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Blending effects of boron carbide and lanthanum(III) oxide reinforcements on the structural, physical, mechanical, and radiation shielding properties of 316L-stainless steel composites for nuclear applications

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Annals of Nuclear Energy 2025年 221卷

作者： ALMisned, Ghada Yayla, Nihal Albayrak, M.Gökhan Güler, Ömer Baykal, Duygu Sen Alkarrani, Hessa Tekin, H.O. Department of Physics College of Science Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Munzur University Department of Strategic Raw Materials and Advanced Technology Applications Tunceli Turkey Fırat University Metallurgical and Materials Engineering Department Engineering Faculty Elazığ Turkey Rare Earth Elements Application and Research Center Munzur University Tunceli62000 Turkey Istanbul Nisantasi University Faculty of Engineering and Architecture Mechatronics Engineering Istanbul34398 Turkey Research Institute for Medical and Health Sciences University of Sharjah Sharjah Sharjah27272 United Arab Emirates Medical Diagnostic Imaging Department College of Health Sciences University of Sharjah Sharjah27272 United Arab Emirates Istinye University Faculty of Engineering and Natural Sciences Computer Engineering Department Istanbul34396 Turkey

Four composite samples were fabricated, maintaining 10 % B4C and varying La2O3 content from 0 % to 20 %. X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), and Energy Dispersive X-ray Spectroscopy (EDX) analyses confirmed the successful incorporation of reinforcements, preserving the austenitic structure of 316L-SS. Elastic modulus calculations revealed a gradual reduction from 225.0 GPa to 215.0 GPa with increasing La2O3 due to clustering and reduced homogeneity. Gamma-ray shielding properties demonstrated significant improvements, particularly at low and intermediate photon energies, with Sample 4 with 20 % La2O3 addition, achieving a 13.6 % higher linear attenuation coefficients (LAC) at 0.015 MeV. Neutron shielding highlighted Sample 4 as one of the most effective materials, outperforming conventional alloys. This study demonstrates the potential of 316L Stainless-Steel (316L-SS) + B4C + La2O3 composites as multifunctional materials for radiation-intensive applications, offering superior attenuation and structural stability. It can be concluded that optimization of reinforcement distribution would play an important role in balancing mechanical and shielding performance in next-generation nuclear materials. © 2025 Elsevier Ltd

关键词： Reactor shielding

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Performance of multimodal prediction models for intracerebral hemorrhage outcomes using real-world data

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International Journal of Medical Informatics 2025年 202卷 105989-105989页

作者： Matsumoto, Koutarou Suzuki, Masahiro Ishihara, Kazuaki Tokunaga, Koki Matsuda, Katsuhiko Chen, Jenhui Yamashiro, Shigeo Soejima, Hidehisa Nakashima, Naoki Kamouchi, Masahiro Department of Health Care Administration and Management of Medical Sciences Kyushu University Fukuoka Japan Graduate Degree Program of Applied Data Sciences Sophia University Tokyo Japan Department of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan Department of Pharmacy Saiseikai Kumamoto Hospital Kumamoto Japan Department of Radiology Saiseikai Kumamoto Hospital Kumamoto Japan Division of Neurosurgery Saiseikai Kumamoto Hospital Kumamoto Japan Institute for Medical Information Research and Analysis Saiseikai Kumamoto Hospital Kumamoto Japan Medical Information Center Kyushu University Hospital Fukuoka Japan Department of Medical Informatics of Medical Sciences Kyushu University Fukuoka Japan Center for Cohort Studies of Medical Sciences Kyushu University Fukuoka Japan

Background: We aimed to develop and validate multimodal models integrating computed tomography (CT) images, text and tabular clinical data to predict poor functional outcomes and in-hospital mortality in patients with intracerebral hemorrhage (ICH). These models were designed to assist non-specialists in emergency settings with limited access to stroke specialists. Methods: A retrospective analysis of 527 patients with ICH admitted to a Japanese tertiary hospital between April 2019 and February 2022 was conducted. Deep learning techniques were used to extract features from three-dimensional CT images and unstructured data, which were then combined with tabular data to develop an L1-regularized logistic regression model to predict poor functional outcomes (modified Rankin scale score 3–6) and in-hospital mortality. The model's performance was evaluated by assessing discrimination metrics, calibration plots, and decision curve analysis (DCA) using temporal validation data. Results: The multimodal model utilizing both imaging and text data, such as medical interviews, exhibited the highest performance in predicting poor functional outcomes. In contrast, the model that combined imaging with tabular data, including physiological and laboratory results, demonstrated the best predictive performance for in-hospital mortality. These models exhibited high discriminative performance, with areas under the receiver operating curve (AUROCs) of 0.86 (95% CI: 0.79–0.92) and 0.91 (95% CI: 0.84–0.96) for poor functional outcomes and in-hospital mortality, respectively. Calibration was satisfactory for predicting poor functional outcomes, but requires refinement for mortality prediction. The models performed similar to or better than conventional risk scores, and DCA curves supported their clinical utility. Conclusion: Multimodal prediction models have the potential to aid non-specialists in making informed decisions regarding ICH cases in emergency departments as part of clinical dec

