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International Symposium on Antennas and Propagation (ISAP)

作者： Patchadaporn Sangpet Prayoot Akkaraekthalin Wanwisa Thaiwirot Nutapong Somjit Sukanya Chudpooti Nonchanutt Chudpooti Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand School of Electronic and Electrical Engineering University of Leeds Leeds United Kingdom Department of Industrial Physics and Medical Instrumentation Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand

The Fused Deposition Modeling (FDM) method is utilized in this research to present dielectric lens antennas that were 3D printed. The ultra-wideband (UWB) stub antenna’s antenna characteristic is intended to be improved by the 3D-printed dielectric lens antennas. The proportion of ethanol in the liquid mixture sample is categorized using the transmission-line measuring technique. The device-under-test (DUT) is placed between two 3D-printed dielectric lens antennas. The glass bottle is filled with a 100-ml liquid sample and covered with a plastic cap. The 3D EM Simulation CST Studio is used to optimize the 10 mm gap between the dielectric lens antennas and the DUT. Six concentrations of the ethanol/water mixture, such as empty, 60%, 65%, 70%, and 80%, are measured to examine the measurement system. The measurement outcomes indicate the various S21 (transmission coefficient) levels between 9 GHz and 11 GHz. According to the trend of the suggested system, the level of the transmission coefficient, S21, will change downward when the proportion of ethanol is increased. Several better advantages, such as a non-destructive method, non-contact measurement, and support with a real-time monitoring system, are provided by the transmission-line measuring methodology using 3D-printed dielectric lens antennas.

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Limited Dissolution of Transition Metals in the Nanocrystalline Cerium (IV) Oxide

SSRN

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SSRN 2024年

作者： Ducka, Agata Blaszczak, Patryk Zając, Marcin Mizera, Adrian d'Acapito, Francesco Bochentyn, Beata Faculty of Applied Physics and Mathematics Gdansk University of Technology ul. Narutowicza 11/12 Gdańsk80-233 Poland National Synchrotron Radiation Centre SOLARIS Jagiellonian University Czerwone Maki 98 Kraków30-392 Poland Faculty of Materials Science and Ceramics AGH University of Science and Technology Al. A. Mickiewicza 30 Kraków30-059 Poland 2CNR-IOM-OGG c/o ESRF-The European Synchrotron 71 Avenue des Martyrs Grenoble38000 France

Nanocrystalline transition metal-doped (Mn, Fe, Co, Ni, or Cu) cerium (IV) oxides were synthesized by the reverse microemulsion method and were characterized using complementary techniques to verify the formation of multiphase material. The studies on as-synthesized oxides revealed the formation of finely dispersed oxides on the ceria surface. Due to nanocrystallinity, no additional phases were found in the as-synthesized diffraction patterns. The nanocrystallinity precludes proper phase identification, thus the XRD measurement was repeated on the recrystallized oxides and revealed the formation of small amount of respective transition metals oxides. By means of both diffraction and spectroscopy analyses, it was found that the highest share of Mn was incorporated into the ceria lattice, while Cu tends to form surficial oxides. The work presented herein states that 10 mol.% cannot be fully incorporated into the ceria lattice to form a solid solution without the segregation of other phases on the surface. © 2024, The Authors. All rights reserved.

关键词： Transition metals

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physics-Informed Neural Network for Solving Buckley-Leverett Equation of Plane Radial Flow

Physics-Informed Neural Network for Solving Buckley-Leverett...

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Data-driven Optimization of Complex Systems (DOCS), International Conference on

作者： Jianpeng Zang Yizhuo Song Junting Bai Kai Zhang Jian Wang Sergey V. Ablameyko School of Petroleum Engineering China University of Petroleum (East China) Qingdao China Engineering and Computer Science New York University Shanghai Shanghai China College of Science China University of Petroleum (East China) Qingdao China School of Petroleum Engineering China University of Petroleum (East China) Civil Engineering School Qingdao University of Technology Qingdao China Faculty of Applied Mathematics and Computer Science Belarusian State University Minsk Belarus

