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XANES reference library of sulphur-containing compounds for biological research: a status report from the ASTRA beamline at the SOLARIS National Synchrotron Radiation Centre

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RSC Advances 2025年第17期15卷 13513-13524页

作者： Klonecka, Agnieszka Slawek, Joanna Gazdowicz, Grzegorz Maximenko, Alexey Slawek, Andrzej Piszak, Marcel Lichtenberg, Henning Kozak, Maciej SOLARIS National Synchrotron Radiation Centre Kraków Poland The Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Kraków Poland Doctoral School of Exact and Natural Science Jagiellonian University Kraków Poland AGH University of Science and Technology Academic Centre for Materials and Nanotechnology Kraków Poland Hochschule Niederrhein University of Applied Sciences Krefeld Germany Department of Biomedical Physics Faculty of Physics Adam Mickiewicz University Poznań Poland

Sulphur is present in a vast multitude of biological compounds, and X-ray absorption spectroscopy (XAS) is a powerful and well-established characterization technique to study the local atomic environment of this chemical element in such systems in detail, with a high potential for advancing knowledge in medicine, biotechnology and environmental research. For this project, X-ray absorption near edge structure (XANES) spectroscopy at the sulphur K-absorption edge was used to investigate sulphur-containing compounds of biological interest at the ASTRA beamline (SOLARIS National Synchrotron Radiation Centre, Kraków, Poland). XANES provides valuable insights into the bonding environment of sulphur and thereby contributes to a deeper understanding of the structural characteristics and functional roles of sulphur in biological systems. The ASTRA beamline, specifically optimized for XAS in the tender energy range, offers ideal conditions for further research in this field, enabling detailed analysis of the composition and changes in sulphur containing compounds. © 2025 The Royal Society of Chemistry.

关键词： X ray absorption near edge structure spectroscopy

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Video classification for efficient data storage using deep learning: a comparison of sequential and simultaneous feature extraction methods

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Multimedia Tools and Applications 2025年第6期84卷 3071-3094页

作者： Dang Thi, Phuc Nguyen Dang, Hoang Thi Doan Huu, Phuc Dau Sy, Hieu Department of Computer Science Faculty of Information Technology Industrial University of Ho Chi Minh City 12 Nguyen Van Bao Ho Chi Minh City7000 Viet Nam Department of Applied Physics Faculty of Applied Science University of Technology - Viet Nam National University HCMC To Hien Thanh Ho Chi Minh City7000 Viet Nam

Optimizing camera information storage is a critical issue due to the increasing data volume and a large number of daily surveillance videos. In this study, we propose a deep learning-based system for efficient data storage. Videos captured by cameras are classified into four categories: no action, normal action, human action, and dangerous action. Videos without action or with normal action are stored temporarily and then deleted to save storage space. Videos with human action are stored for easy retrieval, while videos with dangerous action are promptly alerted to users. In the paper, we propose two approaches using deep learning models to address the video classification problem. The first approach is a separate approach, where pretrained CNN models extract features from video frame images. These features are then passed through RNN, Transformer models to extract relationships between them. The goal of this approach is to delve into extracting features of objects in the video. The proposed models include VGG16, InceptionV3 combined with LSTM, BiLSTM, Attention, and Vision Transformer. The next approach combines CNN and LSTM layers simultaneously through models like ConvLSTM and LRCN. This approach aims to help the model simultaneously extract object features and their relationships, with the goal of reducing model size, accelerating the training process, and increasing object recognition speed when deployed in the system. In Approach 1, we construct and refine network architectures such as VGG16+LSTM, VGG16+Attention+LSTM, VGG16+BiLSTM, VGG16+ViT, InceptionV3+LSTM, InceptionV3+Attention+LSTM, InceptionV3+BiLSTM. In Approach 2, we build a new network architecture based on the ConvLSTM and LRCN model. The training dataset, collected from real surveillance cameras, comprises 3315 videos labeled into four classes: no action (1018 videos), actions involving people (832 videos), dangerous actions (751 videos), and normal actions (714 videos). Experimental results show t

关键词： Deep learning

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DISTANCE BETWEEN SPECIES BY CONFUSION OPERATORS OF MULTI-CLASS CLASSIFIERS

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UPB Scientific Bulletin, Series A: applied Mathematics and physics 2025年第1期2025卷 171-182页

作者： Tarbă, Nicolae Irimescu, Ionela N. Pleavă, Ana M. Scarlat, Eugen N. Mihăilescu, Mona Doctoral School Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology POLITEHNICA Bucharest Romania Applied Sciences Doctoral School National University of Science and Technology POLITEHNICA Bucharest Romania CAMPUS Research Center National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept Research Center for Applied Sciences in Engineering National University of Science and Technology POLITEHNICA Bucharest Romania

We introduce a method to evaluate the similarities between classes of objects based on the confusion matrices coming from the multi-class machine learning (ML) predictors that operate in the vector space generated by the classes. Operation in the confusion domain allows us to use the distance as evaluation metric of the closeness of the relevant properties carried by the classes. We applied the method to semiquantitatively evaluate the effects of X-ray and carbon ion radiation on cultured SW1353 chondrosarcoma cells with respect to the non-irradiated reference. We prove that it is possible to draw useful information on the strength of cells nuclei changes. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Radiation effects

