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arXiv 2023年

作者： Samanta, Rupam Bozek, Piotr AGH University of Science and Technology Faculty of Physics and Applied Computer Science aleja Mickiewicza 30 Cracow30-059 Poland

Flow fluctuations in ultra-relativistic heavy-ion collision can be probed by studying the momentum dependent correlations or the factorization-breaking coefficients between flow harmonics in separate kinematic bins (transverse momentum p or pseudorapidity η). We study such factorization-breaking coefficients for collisions of deformed 238U+238U nuclei to see the effect of the nuclear deformation on momentum dependent coefficients. We also study momentum dependent mixed-flow correlations for the isobar collision system: 96Ru+96Ru and 96Zr+96Zr, which have the same mass number but different nuclear structure, thus providing the ideal scenario to study nuclear deformation effect on such observables. We use the TRENTO + MUSIC model for simulations and event-by-event analysis of those observables. We find that these momentum dependent correlation coefficients are not only excellent candidates to probe the fluctuation in heavy-ion collision, but also show significant sensitivity to the nuclear deformation. © 2023, CC BY.

关键词： Factorization

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Data science in wind energy: A case study for Norwegian offshore wind

Data science in wind energy: A case study for Norwegian offs...

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2023 International Conference on applied Statistics, Modeling and Advanced Algorithms, ASMA 2023

作者： Chen, Hao Birkelund, Yngve Zhang, Qixia Department of Technology and Safety Faculty of Science and Technology UiT the Arctic University of Norway Tromsø9019 Norway Department of Physics and Technology Faculty of Science and Technology UiT the Arctic University of Norway Tromsø9019 Norway Department of Computer Science Faculty of Science and Technology UiT the Arctic University of Norway Tromsø9019 Norway

In the digital and green transitions, rapidly growing renewable energies are accumulating more and more data. Big data gives room to apply emerging data science to solve challenges in the energy sector. Offshore wind power receives accelerating attention due to its sufficient resources and cleanness. This paper uses data science, including statistical analysis and machine learning, to systematically analyse three coastal wind sites in Norway. The results show that although Norway possesses ample offshore resources, its development could be improved by natural, technical, and economic challenges that can be addressed with the help of data science. Technically, the statistical attributes and forecasting intricacy of offshore wind resources differ across various regions of Norway. © Published under licence by IOP Publishing Ltd.

关键词： Wind power

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A southern, middle, and northern Norwegian offshore wind energy resources analysis by a transfer learning method for Energy Internet 2

A southern, middle, and northern Norwegian offshore wind ene...

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2023 2nd International Conference on Energy Internet and Power Systems, ICEIPS 2023

作者： Chen, Hao Birkelund, Yngve Ricaud, Benjamin Zhang, Qixia Department of Technology and Safety Faculty of Science and Technology UiT the Arctic University of Norway Tromsø9019 Norway Department of Physics and Technology Faculty of Science and Technology UiT the Arctic University of Norway Tromsø9019 Norway Department of Computer Science Faculty of Science and Technology UiT the Arctic University of Norway Tromsø9019 Norway

As renewable energy sources offshore wind energy develop quickly, countries like Norway with long coastlines are exploring their potential. However, the diverse wind resources across different regions of Norway present challenges for study for effective utilization of offshore wind energy. This study proposes a novel method that utilizes transfer learning techniques to analyse the resource differences between these areas for optimum energy generation. The suggested approach is tested using real-world wind data from Norway's southern, middle, and northern regions. The results show that transfer learning successfully bridges resource discrimination, boosting wind resource prediction precision in the target domains. The work can contribute to optimizing offshore wind energy utilization in Norway by addressing the resource disparities and forecasting between the different regions. © 2023 Published under licence by IOP Publishing Ltd.

关键词： Energy utilization

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Interpreting pulsar timing array data of gravitational waves with Ekpyrosis-bouncing cosmology

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Physical Review D 2025年第4期111卷 043508-043508页

作者： Taotao Qiu Mian Zhu School of Physics Huazhong University of Science and Technology Wuhan 430074 China Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University 30-348 Krakow Poland

Recent pulsar timing array (PTA) experiments have reported strong evidence of the stochastic gravitational wave background (SGWB). If interpreted as primordial gravitational waves, the signal favors a strongly blue-tilted spectrum. On the other hand, the Ekpyrosis-bouncing cosmology with a strongly blue-tilted gravitational wave spectrum, i.e., nT≃2, offers a potential explanation for the observed SGWB signal. In this paper, we construct a concrete Ekpyrosis-bouncing model, and show its capacity to intepret the PTA result without pathologies. Both tensor and scalar perturbations are analyzed with constraints from the current observations.