ISBN: (数字)9798350377842

ISBN: (纸本)9798350377859

The Buckley-Leverett (BL) equation of plane radial flow describes the flow state of two phases of oil and water in an ideal planar porous medium. It is a nonlinear hyperbolic conservation partial differential equation (PDE) with a jump value, making it difficult to solve numerically. This paper presents a physics-informed neural network (PINN) with entropy constraints to solve the BL equation of plane radial flow. Specifically, the Oleinik entropy condition is used to limit the change of the relationship between water content and saturation in accordance with the physical law, which does not change the structure of the target PDE to be fitted by the model. Benefit from the interpretability of PINN and its fit in solving PDE problems, the model can be trained in an unsupervised way, thus eliminating the effort of obtaining sample labels. The experimental results show that the root mean square error fluctuates between the interval [0.03436,0.16054], indicating a good fitting effect. Especially the jump value, which refers to the position of the leading edge of water saturation, can be clearly output by the model.

关键词： Partial differential equations Oils Neural networks Fitting Water conservation Mathematical models Entropy Surface fitting Root mean square Optimization

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Positronium and Quantum Entanglement Imaging: A New Trend in Positron Emission Tomography

Positronium and Quantum Entanglement Imaging: A New Trend in...

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2021 IEEE Nuclear science Symposium and Medical Imaging Conference, NSS/MIC 2021

作者： Moskal, Pawel Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Cracow Poland Jagiellonian University Total-Body Jagiellonian-PET Laboratory Cracow Poland Jagiellonian University Theranostics Center Cracow Poland

ISBN: (纸本)9781665421133

We report on the progress in the development of positronium imaging and quantum entanglement imaging achieved thus far by the J-PET collaboration. Positronium (a bound state of electron and positron) is copiously produced in the human body during positron emission tomography. Its properties depend on the size of the intra-molecular voids and concentration in them of molecules as e.g. molecular oxygen. Therefore, positronium may serve as a biomarker of tissue pathology and hypoxia. Recently, the positronium imaging method was developed and the first positronium images of phantoms comprised of tumor cardiac myxoma and healthy adipose tissues were created by means of the J-PET tomograph. A significant difference in mean positronium lifetime in cardiac myxoma and adipose tissues opens promising perspectives for the application of positronium as a cancer diagnostic indicator. It is also important to note that photons originating from the decay of positronium are quantum entangled in polarisation. Thus, it can be hypothesized that by measuring the degree of entanglement of photons from positronium annihilation, additional information can be obtained about the molecular environment in which the positronium is formed. J-PET is the first PET system enabling the determination of linear polarisation of annihilation photons. The measurements performed with the J-PET detector confirm that the distribution of relative angle between the polarization of 511 keV back-to-back photons is consistent with the assumption that these photons are quantum entangled. These results open up prospects for the study of quantum entanglement of photons from positronium annihilation in living organisms. © 2021 IEEE.

关键词： Photons

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PERSPECTIVE OF CP VIOLATION SEARCH BY MODULAR J-PET DETECTOR IN THE ORTHO-POSITRONIUM DECAY

Acta Physica Polonica B, Proceedings Supplement

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Acta Physica Polonica B, Proceedings Supplement 2022年第4期15卷

作者： Eliyan, Kavya V. Skurzok, Magdalena Moskal, Paweł Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Łojasiewicza 11 30-348 Kraków Poland and Center for Theranostics Jagiellonian University Kraków Poland

The positronium atom, a bound state of electron and positron, is a suitable leptonic test site for Charge-Parity (CP) discrete symmetry research. According to the Standard Model, the photon–photon interaction in the final state due to the vacuum polarization may mimic CP violation of the order of 10−9, while weak interaction effects lead to a violation of the order of 10−14. So far, the experimental limits on CP symmetry violation in the decay of o-Ps are set at the level of 10−3. The J-PET detector can be used to explore discrete symmetries by looking for probable non-zero expectation values of the symmetry-odd operators, constructed from spin of ortho-Positronium (o-Ps) and momentum, and polarization vectors of gamma (γ) quanta resulting from o-Ps annihilation. The upgraded version of the J-PET detector, with an additional fourth layer of detection modules increases signal acceptance, which allows to triple the efficiency of γ quanta detection for CP discrete symmetry studies. © 2022 Jagellonian university. All rights reserved.

