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Calorimetric Studies and Thermodynamic Modeling of Ag–Mg–Ti Liquid Alloys

Materials 2024年第8期17卷 1786页

作者： Gozdur, Weronika Gąsior, Wladyslaw Zrobek, Maciej Budziak, Andrzej Dębski, Roman Gierlotka, Wojciech Pęska, Magda Polański, Marek Dębski, Adam Institute of Metallurgy and Materials Science Polish Academy of Sciences 25 Reymonta Street Kraków30-059 Poland Faculty of Energy and Fuels AGH University of Krakow Al. Mickiewicza 30 Kraków30-059 Poland Institute of Computer Science AGH University of Krakow Al. Mickiewicza 30 Kraków30-059 Poland Department of Materials Science and Engineering National Dong Hwa University Hualien970024 Taiwan Department of Functional Materials and Hydrogen Technology Military University of Technology 2 Kaliskiego St. Warsaw00-908 Poland

Due to the absence of thermodynamic data concerning the Ag–Mg–Ti system in the existing literature, this study aims to fill this gap by offering the outcomes of calorimetric investigations conducted on ternary liquid solutions of these alloys. The measurements were performed using the drop calorimetry method at temperatures of 1294 K and 1297 K for the liquid solutions with the following constant mole fraction ratio: xAg/xMg = 9/1, 7/3, 1/1, 3/7 [(Ag0.9Mg0.1)1−xTix, (Ag0.7Mg0.3)1−xTix, (Ag0.5Mg0.5)1−xTix, (Ag0.3Mg0.7)1−xTix)], and xAg/xTi = 19/1 [(Ag0.95Ti0.05)1−xMgx]. The results show that the mixing enthalpy change is characterized by negative deviations from the ideal solutions and the observed minimal value equals −13.444 kJ/mol for the Ag0.95Ti0.05 alloy and xMg = 0.4182. Next, based on the thermodynamic properties of binary systems described by the Redlich–Kister model and the determined experimental data from the calorimetric measurements, the ternary optimized parameters for the Ag–Mg–Ti liquid phase were calculated by the Muggianu model. Homemade software (TerGexHm 1.0) was used to optimize the calorimetric data using the least squares method. Next, the partial and molar thermodynamic functions were calculated and are presented in the tables and figures. Moreover, this work presents, for comparative purposes, the values of the enthalpy of mixing of liquid Ag–Mg–Ti alloys, which were calculated using Toop’s model. It was found that the best agreement between the modeled and experimental data was observed for the cross-sections xAg/xTi = 19/1 [(Ag0.95Ti0.05)1−xMgx] and xAg/xMg = 9/1 [(Ag0.9Mg0.1)1−xTix]. The results of the experiments presented in this paper are the first step in the investigation and future evaluation of the thermodynamics of phases and the calculation of the phase diagram of the silver–magnesium–titanium system. © 2024 by the authors.

关键词： Calorimetry

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Text-Guided Graph Temporal Modeling for Few-Shot Video Classification

SSRN

引用

SSRN 2024年

作者： Fuqin, Deng Jiaming, Zhon Li, Nannan Lanhui, Fu Jiang, Bingchun Yi, Ningbo Feng, Qi He, Xin Lun, Lam Tin School of Intelligent Manufacturing the Wuyi University Jiangmen China School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology China School of Textile Materials and Engineering the Wuyi University Jiangmen China School of Applied Physics and Materials Science the Wuyi University Jiangmen China School of Mechanical and Electrical Engineering Guangdong University of Science and Technology Dongguan China Shenzhen Vatop Semicon Tech Co. Ltd. Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society School of Science and Engineering The Chinese University of Hong Kong Shenzhen China

Large-scale pre-trained models and graph neural networks have recently demonstrated remarkable success in few-shot video classification tasks. However, they generally suffer from two key limitations: i) the temporal relations between adjacent frames tends to be ambiguous due to the lack of explicit temporal modeling. ii) the absence of multi-modal semantic knowledge in query videos results in inaccurate prototypes construction and an inability to achieve multi-modal temporal alignment metrics. To address these issues, we develop a Text-guided Graph Temporal Modeling (TgGTM) method that consists of two crucial components: a text-guided feature refinement module and a learnable query text-token contrastive objective. Specifically, the former leverages the temporal masking layer to guide the model in learning temporal relationships between adjacent frames. Additionally, it utilizes multi-modal information to refine video prototypes for comprehensive few-shot video classification. The latter addresses the feature discrepancy between multi-modal support features and single-modal query features by aligning a learnable query text-token with corresponding base class text descriptions. Extensive experiments on three commonly used benchmarks demonstrate the effectiveness of our proposed method, which significantly outperforms existing state-of-the-art methods under various settings. A detailed ablation showcases the importance of learning temporal relationships between adjacent frames and obtaining query text-token. The source code and models will be publicly available at https://***/JiaMingZhong2621/TgGTM. © 2024, The Authors. All rights reserved.