关键词： Logistic regression

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Conditional Generative Modeling for Amorphous Multi-Element Materials

arXiv

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arXiv 2025年

作者： Li, Honglin Liu, Chuhao Guo, Yongfeng Luo, Xiaoshan Chen, Yijie Liu, Guangsheng Li, Yu Wang, Ruoyu Wang, Zhenyu Wu, Jianzhuo Ma, Cheng Xie, Zhuohang Lv, Jian Ding, Yufei Zhang, Huabin Luo, Jian Zhong, Zhicheng Li, Mufan Wang, Yanchao Li, Wan-Lu Key Laboratory of Material Simulation Methods and Software Ministry of Education College of Physics Jilin University Changchun China Aiiso Yufeng Li Family Department of Chemical and Nano Engineering University of California La Jolla San DiegoCA United States Institute of Molecular Engineering Plus College of Chemistry Fuzhou University Fuzhou China College of Chemistry and Molecular Engineering Peking University Beijing China School of Physics Nankai University Tianjin China Institute of Modern Physics Fudan University Shanghai China Program in Materials Science and Engineering University of California La Jolla San DiegoCA United States School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China International Center of Future Science Jilin University Changchun China Department of Computer Science and Engineering University of California La Jolla San DiegoCA United States Center for Renewable Energy and Storage Technologies Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Suzhou Lab Suzhou215123 China

Amorphous multi-element materials offer unprecedented tunability in composition and properties, yet their rational design remains challenging due to the lack of predictive structure-property relationships and the vast configurational space. Traditional modeling struggles to capture the intricate short-range order that dictates their stability and functionality. We here introduce ApolloX, a pioneering predictive framework for amorphous multi-element materials, establishing a new paradigm by integrating physics-informed generative modeling with particle swarm optimization, using chemical short-range order as an explicit constraint. By systematically navigating the disordered energy landscape, ApolloX enables the targeted design of thermodynamically stable amorphous configurations. It accurately predicts atomic-scale arrangements, including composition-driven metal clustering and amorphization trends, which are well-validated by experiments, while also guiding synthesis by leveraging sluggish diffusion to control elemental distribution and disorder. The resulting structural evolution, governed by composition, directly impacts catalytic performance, leading to improved activity and stability with increasing amorphization. This predictive-experimental synergy transforms the discovery of amorphous materials, unlocking new frontiers in catalysis, energy storage, and functional disordered systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Amorphization

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ChatAug: Leveraging ChatGPT for Text data Augmentation

arXiv

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arXiv 2023年

作者： Dai, Haixing Liu, Zhengliang Liao, Wenxiong Huang, Xiaoke Wu, Zihao Zhao, Lin Liu, Wei Liu, Ninghao Li, Sheng Zhu, Dajiang Cai, Hongmin Li, Quanzheng Shen, Dinggang Liu, Tianming Li, Xiang The School of Computing University of Georgia AthensGA United States The School of Computer Science and Engineering South China University of Technology China The Department of Radiation Oncology Mayo Clinic PhoenixAZ United States The School of Data Science University of Virginia CharlottesvilleVA United States The Department of Computer Science and Engineering The University of Texas at Arlington ArlingtonTX United States The Department of Radiology Massachusetts General Hospital Harvard Medical School BostonMA United States School of Biomedical Engineering ShanghaiTech University Shanghai201210 China Shanghai United Imaging Intelligence Co. Ltd. Shanghai200230 China Shanghai Clinical Research and Trial Center Shanghai201210 China