关键词： Positrons

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Improving efficiency of the path optimization method for a gauge theory

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Physical Review D 2023年第3期107卷 034509-034509页

作者： Yusuke Namekawa Kouji Kashiwa Hidefumi Matsuda Akira Ohnishi Hayato Takase Education and Research Center for Artificial Intelligence and Data Innovation Hiroshima University Hiroshima 730-0053 Japan Department of Physics Faculty of Science Kyoto University Kyoto 606-8502 Japan Yukawa Institute for Theoretical Physics Kyoto University Kyoto 606-8502 Japan Department of Computer Science and Engineering Faculty of Information Engineering Fukuoka Institute of Technology Fukuoka 811-0295 Japan Department of Physics and Center for Field Theory and Particle Physics Fudan University Shanghai 200433 China

We investigate efficiency of a gauge-covariant neural network and an approximation of the Jacobian in optimizing the complexified integration path toward evading the sign problem in lattice field theories. For the construction of the complexified integration path, we employ the path optimization method. The two-dimensional U(1) gauge theory with the complex gauge-coupling constant is used as a laboratory to evaluate the efficiency. It is found that the gauge-covariant neural network, which is composed of the Stout-like smearing, can enhance the average phase factor, as the gauge-invariant input does. For the approximation of the Jacobian, we test the most drastic case in which we perfectly drop the Jacobian during the learning process. It reduces the numerical cost of the Jacobian calculation from O(N3) to O(1), where N means the number of degrees of freedom of the theory. The path optimization using this Jacobian approximation still enhances the average phase factor at expense of a slight increase of the statistical error.

关键词： Lattice gauge theory Machine learning

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Punctuation patterns in Finnegans Wake by James Joyce are largely translation-invariant

arXiv

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arXiv 2025年

作者： Bartnicki, Krzysztof Drozdz, Stanislaw Kwapien, Jaroslaw Stanisz, Tomasz Poland Complex Systems Theory Department Institute of Nuclear Physics Polish Academy of Sciences Kraków31-342 Poland Faculty of Computer Science and Telecommunications Cracow University of Technology Kraków31-155 Poland

The complexity characteristics of texts written in natural languages are significantly related to the rules of punctuation. In particular, the distances between punctuation marks measured by the number of words quite universally follow the family of Weibull distributions known from survival analyses. However, the values of two parameters marking specific forms of these distributions distinguish specific languages. This is such a strong constraint that the punctuation distributions of texts translated from the original language into another adopt quantitative characteristics of the target language. All these changes take place within Weibull distributions such that the corresponding hazard functions are always increasing. Recent previous research shows that James Joyce's famous Finnegans Wake is subject to such extreme distribution from the Weibull family that the corresponding hazard function is clearly decreasing. At the same time, the distances of sentence ending punctuation marks, determining the variability of sentence length, have an almost perfect multifractal organization, so far to such an extent found nowhere else in the literature. In the present contribution based on several available translations (Dutch, French, German, Polish, Russian) of Finnegans Wake, it is shown that the punctuation characteristics of this work remain largely translation invariant, contrary to the common cases. These observations may constitute further evidence that Finnegans Wake is a translinguistic work in this respect as well, in line with Joyce's original intention. Copyright © 2025, The Authors. All rights reserved.

关键词： Hazards

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Block-Circulant Complex Hadamard Matrices

arXiv

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arXiv 2022年

作者： Bruzda, Wojciech Jagiellonian University Faculty of Physics Astronomy and Applied Computer Science ul. Lojasiewicza 11 Kraków30-348 Poland

A new method of obtaining a sequence of isolated complex Hadamard matrices (CHM) for dimensions N ≥ 7, based on block-circulant structures, is presented. We discuss, several analytic examples resulting from a modification of the Sinkhorn algorithm. In particular, we present new isolated matrices of orders 9, 10 and 11, which elements are not roots of unity, and also several new multiparametric families of order 10. We note novel connections between certain eight-dimensional matrices and provide new insights towards classification of CHM for N ≥ 7. These contributions can find real applications in Quantum Information Theory and constructions of new families of Mutually Unbiased Bases or Unitary Error *** Codes 05B20, 65H10 © 2022, CC BY-NC-SA.