关键词： Semantics

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Decomposition of the Laves Phase in the Fusion Zone of the Inconel 718/Ati 718plus Welded Joint During Isothermal Holding at a Temperature of 649 ° C

SSRN

引用

SSRN 2022年

作者： Kruk, Adam Cempura, Grzegorz AGH. University of Science and Technology Faculty of Metals Engineering and Industrial Computer Science Al. A. Mickiewicza 30 Kraków30-059 Poland

The results of a microstructural investigation of the Laves phase decomposition during annealing at 649 °C using analytical and high-resolution transmission electron microscopy methods are presented. 3D imaging using the FIB-SEM tomography technique was used to analyze the morphology of the Laves particles and to visualize the spatial distribution of its decay products. The research showed that the Laves phase decays while annealing into other phases such as d, α-Cr and secondary carbides. The results of the 3D analysis showed the complex morphology of the Laves phase precipitation and the spatial distribution of the precipitated Cr-rich phase particles. The d phase had plate-like morphology nucleates and grew into the surrounding Laves phase that had a longer annealing time and continued to grow in the g matrix. A high-resolution analysis of the d/Laves and d/g interphase boundaries was performed. © 2022, The Authors. All rights reserved.

关键词： High resolution transmission electron microscopy

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A novel method of experimental determination of grain stresses and critical resolved shear stresses for slip and twin systems in a magnesium alloy

arXiv

引用

arXiv 2023年

作者： Kot, P. Wroński, M. Baczmański, A. Ludwik, A. Wroński, S. Wierzbanowski, K. Scheffzük, Ch. Pilch, J. Farkas, G. NOMATEN Centre of Excellence National Centre of Nuclear Research A. Soltana 7 Otwock-Swierk05-400 Poland AGH University of Krakow Faculty of Physics and Applied Computer Science al. Mickiewicza 30 Krakow30-059 Poland Karlsruhe Institute of Technology Institute of Applied Geosciences Adenauerring 20b Karlsruhe76131 Germany The Czech Academy of Science Nuclear Physics Institute Hlavní 130 Řež250 68 Czech Republic

A novel original method of determination of stresses and critical resolved shear stresses (CRSSs) using neutron diffraction was proposed. In this method, based on the crystallite group method, the lattice strains were measured in different directions and using different reflections hkl during uniaxial deformation of magnesium alloy AZ31. The advantage of this method is that the stresses for groups of grains having similar orientations can be determined directly from measurement without any models used for data interpretation. The obtained results are unambiguous and do not depend on the models’ assumptions as in previous works. Moreover, it was possible for the first time to determine the uncertainty of the measured CRSS values and local stresses at groups of grains. The used methodology allowed for the determination of stress partitioning between grains having different orientations and for an explanation of the anisotropic mechanical behaviour of the strongly textured alloy. Finally, the CRSS values allowed for the validation of the type of intergranular interaction assumed in the elastic-plastic self-consistent model and for a significant reduction of the number of unknown parameters when the model is adjusted to the experimental data. © 2023, CC BY-NC-ND.

关键词： Magnesium alloys

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A NEW CLASS OF DISTANCES ON COMPLEX PROJECTIVE SPACES

arXiv

引用

arXiv 2023年

作者： Bistron, Rafal Eckstein, Michal Friedland, Shmuel Miller, Tomasz Życzkowski, Karol Institute of Theoretical Physics Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Krakow Poland Doctoral School of Exact and Natural Sciences Jagiellonian University Poland Department of Mathematics Statistics and Computer Science University of Illinois at Chicago ChicagoIL60607-7045 United States Copernicus Center for Interdisciplinary Studies Jagiellonian University Krakow Poland Center for Theoretical Physics Polish Academy of Science Warsaw Poland

The complex projective space (ℂn) can be interpreted as the space of all quantum pure states of size n. A distance on this space, interesting from the perspective of quantum physics, can be induced from a classical distance defined on the n-point probability simplex by the 'earth mover problem'. We show that this construction leads to a quantity satisfying the triangle inequality, which yields a true distance on complex projective space belonging to the family of quantum 2-Wasserstein distances. © 2023, CC BY.

