jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tong Wu | Deying Wang | Nan Zhang | Fengjuan Chen | Fan Yang | Ran Zhang | Jiaqi Wang
Source:  (article, 2024-12-27)
Field: Materials Science — Materials Chemistry

To Build Digital Twin of Nuclear Reactor Decommissioning: Cutting and Packaging Method Based on Simulated Annealing Algorithm The decommissioning of nuclear reactors is a worldwide problem because of process complex, high radioactive, high cost, leak of experience etc. Due to varying degrees of activation and radioactive nuclide contamination of the components within the nuclear reactor, appropriate treatment measures must be taken based on the radioactive levels of each component to ensure safe storage. The goal of this study is to design the cutting method for reactor components based on the dimensions of the solid waste packaging steel boxes to achieve optimal utilization of the cut blocks. For this specific purpose, a simulated annealing algorithm is adopted to optimize the packing of cut blocks, simplifying the complex three-dimensional irregular parts packing problem into a two-dimensional plane study to obtain the optimal number of cut blocks in each area of the reactor components. By determining the optimal number of cut blocks, a cutting plan is formulated, and a global packing plan for all cut blocks is derived. The research results show that this method can not only obtain the optimal cutting plan for different cutting areas of reactor components, but also determine the best placement of the cut blocks in the steel boxes while satisfying all cutting constraints. This method provides an effective solution for handling the cutting and packing issues during the decommissioning process of nuclear reactors and has potential for practical applications. This simulation and method will be applied to build of digital twin of nuclear reactor decommissioning. Nuclear decommissioning ; Annealing (glass) ; Computer science ; Simulated annealing ; Nuclear engineering ; Materials science ; Algorithm ; Engineering ; Waste management ; Metallurgy Graphite, nuclear technology, radiation studies ; Advanced Manufacturing and Logistics Optimization ; Optimization and Packing Problems
Authors: Chandra Chekuri | Anand Louis
Source: arXiv (Cornell University) (preprint, 2024-10-27)
Field: Materials Science — Polymers and Plastics

On Sparsest Cut and Conductance in Directed Polymatroidal Networks We consider algorithms and spectral bounds for sparsest cut and conductance in directed polymatrodal networks. This is motivated by recent work on submodular hypergraphs \cite{Yoshida19,LiM18,ChenOT23,Veldt23} and previous work on multicommodity flows and cuts in polymatrodial networks \cite{ChekuriKRV15}. We obtain three results. First, we obtain an $O(\sqrt{\log n})$-approximation for sparsest cut and point out how this generalizes the result in \cite{ChenOT23}. Second, we consider the symmetric version of conductance and obtain an $O(\sqrt{OPT \log r})$ approximation where $r$ is the maximum degree and we point out how this generalizes previous work on vertex expansion in graphs. Third, we prove a non-constructive Cheeger like inequality that generalizes previous work on hypergraphs. We provide a unified treatment via line-embeddings which were shown to be effective for submodular cuts in \cite{ChekuriKRV15}. Conductance ; Mathematics ; Computer science ; Statistical physics ; Combinatorics ; Physics Conducting polymers and applications
Authors: Fei Xu
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2024-06-06)
Field: Materials Science — Materials Chemistry

Annular Metallic Nuclear Fuel Informatics at 50 nm Resolution U-10wt.% Zr (U-10Zr) based metallic fuel is the leading candidate for next-generation sodium cooled fast reactor in United States. Advanced post-irradiation characterization (from sub-nanometer to micrometer) helps to understand fuel microstructure and property change during irradiation, benefiting fuel qualification for commercial application. With high velocity image data generating method, an automatic way to extract the microstructural information quantitively can better serve the needs from post irradiation characterization. A trained machine learning model, named Decision Tree, is employed to categorize pores caused by fission gas release and to aid phase identification. This work presents a showcase of this approach on different irradiated U-10Zr metallic fuels. This quantitative data offers insights into the fission product migration and potentially thermal conductivity degradation. This information from machine learning will be fed into fuel design code for better prediction of fuel performance. Nuclear fuel ; Metal ; Materials science ; Informatics ; Nuclear engineering ; Metallurgy ; Engineering ; Electrical engineering Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear and radioactivity studies
Authors: Xi Guo | Xun Chen | Dong Xiang | Miguel Ángel Martín Contreras | Xiaohua Li
Source: arXiv (Cornell University) (preprint, 2024-06-07)
Field: Materials Science — Materials Chemistry

