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arXiv 2024年

作者： Yousefi, Milad Maleki, Shadi Farabi Jafarizadeh, Ali Youshanlui, Mahya Ahmadpour Jafari, Aida Pedrammehr, Siamak Alizadehsani, Roohallah Tadeusiewicz, Ryszard Plawiak, Pawel Faculty of Mathematics Statistics and Computer Sciences University of Tabriz Tabriz Iran Nikookari Eye Center Tabriz University of Medical Sciences Tabriz Iran Immunology Research Center Tabriz University of Medical Sciences Tabriz Iran Faculty of Design Tabriz Islamic Art University Tabriz Iran Deakin University VIC3216 Australia AGH University of Science and Technology Department of Biocybernetics and Biomedical Engineering Krakow Poland Department of Computer Science Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 Krakow31-155 Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland

Thyroid cancer is an increasing global health concern that requires advanced diagnostic methods. The application of AI and radiomics to thyroid cancer diagnosis is examined in this review. A review of multiple databases was conducted in compliance with PRISMA guidelines until October 2023. A combination of keywords led to the discovery of an English academic publication on thyroid cancer and related subjects. 267 papers were returned from the original search after 109 duplicates were removed. Relevant studies were selected according to predetermined criteria after 124 articles were eliminated based on an examination of their abstract and title. After the comprehensive analysis, an additional six studies were excluded. Among the 28 included studies, radiomics analysis, which incorporates ultrasound (US) images, demonstrated its effectiveness in diagnosing thyroid cancer. Various results were noted, some of the studies presenting new strategies that outperformed the status quo. The literature has emphasized various challenges faced by AI models, including interpretability issues, dataset constraints, and operator dependence. The synthesized findings of the 28 included studies mentioned the need for standardization efforts and prospective multicenter studies to address these concerns. Furthermore, approaches to overcome these obstacles were identified, such as advances in explainable AI technology and personalized medicine techniques. The review focuses on how AI and radiomics could transform the diagnosis and treatment of thyroid cancer. Despite challenges, future research on multidisciplinary cooperation, clinical applicability validation, and algorithm improvement holds the potential to improve patient outcomes and diagnostic precision in the treatment of thyroid cancer. © 2024, CC BY-NC-SA.

关键词： Machine learning

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Temperature dependent deformation behavior and texture evolution in AA6082 aluminum alloy: An integrated experimental and crystal plasticity simulation approach

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International Journal of Plasticity 2024年 176卷

作者： Lypchanskyi, Oleksandr Chiu, Chen-Chun Qayyum, Faisal Korpala, Grzegorz Guk, Sergey Chao, Ching-Kong Prahl, Ulrich Institute of Metal Forming TU Bergakademie Freiberg Freiberg09599 Germany Faculty of Metals Engineering and Industrial Computer Science AGH University of Krakow Krakow30-059 Poland Department of Mechanical Engineering National Taiwan University of Science and Technology Taipei106335 Taiwan

This research provides a comprehensive analysis of the texture and temperature dependent deformation behavior of the aluminum alloy AA6082. The study is performed using a combination of experimental deformation tests and computational simulations using a crystal plasticity (CP) framework. The primary objective is to identify the critical influence of temperature on local stress states and dislocation density within the material during tensile tests. From an experimental perspective, the study employs ex-situ deformation tests and subsequent electron backscatter diffraction (EBSD) data in combination with energy dispersive x-ray spectrometer (EDS) analysis at 200 °C and 400 °C. The experimental EBSD data are adopted, preprocessed and converted into a geometry file for the numerical simulations. On the computational front, a dislocation density-based material model is adopted for CP simulations. The physical and fitting parameters of the model are calculated, adopted from the literature, or calibrated by comparing the global simulation results with experimental stress-strain observations under uniaxial tensile load at room temperature, 200 °C and 400 °C. Empirical functions for solid solution strengthening, dislocation density, fitting parameters controlling mean free path and dislocation annihilation have been derived that can be used to quickly interpolate them for any intermediate temperature. These functions combined with other model parameters can now be used for temperature-dependent CP modeling of AA6082. The isothermal grain-scale simulation and ex-situ experimental results confirmed a noteworthy texture transformation at higher temperatures, characterized by a reduction in the primary Cube orientation and its transition into a Copper orientation due to the stretching process. The correlation between the experimental results and the simulations on macro- and micro-scales is reasonable, indicating the accuracy and effectiveness of the CP approach in predicting

关键词： Aluminum alloys

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Design and Demonstrate an Attack Strategy to Control a Vehicle’s computer by Targeting Its Electrical Network 3rd

Design and Demonstrate an Attack Strategy to Control a Vehic...

