jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: V. K. Bityukov | Aleksey V. Dolmatov | A. A. Zadernovsky | A. I. Starikovsky | R. M. Uvaysov
Source: Russian Technological Journal (article, 2023-12-14)
Field: Materials Science — General Materials Science

Calculating permissible deviations of vibration accelerations of printed circuit assemblies by simulation modeling Objectives . A variety of technical condition control methods are used in the production and operation of printed circuit assemblies (PCA) for radio-electronic means (REM). The main methods are optical, electrical, and thermal. However, not all possible defects can be detected using these methods. For example, a weakened PCA fastener in a block or the incorrect installation of an electric radioelement (ERE) on a printed circuit board (PCB) can be detected only by analyzing the mechanical characteristics of the REM. These factors, in particular, are the values of the vibration acceleration amplitudes on ERE or at selected PCB control points (hereinafter referred to as the PCA vibration acceleration amplitude). In order to draw a conclusion about the presence of a defect, the measured values of the vibration acceleration amplitudes obtained as a result of testing PCA for the effects of harmonic vibration are compared with the permissible values calculated during the simulation of mechanical processes in PCA. This takes into account the variations in the physical and mechanical parameters of materials and geometric parameters of the PCA design. The aim of this paper is to determine the permissible values of PCA vibration acceleration amplitudes to be compared with the measured values. Methods . The Monte Carlo simulation method is used to calculate the permissible deviations of vibration accelerations. This consists in repeatedly calculating the values of the vibration acceleration amplitudes at random values of the physical and mechanical parameters of materials and geometric parameters of the PCA design within their tolerances. Results . Experimental verification of this method was carried out using the SolidWorks software for modeling mechanical processes. This enabled the tolerance values for PCA vibration acceleration at the control point at the first resonant frequency to be established and experimental data to be obtained when introducing various defects. The results of comparing the measured values with the calculated tolerance enabled conclusions to be made with regard to the possibility of detecting PCA defects. Conclusions . Using this method of calculating tolerances for the PCA vibration acceleration amplitude allows the presence of defects in REM that do not affect the electrical or thermal characteristics of REM to be determined, thus increasing the efficiency of technical condition control. Vibration ; Acceleration ; Amplitude ; Monte Carlo method ; Random vibration ; Acoustics ; Engineering ; Structural engineering ; Mathematics ; Physics ; Statistics ; Optics Material Properties and Applications ; Transport and Logistics Innovations ; Manufacturing Process and Optimization
Authors: M. Pradeep | M. Siddappa
Source: International Journal of Engineering Technology and Management Sciences (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

CLASSIFICATION OF RICE USING CONVOLUTIONAL NEURAL NETWORK (CNN) This paper describes the technique for automatic recognition and classification of different rice grain samples using neural network classifier. The Red Green Blue (RGB), Hue Saturation Intensity (HSI) and Hue Saturation Value (HSV) color models of the image were considered for extracting 18 color features. The classification was carried out using color and texture features separately. The color image was converted to Gray scale image and the Gray Level Co-occurrence Matrixes (GLCM) for four different directions was calculated. A total of eight texture features were calculated from the Co-occurrence matrices. Convolutional Neural Network (CNN) is used for the classification process. The classification accuracy with color features and texture features were compared. Result shows that the classification base on texture features outperform the color feature-based classification even with lesser number of features. It is found that Convolutional Neural network was able to classify two varieties of rice with 100% accuracy using texture features and the edge detection with Sobel and Canny edge detection of the fiber features in the food grain. Artificial intelligence ; Hue ; Pattern recognition (psychology) ; RGB color model ; Convolutional neural network ; Computer science ; Sobel operator ; Grayscale ; HSL and HSV ; Computer vision ; Color space ; Canny edge detector ; Artificial neural network ; Color image ; Edge detection ; Image processing ; Pixel ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Suherman Suherman | Habib Faisal Yahya
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2023-01-17)
Field: Materials Science — Polymers and Plastics

