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Effect of order–disorder transition on thermodynamic and electronic properties of σ and χ phases in W–Re alloy: the first-principles calculation

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Tungsten 2025年第1期7卷 195-213页

作者： Zhi-Peng Zhu Dian Jia William Yi Wang Jun-Lei Yin Xing-Yu Gao Shu-Feng Yang Hai-Feng Song Jin-Shan Li State Key Laboratory of Solidification Processing Northwestern Polytechnical University Laboratory of computational Physics Institute of Applied Physics and Computational Mathematics School of Metallurgical and Ecological Engineering University of Science and Technology Beijing

The Tungsten-Rhenium(W-Re) alloys,celebrated for their high melting point,strength at elevated temperatures,electrical resistivity,and radiation resistance,have been widely utilized in high-temperature components,aerospace,electronics,and nuclear *** constituents of the topologically close-packed(TCP) phases,the sigma phase(σ) and chi phase(χ) formed within W-Re alloys wield considerable influence on the mechanical properties and the stability of the *** calculations were utilized in the present work to explore the structural,thermodynamic,and electronic properties of both ordered and disordered configurations within the σ and χ phases,culminating in a systematic elucidation of the higher phase stability exhibited by the ordered *** is found that the bulk modulus of these two phases is directly proportional to the concentration of Re in the alloy,while the equilibrium volume is inversely *** thermodynamic parameters of the σ and χ phases are calculated via the mean-field potential *** similar trends observed in the isobaric heat capacity,enthalpy increment,and entropy change curves for these two phases suggest they possess comparable thermodynamic *** is noteworthy that the contribution of ionic vibrations predominantly affects the isobaric heat capacity,while the contribution of thermal electronic excitations increases linearly with *** the structure and thermodynamic properties of TCP phases in W-Re alloys at low temperatures has profound significance for optimizing material performance,microstructures features,establishing theoretical foundations,and predicting material behavior.

关键词： First-principles calculations Phase stability TCP phases Thermodynamic properties W–Re alloy

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Transforming Education with Photogrammetry:Creating Realistic 3D Objects for Augmented Reality Applications

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Computer Modeling in Engineering & sciences 2025年第1期142卷 185-208页

作者： Kaviyaraj Ravichandran Uma Mohan Department of computational Intelligence School of ComputingSRM Institute of Science and TechnologyKattankulathur603203India

Augmented reality(AR)is an emerging dynamic technology that effectively supports education across different *** increased use of mobile devices has an even greater *** the demand for AR applications in education continues to increase,educators actively seek innovative and immersive methods to engage students in ***,exploring these possibilities also entails identifying and overcoming existing barriers to optimal educational ***,this surge in demand has prompted the identification of specific barriers,one of which is three-dimensional(3D)*** 3D objects for augmented reality education applications can be challenging and time-consuming for the *** address this,we have developed a pipeline that creates realistic 3D objects from the two-dimensional(2D)*** for augmented and virtual reality can then utilize these created 3D *** evaluated the proposed pipeline based on the usability of the 3D object and performance ***,with 117 respondents,the co-creation team was surveyed with openended questions to evaluate the precision of the 3D object created by the proposed photogrammetry *** analyzed the survey data using descriptive-analytical methods and found that the proposed pipeline produces 3D models that are positively accurate when compared to real-world objects,with an average mean score above *** study adds new knowledge in creating 3D objects for augmented reality applications by using the photogrammetry technique;finally,it discusses potential problems and future research directions for 3D objects in the education sector.