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My part is bigger than yours - assessment within a group of peers

arXiv

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arXiv 2024年

作者： Kulakowski, Konrad Szybowski, Jacek AGH University of Krakow WEAIiIB Applied Computer Science Department Poland AGH University of Krakow Faculty of Applied Mathematics Poland

A project (e.g., writing a collaborative research paper) is often a group effort. At the end, each contributor identifies their contribution, often verbally. The reward, however, is very frequently financial. It leads to the question of what (percentage) share in the creation of the paper is due to individual authors. Different authors may have various opinions on the matter;even worse, their opinions may have different relevance. In this paper, we present simple models that allow aggregation of experts’ views, linking the priority of his preference directly to the assessment made by other experts. In this approach, the more significant the contribution of a given expert, the greater the importance of his opinion. The presented method can be considered an attempt to find consensus among peers involved in the same project. Hence, its applications may go beyond the proposed study example of writing a scientific paper. © 2024, CC BY.

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Exploration of soliton structures and modulation instability analysis for the highly dispersive perturbed NLSE with sextic-power law refractive index

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Optical and Quantum Electronics 2025年第1期57卷 1-18页

作者： Abdullah, Eman H. M. Ahmed, Hamdy M. Zaghrout, Afaf A. S. Bahnasy, Amal Ibrahim Ahmed Rabie, Wafaa B. Hashemi, M.S. Bayram, M. Department of Physics and Engineering Mathematics Higher Institute of Engineering El Shorouk Academy Cairo Egypt Department of Mathematics Faculty of Science Al Azhar University Cairo Egypt Department of Physics and Engineering Mathematics Higher Institute of Engineering and Technology Tanta Egypt Department of Mathematics Basic Science Faculty University of Bonab Bonab Iran Computer Engineering Biruni University Istanbul Turkey

This study introduces some novel soliton solutions and other analytic wave solutions for the highly dispersive perturbed nonlinear Schrödinger equation with generalized nonlocal laws and sextic-power law refractive index. This equation models the behavior of complex wave propagation. The improved modified extended tanh function governs the transmission of various solitons with different effects. New optical solitons are constructed as bright solitons, dark solitons, combo bright-dark solitons and singular solitons. Furthermore, exponential solutions, triangular solutions and rational wave solutions are shown. The findings conferred in this article show a crucial advancement in understanding the propagation of waves in non-linear media. The study also includes modulation instability analysis to examine the stability of the derived wave solutions, supplemented by graphical simulations that depict their distinctive features. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Solitons

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Machine Learning Model for Wind Power Forecasting Using Enhanced Multilayer Perceptron

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computers, Materials & Continua 2025年第5期83卷 2287-2303页

作者： Ahmed A.Ewees Mohammed A.A.Al-Qaness Ali Alshahrani Mohamed Abd Elaziz Computer Science and Artificial Intelligence Department College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia Department of Computer Damietta UniversityDamietta34517Egypt College of Physics and Electronic Information Engineering Zhejiang Normal UniversityJinhua321004China Department of Mathematics Faculty of ScienceZagazig UniversityZagazig44519Egypt

Wind power forecasting plays a crucial role in optimizing the integration of wind energy into the grid by predicting wind patterns and energy *** enhances the efficiency and reliability of renewable energy *** approaches inform energy management strategies,reduce reliance on fossil fuels,and support the broader transition to sustainable energy *** primary goal of this study is to introduce an effective methodology for estimating wind power through temporal data *** research advances an optimized Multilayer Perceptron(MLP)model using recently proposedmetaheuristic optimization algorithms,namely the FireHawk Optimizer(FHO)and the Non-Monopolize Search(NO).A modified version of FHO,termed FHONO,is developed by integrating NO as a local search mechanism to enhance the exploration capability and address the shortcomings of the original *** developed FHONO is then employed to optimize the MLP for enhanced wind power *** effectiveness of the proposed FHONO-MLP model is validated using renowned datasets from wind turbines in *** results of the comparative analysis between FHONO-MLP,conventionalMLP,and other optimized versions of MLP show that FHONO-MLP outperforms the others,achieving an average RootMean Square Error(RMSE)of 0.105,Mean Absolute Error(MAE)of 0.082,and Coefficient of Determination(R^(2))of 0.967 across all *** findings underscore the significant enhancement in predictive accuracy provided by FHONO and demonstrate its effectiveness in improving wind power forecasting.