Thin Layer Modeling of Corn Grains Using a Hybrid Solar Dryer with Lpg The hybrid sun drying method combines sun drying with additional heating, namely LPG. So that the drying process can take place more quickly and effectively. The three drying methods that will be compared are Open Sun drying, solar dryers, and solar hybrids. The open sun drying operation is carried out using only direct sunlight. The solar drying operation was carried out using a solar dryer without additional heating, while the drying operation with a hybrid solar dryer varied the drying temperature at 40, 50 and 60 °C. The results showed that the average moisture content of corn grains was 13.95% for 10.5 hours, 13.75% for 6 hours, 13.65% for 7 hours, 13.45% for 5 hours, and 13.65 % for 4.5 hours in Open Sun drying, drying without additional sunlight. heater, and solar drying hybrid with temperature variables of 40, 50 and 60oC. The average drying rates were 0.212 g/minute, 0.364 g/minute, 0.318 g/minute, 0.439 g/minute and 0.477 g/minute. The results of the thin layer modeling show that the Two Term model is the most accurate and suitable model for describing the drying characteristics of corn grains for all variables. Solar dryer ; Thin layer ; Layer (electronics) ; Materials science ; Environmental science ; Composite material ; Solar energy ; Engineering ; Electrical engineering Textile materials and evaluations
Authors: Md. Mazharul Islam | Md. Nazmul Islam
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2023-09-01)
Field: Materials Science — Polymers and Plastics

Impact of Different Stitch Types on Seam Strength and Seam Slippage The durability of seams significantly influences the overall performance of garments during their intended use. Seam strength is contingent upon multiple factors, encompassing seam type, stitch type, stitches per inch (SPI), sewing thread count, fabric strength, and fabric type. However, among these factors, the stitch type employed in seam creation holds paramount importance. This study aims to ascertain the impact of distinct stitch types on both seam strength and seam slippage. In pursuit of this objective, a fabric with the following construction (EPI: 186, PPI: 131, Warp Count: 32/1 Ne +40D, and Weft Count: 100D + 40D) has been procured. Subsequently, samples have been meticulously fashioned according to the EN ISO 13936-1 method. The seam strength of each sample has been meticulously assessed utilizing a seam strength tester. Throughout this study, diverse stitch types have been chosen, including lock stitch 301, chain stitch 401, double chain stitch 401x2, and overlock stitch 504. The findings encompassing seam strength have been extracted from the corresponding equipment. Comparative analysis of the findings yielded intriguing insights. This proactive approach ensures the production of high-quality apparel items that align with performance expectations and uphold the standard of excellence. Slippage ; Geology ; Mining engineering ; Engineering ; Structural engineering Textile materials and evaluations ; Additive Manufacturing and 3D Printing Technologies ; 3D Shape Modeling and Analysis
Authors: I. A. Naydanov | A. V. Zakharov | M. A. Milyaev | V. V. Proglyado | И. А. Найданов | А. В. Захаров | М. А. Миляев | В. В. Проглядо
Source: Electronic scientific archive of UrFU (Ural Federal University) (article, 2023-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

PREDICTION OF MAGNETOTRANSPORT PROPERTIES OF SUPERLATTICES BASED ON MACHINE LEARNING METHODS In the course of this work, a regression machine learning model was constructed based on a set of experimental data capable of predicting the magnetotransport properties of CoFeNi/Cu and CoFeNi/CuIn superlattices. Set (abstract data type) ; Regression ; Experimental data ; Support vector machine ; Data set ; Regression analysis Magnetic Properties and Applications ; Magnetic properties of thin films ; Non-Destructive Testing Techniques
Authors: Tamás Kegyes | Zoltán Süle | János Abonyi
Source: Nemzetbiztonsági Szemle (article, 2023-05-16)
Field: Materials Science — Materials Chemistry