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3rd International Conference on Electronic Engineering and Renewable Energy, ICEERE 2022

作者： Karrouchi, Mohammed Messaoudi, Abdelhafid Kassmi, Kamal Nasri, Ismail Atmane, Ilias Blaacha, Jalal Electrical Engineering and Maintenance Laboratory High School of Technology Mohammed First University BP. 473 Oujda Morocco Energy Embedded Systems and Information Processing Laboratory National School of Applied Sciences Mohammed First University Oujda Morocco Department of Physics Faculty of Science Mohammed First University Oujda Morocco

ISBN: (纸本)9789811962226

The functionality of most modern automobiles is controlled by electronic control units (ECUs) that interact with one another via the CAN communication protocol (controller area network), as defined by the ISO 11898 standard, which is one of the fundamental features of most modern cars. This physical bus provides excellent data transfer quality by providing wide propagation reaching all areas of a vehicle in a short amount of time. However, because of the absence of secrecy and ease of access (physical or remote), this protocol does not place a high value on security, which causes the CAN bus control system to be vulnerable. Due to this weakness, it is possible to control the car from the outside and place the vehicle's passengers in jeopardy. The objective of this paper is to describe the existing vulnerability of the security system against attacks, and also a demonstration of a hacking technique used to manage the dashboard of a 2014 DACIA Dokker automobile using the automotive network. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Controllers

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Optimization of Base Station Placement in 4G LTE Broadband Networks Using Adaptive Variable Length Genetic Algorithm

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SN computer science 2023年第2期4卷 121页

作者： Nwelih, Emmanuel Isabona, Joseph Imoize, Agbotiname Lucky Department of Computer Science University of Benin Benin City Nigeria Department of Physics Federal University Lokoja P.M.B 1154 Lokoja Nigeria Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos 100213 Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum 44801 Germany

The growing subscriptions for cellular communication services have been remarkable globally. This development has placed immense pressure on the network providers to optimally position the radio transmitters to deliver quality services to mobile subscribers. The conventional cellular network planning techniques are prone to errors and incapable of guaranteeing the desired quality of service at user equipment terminals. To address this problem, meta-heuristic evolutionary algorithms have been employed to boost the radio transmitter placement for optimal performance. However, the meta-heuristic evolutionary algorithms require experimental measurements for testing and validation. This paper uses a field measurement-based genetic algorithms approach to optimize base station placement in cellular networks. The proposed method explores the combined impact of strong cellular networks influencing parameters, such as capacity, coverage, and transmit power in the base station placement process. Field signal strength measurement was conducted to examine the propagation loss predictive performance of the referenced COST 231 Hata model. This approach provided the means to determine the cell coverage radius and the minimum base station number needed for model optimization. Higher order sectorization (considering six and 12 sectors per site) on cellular network capacity was employed. Generally, results revealed that the system capacity increases compared to the standard approach with three sectors per site. The adaptive variable-length genetic algorithm approach was employed with a weighted fitness function that combines coverage, capacity, and transmit power parameters. The proposed approach outperforms the conventional analytical method based on the referenced COST 231 Hata model in convergence speed, computational efficiency, and algorithm reliability. Improved performance with the projected approach was demonstrated over the benchmarked particle swarm optimization approach. ©

关键词： Cellular networks COST 231 Hata model Field measurements Genetic algorithm Optimal base station placement Swarm particle optimization

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Using Data Mining Techniques to Analyze Facial Expression Motion Vectors 6th

Using Data Mining Techniques to Analyze Facial Expression Mo...

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6th International Conference on Dynamics of Information Systems, DIS 2023

作者： Roshanzamir, Mohamad Alizadehsani, Roohallah Roshanzamir, Mahdi Shoeibi, Afshin Gorriz, Juan M. Khosravi, Abbas Nahavandi, Saeid Acharya, U. Rajendra Department of Computer Engineering Faculty of Engineering Fasa University Fasa74617-81189 Iran Deakin University GeelongVIC Australia Department of Electrical and Computer Engineering University of Tabriz Tabriz Iran Data Science and Computational Intelligence Institute University of Granada Granada Spain Department of Signal Theory Networking and Communications Universidad de Granada Granada Spain Swinburne University of Technology HawthornVIC3122 Australia School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia

