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Decoupling the orientation dependence of deformation-induced martensitic transformation via single-crystal micropillar compression

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Journal of Materials science & Technology 2026年 244卷 80-87页

作者： Guosheng Sun Shilong Lv Yunfei Zhang Lirong Xiao Yong Peng Yuntian Zhu Kehong Wang Jizi Liu School of Materials Science and Engineering Nanjing University of Science and Technology Nanjing 210094 China Center of Analytical Facilities Nanjing University of Science and Technology Nanjing 210094 China Department of Materials Science and Engineering Hong Kong Institute for Advanced Study College of Science and Engineering City University of Hong Kong Kowloon 999077 Hong Kong China Mechanical Behavior Division of Shenyang National Laboratory for Materials Science City University of Hong Kong Kowloon 999077 Hong Kong China Nanjing Research Center for Biomedical Electron Microscopy (NRC-BEM) Nanjing Drum Tower Hospital Medical School Nanjing University Nanjing 210093 China

This study explores the significant orientation dependence of deformation-induced martensitic transformation. By employing single crystalline micropillar compression, three typical low-index crystallographic orientations were selected to examine their influence on the martensitic transformation of metastable austenitic stainless steel. Results demonstrate that the [011] orientation significantly inhibits the formation of α'-martensite, whereas the [001] and [111] orientations more effectively facilitate phase transformation, which is closely related to the transformation mechanisms. Specifically, the [001] orientation, which is prone to stacking fault formation, promotes martensitic transformation significantly with the aid of ε-martensite and the decreased effective stacking fault energy during compression. Meanwhile, the [111] oriented pillars demonstrate the highest yield strength, with shear deformation responsible for the relatively high fraction of α'-martensite. Additionally, the non-Schmid behavior occurred in the [011]- and [111]- oriented micropillars were found and discussed. This research advances the understanding of orientation-dependent martensitic transformation and provides an optional way to tailor the Transformation-induced plasticity effect via grain orientation engineering.

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Melanoma Detection using advanced Deep Neural Network

Melanoma Detection using Advanced Deep Neural Network

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International Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE)

作者： Pallabi Sharma Anmol Gautam Rajashree Nayak Bunil Kumar Balabantaray Computer Science and Engineering National Institute of Technology Meghalaya Shillong India Computer Science and Engineering NIT Meghalaya Shillong India Center for Data Science JIS Institute of Advanced Studies and Research Kolkata

Melanoma is a type of skin cancer that starts in the cells (melanocytes) that govern the color of your skin. Melanoma is the most lethal one among all other skin diseases and the only reason for 77% deaths due to skin cancer. The best way to reduce these deaths is to detect cancer at its early stages so it can be treated and cured with minor treatment or surgeries. To speed up and improve the process of early detection, we propose an automatic classification method for melanoma cancer using an advanced deep neural network. Deep learning models require a large dataset to work efficiently, but due to limited time and the heavy workload of doctors, there is a lack of annotated skin can-cer images. Therefore, the proposed model introduces adversarial training for achieving better accuracy even with a small amount of data. The model removes unnecessary details and noise from the image and amplifies the depth and gradient in the dimensions and the shade of the image. This proposed adversarial method uses the gradients of the loss with respect to the input image to create a new adversarial example image that maximizes the loss for an input image. The synthetically generated images are used in the classification system for training and testing purposes. A comparative analysis of training with an adversarial approach and without an adversarial approach on different pre-trained models, namely VGG16, VGG19, Densenet121, and Resnet101, is also introduced in this work. ResNet101 with adversarial training has shown a state-of-the-art accuracy performance of 84.77% for melanoma classification. Therefore, the proposed approach can be considered an efficient method for classifying benign and malignant melanoma.

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A MgNO Method for Multiphase Flow in Porous Media

arXiv

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arXiv 2024年

作者： Liu, Xinliang Yang, Xia Zhang, Chen-Song Zhang, Lian Zhao, Li Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Xiangtan University Hunan China LSEC ICMSEC Academy of Mathematics and System Sciences Chinese Academy of Sciences University of Chinese Academy of Sciences China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data China

This research investigates the application of Multigrid Neural Operator (MgNO), a neural operator architecture inspired by multigrid methods, in the simulation for multiphase flow within porous media. The architecture is adjusted to manage a variety of crucial factors, such as permeability and porosity heterogeneity. The study extendes MgNO to time-dependent porous media flow problems and validate its accuracy in predicting essential aspects of multiphase flows. Furthermore, the research provides a detailed comparison between MgNO and Fourier Neural Opeartor (FNO), which is one of the most popular neural operator methods, on their performance regarding prediction error accumulation over time. This aspect provides valuable insights into the models' long-term predictive stability and reliability. The study demonstrates MgNO's capability to effectively simulate multiphase flow problems, offering considerable time savings compared to traditional simulation methods, marking an advancement in integrating data-driven methodologies in geoscience applications. © 2024, CC BY.