关键词： Quantum optics

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Model Free Deep Deterministic Policy Gradient Controller for Setpoint Tracking of Non-minimum Phase Systems

arXiv

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arXiv 2024年

作者： Tavakkoli, Fatemeh Sarhadi, Pouria Clement, Benoit Naeem, Wasif The Faculty of Electrical and Computer Engineering Department Babol Noshirvani University of Technology Babol Iran The School of Physics Engineering and Computer Science University of Hertfordshire Hertfordshire HatfieldAL10 9AB United Kingdom ENSTA Bretagne CROSSING ILR CNRS 2010 Flinders University Australia The School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast United Kingdom

Deep Reinforcement Learning (DRL) techniques have received significant attention in control and decision-making algorithms. Most applications involve complex decision-making systems, justified by the algorithms' computational power and cost. While model-based versions are emerging, model-free DRL approaches are intriguing for their independence from models, yet they remain relatively less explored in terms of performance, particularly in applied control. This study conducts a thorough performance analysis comparing the data-driven DRL paradigm with a classical state feedback controller, both designed based on the same cost (reward) function of the linear quadratic regulator (LQR) problem. Twelve additional performance criteria are introduced to assess the controllers' performance, independent of the LQR problem for which they are designed. Two Deep Deterministic Policy Gradient (DDPG)-based controllers are developed, leveraging DDPG's widespread reputation. These controllers are aimed at addressing a challenging setpoint tracking problem in a Non-Minimum Phase (NMP) system. The performance and robustness of the controllers are assessed in the presence of operational challenges, including disturbance, noise, initial conditions, and model uncertainties. The findings suggest that the DDPG controller demonstrates promising behavior under rigorous test conditions. Nevertheless, further improvements are necessary for the DDPG controller to outperform classical methods in all criteria. While DRL algorithms may excel in complex environments owing to the flexibility in the reward function definition, this paper offers practical insights and a comparison framework specifically designed to evaluate these algorithms within the context of control engineering. © 2024, CC BY.

关键词： Controllers

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A Multi-Task Multiple Instance Learning Algorithm to Analyze Large Whole Slide Images from Bright Challenge 2022

A Multi-Task Multiple Instance Learning Algorithm to Analyze...

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Biomedical Imaging Challenges (ISBIC), IEEE International Symposium on

作者： Niccolò Marini Marek Wodzinski Manfredo Atzori Henning Mü ller University of Applied Sciences Western Switzerland Information Systems Institute Sierre Switzerland Department of Computer Science University of Geneva Switzerland Department of Measurement and Electronics AGH University of Science and Technology Krakow Poland Department of Neurosciences University of Padua Italy Medical Faculty University of Geneva Switzerland

ISBN: (数字)9781665451727

ISBN: (纸本)9781665451734

Malignant lesions in breast tissue specimen whole slide images (WSIs), may lead to a dangerous diagnosis, such as cancer. However, WSIs analysis is time-consuming and expensive, requiring the work of expert pathologists. This paper aims to present a method for the 2022 BRIGHT Challenge, that involves the analysis of breast WSIs. The organizers provided over 550 breast WSIs and over 3900 regions of interest (ROIs) to develop and validate methods for breast cancer images. The method presented in this work is based on a Multiple Instance Learning instance-based Convolutional Neural Network (CNN), allowing the combination of strongly-annotated data (from ROIs) and weakly-annotated data (from WSIs) via the optimization of a multi-task loss function. Furthermore, during the CNN training, the input patches are clustered and filtered according to their entropy, to reduce the non-informative content used to train the model. The CNN reaches an averaged $\text{F1-score}=0.63\pm 0.02$ on the 3-class classification task and averaged $\text{F1-score}=0.39 \pm 0.08$ on the 6-class classification task, considering the val-idation partition; an averaged $\text{F1-score}=0.65$ on the cancer risk classification task and averaged $\text{F1-score}=0.45$ on the sub-typing cancer risk classification task, considering the best result achieved on the test partition. These results show that Multiple Instance Learning instance-based CNNs may represent a good resource to tackle this kind of problem.

关键词： Training Breast tissue Multitasking Entropy Partitioning algorithms Classification algorithms Convolutional neural networks

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