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Nanoarchitectonics and radiation mitigation in Sm 2 O 3 -doped lithium borotellurite glass systems: From amorphous harmony to localized order

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Journal of Non-Crystalline Solids 2025年 666卷

作者： G. Kilic Hasan Durmus Selçuk Birdogan E. Ilik Esra Kavaz Perişanoğlu Sevda Saltık Ömer Guler Ghada ALMisned H.O. Tekin Eskisehir Osmangazi University Faculty of Science Department of Physics TR-26040 Eskisehir Türkiye Vocational School of Health Services Izmir University of Economics Izmir Türkiye Sabanci University Nanotechnology Research and Application Center (SUNUM) 34956 Istanbul Türkiye Ataturk University Faculty of Science Department of Physics Erzurum Türkiye Munzur University Rare Earth Elements Application and Research Center 62000 Tunceli Türkiye Department of Physics College of Science Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Medical Diagnostic Imaging Department College of Health Sciences University of Sharjah 27272 Sharjah United Arab Emirates Istinye University Computer Engineering Department Faculty of Engineering and Natural Sciences 34396 Istanbul Türkiye

In this study, a novel series of nano-Sm 2 O 3 -doped lithium borotellurite glasses was synthesized and systematically investigated in terms of their structural, physical, and radiation shielding characteristics. X-ray diffraction confirmed the amorphous nature of all samples, while transmission electron microscopy revealed progressive nanoarchitectural refinement and particle growth with increasing Sm 2 O 3 content, from uniformly dispersed nanodomains to dense agglomerates exhibiting short-range crystalline ordering. Energy-dispersive X-ray spectroscopy and elemental mapping validated the homogeneous incorporation of boron, oxygen, tellurium, and samarium throughout the glass matrix without phase separation. Gamma-ray shielding evaluations showed enhanced mass attenuation coefficients and reduced half-value layers, particularly in the Sm(n)8 sample, with significant performance gains at lower photon energies. Neutron shielding assessments further indicated elevated macroscopic removal cross-section values and dose attenuation, affirming the compositional benefit of Sm 2 O 3 for fast neutron mitigation. It can be concluded that nano-Sm 2 O 3 incorporation would play a multifunctional role on structural and radioprotective properties in addition to morphostructural features of lithium borotellurite glasses.

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Intrinsic mechanical behavior of MgAgSb thermoelectric material:An ab initio study

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Journal of Materiomics 2020年第1期6卷 24-32页

作者： Guodong Li Qi An Umut Aydemir Sergey I.Morozov Bo Duan Pengcheng Zhai Qingjie Zhang William A.Goddard Ⅲ Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanics School of ScienceWuhan University of TechnologyWuhan430070China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of TechnologyWuhan430070China Department of Chemical and Materials Engineering University of Nevada RenoRenoNV89557USA Department of Chemistry Koc UniversitySariyerIstanbul34450Turkey Boron and Advanced Materials Research Center Koc UniversitySariyerIstanbul34450Turkey Department of Computer Simulation and Nanotechnology South Ural State UniversityChelyabinsk454080Russia Materials and Process Simulation Center California Institute of TechnologyPasadenaCA91125USA

α-MgAgSb based thermoelectric(TE)device attracts much attention for its commercial application because it shows an extremely high conversion efficiency of ~8.5% under a temperature difference of 225 ***,the mechanical behavior of α-MgAgSb is another serious consideration for its engineering ***,we apply density functional theory(DFT)simulations to examine the intrinsic mechanical properties of all three MgAgSb phases,including elastic properties,shear-stress-shear-strain relationships,deformation and failure mechanism under ideal shear and biaxial shear *** find that the ideal shear strength of α-MgAgSb is 3.25 GPa along the most plausible(100)<010>slip *** strength is higher than that of β-MgAgSb(0.80 GPa)and lower than that of γ-MgAgSb(3.43 GPa).The failure of α-MgAgSb arises from the stretching and breakage of MgeSb bond α-MgAgSb under pure shear load,while it arises from the softening of MgeAg bond and the breakage of AgeSb bond under biaxial shear *** suggests that the deformation mechanism changes significantly under different loading conditions.