ISBN: (纸本)9783031503191

Automatic recognition of facial expressions is a common problem in human-computer interaction. While humans can recognize facial expressions very easily, machines cannot do it as easily as humans. Analyzing facial changes during facial expressions is one of the methods used for this purpose by the machines. In this research, facial deformation caused by facial expressions is considered for automatic facial expression recognition by machines. To achieve this goal, the motion vectors of facial deformations are captured during facial expression using an optical flow algorithm. These motion vectors are then used to analyze facial expressions using some data mining algorithms. This analysis not only determined how changes in the face occur during facial expressions but can also be used for facial expression recognition. The facial expressions investigated in this research are happiness, sadness, surprise, fear, anger, and disgust. According to our research, these facial expressions were classified into 12 classes of facial motion vectors. We applied our proposed analysis mechanism to the extended Cohen-Kanade facial expression dataset. Our developed automatic facial expression system achieved 95.3%, 92.8%, and 90.2% accuracy using Deep Learning (DL), Support Vector Machine (SVM), and C5.0 classifiers, respectively. In addition, based on this research, it was determined which parts of the face have a greater impact on facial expression recognition. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data mining

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Revisiting the antiferromagnetic structure of Tb14Ag51. The importance of distinguishing alternative symmetries for a multidimensional order parameter

arXiv

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arXiv 2023年

作者： Pomjakushin, V. Perez-Mato, J.M. Fischer, P. Keller, L. Sikora, W. Paul Scherrer Institut PSI VilligenCH-5232 Switzerland Facultad de Ciencia y Tecnología Universidad del País Vasco UPV/EHU Apartado 644 BilbaoE-48080 Spain Faculty of Physics and Applied Computer Science AGH University of Science and Technology KrakowPL-30-059 Poland

We revisit the antiferromagnetic structure of Tb14Ag51 (Fischer et al., 2005) with the propagation vector [ 1313, 0] and parent space group P6/m using both magnetic symmetry and irreducible representation arguments. We have found a new magnetic structure under the hexagonal Shubnikov magnetic space group P6¯0, which fits much better the experimental data. This new solution was obtained by constraining the spin arrangement to one of the three possible magnetic space groups of maximal symmetry that can be realised by a magnetic ordering transforming according to the 4-dimensional physically irreducible representation that is known to be relevant in this magnetic phase. The refined model, parameterised under P6¯0, implicitly includes the presence of a third harmonic with the propagation vector at the gamma point [0, 0, 0], which has an important weight in the final result. The structure consists of 13 symmetry-independent Tb magnetic moments with the same size of 8.48(2)µB, propagating cycloidally in the ab-plane. The modulation has a substantial deviation from being purely sinusoidal due to the contribution of the mentioned third harmonic. Copyright © 2023, The Authors. All rights reserved.

关键词： Vector spaces

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Heat as a witness of quantum properties

arXiv

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arXiv 2024年

作者： Junior, A. de Oliveira Brask, Jonatan Bohr Lipka-Bartosik, Patryk Center for Macroscopic Quantum States bigQ Department of Physics Technical University of Denmark Fysikvej 307 Kgs. Lyngby2800 Denmark Department of Applied Physics University of Geneva Geneva1211 Switzerland Institute of Theoretical Physics Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Krakow30-348 Poland

We present a new approach for witnessing quantum resources, such as entanglement and coherence, based on heat generation. Inspired by Maxwell’s demon, we ask what the optimal heat exchange between a quantum system and a thermal environment is when the process is assisted by a quantum memory. We derive fundamental energy constraints in this scenario and show that quantum states can reveal non-classical signatures via heat exchange. This approach leads to a heat-based witness for quantum properties, offering an alternative to system-specific measurements, as it only relies on fixed energy measurements in a thermal ancilla. We illustrate our findings with the detection of entanglement in isotropic states and coherence in two-spin systems interacting with a single-mode electromagnetic field. © 2024, CC BY.

关键词： Quantum entanglement

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Harmful Algal Bloom Forecasting. A Comparison between Stream and Batch Learning

SSRN

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SSRN 2024年

作者： Molares-Ulloa, Andres Rocruz, Elisabet Rivero, Daniel Padin, Xosé A. Nolasco, Rita Dubert, Jesús Fernandez-Blanco, Enrique Universidade da Coruña Department of Computer Science and Information Technology Faculty of Computer Science A Coruña15071 Spain Centro de investigación CITIC Department of Computer Science and Information Technology University of A Coruña A Coruña15071 Spain Vigo Spain Department of Physics Centro de Estudos do Ambiente e do Mar University of Aveiro Aveiro Portugal