Az információmenedzsment szerepe az ABV-védelemben Az atom-, bio- és vegyi (ABV-) incidensek felderítése kiemelt fontosságú feladat, amely évtizedek óta intenzíven kutatott téma. A folyamatos technológiai, adatfeldolgozási és automatizálási vívmányok újabb és újabb fejlesztési potenciált nyitnak az ABV-védelem terén is, amely napjainkra komplex, interdiszciplináris tudományterületté vált. Ennek megfelelően kémikusok, fizikusok, meteorológusok, katonai szakértők, programozók és adattudósok egyaránt közreműködnek a kutatásokban. A hazai ABV-védelmi képességek hatékony növelésének a kulcsa is abban rejlik, hogy megfelelően strukturált koncepció mentén folyamatos és célirányos fejlesztés történjen. Kutatásunk célja, hogy áttekintést adjunk a modern ABV-védelmi technológiák főbb komponenseiről, ezen belül összefoglaljuk az ABV-felderítés, illetve a döntéstámogatási lépések koncepcionális követelményeit, és bemutatjuk az információmenedzsment szerepét és legújabb lehetőségeit a folyamatokban. Physics ; Humanities ; Philosophy Graphite, nuclear technology, radiation studies
Authors: Yichen Huo | Yongzhi Min | Baoquan Wan | Guo Wang | Jianming Wu | Xin Chen
Source: Research Square (preprint, 2023-09-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

RETRACTED: Vibration and Noise Reduction Method of 750kV Shunt Reactor Based on Optimization of Air Gap Structure Air gap (plumbing) ; Vibration ; Noise reduction ; Materials science ; Magnetic core ; Acoustics ; Inductance ; Voltage ; Noise (video) ; Engineering ; Mechanical engineering ; Electrical engineering ; Physics ; Computer science ; Electromagnetic coil ; Composite material Magnetic Properties and Applications ; High-Voltage Power Transmission Systems ; Non-Destructive Testing Techniques
Authors: Lisard Torró | Cristina Villanova-de-Benavent | Loïs Monnier | Oscar Laurent | Mariana Kelly Segovia-More | Mercy K Sanandres-Flores | Johan S. Ramírez-Briones | Jean Vallance | Stefano Salvi | Patrice Baby | Joaquín A. Proenza
Source: HAL (Le Centre pour la Communication Scientifique Directe) (preprint, 2023-08-28)
Field: Materials Science — Biomaterials

Lithium-bearing micas in the 'Lithium-rich Tuff' from the Macusani Volcanic Field, Puno, Peru International audience Volcano ; Lithium (medication) ; Geology ; Bearing (navigation) ; Field (mathematics) ; Geochemistry ; Geography ; Biology ; Cartography ; Mathematics Clay minerals and soil interactions ; Botany and Geology in Latin America and Caribbean ; Geological and Geochemical Analysis
Authors: Giuseppe Marco Randazzo
Source: The Journal of Open Source Software (article, 2023-10-25)
Field: Chemistry — Analytical Chemistry

libscientific: A Powerful C Library for MultivariateAnalysis Multivariate analysis is a powerful technique that allows researchers to analyze and interpret data with multiple variables.In today's data-driven world, multivariate analysis has become essential for the exploration of complex data sets.libscientific is a powerful library written in C that provides a comprehensive set of multivariate analysis tools based on the NIPALS algorithm.The library includes several multivariate analysis algorithms, such as principal component analysis (PCA), partial least squares regression (PLS), consensus principal component analysis (CPCA), multiblock principal component analysis, and multiblock partial least squares (UPLS).libscientific also includes several other tools to analyze data, such as cluster analysis using KMeans Hierarchical clustering and other methods to run linear algebra calculations.The library also provides a Python foreign function to be used inside Python scripts. Principal component analysis ; Python (programming language) ; Multivariate statistics ; Multivariate analysis ; Computer science ; Scripting language ; Cluster analysis ; Hierarchical clustering ; Partial least squares regression ; Data mining ; Mathematics ; Artificial intelligence ; Machine learning ; Programming language Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Advanced Chemical Sensor Technologies
Authors: Jiarui Zhang | Jiguo Cao | Liangliang Wang
Source: arXiv (Cornell University) (preprint, 2023-07-19)
Field: Chemistry — Analytical Chemistry

