jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Н.В. Бабанин | А.Л. Мелконян | Д.А. Николаев
Source: МОРСКИЕ ИНТЕЛЛЕКТУАЛЬНЫЕ ТЕХНОЛОГИИ (article, 2023-08-28)
Field: Materials Science — General Materials Science

Calculation of vibration parameters of a structure with multistage multichannel damping Работа посвящена разработке расчётного комплекса (модель, алгоритм и программа) для получения параметров совместной вибрации соосного многоопорного валопровода и корпуса судна при учете вращения гребных винтов, а также оценки взаимовлияния валов на параметры их вибрации. Разработана модель, включающая корпус судна и соосный валопровод, в виде совокупности трех квазиодномерных конечно-элементных конструкций. Алгоритм расчета построен на базе метода парциальных откликов в его дискретном варианте. Влияние вращения гребного винта учтено введением дополнительных моментов гироскопической природы. Действие этих моментов учитывалось модификацией инерционно-жесткостных характеристик каждой из квазиодномерных моделей валов. Для решения поставленной задачи были разработаны математическая модель и алгоритм расчёта параметров вибрации принятой модели. Для проведения расчётов параметров вибрации была создана программа «Соосность» и выполнена серия расчетов в задаче прикладного характера. The work is devoted to the design of computational complex (model, algorithm and program) to obtain the vibration parameters of a coaxial multi-supported ship shafting, taking into account the rotation of propellers, as well as estimates of the mutual influence of the ship shafts on their vibration parameters. A model that includes a ship’s hull and a coaxial ship shafting represented by a set of three quasi-one dimensional finite-element structures was developed. The calculation algorithm is based on the discrete version of the partial responses method. The influence of the rotation of the propeller is compensated by applying additional moments of gyroscopic nature. Those moments, in turn, were taken into account by modifying of inertia-stiffness characteristics of each quasi-one-dimensional ship shafting models. The mathematical model and the computational algorithm for calculating the vibration parameters of the adopted model were developed to solve the given problem. The algorithm for calculating the vibration parameters of the adopted model was developed. To calculate the vibration parameters, the "Coaxiality" program was created and a series of calculations were performed in an applied problem. Vibration ; Propeller ; Hull ; Rotation (mathematics) ; Coaxial ; Inertia ; Stiffness ; Finite element method ; Moment of inertia ; Structural engineering ; Engineering ; Mathematics ; Acoustics ; Mechanical engineering ; Physics ; Marine engineering ; Classical mechanics ; Geometry Material Properties and Applications ; Engineering Diagnostics and Reliability ; Magnetic Bearings and Levitation Dynamics
Authors: М.Ю. Миронов | М.В. Цыварев | А.С. Ветлугина
Source: МОРСКИЕ ИНТЕЛЛЕКТУАЛЬНЫЕ ТЕХНОЛОГИИ (article, 2023-08-28)
Field: Materials Science — General Materials Science