The Potential Energy of Heavy Quarkonium in Flavor-Dependent Systems from a Holographic Model Within the framework of the Einstein-Maxwell-Dilaton (EMD) model, which incorporates information on the equation of state and baryon number susceptibility from lattice results, we have conducted a comprehensive analysis of the potential energy, running coupling, and dissociation time for heavy quark-antiquark pairs using gauge/gravity duality. This study encompasses various systems, including pure gluon systems, 2 flavor systems, 2+1 flavor systems, and 2+1+1 flavor systems under finite temperature and chemical potential. The results reveal that the linear component of the potential energy diminishes as the flavor increases. It is also found that our results are extremely close to the recent lattice results for 2+1 flavors at finite temperature. Moreover, we have thoroughly investigated the dissociation distance and running coupling constant of quark-antiquark pairs to gain a comprehensive understanding of their behavior across various flavors. Finally, we have examined real-time dynamics of quark dissociation. The findings indicate that the dissociation time of quark-antiquark pairs is dependent on temperature, chemical potential, and flavor of the systems. Quarkonium ; Holography ; Flavor ; Physics ; Energy (signal processing) ; Particle physics ; Environmental science ; Optics ; Chemistry ; Food science ; Quantum mechanics ; Quantum chromodynamics Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Thermodynamic and Structural Properties of Metals and Alloys
Authors: A.V. Shimokhin | А А Лучинович | S. N. Boltovsky | A S Gritsay
Source: BIO Web of Conferences (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Information system for monitoring number of fruits and their condition using computer vision technology The main objectives of process automation are to improve the quality and productivity of process execution. An automated process has more stable characteristics than a manually executed process. One of the tools of automation is artificial intelligence in general and computer vision in particular. Computer vision (also called machine vision) is a scientific trend in the field of artificial intelligence and related technologies for acquiring images of real-world objects, processing them and using the obtained data to solve various kinds of applied tasks without (full or partial) human participation. From an engineering perspective, computer vision seeks to automate tasks that can be performed by the human visual system. Computer vision tasks include ways of acquiring, processing, analysing, and understanding digital images and extracting data from the real world to produce numerical or symbolic information. In the context of crop farming, automation is primarily capable of increasing yields and improving the quality of the final product, by better controlling environmental conditions and timely detection of any abnormalities in the development and growth of plants and fruits, by increasing productivity, by replacing a variety of more accurate and responsive automated systems (e.g. irrigation or temperature control) with human involvement, and thereby simplifying the role of humans in cultivation, and by improving the quality of the final product. In the paper, the main objective of the designed information system is the recognition in a digital image of plant fruits with subsequent classification based on known colour ranges in advance. In order to achieve this goal, the following tasks should be solved: minimisation of possible negative effects of distortions of the original image; analysing the image to look for tomato fruits on it. Computer science ; Computer vision ; Artificial intelligence ; Human–computer interaction Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing and Land Use
Authors: R. M. Talyshinsky
Source:  (article, 2024-12-12)
Field: Chemistry — Spectroscopy

QUANTUM CHEMICAL RESONANCE ESTIMATION IN CATALYTIC CONVERSION OF ETHANOL The article examines the main factors of the functioning of development banks and disclose their role in the post COVID era.The analysis of international experience is carried out and the activity in achieving the goals of the socio-economic development is revealed.The main problems that banks for development face in the process of supporting various investment projects in contemporary environment are highlighted.Based on the international experience and data received, proposals for meeting the challenges of the contemporary situation are presented, that contribute to the recovery of national economies, overcoming the consequences of the COVID pandemic situation. Ethanol ; Catalysis ; Quantum chemical ; Estimation ; Resonance (particle physics) ; Environmental science ; Materials science ; Chemistry ; Physics ; Atomic physics ; Organic chemistry ; Molecule ; Engineering Molecular spectroscopy and chirality
Authors: Jisun Jang | Enrico Mariconti | Rebecca Watts
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Polymers and Plastics