Text data augmentation is an effective strategy for overcoming the challenge of limited sample sizes in many natural language processing (NLP) tasks. This challenge is especially prominent in the few-shot learning scenario, where the data in the target domain is generally much scarcer and of lowered quality. A natural and widely-used strategy to mitigate such challenges is to perform data augmentation on the training data to better capture the data invariance and increase the sample size. However, current text data augmentation methods either can not ensure the correct labeling of the generated data (lacking faithfulness) or can not ensure sufficient diversity in the generated data (lacking completeness), or both. Inspired by the recent success of large language models, especially the development of ChatGPT, which demonstrated improved language comprehension abilities, in this work, we propose a text data augmentation approach based on ChatGPT (named ChatAug). ChatGPT is trained on data with unparalleled linguistic richness and employs a reinforcement training process with large-scale human feedback, which endows the model with affinity to the naturalness of human language. Our text data augmentation approach ChatAug rephrases each sentence in the training samples into multiple conceptually similar but semantically different samples. The augmented samples can then be used in downstream model training. Experiment results on few-shot learning text classification tasks show the superior performance of the proposed ChatAug approach over state-of-the-art text data augmentation methods in terms of testing accuracy and distribution of the augmented samples. © 2023, CC BY.

关键词： Classification (of information)

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Heavy metals prediction in coastal marine sediments using hybridized machine learning models with metaheuristic optimization algorithm

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Chemosphere 2024年 352卷 141329页

作者： Yaseen, Zaher Mundher Melini Wan Mohtar, Wan Hanna Homod, Raad Z. Alawi, Omer A. Abba, Sani I. Oudah, Atheer Y. Togun, Hussein Goliatt, Leonardo Ul Hassan Kazmi, Syed Shabi Tao, Hai Civil and Environmental Engineering Department King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia Interdisciplinary Research Center for Membranes and Water Security King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Saudi Arabia Department of Civil Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia UKM Selangor Bangi 43600 Malaysia Environmental Management Centre Institute of Climate Change Universiti Kebangsaan Malaysia Selangor UKM Bangi 43600 Malaysia Department of Oil and Gas Engineering Basrah University for Oil and Gas Basra Iraq Department of Thermofluids School of Mechanical Engineering Universiti Teknologi Malaysia UTM Skudai Johor Bahru 81310 Malaysia Department of Computer Sciences College of Education for Pure Science University of Thi-Qar Nasiriyah 64001 Iraq Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Nasiriyah 64001 Iraq Department of Mechanical Engineering College of Engineering University of Baghdad Baghdad Iraq Computational and Applied Mechanics Department Federal University of Juiz de Fora 36036-900 Brazil Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention and Guangdong Provincial Key Laboratory of Marine Biotechnology Shantou University Shantou 515063 China School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun 558000 China Institute of Big Data Application and Artificial Intelligence Qiannan Normal University for Nationalities Guizhou Duyun 558000 China Faculty of Data Science and Information Technology INTI International University 71800 Malaysia

This study proposes different standalone models viz: Elman neural network (ENN), Boosted Tree algorithm (BTA), and f relevance vector machine (RVM) for modeling arsenic (As (mg/kg)) and zinc (Zn (mg/kg)) in marine sediments owing to anthropogenic activities. A heuristic algorithm based on the potential of RVM and a flower pollination algorithm (RVM-FPA) was developed to improve the prediction performance. Several evaluation indicators and graphical methods coupled with visualized cumulative probability function (CDF) were used to evaluate the accuracy of the models. Akaike (aiC) and Schwarz (SCI) information criteria based on Dickey-Fuller (ADF) and Philip Perron (PP) tests were introduced to check the reliability and stationarity of the data. The prediction performance in the verification phase indicated that RVM-M2 (PBaiS = -o.0465, MAE = 0.0335) and ENN-M2 (PBaiS = 0.0043, MAE = 0.0322) emerged as the best model for As (mg/kg) and Zn (mg/kg), respectively. In contrast with the standalone approaches, the simulated hybrid RVM-FPA proved merit and the most reliable, with a 5 % and 18 % predictive increase for As (mg/kg) and Zn (mg/kg), respectively. The study's findings validated the potential for estimating complex HMs through intelligent data-driven models and heuristic optimization. The study also generated valuable insights that can inform the decision-makers and stockholders for environmental management strategies. © 2024 Elsevier Ltd

关键词： Artificial intelligence Heavy metals Sensitivity analysis Soil contamination