Rigidity of the three-layer shell of the boat with different surface shapes of the outer shell Для оболочки корпуса шлюпки, представляющей собой «сэндвич» из заполнителя (экструдированного пенополистирола) и стеклопластиковых обечаек сложной формы выполнены экспериментальные замеры перемещений при лабораторном нагружении. Оно задавалось пятном контактного давления в разных зонах резен-киля и на плоских днищевых участках оболочки. Показаны особенности формируемой поверхности оболочки, связанные с используемой технологией. Сравнение результатов расчета твердотельно-оболочечной конечно-элементной модели с результатами эксперимента позволяет выполнить корректировку этой модели, и получить эффективный аппарат исследования трехслойных конструкций корпуса маломерных судов, например, прогнозировать общую жесткость сходных конструкций с иной формой поверхности. Показано отрицательное влияние переменной толщины сечения клинкерной формы на общую жесткость и концентрацию напряжений незамкнутой транцем оболочки по сравнению с гладкой трехслойной конструкцией эквивалентного объема. Предлагается вариант технологического решения для создания такой конструкции двойной кривизны из листового полуфабриката. Experimental measurements of displacements under laboratory loading were performed for the shell of the boat hull, which is a "sandwich" of filler (extruded polystyrene foam) and fiberglass shells of complex shape. It was set by a contact pressure spot in different zones of the keel and on flat bottom sections of the shell. The features of the formed shell surface associated with the technology used are shown. Comparison of the calculation results of the solid-shell finite element model with the experimental results makes it possible to correct this model and obtain an effective apparatus for studying three-layer hull structures of small vessels, for example, to predict the overall rigidity of similar structures with a different surface shape. The negative effect of the variable thickness of the clinker cross-section on the overall stiffness and stress concentration of the transom-open shell compared with a smooth three-layer structure of equivalent volume is shown. A variant of the technological solution for creating such a double curvature structure from a semi-finished sheet is proposed. Shell (structure) ; Materials science ; Curvature ; Substructure ; Composite material ; Stiffness ; Hull ; Rigidity (electromagnetism) ; Finite element method ; Structural engineering ; Geometry ; Engineering ; Mathematics Material Properties and Applications ; Material Science and Thermodynamics ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry
Authors: Paul C. Jerger | Yuxin Wang | Mykyta Onizhuk | Benjamin S. Soloway | Michael T. Solomon | Christopher Egerstrom | F. Joseph Heremans | Giulia Galli | Aashish A. Clerk | D. D. Awschalom
Source: arXiv (Cornell University) (preprint, 2023-03-03)
Field: Materials Science — Materials Chemistry

Detecting spin bath polarization with quantum quench phase shifts of single spins in diamond Single-qubit sensing protocols can be used to measure qubit-bath coupling parameters. However, for sufficiently large coupling, the sensing protocol itself perturbs the bath, which is predicted to result in a characteristic response in the sensing measurements. Here, we observe this bath perturbation, also known as a quantum quench, by preparing the nuclear spin bath of a nitrogen-vacancy (NV) center in polarized initial states and performing phase-resolved spin echo measurements on the NV electron spin. These measurements reveal a time-dependent phase determined by the initial state of the bath. We derive the relationship between sensor phase and Gaussian spin bath polarization, and apply it to reconstruct both the axial and transverse polarization components. Using this insight, we optimize the transfer efficiency of our dynamic nuclear polarization sequence. This technique for directly measuring bath polarization may assist in preparing high-fidelity quantum memory states, improving nanoscale NMR methods, and investigating non-Gaussian quantum baths. Spins ; Qubit ; Physics ; Polarization (electrochemistry) ; Quantum ; Diamond ; Spin polarization ; Quantum sensor ; Condensed matter physics ; Atomic physics ; Electron ; Chemistry ; Quantum computer ; Quantum mechanics ; Quantum simulator Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Force Microscopy Techniques and Applications
Authors: Yifan Yang | Shuo Chen | Wang, Ming
Source: arXiv (Cornell University) (preprint, 2023-04-17)
Field: Chemistry — Analytical Chemistry

Hypothesis testing under uniform-block covariance structures A block covariance structure is widely observed across large-scale and high-dimensional datasets in diverse fields such as biology, medicine, engineering, economics, and finance. This pattern entails partitioning a covariance matrix into uniform blocks, where each block exhibits equal variances and covariances. The importance of uniform-block structures lies in their ubiquity, interpretability, and ability to accommodate high dimensionality and data missingness. Despite their prevalence, statistical hypothesis testing under uniform-block covariance structures remains largely unexplored, and unknown statistical properties limit their application in research. To address this gap, we develop a comprehensive framework for joint hypothesis tests of both covariance and mean structures, leveraging a novel block Hadamard product representation of uniform-block matrices. Specifically, we derive closed-form likelihood ratio test statistics and information statistics, explicitly establishing their null distributions. Additionally, we perform simultaneous marginal mean tests under a procedure that controls the false discovery proportion (FDP). Extensive simulations validate the consistency between theoretical and empirical distributions of the joint test statistics, assess the performance of the proposed FDP control procedure, and evaluate the robustness of the joint test statistics against structural disruptions and missing data. Lastly, we apply our methodology to hypothesis testing in a high-dimensional imaging dataset. Covariance matrix ; Mathematics ; Covariance ; Estimation of covariance matrices ; Population ; Representation (politics) ; Eigendecomposition of a matrix ; Matrix (chemical analysis) ; Block (permutation group theory) ; Hadamard transform ; Eigenvalues and eigenvectors ; Statistics ; Algorithm ; Applied mathematics ; Combinatorics ; Mathematical analysis Spectroscopy and Chemometric Analyses ; Gene expression and cancer classification ; Sensory Analysis and Statistical Methods
Authors: Haoyi Fu | Lu Tang | Ori Rosen | Alison E. Hipwell | Theodore J. Huppert | Robert T. Krafty
Source: arXiv (Cornell University) (preprint, 2023-01-03)
Field: Chemistry — Analytical Chemistry