'Jsdne': A Novel R Package for Estimating Age Quantitatively with the Auricular Surface by Dirichlet Normal Energy Mathematics ; Surface (topology) ; Dirichlet distribution ; Energy (signal processing) ; Statistics ; Mathematical analysis ; Geometry Textile materials and evaluations ; 3D Shape Modeling and Analysis
Authors: Jerilyn A. Timlin
Source: Microscopy and Microanalysis (article, 2024-07-01)
Field: Chemistry — Analytical Chemistry

Hyperspectral Confocal Fluorescence &amp; Raman Microscopy for Characterizing Plant Response at the Cellular and Subcellular Level: Current Progress and Future Opportunities Hyperspectral imaging ; Raman spectroscopy ; Confocal ; Confocal microscopy ; Microscopy ; Fluorescence microscope ; Biophysics ; Current (fluid) ; Fluorescence ; Nanotechnology ; Materials science ; Remote sensing ; Chemistry ; Biology ; Optics ; Cell biology ; Physics ; Geography Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Essential Oils and Antimicrobial Activity
Authors: Frank Adolf
Source:  (preprint, 2024-02-05)
Field: Materials Science — Materials Chemistry

Structural Analysis of 20S CPs and Assembly Intermediates by Electron Cryo-Microscopy v1 This protocol details methods for structural determination by transmission electron cryo-microscopy of 20S CPs and assembly intermediates. Cryo-electron microscopy ; Electron microscope ; Microscopy ; Nanotechnology ; Chemistry ; Crystallography ; Materials science ; Biophysics ; Computer science ; Physics ; Biology ; Optics ; Biochemistry Enzyme Structure and Function ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Zhizhang Wu | Renaud Raquépas | Jack Xin | Zhiwen Zhang
Source: arXiv (Cornell University) (preprint, 2024-03-28)
Field: Materials Science — Materials Chemistry

Computing large deviation rate functions of entropy production for diffusion processes by an interacting particle method We develop an interacting particle method (IPM) for computing the large deviation rate function of entropy production for diffusion processes, with emphasis on the vanishing-noise limit and high dimensions. The crucial ingredient to obtain the rate function is the computation of the principal eigenvalue $λ$ of elliptic, non-self-adjoint operators. We show that this principal eigenvalue can be approximated in terms of the spectral radius of a discretized evolution operator, which is obtained from an operator splitting scheme and an Euler--Maruyama scheme with a small time step size. We also show that this spectral radius can be accessed through a large number of iterations of this discretized semigroup, which is suitable for computation using the IPM. The IPM applies naturally to problems in unbounded domains and scales easily to high dimensions. We show numerical examples of dimensions up to 16, and the results show that our numerical approximation of $λ$ converges to the analytical vanishing-noise limit within visual tolerance with a fixed number of particles and a fixed time step size. It is numerically shown that the IPM can adapt to singular behaviors in the vanishing-noise limit. We also apply the IPM to explore situations with no explicit formulas of the vanishing-noise limit. Our paper appears to be the first one to obtain numerical results of principal eigenvalue problems for non-self-adjoint operators in such high dimensions. Statistical physics ; Diffusion ; Limit (mathematics) ; Rate function ; Entropy (arrow of time) ; Noise (video) ; Entropy production ; Large deviations theory ; Physics ; Mathematics ; Computer science ; Mathematical analysis ; Quantum mechanics ; Artificial intelligence Material Dynamics and Properties ; Theoretical and Computational Physics ; Advanced Thermodynamics and Statistical Mechanics
Authors: Shonosuke Sugasawa | Francis K. C. Hui | A. H. Welsh
Source: arXiv (Cornell University) (preprint, 2024-07-02)
Field: Chemistry — Analytical Chemistry