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A geometric analysis of neural collapse with unconstrained features

arXiv

引用

arXiv 2021年

作者： Zhu, Zhihui Ding, Tianyu Zhou, Jinxin Li, Xiao You, Chong Sulam, Jeremias Qu, Qing Department of Electrical & Computer Engineering University of Denver Department of Applied Mathematics & Statistics Johns Hopkins University Center for Data Science New York University Google Research New York City United States Department of Biomedical Engineering MINDS Johns Hopkins University Department of Electrical Engineering & Computer Science University of Michigan

We provide the first global optimization landscape analysis of Neural Collapse - an intriguing empirical phenomenon that arises in the last-layer classifiers and features of neural networks during the terminal phase of training. As recently reported in [1], this phenomenon implies that (i) the class means and the last-layer classifiers all collapse to the vertices of a Simplex Equiangular Tight Frame (ETF) up to scaling, and (ii) cross-example within-class variability of last-layer activations collapses to zero. We study the problem based on a simplified unconstrained feature model, which isolates the topmost layers from the classifier of the neural network. In this context, we show that the classical cross-entropy loss with weight decay has a benign global landscape, in the sense that the only global minimizers are the Simplex ETFs while all other critical points are strict saddles whose Hessian exhibit negative curvature directions. In contrast to existing landscape analysis for deep neural networks which is often disconnected from practice, our analysis of the simplified model not only does it explain what kind of features are learned in the last layer, but it also shows why they can be efficiently optimized in the simplified settings, matching the empirical observations in practical deep network architectures. These findings could have profound implications for optimization, generalization, and robustness of broad interests. For example, our experiments demonstrate that one may set the feature dimension equal to the number of classes and fix the last-layer classifier to be a Simplex ETF for network training, which reduces memory cost by over 20% on ResNet18 without sacrificing the generalization performance. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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Single-Carrier Delay Alignment Modulation for Multi-IRS aided Communication

arXiv

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arXiv 2022年

作者： Lu, Haiquan Zeng, Yong Jin, Shi Zhang, Rui The National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing210096 China The Purple Mountain Laboratories Nanjing211111 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

Delay alignment modulation (DAM) is a promising technology to achieve inter-symbol interference (ISI)-free single-carrier communication, by leveraging delay compensation and path-based beamforming, rather than the conventional channel equalization or multi-carrier transmission. In particular, when there exist a few strong time-dispersive channel paths, DAM is able to effectively align different propagation delays and achieve their constructive superposition, thus especially appealing for intelligent reflecting surfaces (IRSs)-aided communications with controllable multi-paths. In this paper, we apply single-carrier DAM to multi-IRS aided communication and study its design and achievable performance. We first provide an asymptotic analysis showing that when the number of base station (BS) antennas is much larger than the number of IRSs, an ISI-free channel can be established from the BS to the user with appropriate delay pre-compensation and the simple path-based maximal-ratio transmission (MRT) beamforming. We then consider the general system setup and study the problem of joint path-based beamforming design at the BS and phase shifts design at the IRSs for DAM transmission, by considering the three classical beamforming techniques on a per-path basis, namely the low-complexity path-based MRT beamforming to maximize the desired signal power, the path-based zero-forcing (ZF) beamforming for ISI-free DAM communication, and the optimal path-based minimum mean-square error (MMSE) beamforming to maximize the signal-to-interference-plus-noise ratio (SINR). As a comparison, orthogonal frequency-division multiplexing (OFDM)-based multi-IRS aided communication is considered for benchmarking. Simulation results are provided which demonstrate the significant performance gain of DAM over OFDM, in terms of spectral efficiency and bit error rate (BER), as well as its lower peak-to-average-power ratio (PAPR). Copyright © 2022, The Authors. All rights reserved.

关键词： Orthogonal frequency division multiplexing

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Dynamic graph transformer network via dual-view connectivity for autism spectrum disorder identification

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computers in Biology and Medicine 2024年 174卷 108415-108415页

作者： Guan, Zihao Yu, Jiaming Shi, Zhenshan Liu, Xiumei Yu, Renping Lai, Taotao Yang, Changcai Dong, Heng Chen, Riqing Wei, Lifang College of Computer and Information Science Fujian Agriculture and Forestry University Fuzhou350002 China Digital Fujian Research Institute of Big Data for Agriculture and Forestry Fujian Agriculture and Forestry University Fuzhou350002 China Department of Radiology The First Affiliated Hospital of Fujian Medical University Fuzhou350002 China Developmental and Behavior Pediatrics Department Fujian Children's Hospital - Fujian Branch of Shanghai Children's Medical Center Fuzhou350002 China College of Clinical Medicine for Obstetrics Gynecology and Pediatrics Fujian Medical University Fuzhou350012 China School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China College of Computer and Control Engineering Minjiang University Fuzhou350108 China