关键词： Augmented reality education immersive learning 3D object creation photogrammetry and StructureFromMotion

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Toward understanding the role of genomic repeat elements in neurodegenerative diseases

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Neural Regeneration Research 2025年第3期20卷 646-659页

作者： Zhengyu An Aidi Jiang Jingqi Chen Institute of science and Technology for Brain-Inspired Intelligence Fudan UniversityShanghaiChina MOE Frontiers Center for Brain science Fudan UniversityShanghaiChina MOE Key Laboratory of computational Neuroscience and Brain-Inspired Intelligence Fudan UniversityShanghaiChina Zhangjiang Fudan International Innovation Center ShanghaiChina

Neurodegenerative diseases cause great medical and economic burdens for both patients and society;however, the complex molecular mechanisms thereof are not yet well understood. With the development of high-coverage sequencing technology, researchers have started to notice that genomic repeat regions, previously neglected in search of disease culprits, are active contributors to multiple neurodegenerative diseases. In this review, we describe the association between repeat element variants and multiple degenerative diseases through genome-wide association studies and targeted sequencing. We discuss the identification of disease-relevant repeat element variants, further powered by the advancement of long-read sequencing technologies and their related tools, and summarize recent findings in the molecular mechanisms of repeat element variants in brain degeneration, such as those causing transcriptional silencing or RNA-mediated gain of toxic function. Furthermore, we describe how in silico predictions using innovative computational models, such as deep learning language models, could enhance and accelerate our understanding of the functional impact of repeat element variants. Finally, we discuss future directions to advance current findings for a better understanding of neurodegenerative diseases and the clinical applications of genomic repeat elements.

关键词： Alzheimer's disease ataxia deep learning long-read sequencing neurodegeneration neurodegenerative diseases Parkinson's disease repeat element structural variant

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Small extrachromosomal circular DNA in amyotrophic lateral sclerosis matter

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Neural Regeneration Research 2025年第5期20卷 1411-1413页

作者： Marcos J.Arauzo-Bravo Daniela Gerovska Matthias Schwab Alexandra Kretz computational Biology and Systems Biomedicine Biodonostia Health Research InstituteSan SebastianSpain Basque Foundation for science IKERBASQUEBilbaoSpainMax Planck Institute for Molecular BiomedicineComputational Biology and Bioinformatics GroupMünsterNorth Rhine-WestphaliaGermany Department of Cell Biology and Histology Faculty of Medicine and NursingUniversity of Basque Country(UPV/EHU)LeioaSpain Department of Neurology Jena University HospitalJenaThuringiaGermany

Comprehensive studies identify motor neuron spectrum disorders including amyotrophic lateral sclerosis(ALS)as globally rising fatal disorders with the highest prevalence in aging populations,influenced by ethnicity and ancestry(GBD 2016 Motor Neuron Disease Colla borators,2018).While~10% of diagnoses involve a family history(fALS),most cases are considered sporadic(sALS).However,population-based studies suggest that even cases without a common index mutation impart heritability(Ryan et al.,2019),indicating a crucial role of rare and as yet unknown genetic denominators.

关键词： amyotrophic sclerosis

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Fractal autoencoder with redundancy regularization for unsupervised feature selection

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science China(Information sciences) 2025年

作者： Meiting SUN Fangyu LI Honggui HAN School of Information science and Technology Beijing University of Technology Beijing Key Laboratory of computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education Beijing Institute of Artificial Intelligence(Beijing)

Feature selection is a crucial step in data preprocessing because feature selection reduces the dimensionality of data by eliminating irrelevant and redundant features. Since manual labeling is expensive, unsupervised feature selection has received increasing attention in recent years. However, existing unsupervised feature selection methods tend to prioritize selecting highly correlated features over exploring feature diversity. Thus, a regularized fractal autoencoder (RFAE) method is proposed to select informative features in an unsupervised way. Specifically, the fractal autoencoder network extends autoencoders to construct a correspondence neural network and a selection neural network. The correspondence neural network exploits interfeature correlations and the selection neural network selects the informative features. A redundancy regularization strategy consists of a redundancy elimination regularization term based on the dependency between features and a sparse regularization term based on the group lasso. The redundancy regularization strategy eliminates feature subset redundancy and enhances network generalization ability. Extensive experimental results on six publicly available datasets show that the proposed RFAE outperforms the compared methods regarding clustering accuracy and classification accuracy. Moreover, the proposed RFAE achieves acceptable computation efficiency.