关键词： Earth (planet)

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Graph Neural Networks in computer Vision - Architectures, Datasets and Common Approaches

arXiv

引用

arXiv 2022年

作者： Krzywda, Maciej Lukasik, Szymon Gandomi, Amir H. Faculty of Physics and Applied Computer Science AGH University of Science and Technology al. Mickiewicza 30 Kraków30-059 Poland Faculty of Engineering and IT University of Technology Sydney 5 Broadway UltimoNSW2007 Australia Systems Research Institute Polish Academy of Sciences ul. Newelska 6 Warsaw01-447 Poland

Graph Neural Networks (GNNs) are a family of graph networks inspired by mechanisms existing between nodes on a graph. In recent years there has been an increased interest in GNN and their derivatives, i.e., Graph Attention Networks (GAT), Graph Convolutional Networks (GCN), and Graph Recurrent Networks (GRN). An increase in their usability in computer vision is also observed. The number of GNN applications in this field continues to expand;it includes video analysis and understanding, action and behavior recognition, computational photography, image and video synthesis from zero or few shots, and many more. This contribution aims to collect papers published about GNN-based approaches towards computer vision. They are described and summarized from three perspectives. Firstly, we investigate the architectures of Graph Neural Networks and their derivatives used in this area to provide accurate and explainable recommendations for the ensuing investigations. As for the other aspect, we also present datasets used in these works. Finally, using graph analysis, we also examine relations between GNN-based studies in computer vision and potential sources of inspiration identified outside of this field. © 2022, CC BY-SA.

关键词： computer vision

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Understanding the Dominant physics Mechanisms on the p-i-n Perovskite Solar Cells Fabricated by Scalable Slot-Die Coating Process in Ambient Air

引用

Solar RRL 2024年第3期8卷

作者： Glowienka, Damian Huang, Shih-Han Lee, Pei-Huan Tsai, Feng-Yu Su, Wei-Fang Department of Materials Science and Engineering National Taiwan University Taipei10617 Taiwan Faculty of Applied Physics and Mathematics Gdańsk University of Technology Narutowicza 11/12 Gdańsk80-233 Poland Department of Materials Engineering MingChi University of Technology New Taipei City243303 Taiwan

Perovskite solar cells (PSC) are emerging technologies that have shown continuous improvement in power conversion efficiency (PCE) and stability. However, a very important aspect that has been seldom considered is the reproducibility of PCE of PSC devices. It is possible to achieve PCE from 10.21% to 17.05% using scalable slot-die-coating technique. However, a spatial distribution of performance is clearly observed for device samples on a 4 × 4 cm substrate. The relatively low PCE is mainly coming from the losses of electrical mechanism. To have in-depth understanding of the losses, the dominant loss analysis techniques including numerical simulations are used to explore the mechanism. In the results, it is indicated that a part of efficiency decrease is due to the increase of bulk defect density which linearly changes with the quality of the perovskite layer and related to recombination process. However, extremely high-charge-carrier transportation losses are found at the HTL/perovskite interface that are related to the Fermi-level pinning mechanism for low-efficiency device. The result of physics insight of perovskite solar cells leads to a strategy, where chemical passivation technique is used to achieve the PCE from 13.81% to 18.07% for the batch of devices with good reproducibility. © 2023 Wiley-VCH GmbH.

关键词： Perovskite solar cells

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A New Prediction Model for Lung Cancer Based on ANN-SVM 5

A New Prediction Model for Lung Cancer Based on ANN-SVM

引用

5th International Conference on computer Information science and Application technology, CISAT 2022

作者： Li, Hua Lu, Yongchen Wang, Junyi Zhang, Yue Faculty of Chemical Engineering Beijing University of Chemical technology 15 Beisanhuan Dong Lu Chaoyang District Beijing100029 China School of Information Studies Syracuse University 900 South Crouse Ave SyracuseNY13244 United States Faculty of Computer Science and Technology Harbin Institute of Technology 2 Wenhua Xi Lu Huancui District Weihai264200 China Faculty of applied physics Jinan University 601 Huangpu Dadao Xi Tianhe District Guangzhou510632 China