Covariate-guided Bayesian mixture model for multivariate time series With rapid development of techniques to measure brain activity and structure, statistical methods for analyzing modern brain-imaging play an important role in the advancement of science. Imaging data that measure brain function are usually multivariate time series and are heterogeneous across both imaging sources and subjects, which lead to various statistical and computational challenges. In this paper, we propose a group-based method to cluster a collection of multivariate time series via a Bayesian mixture of smoothing splines. Our method assumes each multivariate time series is a mixture of multiple components with different mixing weights. Time-independent covariates are assumed to be associated with the mixture components and are incorporated via logistic weights of a mixture-of-experts model. We formulate this approach under a fully Bayesian framework using Gibbs sampling where the number of components is selected based on a deviance information criterion. The proposed method is compared to existing methods via simulation studies and is applied to a study on functional near-infrared spectroscopy (fNIRS), which aims to understand infant emotional reactivity and recovery from stress. The results reveal distinct patterns of brain activity, as well as associations between these patterns and selected covariates. Multivariate statistics ; Covariate ; Bayesian probability ; Computer science ; Smoothing ; Deviance information criterion ; Artificial intelligence ; Gibbs sampling ; Mixture model ; Pattern recognition (psychology) ; Statistics ; Machine learning ; Mathematics ; Bayesian inference Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Time Series Analysis and Forecasting
Authors: Pedro Henrique T. O. Sousa | Camila P. E. de Souza | Ronaldo Dias
Source: arXiv (Cornell University) (preprint, 2023-03-06)
Field: Chemistry — Analytical Chemistry

Bayesian Variable Selection for Function-on-Scalar Regression Models: a comparative analysis In this work, we developed a new Bayesian method for variable selection in function-on-scalar regression (FOSR). Our method uses a hierarchical Bayesian structure and latent variables to enable an adaptive covariate selection process for FOSR. Extensive simulation studies show the proposed method's main properties, such as its accuracy in estimating the coefficients and high capacity to select variables correctly. Furthermore, we conducted a substantial comparative analysis with the main competing methods, the BGLSS (Bayesian Group Lasso with Spike and Slab prior) method, the group LASSO (Least Absolute Shrinkage and Selection Operator), the group MCP (Minimax Concave Penalty), and the group SCAD (Smoothly Clipped Absolute Deviation). Our results demonstrate that the proposed methodology is superior in correctly selecting covariates compared with the existing competing methods while maintaining a satisfactory level of goodness of fit. In contrast, the competing methods could not balance selection accuracy with goodness of fit. We also considered a COVID-19 dataset and some socioeconomic data from Brazil as an application and obtained satisfactory results. In short, the proposed Bayesian variable selection model is highly competitive, showing significant predictive and selective quality. Feature selection ; Bayesian probability ; Regression analysis ; Lasso (programming language) ; Latent variable ; Regression ; Bayesian linear regression ; Scad ; Computer science ; Statistics ; Mathematics ; Econometrics ; Machine learning ; Bayesian inference Spectroscopy and Chemometric Analyses ; Statistical Methods and Inference ; Advanced Statistical Methods and Models
Authors: Pooja Kadam | V.K. Gupta | Natarajan Venkateswaran
Source:  (article, 2023-08-11)
Field: Materials Science — Materials Chemistry