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Three-Stage Transfer Learning with AlexNet50 for MRI Image Multi-Class Classification with Optimal Learning Rate

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Computer Modeling in Engineering & sciences 2025年第1期142卷 155-183页

作者： Suganya Athisayamani A.Robert Singh Gyanendra Prasad Joshi Woong Cho School of Computing Sastra Deemed to be UniversityThanjavur613401India Department of computational Intelligence SRM Institute of Science and TechnologyKattankulathur603203India Department of AI and Software Engineering Kangwon National UniversitySamcheok25913Republic of Korea Department of Electronics Information and Communication EngineeringKangwon National UniversitySamcheok25913Republic of Korea

In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological *** is particularly effective for detecting soft tissue ***,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of *** address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI *** manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning *** are three stages for learning;in the first stage,the whole dataset is used to learn the *** the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented *** method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical Engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for *** hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning ***-dataset registers maximum classification *** evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.

关键词： MRI tumors classification AlexNet50 transfer learning hyperparameter tuning optimizer

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Molecular dynamics simulation of quartz deformation under slow earthquake background

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science China Earth sciences 2025年

作者： Jingxian SUN Qianqian GUO Quanlin HOU College of Earth and Planetary sciences University of Chinese Academy of Sciences Key Laboratory of computational Geodynamics CAS

Slow earthquakes are the primary mechanism of slow energy release, and research on the focal mechanism has been inconclusive. Studies have primarily focused on the friction law based on physical mechanisms and have suggested that slow earthquakes are caused by brittle faults. However, the focal strength and structural characteristics of slow earthquakes in subduction zones provide evidence of plastic deformation. What is the role of plastic deformation in the focal mechanisms of slow earthquakes? Mechanochemical study have shown that mechanical forces can directly affect chemical bonds. In this study,we examine the storage and release of chemical energy during plastic deformation and consider a mechanochemical process in the focal mechanism of slow earthquakes. Combined with the Tersoff potential, molecular dynamics simulation on the shear deformation process of two α-quartz crystals show that the shear modulus of α-quartz is 18 GPa, and that the crystal model primarily exhibits atoms flowing and changing in the direction of chemical bonds during the steady-state flow stage. The molecular potential energy and stress vary in an oscillating up-and-down curve during shear, indicating that chemical energy can be stored and released during plastic deformation. This is consistent with the energy variation during slow earthquakes. Under the initial simple-shear loading condition, α-quartz crystals undergo general shear deformation instead of plane strain and the angle between the longest instantaneous stretching axis （ISA1） and the shearing direction is approximately 30°, not 45°. Both the deformation type and direction of ISA1are contrary to basic deformation theory, which may provide clues for future *** study reveals the process of quartz elastic-plastic shear deformation on an atomic scale. This information is useful for understanding focal mechanisms of slow earthquakes. This study is part of a series of investigations on tectonic stress chemistry.

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Emerging structures and dynamic mechanisms ofγ-secretase for Alzheimer’s disease

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Neural Regeneration Research 2025年第1期20卷 174-180页

作者： Yinglong Miao Michael S.Wolfe computational Medicine Program and Department of Pharmacology University of North Carolina–Chapel HillChapel HillNCUSA Department of Medicinal Chemistry University of KansasLawrenceKSUSA

γ-Secretase,called“the proteasome of the membrane,”is a membrane-embedded protease complex that cleaves 150+peptide substrates with central roles in biology and medicine,including amyloid precursor protein and the Notch family of cell-surface *** inγ-secretase and amyloid precursor protein lead to early-onset familial Alzheimer’s disease.γ-Secretase has thus served as a critical drug target for treating familial Alzheimer’s disease and the more common late-onset Alzheimer’s disease as ***,critical gaps remain in understanding the mechanisms of processive proteolysis of substrates,the effects of familial Alzheimer’s disease mutations,and allosteric modulation of substrate cleavage byγ-*** this review,we focus on recent studies of structural dynamic mechanisms ofγ-*** mechanisms,including the“Fit-Stay-Trim,”“Sliding-Unwinding,”and“Tilting-Unwinding,”have been proposed for substrate proteolysis of amyloid precursor protein byγ-secretase based on all-atom molecular dynamics *** an incorrect registry of the Notch1 substrate was identified in the cryo-electron microscopy structure of Notch1-boundγ-secretase,molecular dynamics simulations on a resolved model of Notch1-boundγ-secretase that was reconstructed using the amyloid precursor protein-boundγ-secretase as a template successfully capturedγ-secretase activation for proper cleavages of both wildtype and mutant Notch,being consistent with biochemical experimental *** approach could be potentially applied to decipher the processing mechanisms of various substrates byγ-*** addition,controversy over the effects of familial Alzheimer’s disease mutations,particularly the issue of whether they stabilize or destabilizeγ-secretase-substrate complexes,is ***,an outlook is provided for future studies ofγ-secretase,including pathways of substrate binding and product release,effects of modulators on familial Alzheimer’s disease mutations