关键词： Porous materials

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The Transition of Threshold Voltage Shift of Al2O3/Si3N4AlGaN/GaN MIS-HEMTs Under Negative Gate Bias Stress From DC to AC

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IEEE Transactions on Electron Devices 2024年第3期71卷 1792-1797页

作者： Lee, Ya-Huan Chang, Kai-Chun Tai, Mao-Chou Wang, Yu-Xuan Lin, Hsin-Ni Yeh, Yu-Hsuan Kuo, Hung-Ming Tsai, Xin-Ying Lee, Jason Huang, I-Yu Chang, Ting-Chang Sze, Simon National Sun Yat-sen University Department of Physics Kaohsiung80424 Taiwan National Sun Yat-sen University Department of Photonics Kaohsiung80424 Taiwan Institute of Electronics National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Institute of Electronics National Yang Ming Chiao Tung University Department of Materials and Optoelectronic Science Hsinchu30010 Taiwan Boston University Department of Computer Science BostonMA02215 United States National Sun Yat-sen University College of Semiconductor and Advanced Technology Research Kaohsiung80424 Taiwan National Sun Yat-sen University Department of Physics Center of Crystal Research Kaohsiung80424 Taiwan National Chiao-Tung University Department of Electronics Engineering Institute of Electronics Hsinchu30010 Taiwan

In this study, the reliability issues are discussed under dc and ac negative gate bias stress (ac-NGBS) in Al2O3/Si3N4 metal insulator semiconductor high electron mobility transistors (MIS-HEMTs). Converse degradation between these two stress conditions is discovered. The holes generated by trap-assisted thermionic field emission (TA-TFE) are trapped into the Al2O3 layer, so that the threshold voltage (Vth) decreases under dc negative gate bias stress (dc-NGBS). Vth increases because of the hot electrons injected into the GaN layer, while the device is turned on quickly under ac-NGBS. In addition, the degradation mechanisms under dc-NGBS and ac-NGBS are confirmed by the OFF-state gate and drain leakages, respectively. Silvaco TCAD is used to validate the degradation mechanism under ac-NGBS. Finally, the characteristics of the Vth shift transition from dc to ac NGBS are discussed. © 1963-2012 IEEE.

关键词： Threshold voltage

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Stability Criteria for Neural Networks with Stochastic Impulsive Intensity and Impulsive Density

Stability Criteria for Neural Networks with Stochastic Impul...

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Neuromorphic Computing (ICNC), International Conference on

作者： Hao Deng Jingxin Liu Jun Peng Yingxu Wang Fei Chang School of Computer Science and Engineering Chongqing University of Science and Technology Chongqing China College of Electronic and Information Engineering Southwest University Chongqing China Center for Applied Mathematics of Guangxi Guangxi Minzu University Nanning China School of Mathematics and Physics Sciences Chongqing University of Science and Technology Chongqing China Chongqing Research Institute of Intelligent Mathematics and Autonomous AI (RI-IM·AI*) Chongqing China

ISBN: (数字)9798331508661

ISBN: (纸本)9798331508678

This paper studies stability conditions for neural networks with stochastic impulsive intensity and impulsive density. Firstly, the pth moment exponential stability conditions are given, where the impulsive intensities of impulsive neural networks are independent and identically distributed, and the impulsive sequence is defined by impulsive density. Then, taking a linear time-varying impulsive system as an example, it is shown that impulsive sequence defined by average impulsive interval is sometime insufficient in terms of stabilizing such linear time-varying systems. Finally, two examples are presented to verify that impulsive density method is more effective and universal than average impulsive interval method when dealing with time-varying systems.