Robust Bayesian Functional Principal Component Analysis We develop a robust Bayesian functional principal component analysis (RB-FPCA) method that utilizes the skew elliptical class of distributions to model functional data, which are observed over a continuous domain. This approach effectively captures the primary sources of variation among curves, even in the presence of outliers, and provides a more robust and accurate estimation of the covariance function and principal components. The proposed method can also handle sparse functional data, where only a few observations per curve are available. We employ annealed sequential Monte Carlo for posterior inference, which offers several advantages over conventional Markov chain Monte Carlo algorithms. To evaluate the performance of our proposed model, we conduct simulation studies, comparing it with well-known frequentist and conventional Bayesian methods. The results show that our method outperforms existing approaches in the presence of outliers and performs competitively in outlier-free datasets. Finally, we demonstrate the effectiveness of our method by applying it to environmental and biological data to identify outlying functional observations. The implementation of our proposed method and applications are available at https://github.com/SFU-Stat-ML/RBFPCA. Functional principal component analysis ; Outlier ; Computer science ; Functional data analysis ; Hyperparameter ; Principal component analysis ; Bayesian probability ; Skewness ; Model selection ; Frequentist inference ; Bayesian inference ; Prior probability ; Artificial intelligence ; Data mining ; Pattern recognition (psychology) ; Machine learning ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses ; Advanced Statistical Methods and Models ; Sensory Analysis and Statistical Methods
Authors: Pierre Nickmilder | Philippe Leclère |  Yang Lanti | Thomas De Muijlder
Source: ORBi UMONS (article, 2023-04-07)
Field: Materials Science — Polymers and Plastics

Study of the mechanical and viscoelastic properties of complex heterogeneous polymeric systems at the nanoscale and automated population identification Les mélanges de polymères offrent plus de possibilités d'applications et de conception que les systèmes polymères simples grâce à la présence de microstructures, de phases et d'interfaces qui, influencent les propriétés macroscopiques. Pour comprendre la relation structure-propriétés de ces matériaux et accélérer leur développement, il est important de cartographier et d'identifier avec précision ces phases et interfaces, ainsi que leurs propriétés mécaniques locales. Plus spécifiquement dans le cas des polymères, il est nécessaire de s’intéresser à leurs propriétés mécaniques mais aussi viscoélastiques locales comme leurs modules de stockage E’, leurs modules de perte E’’ et leurs rapports E’’/E’ correspondant à leur facteur d’amortissement (tan . La nano-DMA (nano-Dynamic Mechanical Analysis) [1] a récemment été développée pour mesurer ces propriétés à l'échelle locale en appliquant une sollicitation dynamique de la sonde à une fréquence donnée lors du contact prolongé avec l'échantillon. La nano-DMA peut ainsi produire des cartographies de propriétés à une fréquence donnée ou une évolution de ceux-ci en fonction de la fréquence de sollicitation en appliquant un « rampscript » de fréquences. A titre d’exemple illustratif, notre étude a porté sur des échantillons industriels de polypropylène à haute résistance d'impact (High Impact PolyPropylene - HiPP). Ce type de matériau est souvent utilisé pour absorber les impacts, tels que les pare-chocs, et se compose généralement d'une matrice rigide de poly(propylène) avec des inclusions molles de caoutchouc (Figure 1). On peut observer des zones cristallisées plus solides au sein de ces inclusions molles. Pour les analyses, ces échantillons ont été cryomicrotomés et analysés en nano-DMA en cartographiant l'échantillon avec une résolution de 128 x 128 pixels après une analyse préalable en Peak Force Tapping afin d’identifier les zones d’intérêt. L’identification précise des différentes zones qui constitue l’échantillon ainsi que l’attribution de leur interphase peut poser un problème. Nous avons développé un logiciel (code Python) de data clustering qui, à partir des cartographies des différentes propriétés partitionne les données en différentes populations statistiques indépendantes qu’il est alors possible d’attribuer aux différentes zones caractéristiques. In fine, ce logiciel peut également être utilisé pour déterminer pour chaque pixel une valeur de coefficient de Tabor [2], permettant par la suite d’attribuer le modèle de contact mécanique le mieux adapté. Cela permet une évaluation plus précise du module de rigidité d’un échantillon hétérogène. Population ; Statistical analysis ; Viscoelasticity ; Cobaltite Polymer crystallization and properties ; Force Microscopy Techniques and Applications ; Polymer Nanocomposites and Properties
Authors: Thomas Devereaux | Martin Claassen | Xu-Xin Huang | Michael P. Zaletel | Joel E. Moore | Dirk K. Morr | Fahad Mahmood | Peter Abbamonte | Zhi‐Xun Shen
Source: arXiv (Cornell University) (preprint, 2023-08-12)
Field: Materials Science — Surfaces, Coatings and Films