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that requires objective and accurate identification methods for effective early intervention. Previous population-based methods via functional connectivity (FC) analysis ignore the differences between positive and negative FCs, which provide the potential information complementarity. And they also require additional information to construct a pre-defined graph. Meanwhile, two challenging demand attentions are the imbalance of performance caused by the class distribution and the inherent heterogeneity of multi-site data. In this paper, we propose a novel dynamic graph Transformer network based on dual-view connectivity for ASD Identification. It is based on the Autoencoders, which regard the input feature as individual feature and without any inductive bias. First, a dual-view feature extractor is designed to extract individual and complementary information from positive and negative connectivity. Then Graph Transformer network is innovated with a hot plugging K-Nearest Neighbor (KNN) algorithm module which constructs a dynamic population graph without any additional information. Additionally, we introduce the PolyLoss function and the Vrex method to address the class imbalance and improve the model's generalizability. The evaluation experiment on 1102 subjects from the ABIDE I dataset demonstrates our method can achieve superior performance over several state-of-the-art methods and satisfying generalizability for ASD identification. © 2024 Elsevier Ltd

关键词： Diseases

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applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience

arXiv

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arXiv 2022年

作者： Wang, Rongguang Bashyam, Vishnu Yang, Zhijian Yu, Fanyang Tassopoulou, Vasiliki Chintapalli, Sai Spandana Skampardoni, Ioanna Sreepada, Lasya P. Sahoo, Dushyant Nikita, Konstantina Abdulkadir, Ahmed Wen, Junhao Davatzikos, Christos Center for AI and Data Science for Integrated Diagnostics University of Pennsylvania Philadelphia United States Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia United States School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Department of Clinical Neurosciences Lausanne University Hospital University of Lausanne Lausanne Switzerland Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia United States

Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields. They belong to the broader family of generative methods, which learn to generate realistic data with a probabilistic model by learning distributions from real samples. In the clinical context, GANs have shown enhanced capabilities in capturing spatially complex, nonlinear, and potentially subtle disease effects compared to traditional generative methods. This review critically appraises the existing literature on the applications of GANs in imaging studies of various neurological conditions, including Alzheimer’s disease, brain tumors, brain aging, and multiple sclerosis. We provide an intuitive explanation of various GAN methods for each application and further discuss the main challenges, open questions, and promising future directions of leveraging GANs in neuroimaging. We aim to bridge the gap between advanced deep learning methods and neurology research by highlighting how GANs can be leveraged to support clinical decision making and contribute to a better understanding of the structural and functional patterns of brain diseases. © 2022, CC BY.

关键词： Generative adversarial networks

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On the Stability and Generalization of Triplet Learning

arXiv

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arXiv 2023年

作者： Chen, Jun Chen, Hong Jiang, Xue Gu, Bin Li, Weifu Gong, Tieliang Zheng, Feng College of Informatics Huazhong Agricultural University Wuhan430070 China College of Science Huazhong Agricultural University Wuhan430070 China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Wuhan430070 China Key Laboratory of Smart Farming for Agricultural Animals Wuhan430070 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates School of Computer Science and Technology Xi'an Jiaotong University Xi’an710049 China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Ministry of Education Xi’an710049 China

Triplet learning, i.e. learning from triplet data, has attracted much attention in computer vision tasks with an extremely large number of categories, e.g., face recognition and person re-identification. Albeit with rapid progress in designing and applying triplet learning algorithms, there is a lacking study on the theoretical understanding of their generalization performance. To fill this gap, this paper investigates the generalization guarantees of triplet learning by leveraging the stability analysis. Specifically, we establish the first general high-probability generalization bound for the triplet learning algorithm satisfying the uniform stability, and then 1 obtain the excess risk bounds of the order (Equation presented). for both stochastic gradient descent (SGD) and regularized risk minimization (RRM), where 2n is approximately equal to the number of training samples. Moreover, an optimistic generalization bound in expectation as fast as O(n−1) is derived for RRM in a low noise case via the on-average stability analysis. Finally, our results are applied to triplet metric learning to characterize its theoretical underpinning. Copyright © 2023, The Authors. All rights reserved.

关键词： Learning algorithms

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