关键词： Sufficient conditions Stability Neuromorphic engineering Neural networks Stability criteria Control theory Time-varying systems Numerical stability

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Rational Design of Hybrid Electrolyte for All-Solid-State Lithium Battery Based on Investigation of Lithium-Ion Transport Mechanism

SSRN

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SSRN 2024年

作者： Park, Jinkyu Shim, Yoonsu Chang, Joon Ha Kim, Se-Hee Kang, Yongku Lee, Jin Woong Jung, Dae Soo Yuk, Jong Min Lee, Chan-Woo Suk, Jungdon Advanced Energy Materials Research Center Advanced Materials Division Korea Research Institute of Chemical Technology 141 Gajeong-ro Yuseong-gu Daejeon34114 Korea Republic of Department of Materials Science and Engineering Korea Advanced Institute of Science and Technology 291 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of Department of Advanced Materials University of Science and Technology 217 Gajeong-ro Yuseong-gu Daejeon34113 Korea Republic of Research Institute of Industrial Science and Technology Korea Republic of Energy Storage Materials Center Korea Institute of Ceramic Engineering and Technology Gyeongnam Jinju52851 Korea Republic of Computational Science & Engineering Laboratory Korea Institute of Energy Research 152 Gajeong-ro Yuseong-Gu Daejeon34129 Korea Republic of

Lithium all-solid-state batteries (ASSBs) are a promising technology for achieving high energy density, long cycle life, and safe rechargeable battery systems. Among these, Li ASSBs using solid polymer electrolytes (SPEs) have gained attention owing to their processability, lightweight nature, flexibility, and favorable electrode contacts. However, SPEs have low ionic conductivity, low Li+ transference number, and lack mechanical strength, limiting cell performance. Therefore, the present study focuses on the development of hybrid polymer electrolytes (HPEs) by incorporating Li1+xAlxTi2-x(PO4)3 (LATP) of Li/Na superionic conductor-type materials into SPEs. The HPEs with LATP 10 wt% exhibited significant improvements with an ionic conductivity of 7.23 × 10-4 S cm-1 at 45 ℃ and a Li+ transference number of 0.61. Density functional theory calculations supported the enhanced Li-ion migration through the polymer-LATP interface achieved by the optimized LATP content. The addition of LATP particles enhanced the mechanical strength of the electrolytes, effectively suppressing dendrite growth, resulting in a 65.42% capacity retention after 320 cycles, highlighting the crucial role of high-performance HPEs in advanced ASSBs. By addressing the limitations of SPEs, HPEs offer promising opportunities to unlock the full potential of solid-state battery technologies for various energy storage systems. © 2024, The Authors. All rights reserved.

关键词： Ionic conductivity

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Optimizations of reinforcing particulates and processing parameters for stir casting of aluminium metal matrix composites for sustainable properties

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Materials Today: Proceedings 2022年 68卷 1172-1179页

作者： Kumar, Jatinder Kumar, Vipan Sharma, Shubham Chohan, Jasgurpreet Kumar, Raman Singh, Sandeep Obaid, Ahmed J. Akram, Shaik Vaseem Department of Mechanical Engineering St. Soldier Institute of Engineering and Technology Punjab Jalandhar144011 India Department of Mechanical Engineering IK Gujral Punjab Technical University Jalandhar-Kapurthala Road Punjab Kapurthala144603 India Chandigarh University Gharuan140413 India Department of Civil Engineering Chandigarh University Gharuan140413 India Faculty of Computer Science and Mathematics University of Kufa Iraq Division of Research and Innovation Uttaranchal University Uttarakhand Dehradun248007 India

Due to its exceptional characteristics, aluminium matrix composites are used in defence, automotive, and structural applications. Stir casting for SiC and Gr reinforced AMCs was optimised. SiC weight was kept constant during manufacturing, while Gr was 1, 3, and 5% by weight. Stir casting was used to formulate AMCs by varying graphite amounts, stirring time, and stirring speed according to Taguchi's L9 OA. Hardness, tensile strength, and porosity levels of composites were examined. Taguchi analysis and Grey Relational Analysis optimised stir casting. Stirring speed, duration, and graphite content were found to affect AMC hardness. Graphite particles influence AMC strength more than stirring speed and time. GRA results show that include Gr in AMCs is the most important component impacting hybrid AMC characteristics, contributing 63.83 percent to optimal response values. Stirring speed and time contribute 17.27% and 11.1% to optimal response values. Comparing anticipated and experimental GRG validates results. © 2022

关键词： Silicon carbide

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Time and Space Complexity of Deterministic and Nondeterministic Decision Trees: Local Approach

Time and Space Complexity of Deterministic and Nondeterminis...