Angle-Resolved Pair Photoemission Theory for Correlated Electrons In this paper we consider the possibility and conditions for pair photoemission whereby two incident photons emit pairs of electrons from a candidate material as a novel method to measure and visualize electronic correlations. As opposed to double photoemission - where a single photon precipitates the ejection of a pair electrons via a subsequent electron energy loss scattering process - we show that pair photoemission need not be limited to interference between initial photoelectrons and valence electrons, and moreover, can occur without the energy penalty of two work functions. This enables detection of pairs of electrons at high energy resolution that may be correlated in the same quantum many-body states. Photoelectric effect ; Electron ; Angle-resolved photoemission spectroscopy ; Inverse photoemission spectroscopy ; Atomic physics ; Photon ; Physics ; Photon energy ; Core electron ; Valence electron ; Electronic structure ; Condensed matter physics ; Quantum mechanics Electron and X-Ray Spectroscopy Techniques ; Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science
Authors: Sivert Dagenborg | Yu Liu | Ingrid Hallsteinsen | Gregory Nordahl | Magnus Nord
Source: Microscopy and Microanalysis (article, 2023-07-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Acquisition and Processing of Magnetic Data from LN2 Cooled Perovskite Thin Films Using STEM-DPC Scanning transmission electron microscopy (STEM) is used to study the composition and structure of materials at nanoscale resolution, enabling unparalleled insight into material characteristics.However, the study of functional properties of materials, such as ferromagnetic domains, have historically received less attention.One main way to study these in STEM is via the differential phase contrast (DPC) technique, which measures the deflection by the Lorentz force on the electron beam due to interaction with local sample magnetization [1].This results in a small shift of the electron beam in the back focal plane, typically on the order of a couple of microradians.Due to advances in fast pixelated direct electron detectors in recent years [2], these types of experiments Norwegian ; Library science ; Engineering physics ; Engineering ; Computer science ; Philosophy Magnetic and transport properties of perovskites and related materials ; Electronic and Structural Properties of Oxides
Authors: Wataru Kobayashi
Source: arXiv (Cornell University) (preprint, 2023-02-27)
Field: Materials Science — Materials Chemistry

Analytical expression of negative differential thermal resistance in a macroscopic heterojunction Heat flux ($J$) generally increases with temperature difference in a material. A differential coefficient of $J$ against temperature ($T$) is called differential thermal conductance ($k$), and an inverse of $k$ is differential thermal resistance ($r$). Although $k$ and $r$ are generally positive, they can be negative in a macroscopic heterojunction with positive $T$-dependent interfacial thermal resistance (ITR). The negative differential thermal resistance (NDTR) effect is an important effect that can realize thermal transistor, thermal memory, and thermal logic gate. In this paper, we examine analytical expressions of $J$, $k$, $r$, and other related quantities as a function of parameters related to thermal conductivity ($κ$) and ITR in a macroscopic heterojunction to precisely describe the NDTR effect. Thermal resistance ; Heterojunction ; Thermal conductivity ; Thermal ; Condensed matter physics ; Materials science ; Inverse ; Differential (mechanical device) ; Function (biology) ; Thermal contact conductance ; Thermodynamics ; Physics ; Mathematics ; Biology ; Geometry Thermal properties of materials ; Composite Material Mechanics ; Surface and Thin Film Phenomena