ISBN: (纸本)9781510660076

Lung cancer has become a major threat to human life and health due to its characteristics of difficulty in early diagnosis and high mortality. Therefore, to develop a reliable method to predict lung cancer is very important. This study provides a new prediction method based on artificial intelligence technology. Firstly, a data set of patients' physical health status was collected, and the correlation between various variables and lung cancer was analyzed using random forest model. Then, the data sets were brought into some common data sets for training and the accuracy of these models was obtained. Then, the neural network technology is used to preliminarily process the data into feature vectors, and then the feature vectors are input into the support vector machine model, and the ANN-SVM hybrid model is established and trained. The results of this model are optimized by adjusting various model parameters and compared with the results of other common prediction models. Finally, a lung cancer prediction APP is made by using this model. Random forest results show that gender, alcohol consuming, and peer pressure are the main factors leading to lung cancer. The prediction accuracy of Ann-SVM model is up to 95.1613% shown by the cross-validation, which is higher than 2.8780%-8.5446% compared with other common prediction models, indicating that the model and prediction APP have practical value. © 2022 SPIE.

关键词： Decision trees

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Introducing a deep learning method for brain tumor classification using MRI data towards better performance

引用

Informatics in Medicine Unlocked 2024年 44卷

作者： Dikande Simo, Alain Marcel Tchagna Kouanou, Aurelle Monthe, Valery Kameni Nana, Michael Moffo Lonla, Bertrand Africa Institute of Mathematical Science (AIMS)-Cameroon Limbe Cameroon Department of Physics Faculty of Science University of Buea Cameroon Department of Computer Engineering College of Technology University of Buea Cameroon Department of Training Research Development and Innovation InchTech's Solutions Yaoundé Cameroon Department of Computer Science Faculty of Sciences University of Yaoundé 1 Yaoundé Cameroon

Magnetic resonance imaging (MRI) is widely used to detect brain tumors, but its accuracy depends on the physician's expertise and often requires biopsy confirmation. Deep learning, especially in the field of computer vision, has revolutionized the diagnosis and classification of brain tumors using MRI. This study aims to design a sequential brain tumor detection and classification model based on deep learning and using fully convolutional neural networks. The proposed model consists of two steps: distinguishing non-neoplastic brain from neoplastic brain and determining the tumor type of the latter. Two models were trained using the Brain Tumor MRI Dataset. Four optimizers are studied for three classification tasks (Adam, Nesterov momentum, root-mean-square propagation, and adaptive gradient) to achieve the best results. Adam performed best at distinguishing tumor from non-tumor brains, with 100 % training accuracy and 98 % validation and test accuracy. Nesterov Momentum performed best at differentiating the three tumor types, with 100 % training accuracy and 92 % validation and testing accuracy. Nesterov also performed best on the third classification task, with 100 % training accuracy and 95 % validation and test accuracy. Nesterov-based sequence models show significant results compared to literature works. The proposed Nesterov momentum-based sequence model achieves high accuracy in MRI brain tumor detection and classification. © 2023 The Authors

关键词： Brain tumors classification Deep learning Fully convolutional neural network Magnetic resonance image Nesterov momentum optimization

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Higher-order correction to weak-field lensing of an Ellis-Bronnikov wormhole

arXiv

引用

arXiv 2023年

作者： Cai, Tingqi Huang, Hyat Wang, Zun Zhu, Mian Department of Physics The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Jockey Club Institute for Advanced Study The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong College of Physics and Communication Electronics Jiangxi Normal University Nanchang330022 China Institute of Physics University of Oldenburg Postfach 2503 OldenburgD-26111 Germany Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Krakow30-348 Poland

The gravitational lensing effect at higher order under weak-field approximation is believed to be important to distinguish black holes and other compact objects such as wormholes. The deflection angle of a generic wormhole is difficult to solve analytically;thus approximation methods are implemented. In this paper, we investigate the weak-field deflection angle of a specific wormhole, the Ellis-Bronnikov wormhole, up to the 1/b4 order. We use different approximation formalisms, study their precision at 1/b4 order by a comparison to a purely numerical result, and finally rank these formalisms by their accuracy. Moreover, we find that certain formalisms are sensitive to the choice of coordinate system;thus it is important to choose the coordinate system appropriately for the evaluating of lensing physics. © 2023, CC BY.

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