Application of NLP Based Knowledge Extraction for Polypropylene and Polybutadiene Research Smarter AI based techniques are need of present time to scan and extract useful domain specific information for research from vast amount of technical information in literature. Natural Language Processing (NLP) based knowledge extraction have attracted more attention in recent past. In this work, we have applied the NLP based techniques to extract useful information from poly-olefin & -diolefin research literature. Poly-olefins and -diolefins are industrially important materials of, widespread commercial applications with continued generation of useful research literature with each passing day. We have developed NLP model utilizing “word2vec” techniques using data from thousands of poly-olefins and -diolefins research abstracts. Model development and its application require collection of relevant information and pre-processing of input data. We have extracted catalysts and cocatalysts used in poly-olefin and -diolefin synthesis from literature and visualized them using Principal Component Analysis (PCA) technique. Further, we have determined pairs of “catalyst- cocatalyst” combinations from literature and validated. The number of co-catalysts identified were much less compared to catalysts for both poly-olefins and -diolefins. Based on these observations, there is potential for research to focus on the development of novel cocatalysts aimed at enhancing their performance as compared to catalysts. This perspective aligns with a commercial technology development strategy in the field of high-performance poly-olefin and -diolefin materials, catering to various growing sectors such as packaging, transportation, energy transition, agriculture, defense, and more. Word2vec ; Computer science ; Olefin fiber ; Artificial intelligence ; Domain (mathematical analysis) ; Polybutadiene ; Catalysis ; Chemistry ; Organic chemistry ; Polymer ; Copolymer ; Mathematics Machine Learning in Materials Science ; Software Engineering Research ; Computational Drug Discovery Methods
Authors: Andreas Marek | Markus Rampp | Klaus Reuter | Erwin Laure
Source:  (article, 2023-09-25)
Field: Materials Science — Materials Chemistry

Beyond the Fourth Paradigm — the Rise of AI Thanks to the availability of huge amounts of data and improved computational resources, AI methods are gaining importance in scientific workflows, from image recognition and natural language processing to materials science. In many domains the usage of AI is under active investigation and first results show a tremendous potential, suggesting that AI will have significant impact way beyond the currently dominating examples of image and language processing. Workflow ; Computer science ; Artificial intelligence ; Data science ; Image processing ; Image (mathematics) ; Database Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Zhixin Jia | Zengtao Ji | Xinting Yang | Ce Shi | Xia Sun | Yemin Guo
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Biomaterials

Preparation of a Water-Based Fluorescent Ink Via Lanthanide and Nitrogen Co-Modified Graphene Quantum Dots Grafted Onto Bacterial Cellulose Nanocrystals for Dual Anticounterfeiting Nanocrystal ; Quantum dot ; Fluorescence ; Cellulose ; Graphene ; Materials science ; Inkwell ; Lanthanide ; Nanotechnology ; Dual (grammatical number) ; Chemical engineering ; Chemistry ; Organic chemistry ; Composite material Advanced Cellulose Research Studies ; Dyeing and Modifying Textile Fibers ; Nanomaterials for catalytic reactions
Authors: Teodoro Baldazzi | Luigi Bellomarini | Stefano Ceri | Andrea Colombo | Andrea Gentili | Emanuel Sallinger
Source: arXiv (Cornell University) (preprint, 2023-06-19)
Field: Materials Science — Materials Chemistry

Fine-tuning Large Enterprise Language Models via Ontological Reasoning Large Language Models (LLMs) exploit fine-tuning as a technique to adapt to diverse goals, thanks to task-specific training data. Task specificity should go hand in hand with domain orientation, that is, the specialization of an LLM to accurately address the tasks of a given realm of interest. However, models are usually fine-tuned over publicly available data or, at most, over ground data from databases, ignoring business-level definitions and domain experience. On the other hand, Enterprise Knowledge Graphs (EKGs) are able to capture and augment such domain knowledge via ontological reasoning. With the goal of combining LLM flexibility with the domain orientation of EKGs, we propose a novel neurosymbolic architecture that leverages the power of ontological reasoning to build task- and domain-specific corpora for LLM fine-tuning. Computer science ; Task (project management) ; Exploit ; Flexibility (engineering) ; Domain (mathematical analysis) ; Artificial intelligence ; Realm ; Orientation (vector space) ; Architecture ; Human–computer interaction ; Natural language processing ; Knowledge management ; Computer security ; Engineering ; Systems engineering Machine Learning in Materials Science ; Topic Modeling ; Ferroelectric and Negative Capacitance Devices
Authors: Manish Singh Kaushik | Prashant Singh | Vishal Singh | Amaresh Kumar Sahoo | Kamlesh Kumar Soni
Source: Universal Journal of Catalysis Science (article, 2023-03-27)
Field: Chemistry — Spectroscopy