Robust Linear Mixed Models using Hierarchical Gamma-Divergence Linear mixed models (LMMs) are a popular class of methods for analyzing longitudinal and clustered data. However, such models can be sensitive to outliers, and this can lead to biased inference on model parameters and inaccurate prediction of random effects if the data are contaminated. We propose a new approach to robust estimation and inference for LMMs using a hierarchical gamma-divergence, which offers an automated, data-driven approach to downweight the effects of outliers occurring in both the error and the random effects, using normalized powered density weights. For estimation and inference, we develop a computationally scalable minorization-maximization algorithm for the resulting objective function, along with a clustered bootstrap method for uncertainty quantification and a Hyvarinen score criterion for selecting a tuning parameter controlling the degree of robustness. Under suitable regularity conditions, we show the resulting robust estimates can be asymptotically controlled even under a heavy level of (covariate-dependent) contamination. Simulation studies demonstrate hierarchical gamma-divergence consistently outperforms several currently available methods for robustifying LMMs. We also illustrate the proposed method using data from a multi-center AIDS cohort study. Generalized linear mixed model ; Divergence (linguistics) ; Mixed model ; Linear model ; Mathematics ; Computer science ; Statistics Spectroscopy and Chemometric Analyses ; Advanced Statistical Methods and Models ; Statistical Methods and Inference
Authors: Siyao Wang | Miles E. Lopes
Source: arXiv (Cornell University) (preprint, 2024-08-10)
Field: Materials Science — Surfaces, Coatings and Films

Testing Elliptical Models in High Dimensions Due to the broad applications of elliptical models, there is a long line of research on goodness-of-fit tests for empirically validating them. However, the existing literature on this topic is generally confined to low-dimensional settings, and to the best of our knowledge, there are no established goodness-of-fit tests for elliptical models that are supported by theoretical guarantees in high dimensions. In this paper, we propose a new goodness-of-fit test for this problem, and our main result shows that the test is asymptotically valid when the dimension and sample size diverge proportionally. Remarkably, it also turns out that the asymptotic validity of the test requires no assumptions on the population covariance matrix. With regard to numerical performance, we confirm that the empirical level of the test is close to the nominal level across a range of conditions, and that the test is able to reliably detect non-elliptical distributions. Moreover, when the proposed test is specialized to the problem of testing normality in high dimensions, we show that it compares favorably with a state-of-the-art method, and hence, this way of using the proposed test is of independent interest. Computer science ; Geology Electron and X-Ray Spectroscopy Techniques
Authors: Wenbo Ouyang | Ruiyang Wu | Ning Hao | Hao Helen Zhang
Source: arXiv (Cornell University) (preprint, 2024-11-04)
Field: Chemistry — Analytical Chemistry

Dynamic Supervised Principal Component Analysis for Classification This paper introduces a novel framework for dynamic classification in high dimensional spaces, addressing the evolving nature of class distributions over time or other index variables. Traditional discriminant analysis techniques are adapted to learn dynamic decision rules with respect to the index variable. In particular, we propose and study a new supervised dimension reduction method employing kernel smoothing to identify the optimal subspace, and provide a comprehensive examination of this approach for both linear discriminant analysis and quadratic discriminant analysis. We illustrate the effectiveness of the proposed methods through numerical simulations and real data examples. The results show considerable improvements in classification accuracy and computational efficiency. This work contributes to the field by offering a robust and adaptive solution to the challenges of scalability and non-staticity in high-dimensional data classification. Principal component analysis ; Component (thermodynamics) ; Computer science ; Pattern recognition (psychology) ; Artificial intelligence ; Physics Spectroscopy and Chemometric Analyses
Authors: David R. Beers | Heather A. Harrington | Jacob Leygonie | Uzu Lim | Louis Theran
Source: arXiv (Cornell University) (preprint, 2024-11-12)
Field: Materials Science — Polymers and Plastics