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IEEE International Conference on Big Data

作者： Kerven Durdymyradov Mikhail Moshkov Computer Electrical and Mathematical Sciences & Engineering Division and Computational Bioscience Research Center King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

Extensive research has been conducted on rough set theory, specifically focusing on the study of decision trees (DTs) and decision rule systems (DRSs). This theory addresses several important questions, including the relationship between DTs and DRSs, the tradeoff between time and space for DTs, and the tradeoff for *** objective of this paper is to investigate infinite binary information systems (ISs), each consisting of an infinite set (universe) and an infinite set of two-valued functions (features) defined on the universe. We examine the concept of a task within an IS, which is described by a finite number of features and a mapping that assigns a decision to each combination of feature values. Our analysis focuses on deterministic and nondeterministic DTs (DDTs and NDTs) as algorithms for task-solving, with the restriction that only features from the task description are *** serve as representations of DRSs and can sometimes have lower space complexity compared to the original DRSs. We study the time and space complexity of DTs, specifically examining the depth and the number of nodes in these trees. The obtained results allow us to classify the set of all ISs into three families, enabling the identification of nontrivial relationships between DDTs and DRSs represented by NDTs. For each family, we investigate the tradeoff between time and space for both DDTs and NDTs.

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Detection of small holes by the scale-invariant robust density-aware distance (RDAD) filtration

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Journal of Applied and Computational Topology 2024年第6期8卷 1793-1836页

作者： Siu, Chunyin Samorodnitsky, Gennady Yu, Christina Lee Yao, Andrey Center for Applied Mathematics Cornell University Frank H.T. Rhodes Hall Ithaca 14853 NY United States School of Operations Research and Information Engineering Cornell University Frank H.T. Rhodes Hall Ithaca 14853 NY United States Department of Computer Science University of Wisconsin – Madison CS Building 1210 W. Dayton St Madison 53703 WI United States

A novel topological-data-analytical (TDA) method is proposed to distinguish, from noise, small holes surrounded by high-density regions of a probability density function. The proposed method is robust against additive noise and outliers. Traditional TDA tools, like those based on the distance filtration, often struggle to distinguish small features from noise, because both have short persistences. An alternative filtration, called the Robust Density-Aware Distance filtration, is proposed to prolong the persistences of small holes of high-density regions. This is achieved by weighting the distance function by the density in the sense of Bell et al. The concept of distance-to-measure is incorporated to enhance stability and mitigate noise. The persistence-prolonging property and robustness of the proposed filtration are rigorously established, and numerical experiments are presented to demonstrate the proposed filtration’s utility in identifying small holes. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Distance-to-measure Random topology Topological bootstrapping Topological data analysis Topological inference Weighted filtration

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Near infrared radiation induced attenuation in P-doped and Al-doped optical fibers at cryogenic temperature

Optics Continuum

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Optics Continuum 2025年第7期4卷 1411-1425页

作者： S. Zilberman W. Lo K. N. Herrera Guzman Z. Van Horn R. Dror A. Bruner F. Scurti Ken and Mary Alice Lindquist Department of Nuclear Engineering The Pennsylvania State University University Park PA 16802 USA Applied Physics Division Soreq Nuclear Research Center Yavne 81800 Israel Department of Engineering Science and Mechanics The Pennsylvania State University University Park PA 16802 USA Radiation Science and Engineering Center The Pennsylvania State University University Park PA 16802 USA Israeli Center for Advanced Photonics Yavne 81800 Israel Materials Research Institute The Pennsylvania State University University Park PA 16802 USA

This manuscript presents a study on the effects of cryogenic irradiation on P-doped and Al-doped optical fibers, focusing on radiation-induced attenuation (RIA) when exposed to gamma radiation in the near-infrared (NIR) telecom wavelength 1550 nm. Results are compared to those obtained at room temperature under equivalent conditions. A novel P-doped fiber from the Israeli Institute for advanced Photonics (ICAP) was tested for what we believe is the first time, along with commercially available P- and Al-doped optical fibers. All results are explained in terms of the defects that are associated with specific spectral ranges of each fiber chemistry. Results from the P-doped fiber show no recovery at room temperature and slight opposite recovery at cryogenic temperatures. The Al-doped fiber showed a slight recovery at both room and cryogenic temperatures. A kinetic model was used to extrapolate data up to higher doses, revealing higher RIA at cryogenic temperatures for both fiber types, with P-doped fiber exhibiting a 100% increase in expected saturation dose at cryogenic temperatures. By reporting the first experimental measurements of cryogenic RIA in P-doped and Al-doped fibers, this study sheds light on the underlying defects responsible for RIA at cryogenic temperature and represents an initial step toward developing in-situ cryogenic radiation dosimeters for nuclear fusion reactors, particle colliders, and space missions.

关键词： Fiber optic sensors Light transmission Multimode fibers Near infrared Optical fibers Spatial resolution

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