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Finite-size catalysis in quantum resource theories

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Physical Review A 2025年第2期111卷 022440-022440页

作者： Patryk Lipka-Bartosik Kamil Korzekwa Department of Applied Physics University of Geneva 1211 Geneva Switzerland Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University 30-348 Kraków Poland

Quantum catalysis, the ability to enable previously impossible transformations by using auxiliary systems without degrading them, has emerged as a powerful tool in various resource theories. Although catalytically enabled state transformations have been formally characterized by the monotonic behavior of entropic quantifiers (e.g., the von Neumann entropy or nonequilibrium free energy), such characterizations often rely on unphysical assumptions, namely, the ability of using catalysts of infinitely large dimension. This approach offers very limited insights into the practical significance of using catalysis for quantum information processing. Here, we address this problem across a broad class of quantum resource theories. Leveraging quantum information tools beyond the asymptotic regime, we establish sufficient conditions for the existence of catalytic transformations with finite-size catalysts. We further unveil connections between finite-size catalysis and multicopy transformations. Notably, we discover a phenomenon of catalytic resonance: by carefully tailoring the catalysts's state, one can drastically reduce the required dimension of the catalyst, thus enabling efficient catalytic transformations with minimal resources. Finally, we illustrate our findings with examples from the resource theories of entanglement and thermodynamics, as well in the context of catalytic unitary transformations.

关键词： Free energy

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High energy γγ interactions at the LHeC 31

High energy γγ interactions at the LHeC

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31st International Workshop on Deep Inelastic Scattering, DIS 2024

作者： Forthomme, Laurent Khanpour, Hamzeh Piotrzkowski, Krzysztof Yamazaki, Yuji AGH University Faculty of Physics and Applied Computer Science Al. Mickiewicza 30 Kraków30-055 Poland Physics Department Graduate School of Science Kobe University 1-1 Rokkodai-cho Nada-ku Kobe Japan

The future Large Hadron-electron Collider (LHeC) will operate at a center-of-mass energy of 1.2 TeV, delivering an integrated electron-proton luminosity of approximately 1 ab-1. The high luminosity and clean experimental environment at LHeC promise unique research opportunities, requiring a comprehensive survey of high-energy γγ interactions up to center-of-mass energies of 1 TeV. In this contribution, the potential for scientific discovery is briefly assessed by discussing the γγ processes, particularly the two-photon exclusive production of supersymmetric particles. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).

关键词： Hadrons

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Heider balance on Archimedean lattices and cliques

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Physical Review E 2025年第1期111卷 014310-014310页

作者： Krzysztof Malarz Maciej Wołoszyn Krzysztof Kułakowski AGH University Faculty of Physics and Applied Computer Science al. Mickiewicza 30 30-059 Kraków Poland

We investigate the work function U(T) for the Heider balance, driven by a thermal noise T, on several planar networks that contain separated triangles, pairs of triangles, chains of triangles, and complex structures of triangles. In simulations, the heat-bath algorithm is applied. Two schemes of link values updating are considered: synchronous and asynchronous (sequential). The latter results are compared with analytical calculations for small cliques. We argue that the actual shape of U(T) is a consequence of a local topology rather than of a macroscopic ordering. Finally, we present the mathematical proof that for any planar lattice, perfect structural (Heider) balance is unreachable at T>0.

关键词： Crystal lattices

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Parameters Extraction of Photovoltaic Cell Using Particle Swarm Optimization 4th

Parameters Extraction of Photovoltaic Cell Using Particle Sw...

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4th International Conference on Electronic Engineering and Renewable Energy Systems, ICEERE 2024

作者： Chermite, Charaf Douiri, Moulay Rachid Elamri, Oumaymah Control and Computer Science for Intelligent Systems and Green Energy Department of Applied Physics Faculty of Science and Technology Cadi Ayyad University Marrakech Morocco LAMIGEP EMSI of Marrakech Systems and Applications Engineering Laboratory National School of Applied Sciences Cadi Ayyad University Marrakech Marrakesh Morocco

ISBN: (纸本)9789819799749

Extracting parameters accurately and effectively from solar photovoltaic (PV) models is crucial for detailed simulation, evaluation, and management of PV systems. Although there has been an increase in the development of analytical, numerical, and metaheuristic methods targeting this task, accurately determining these parameters continues to be a significant challenge. This study focuses on applying Particle Swarm Optimization (PSO) for the extraction of key parameters of RTC France solar cells, a critical step towards enhancing PV system efficiency and reliability. Additionally, the PSO's performance is evaluated against other metaheuristic algorithms including Enhanced Chaotic JAYA (CJAYA), Performance-Guided JAYA (PGJAYA), and Enhanced Gradient-Based Optimizer (EGBO), underscoring the effectiveness of the proposed PSO-based approach. Utilizing MATLAB-SIMULINK, comprehensive analysis and results are presented, shedding light on the efficacy of PSO in addressing the intricacies of parameter extraction in PV systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Particle swarm optimization (PSO)