关键词： MgAgSb thermoelectric materials Density functional theory Ideal strength Deformation mechanism

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Multiview feature selection combining latent space and similarity structure learning

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Pattern Recognition 2026年 169卷

作者： Zhuowen Li Hongmei Chen Tengyu Yin Zhong Yuan Chuan Luo Shi-Jinn Horng Tianrui Li School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu 611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu 611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu 611756 PR China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu 611756 PR China College of Computer Science Sichuan University Chengdu 610065 China Department of Computer Science and Information Engineering Asia University Taichung 41354 Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung 404327 Taiwan

Unsupervised multiview feature selection dependent on similar or clustering structures has dramatically progressed, but both ignore the mutually reinforcing relationship between structure learning. Firstly, the two methods of appeal ignore the possibility of shared joint learning, although they can learn coherent information. Secondly, they are inadequate for data structure learning and lack awareness of co-learning global and local structures. This paper proposes an unsupervised feature selection method (SCSF_FS) that integrates similarity and clustering structures learning to capture intrinsic information in data. Specifically, we use latent space learning to partition multiple data matrices into view-specific base matrices and clustering metrics. Structural learning is used to transform specific clustering metrics into coherent clustering structures. In addition, adaptive weights are used on each view’s similarity matrix to learn a consistent similarity structure, while laplacian graph learning is introduced to unify the similarity and clustering structures. Consequently, a unified framework is designed to unify multiview consistency learning, global structure, and local structure preservation. Moreover, an optimization iteration algorithm is designed to solve it. Comparison with eight algorithms shows the effectiveness of the proposed method.

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The INTERSPEECH 2020 far-field speaker verification challenge

arXiv

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arXiv 2020年

作者： Qin, Xiaoyi Li, Ming Bu, Hui Rao, Wei Das, Rohan Kumar Narayanan, Shrikanth Li, Haizhou Data Science Research Center Duke Kunshan University Kunshan China Department of Electrical & Computer Engineering National University of Singapore Singapore Signal Analysis and Interpretation Lab University of Southern California Los Angeles United States AI Shell Foundation Beijing China School of Computer Science Wuhan University Wuhan China

The INTERSPEECH 2020 Far-Field Speaker Verification Challenge (FFSVC 2020) addresses three different research problems under well-defined conditions: far-field text-dependent speaker verification from single microphone array, far-field text-independent speaker verification from single microphone array, and far-field text-dependent speaker verification from distributed microphone arrays. All three tasks pose a cross-channel challenge to the participants. To simulate the real-life scenario, the enrollment utterances are recorded from close-talk cellphone, while the test utterances are recorded from the far-field microphone arrays. In this paper, we describe the database, the challenge, and the baseline system, which is based on a ResNet-based deep speaker network with cosine similarity scoring. For a given utterance, the speaker embeddings of different channels are equally averaged as the final embedding. The baseline system achieves minDCFs of 0.62, 0.66, and 0.64 and EERs of 6.27%, 6.55%, and 7.18% for task 1, task 2, and task 3, respectively. Copyright © 2020, The Authors. All rights reserved.

关键词： Speech recognition

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Using Artificial Intelligence to Predict the Next Deceptive Movement Based on Video Sequence Analysis: A Case Study on a Professional Cricket Player's Movements

Journal of Engineering Research

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Journal of engineering research 2025年