关键词： Alzheimer’s disease amyloid precursor protein cryo-EM structures drug design intramembrane proteolysis molecular dynamics Notch

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Multi-Scale Dilated Convolution Network for SPECT-MPI Cardiovascular Disease Classification with Adaptive Denoising and Attenuation Correction

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Computer Modeling in Engineering & sciences 2025年第1期142卷 299-327页

作者： A.Robert Singh Suganya Athisayamani Gyanendra Prasad Joshi Bhanu Shrestha Department of computational Intelligence SRM Institute of Science and TechnologyKattankulathur603203Tamil NaduIndia School of Computing Sastra Deemed to be UniversityThanjavur613401Tamil NaduIndia Department of Artificial Intelligence&Software Kangwon National UniversitySamcheok25913Republic of Korea Department of Information Convergence System Graduate School of Smart ConvergenceKwangwoon UniversitySeoul01897Republic of Korea

Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronary artery disease(CAD).The automatic classification of SPECT images for different techniques has achieved near-optimal accuracy when using convolutional neural networks(CNNs).This paper uses a SPECT classification framework with three steps:1)Image denoising,2)Attenuation correction,and 3)Image *** denoising is done by a U-Net architecture that ensures effective image *** correction is implemented by a convolution neural network model that can remove the attenuation that affects the feature extraction process of ***,a novel multi-scale diluted convolution(MSDC)network is *** merges the features extracted in different scales and makes the model learn the features more *** scales of filters with size 3×3 are used to extract *** three steps are compared with state-of-the-art *** proposed denoising architecture ensures a high-quality image with the highest peak signal-to-noise ratio(PSNR)value of *** proposed classification method is compared with the five different CNN models,and the proposed method ensures better classification with an accuracy of 96%,precision of 87%,sensitivity of 87%,specificity of 89%,and F1-score of 87%.To demonstrate the importance of preprocessing,the classification model was analyzed without denoising and attenuation correction.

关键词： SPECT-MPI CAD MSDC denoising attenuation correction classification

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New solution bounds of the continuous algebraic Riccati equation and their applications in redundant control input systems

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science China(Information sciences) 2019年第10期62卷 69-85页

作者： Jianzhou LIU Li WANG School of Mathematics and computational science Xiangtan University School of Mathematics and computational science Hunan University of Science and Technology

Redundant control inputs have wide applications in engineering, and have significant effects in quadratic performance optimal control, Hcontrol, tracking control, and many uncertain systems. In this paper, using some properties related to the inequality of the special matrix and the transformation of the matrix equation, we present new bounds of the solution of the continuous algebraic Riccati equation. The new bounds are more precise than those proposed in some existing studies on this topic. Subsequently, two lower bounds are presented for the optimal controller gain of the system(1), and a new upper bound is obtained for the optimal controller gain of the extended control system(3). Several examples are provided to demonstrate that our bounds of the optimal controller gain are superior to some previous results. Subsequently, we provide the applications of the new bounds in relevant redundant control problems and obtain good results. Finally,the corresponding numerical examples are provided to illustrate the effectiveness of our results and to compare them with the existing results.

关键词： Riccati equation bounds redundant control input controller gain inequality

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