Fibers of point cloud persistence Persistent homology (PH) studies the topology of data across multiple scales by building nested collections of topological spaces called filtrations, computing homology and returning an algebraic object that can be vizualised as a barcode--a multiset of intervals. The barcode is stable and interpretable, leading to applications within mathematics and data science. We study the spaces of point clouds with the same barcode by connecting persistence with real algebraic geometry and rigidity theory. Utilizing a semi-algebraic setup of point cloud persistence, we give lower and upper bounds on its dimension and provide combinatorial conditions in terms of the local and global rigidity properties of graphs associated with point clouds and filtrations. We prove that for generic point clouds in $\mathbb{R}^d$ ($d \geq 2$), a point cloud is identifiable up to isometry from its VR persistence if the associated graph is globally rigid, and locally identifiable up to isometry from its Čech persistence if the associated hypergraph is rigid. Persistence (discontinuity) ; Point cloud ; Cloud computing ; Point (geometry) ; Computer science ; Mathematics ; Geology ; Artificial intelligence ; Geometry ; Geotechnical engineering ; Operating system Textile materials and evaluations
Authors: Qi Wang | Luning Hao | Hongye Zhang | Gang Sun | Haigening Wei | Yuyang Wu | Zhipeng Huang | Jintao Hu | Tim Coombs
Source: arXiv (Cornell University) (preprint, 2024-12-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optimisation and Loss Analyses of Pulsed Field Magnetisation in a Superconducting Motor with Cryocooled Iron Cores A 2D electromagnetic-thermal coupled numerical model has been developed using the finite element method and validated against experimental data to investigate a superconducting machine featuring high-temperature superconducting (HTS) tape stacks and cryocooled iron cores. The HTS stacks are transformed into trapped field stacks (TFSs) through pulsed field magnetisation (PFM), generating rotor fields. After PFM, the superconducting motor operates on the same principle as permanent magnet synchronous motors. This study explores the behaviour of HTS stacks by altering the stack's layer number from one to nine and adjusting the pulsed current amplitude from 250 A to 1000 A. The primary objective of this paper is to identify the optimal combination of pulsed current amplitudes and TFS layer numbers for achieving maximum magnetisation fields. The secondary objective is to evaluate the overall losses in both superconducting and non-superconducting parts of the machine during magnetisation, including heat generated in various layers of the TFS, and losses in the motor's active materials (copper windings and iron cores). Two motor configurations were proposed, and two calculation methods using linear interpolation of iron losses and steel grades were introduced to estimate the iron losses for the studied iron material, M270-35A. This pioneering study is expected to serve as a valuable reference for loss analyses and structural design considerations in developing superconducting machines. Magnetization ; Superconductivity ; Condensed matter physics ; Field (mathematics) ; Materials science ; Magnetic field ; Engineering physics ; Physics ; Nuclear engineering ; Engineering Magnetic Properties and Applications ; Magnetic Properties of Alloys ; Electric Motor Design and Analysis
Authors: Sunday Amaju Ojobor | Israel Uzuazor Siloko
Source: Science World Journal (article, 2024-10-16)
Field: Materials Science — Polymers and Plastics

A study on the efficiency of new beta polynomial kernels Kernel density estimation is a widely used nonparametric method for estimating the probability density functions of observed data. The efficiency of the kernel method is significantly influenced by the choice of the kernel function and other statistical properties such as its roughness and variance. This study investigated the new beta polynomial family's efficiency and compared the efficiency values with the classic beta kernel family. The roughness and variance of the new core functions were determined to calculate the efficiency values. The numerical values of the efficiency of the classic beta family and the new family were determined and compared for univariate and bivariate kernel functions. The results of the study showed that the new beta family has higher efficiency values compared to the classic beta family. The higher efficiency of the proposed beta family is due to their coefficients being larger than the classic kernel functions. BETA (programming language) ; Mathematics ; Polynomial ; Combinatorics ; Computer science ; Mathematical analysis ; Programming language Textile materials and evaluations ; Heat Transfer and Numerical Methods ; Heat Transfer and Optimization