关键词： Wind power forecasting multilayer perceptron fire hawk optimizer non-monopolize search

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Fine structure of phase diagram for social impact theory

arXiv

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arXiv 2025年

作者： Malarz, Krzysztof Woloszyn, Maciej AGH University Faculty of Physics and Applied Computer Science al. Mickiewicza 30 Kraków30-059 Poland

In this paper, the social impact theory introduced by Latané is reconsidered. A fully differentiated society is considered;that is, initially every actor has their own opinion. The equivalent of Muller’s ratchet guards that—even for the non-deterministic case (with a positive social temperature)—any opinion once removed from the opinion space does not appear again. With computer simulation, we construct the phase diagram for Latané model based on the number of surviving opinions after various evolution times. The phase diagram is constructed on the two-dimensional plane of model control parameters responsible for the effective range of interaction among actors and the social temperature. Introducing the Muller’s ratchet-like mechanism gives a non-zero chance for any opinion to be removed from the system. We believe that in such a case, for any positive temperature, ultimately a consensus is reached. However, even for a moderate system size, the time to reach consensus is very long. In contrast, for the deterministic case (without social temperature), the system may be frozen with clusters of actors having several different opinions, or even reach the cycle limit (with blinking structures). Copyright © 2025, The Authors. All rights reserved.

关键词： Sociology

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Optimization of Cognitive Radio System Using Enhanced Firefly Algorithm

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Intelligent Automation & Soft Computing 2023年第9期37卷 3159-3177页

作者： Nitin Mittal Rohit Salgotra Abhishek Sharma Sandeep Kaur SSAskar Mohamed Abouhawwash University Center for Research and Development Chandigarh UniversityMohaliPunjab140413India Faculty of Physics and Applied Computer Science AGH University of Science&TechnologyKrakowPoland Faculty of Information Technology Middle East UniversityAmman11813Jordan Department of Computer Engineering and Applications GLA UniversityMathura281406India Department of Computer Engineering&Technology Guru Nanak Dev UniversityAmritsarPunjab143005India Department of Statistics and Operations Research College of ScienceKing Saud UniversityP.O.Box 2455Riyadh11451Saudi Arabia Department of Computational Mathematics Science and EngineeringCollege of EngineeringMichigan State UniversityEast LansingMI48824USA Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt

The optimization of cognitive radio(CR)system using an enhanced firefly algorithm(EFA)is presented in this *** Firefly algorithm(FA)is a nature-inspired algorithm based on the unique light-flashing behavior of *** has already proved its competence in various optimization prob-lems,but it suffers from slow convergence *** improve the convergence performance of FA,a new variant named EFA is *** effectiveness of EFA as a good optimizer is demonstrated by optimizing benchmark functions,and simulation results show its superior performance compared to biogeography-based optimization(BBO),bat algorithm,artificial bee colony,and *** an application of this algorithm to real-world problems,EFA is also applied to optimize the CR *** is a revolutionary technique that uses a dynamic spectrum allocation strategy to solve the spectrum scarcity ***,it requires optimization to meet specific performance *** results obtained by EFA in CR system optimization are compared with results in the literature of BBO,simulated annealing,and genetic *** results further prove that the proposed algorithm is highly efficient and provides superior results.

关键词： Firefly algorithm cognitive radio bit error rate genetic algorithm simulated annealing biogeography-based optimization

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physics Informed Neural Networks with strong and weak residuals for advection-dominated diffusion problems

arXiv

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arXiv 2023年

作者： Sikora, Maciej Krukowski, Patryk Paszyńska, Anna Paszyński, Maciej Institute of Computer Science AGH University of Science and Technology Kraków Poland Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Kraków Poland

This paper deals with the following important research questions. Is it possible to solve challenging advection-dominated diffusion problems in one and two dimensions using physics Informed Neural Networks (PINN) and Variational physics Informed Neural Networks (VPINN)? How does it compare to the higher-order and continuity Finite Element Method (FEM)? How to define the loss functions for PINN and VPINN so they converge to the correct solutions? How to select points or test functions for training of PINN and VPINN? We focus on the one-dimensional advection-dominated diffusion problem and the two-dimensional Eriksson-Johnson model problem. We show that the standard Galerkin method for FEM cannot solve this problem. We discuss the stabilization of the advection-dominated diffusion problem with the Petrov-Galerkin (PG) formulation and present the FEM solution obtained with the PG method. We employ PINN and VPINN methods, defining several strong and weak loss functions. We compare the training and solutions of PINN and VPINN methods with higher-order FEM methods. © 2023, CC BY.

关键词： Advection

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