In Silico Structural and Functional Annotations of Hypothetical Protein All4871 as a 1-Acyl-sn-Glycerol-3-Phosphate Acyltransferase from Nostoc sp. PCC 7120 In this study, the in silico structural and functional characterization of a hypothetical protein All4871 from Nostoc sp. PCC 7120 was performed. The 287 amino acid residue protein sequence revealed considerable homology with 1-acyl-sn-glycerol-3-phosphate acyltransferase (AGPAT) from different organisms. Multiple sequence alignment of All4871 with the AGPAT isomers showed the presence of conserved motifs (motif I-IV) crucial for acyltransferase activity. The All4871 protein depicted the characteristic topology of membrane-bound AGPATs. Both N- and C-terminus of the All4871 protein face the inner side of the membrane. The predicted tertiary structure of All4871 has an N-terminal helix and αβ catalytic core, which are characteristic features of AGPATs. Amongst the CASTp predicted binding pockets, pocket 1 acts as a groove-spanning active site crucial for acyltransferase catalysis, lysophosphatidic acid (LPA), and acyl coA (MCL) binding site. Docking studies further supported pocket 1 as the binding site for both LPA and MCL, which act as substrates for acyltransferase catalysis. So, the overall analyses have pointed toward the possibility that the hypothetical protein All4871 from Nostoc sp. PCC7120 may act as a 1-acyl-sn-glycerol-3-phosphate acyltransferase. Acyltransferase ; Biochemistry ; Docking (animal) ; Homology modeling ; Active site ; Biology ; Stereochemistry ; Binding site ; Chemistry ; Enzyme Advanced Proteomics Techniques and Applications ; Enzyme Production and Characterization ; Enzyme Catalysis and Immobilization
Authors: Luiz Filipe Alves Cordeiro | Gérsika Bitencourt Santos | Waléria Guerreiro Lima | Rafael Rodrigues da Silva | Liliana Andréa dos Santos
Source: Revista Ibero-Americana de Ciências Ambientais (article, 2023-01-08)
Field: Materials Science — Materials Chemistry

Energia nuclear: uma análise bibliométrica dos últimos dez anos (2012-2022) O presente trabalho apresenta um estudo de análise bibliométrica do uso da energia nuclear e sua importância nos aspectos socioambientais, econômicos e de segurança desta fonte de energia elétrica, visando verificar os impactos de seu uso nas publicações científicas sobre o assunto em o período de 10 anos (2012 - 2022). Assim, este estudo tem como objetivo geral realizar uma análise bibliométrica sobre o uso da energia nuclear e os potenciais benefícios ambientais e econômicos de sua implementação nos últimos 10 anos, especificamente, mensurar os benefícios e malefícios econômicos e ambientais do uso de energia nuclear. energia nuclear; contextualizar historicamente a energia nuclear e sua implementação no Brasil; apresentar a análise bibliométrica dos principais autores que abordaram o tema energia nuclear nos últimos 10 anos, e dos principais países e organizações com publicações científicas. Adotando, como método de pesquisa, uma revisão de literatura de abordagem focal, com leitura de material publicado, livros, artigos e outros, neste sentido, a partir do levantamento e análise de artigos científicos. Como resultado, obteve-se uma tendência de crescimento nos números de artigos publicados, o que revela uma preocupação em aprofundar a reflexão sobre esta temática, especialmente nos anos de 2020 a 2022. Political science Graphite, nuclear technology, radiation studies