作者： A.M. Mutawa Korupalli V Rajesh Kumar K Hemachandran M. Murugappan Computer Engineering Department College of Engineering and Petroleum Kuwait University Safat 5969 13060 Kuwait CoE- Sports Analytics and Management and AI Research Centre School of Business Woxsen University Hyderabad India Intelligent Signal Processing (ISP) Research Lab Department of Electronics and Communication Engineering Kuwait College of Science and Technology Block 4 Doha 13133 Kuwait Department of Electronics and Communication Engineering School of Engineering Vels Institute of Sciences Technology and Advanced Studies Chennai Tamilnadu India Center of Excellence for Unmanned Aerial Systems (CoEUAS) Universiti Malaysia Perlis 02600 Perlis Malaysia

This research develops an artificial intelligence-based model to predict the next deceptive movement of athletes by analyzing video sequences of previous movements. Utilizing advanced deep neural network models, we analyze deceptive movements to forecast the next move, with a practical application on the deceptive movements of a professional cricket player. The model employs machine learning techniques such as Random Forest (RF), Decision Trees (DT), and K-Nearest Neighbor (KNN) to enhance prediction accuracy. Achieving up to 70% accuracy, this model rivals human capability, as even highly skilled players can easily fall for deceptive actions. The ability to predict deceptive movements sets humans apart from many intelligent creatures, allowing athletes to avoid predictable actions and gain an edge in various sports. This study applies this concept to cricket, leveraging video data to improve training methods. The results highlight the potential of artificial intelligence in revolutionizing training and performance optimization in sports.

关键词： Artificial Intelligence Deceptive Movements Machine Learning Cricket Performance Optimization

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XCloud-VIP: Virtual Peak Enables Highly Accelerated NMR Spectroscopy and Faithful Quantitative Measures

arXiv

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arXiv 2021年

作者： Guo, Di Tu, Zhangren Guo, Yi Zhou, Yirong Wang, Jian Wang, Zi Qiu, Tianyu Xiao, Min Chen, Yinran Feng, Liubin Huang, Yuqing Lin, Donghai Hong, Qing Goldbourt, Amir Lin, Meijin Qu, Xiaobo The School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China Department of Electronic Science Biomedical Intelligent Cloud R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen361102 China School of Opto-Electronic and Communication Engineering Xiamen University of Technology Xiamen361024 China Department of Computer Science School of Information Xiamen University Xiamen361102 China College of Chemistry and Chemical Engineering Key Laboratory for Chemical Biology of Fujian Province High-field NMR Center Xiamen University Xiamen361005 China China Mobile Group Xiamen361005 China School of Chemistry Tel Aviv University Tel Aviv6997801 Israel Department of Applied Marine Physics and Engineering College of Ocean and Earth Sciences Xiamen University Xiamen361102 China

—Nuclear Magnetic Resonance (NMR) spectroscopy is an important bio-engineering tool to determine the metabolic concentrations, molecule structures and so on. The data acquisition time, however, is very long in multi-dimensional NMR. To accelerate data acquisition, non-uniformly sampling is an effective way but may encounter severe spectral distortions and unfaithful quantitative measures when the acceleration factor is high. By modelling the acquired signal as the superimposed exponentials, we proposed a virtual peak (VIP) approach to self-adapt the prior spectral information, such as the resonance frequency and peak lineshape, and then feed these information into the reconstruction. The proposed method is further implemented with cloud computing to facilitate online, open, and easy access. Results on simulated and experimental data demonstrate that, compared with the low-rank Hankel matrix method, the new approach reconstructs high-fidelity NMR spectra from highly undersampled data and achieves more accurate quantification. The maximum quantitative errors of distances between nuclear pairs and concentrations of metabolites in mixtures have been reduced by 61.1% and 57.7%, respectively. Copyright © 2021, The Authors. All rights reserved.