Diarrhetic Shellfish Poisoning (DSP) is a global health threat arising from shellfish contaminated with toxins produced by dinoflagellates. The condition, with its widespread incidence, high morbidity rate, and persistent shellfish toxicity, poses risks to public health and the shellfish industry. High biomass of toxin-producing algae such as DSP are known as Harmful Algal Blooms (HABs). Monitoring and forecasting systems are crucial for mitigating HABs impact. The study of phytoplankton blooms and their causes is challenging, prompting the exploration of advanced machine learning techniques. Predicting harmful algal blooms involves a time-series-based problem with a strong historical seasonal component, however, recent anomalies due to changes in meteorological and oceanographic events have been observed. Stream Learning stands out as one of the most promising approaches for addressing time-series-based problems with concept drifts. However, its efficacy in predicting HABs remains unproven and needs to be tested in comparison with Batch Learning. Historical data availability is a critical point in developing predictive systems. In oceanography, the available data collection can have some constrains and limitations, which has led to exploring new tools to obtain more exhaustive time series. Ocean hydrodynamic models, such as CROCO, has proven its ability to reproduce the ocean conditions with a high temporal and spatial frequency. Model explainability is increasingly crucial for decision acceptance, particularly in the context of alarm systems like HAB forecasting. In this study, a machine learning workflow for predicting the number of cells of a toxic dinoflagellate, Dinophysis acuminata, was developed with several key advancements. Seven machine learning algorithms were compared within two learning paradigms. Notably, the output data from CROCO, the ocean hydrodynamic model, was employed as the primary dataset, palliating the limitation of time-continuous historic

关键词： Forecasting

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Fe Doping of Γ-Cui: Engineering of Thermoelectric Properties Via the Complex Defect Formation

SSRN

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SSRN 2024年

作者： Byeon, Seokyeong Pryga, Kacper Kim, Jin Hee Rhyee, Jong-Soo Komędera, Kamila Wiendlocha, Bartlomiej Jin, Hyungyu Pohang37673 Korea Republic of AGH University of Krakow Faculty of Physics and Applied Computer Science al. A. Mickiewicza 30 Krakow30-059 Poland Department of Applied Physics Institute of Natural Sciences Kyung Hee University Yongin17104 Korea Republic of Mössbauer Spectroscopy Laboratory Institute of Technology University of the National Education Commission ul.Podchorążych 2 Kraków30-084 Poland

We investigated the effects of Fe doping in a CuI system comprising relatively inexpensive and abundant elements, primarily to leverage the potential performance enhancement indicated by enhanced power factor of Fe doping in previous research and to explore the possibility of modifying the electronic band structure due to magnetic properties of Fe atoms. Our observations indicate that CuI, which exhibits p-type behavior owing to its intrinsic Cu vacancies, forms even more Cu vacancies when doped with Fe atoms, suggesting that CuI, despite its wide bandgap of approximately 3 eV, exhibits heavily doped semiconductor-type behavior. This is related to a complex behavior of Fe impurity, as only a fraction of Fe substitutes Cu with the majority located at the interstitial sites catalyzing the formation of Cu vacancies. Modifications of band structure due to Fe doping is realized enhancing the effective mass by approximately two times that of the undoped system. This adaptation minimizes the reduction in the Seebeck coefficient owing to the increased carrier concentration and yields a significant enhancement in the electrical conductivity by an order of magnitude. Additionally, introducing Fe atoms resulted in a finer nanostructure of the CuI system, amplifying boundary scattering and consequently playing a substantial role in reducing thermal conductivity. Through these combined effects, we realized a strategy of the ‘Phonon-glass-electron-crystal’, underpinning a marked enhancement in thermoelectric performance. © 2024, The Authors. All rights reserved.

关键词： Nanostructures

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Thermoelectric properties of the Corbino disk in graphene

arXiv

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arXiv 2023年

作者： Rycerz, Adam Rycerz, Katarzyna Witkowski, Piotr Institute for Theoretical Physics Jagiellonian University Lojasiewicza 11 KrakówPL–30348 Poland Institute of Computer Science AGH University of Science and Technology al. Mickiewicza 30 KrakówPL–30059 Poland

Thermopower and the Lorentz number for an edge-free (Corbino) graphene disk in the quantum Hall regime is calculated within the Landauer-Büttiker formalism. By varying the electrochemical potential, we find that amplitude of the Seebeck coefficient follows a modified Goldsmid-Sharp relation, with the energy gap defined by the interval between the zero and the first Landau levels in bulk graphene. An analogous relation for the Lorentz number is also determined. Thus, these thermoelectric properties are solely defined by the magnetic field, the temperature, the Fermi velocity in graphene, and fundamental constants including the electron charge, the Planck and Boltzmann constants, being independent on geometric dimensions of the system. This suggests that the Corbino disk in graphene may operate as a thermoelectric thermometer, allowing to measure small temperature differences between two reservoirs, if the mean temperature and the magnetic field are known. Copyright © 2023, The Authors. All rights reserved.

关键词： Graphene

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