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Enhanced Emotion Recognition Using a Hybrid Autoencoder-LSTM Model Optimized with a Hybrid ACO-WOA Algorithm for Hyperparameter Tuning

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International Journal of Advanced computer science and Applications 2025年第4期16卷 861-876页

作者： Waiker, Vinod Ramesh, Janjhyam Venkata Naga Bala, Ms Kiran Krishnaiah, V.V. Jaya Rama Jackulin, T. Muniyandy, Elangovan Shahin, Osama R. Datta Meghe Institute of Management Studies Maharashtra Nagpur India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed To Be University Uttarakhand Dehradun248002 India Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of CSE Panimalar Engineering College Tamil Nadu Chennai India Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science College of Computer and Information Sciences Jouf University Saudi Arabia Physics and Mathematics Department Faculty of Engineering Helwan University Helwan Egypt

Emotion recognition is vital in the human computer interaction because it improves interaction. Therefore, this paper proposes an improved method for emotion recognition regarding the Hybrid Autoencoder-Long Short-Term Memory (LSTM) model and the newly developed hybrid approach of the Ant Colony Optimization (ACO) and Whale Optimization Algorithm (WOA) for hyperparameters tuning. In this case, Autoencoder can reduce input data dimensionality for input data and find the features relevant for the model’s work. In addition, LSTM is able to work with temporal structures of sequential inputs like speech and videos. The contribution of this research lies in the novel combination method of ACO-WOA which aims at tweaking hyperparameters of Autoencoder-LSTM model. Global aspect of ACO and WOA thereby improve the search efficiency and the accuracy of the proposed emotion recognition system and its generalization capacity. In context with the benchmark dataset for the experimentations of emotion recognition, it has established the efficiency of the proposed model in terms of the conventional methods. Recall rates in recognitive intended various emotions and different modalities were also higher in the hybrid Autoencoder-LSTM model. The optimization algorithms like the ACO-WOA also supported in reducing the computational cost which arose due to hyperparameters tuning. The implementation of this paper is done through Python Software. This implementation shows a high accuracy of 94.12% and 95.94% for audio datasets and image datasets respectively when compared with other deep learning models of Conv LSTM and VGG16. Therefore, the research shows that the presented hybrid approach can be a useful solution for successfully employing emotion recognition for enhancing the creation of the empathetic AI systems and for improving user interactions within various fields including healthcare, entertainment, and customer support. © (2025), (science and Information Organization). All rights

关键词： Ant colony optimization

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Chern-Simons gravitational term coupled to an isocurvature field

arXiv

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arXiv 2025年

作者： Orlando, Giorgio Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Krakow30-348 Poland

The Chern-Simons gravitational term during inflation is usually coupled to the inflaton field. The resulting theory suffers from ghost-field formation in the tensor sector, which limits the observational effects of P-violation on cosmological correlators. In this work, we consider the Chern-Simons term coupled to an isocurvature component in a multi-field model of inflation. Since the resulting theory does not affect the quadratic action of tensor perturbations, ghost fields do not appear. This operator provides (P-violating) interactions between the isocurvature perturbation and the curvature and tensor perturbations. We show that combining these couplings with interactions between the curvature and isocurvature components coming from a turning trajectory, the resulting 〈sst〉PV non-Gaussianities can reach fNLsst,PV = BPVζζh(k, k, k)/Pζ2(k) ∼ O(1) within the parameter space of the theory. Our result motivates the systematic study of the Chern-Simons gravitational term coupled to isocurvature fields in multi-field models of inflation with couplings between the curvature and isocurvature fields or other mechanisms that transfer effects on the isocurvature field into the curvature field. Copyright © 2025, The Authors. All rights reserved.

关键词： Tensors

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Primordial full bispectra from the general bounce cosmology

arXiv

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arXiv 2025年

作者： Akama, Shingo Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Krakow30-348 Poland

Primordial non-Gaussianities are key quantities to test early universe scenarios. In this paper, we compute full bispectra of scalar and tensor perturbations generated during a contracting phase in a general bounce model. The general bounce model consists of two branches: one realizes scale-invariant scalar and tensor power spectra from perturbations whose amplitudes become constant on superhorizon scales, as in de Sitter inflation, while the other realizes scale-invariant power spectra from perturbations whose amplitudes grow on superhorizon scales, as in matter bounce cosmology. We study the auto- and cross-bispectra originating from the scale-invariant scalar and tensor perturbations in these two branches. We investigate the amplitudes and shapes of non-Gaussianities and find that the differences between the two branches manifest for equilateral and squeezed momentum triangle configurations. In particular, one of the branches in which the superhorizon perturbations are conserved reproduces the so-called Maldacena’s consistency relation. By examining perturbativity conditions and considering current observational constraints on primordial non-Gaussianities, we also find a viable parameter space in which both theoretical and observational constraints are satisfied simultaneously. © 2025, CC BY.

关键词： Tensors

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