关键词： Nuclear magnetic resonance spectroscopy

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BraTS-Path Challenge: Assessing Heterogeneous Histopathologic Brain Tumor Sub-regions

arXiv

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arXiv 2024年

作者： Bakas, Spyridon Thakur, Siddhesh P. Faghani, Shahriar Moassefi, Mana Baid, Ujjwal Chung, Verena Pati, Sarthak Innani, Shubham Baheti, Bhakti Albrecht, Jake Karargyris, Alexandros Kassem, Hasan Nasrallah, MacLean P. Ahrendsen, Jared T. Barresi, Valeria Gubbiotti, Maria A. Lpez, Giselle Y. Lucas, Calixto-Hope G. Miller, Michael L. Cooper, Lee A.D. Huse, Jason T. Bell, William R. Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Neurological Surgery Indiana University School of Medicine IndianapolisIN United States Department of Radiology and Imaging Sciences Indiana University School of Medicine IndianapolisIN United States Department of Biostatistics and Health Data Science Indiana University School of Medicine IndianapolisIN United States Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IndianapolisIN United States Medical Research Group MLCommons San FranciscoCA United States Department of Radiology Mayo Clinic MN United States Sage Bionetworks United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology Feinberg School of Medicine Northwestern University ChicagoIL United States Department of Diagnostics and Public Health University of Verona Verona Italy Division of Pathology and Laboratory Medicine Department of Anatomical Pathology University of Texas MD Anderson Cancer Center TX United States The Preston Robert Tisch Brain Tumor Center at Duke DurhamNC United States Department of Pathology Duke University School of Medicine DurhamNC United States Department of Neurosurgery Duke University Medical School DurhamNC United States Division of Neuropathology Department of Pathology Johns Hopkins University School of Medicine BaltimoreMD United States Department of Pathology and Cell Biology Columbia University Irving Medical Center New YorkNY United States Department of Translational Molecular Pathology University of Texas MD Anderson Cancer Center TX United States

Glioblastoma is the most common primary adult brain tumor, with a grim prognosis - median survival of 12-18 months following treatment, and 4 months otherwise. Glioblastoma is widely infiltrative in the cerebral hemispheres and well-defined by heterogeneous molecular and micro-environmental histopathologic profiles, which pose a major obstacle in treatment. Correctly diagnosing these tumors and assessing their heterogeneity is crucial for choosing the precise treatment and potentially enhancing patient survival rates. In the gold-standard histopathology-based approach to tumor diagnosis, detecting various morpho-pathological features of distinct histology throughout digitized tissue sections is crucial. Such "features" include the presence of cellular tumor, geographic necrosis, pseudopalisading necrosis, areas abundant in microvascular proliferation, infiltration into the cortex, wide extension in subcortical white matter, leptomeningeal infiltration, regions dense with macrophages, and the presence of perivascular or scattered lymphocytes. With these features in mind and building upon the main aim of the BraTS Cluster of Challenges https://***/brats2024, the goal of the BraTS-Path challenge is to provide a systematically prepared comprehensive dataset and a benchmarking environment to develop and fairly compare deep-learning models capable of identifying tumor sub-regions of distinct histologic profile. These models aim to further our understanding of the disease and assist in the diagnosis and grading of conditions in a consistent manner. © 2024, CC BY.

关键词： Tumors

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The FFSVC 2020 evaluation plan

arXiv

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arXiv 2020年

作者： Qin, Xiaoyi Li, Ming Bu, Hui Das, Rohan Kumar Rao, Wei Narayanan, Shrikanth Li, Haizhou Data Science Research Center Duke Kunshan University Kunshan China Department of Electrical & Computer Engineering National University of Singapore Singapore Signal Analysis and Interpretation Lab University of Southern California Los Angeles United States AI Shell Foundation Beijing China

Speaker verification is a key technology in speech processing and biometric authentication, which has broad impact on our daily lives, e.g. security, customer service, mobile devices, smart speakers. Recently, speech based human computer interaction has become more and more popular in far-field smart home and smart city applications, e.g. mobile devices, smart speakers, smart TVs, automobiles. Due to the usage of deep learning methods, the performances of speaker verification in telephone channel and close-talking microphone channel have been enhanced dramatically. However, there are still some open research questions that can be further explored for speaker verification in the far-field and complex environments, including but not limited to • Far-field text-dependent speaker verification for wake up control • Far-field text-independent speaker verification with complex environments • Far-field speaker verification with cross-channel enrollment and test • Far-field speaker verification with single multi-channel microphone array • Far-field speaker verification with multiple distributed microphone arrays • Far-field speaker verification with front-end speech enhancement methods • Far-field speaker verification with end-to-end modeling using data augmentation • Far-field speaker verification with front-end and backend joint modeling • Far-field speaker verification with transfer learning and domain adaptation. Copyright © 2020, The Authors. All rights reserved.

关键词： Speech recognition

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Corrigendum to "A multimodal neuroimaging-based risk score for mild cognitive impairment" [NeuroImage: Clinical 45 (2025) 103719]

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NeuroImage. Clinical 2025年 45卷 103728页

作者： Elaheh Zendehrouh Mohammad S E Sendi Anees Abrol Ishaan Batta Reihaneh Hassanzadeh Vince D Calhoun Tri-institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology Emory University Atlanta GA United States Department of Electrical and Computer Engineering at Georgia Institute of Technology Atlanta GA United States. Electronic address: ezendehrouh3@gatech.edu. Harvard Medical School and McLean Hospital Boston MA United States. Tri-institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology Emory University Atlanta GA United States. Department of Electrical and Computer Engineering at Georgia Institute of Technology Atlanta GA United States. Department of Electrical and Computer Engineering at Georgia Institute of Technology Atlanta GA United States Departments of Psychology and Computer Science Georgia State University Atlanta GA United States. Electronic address: vcalhoun@gsu.edu.

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The Sensorium competition on predicting large-scale mouse primary visual cortex activity

arXiv

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arXiv 2022年

作者： Willeke, Konstantin F. Fahey, Paul G. Bashiri, Mohammad Pede, Laura Burg, Max F. Blessing, Christoph Cadena, Santiago A. Ding, Zhiwei Lurz, Konstantin-Klemens Ponder, Kayla Muhammad, Taliah Patel, Saumil S. Ecker, Alexander S. Tolias, Andreas S. Sinz, Fabian H. International Max Planck Research School for Intelligent Systems University of Tübingen Germany Institute for Bioinformatics and Medical Informatics University of Tübingen Germany Institute of Computer Science Campus Institute Data Science University of Göttingen Germany Department of Neuroscience Baylor College of Medicine HoustonTX United States Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States Institute for Theoretical Physics University of Tübingen Germany Max Planck Institute for Dynamics and Self-Organization Göttingen Germany Electrical and Computer Engineering Rice University Houston United States

The neural underpinning of the biological visual system is challenging to study experimentally, in particular as the neuronal activity becomes increasingly nonlinear with respect to visual input. Artificial neural networks (ANNs) can serve a variety of goals for improving our understanding of this complex system, not only serving as predictive digital twins of sensory cortex for novel hypothesis generation in silico, but also incorporating bio-inspired architectural motifs to progressively bridge the gap between biological and machine vision. The mouse has recently emerged as a popular model system to study visual information processing, but no standardized large-scale benchmark to identify state-of-the-art models of the mouse visual system has been established. To fill this gap, we propose the SENSORIUM benchmark competition. We collected a large-scale dataset from mouse primary visual cortex containing the responses of more than 28,000 neurons across seven mice stimulated with thousands of natural images, together with simultaneous behavioral measurements that include running speed, pupil dilation, and eye movements. The benchmark challenge will rank models based on predictive performance for neuronal responses on a held-out test set, and includes two tracks for model input limited to either stimulus only (SENSORIUM) or stimulus plus behavior (SENSORIUM+). We provide a starting kit to lower the barrier for entry, including tutorials, pre-trained baseline models, and APIs with one line commands for data loading and submission. We would like to see this as a starting point for regular challenges and data releases, and as a standard tool for measuring progress in large-scale neural system identification models of the mouse visual system and beyond. © 2022, CC BY-NC-ND.

关键词： Neural networks

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