jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Maryam Ghebleh Goydaragh | Ali Asghar Jafarzadeh | Farzin Shahbazi | Shahin Oustan | Ruhollah Taghizadeh‐Mehrjardi | Marcos Lado
Source: Revista Brasileira de Engenharia Agrícola e Ambiental (article, 2019-06-01)
Field: Materials Science — Biomaterials

Estimation of elemental composition of agricultural soils from West Azerbaijan, Iran, using mid-infrared spectral models ABSTRACT Characterizing the elemental composition provides useful information about the weathering degree of soils. In Miandoab County, Northern Iran, this characterization was missing, and thus the objectives of this work were to evaluate the weathering degrees for the most typical soils in the area from their elemental compositions, and to estimate this elemental composition using Fourier transform infrared spectroscopy and Random Forest models. Five soil profiles, including Aridisols and Inceptisols, were selected as the most representative of the area. Major elemental oxides were determined in each genetic horizon by X-ray fluorescence, showing that these soils were at early developmental stages. Only Al2O3 and CaO were accurately estimated, with R2 values of 0.8, and out-of-bag mean square errors of 0.2 and 1.1, respectively. The other oxides were not predicted satisfactorily, probably due to small differences in their elemental compositions. Random Forest provided the important spectral bands related to the content of each element. For Al2O3, these bands were between 500 and 650 cm-1, which represent out-of-plane OH bending vibrations and Al-O gibbsite and alumino-silicate vibrations. For CaO, the most important bands are related to carbonate content. A combination of Fourier transform infrared spectra and Random Forest models can be used as a rapid and low-cost technique to estimate the elemental composition of arid and semi-arid soils of Northern Iran. Soil water ; Weathering ; Elemental analysis ; Mineralogy ; Soil science ; Carbonate ; Environmental science ; Geology ; Environmental chemistry ; Chemistry ; Materials science ; Geochemistry ; Metallurgy Clay minerals and soil interactions ; Heavy metals in environment ; Geochemistry and Geologic Mapping
Authors: Daniel Medyński | Bartłomiej Samociuk | A. Janus | Jacek Chęcmanowski
Source: Materials (article, 2019-10-25)
Field: Materials Science — Materials Chemistry

Effect of Cr, Mo and Al on Microstructure, Abrasive Wear and Corrosion Resistance of Ni-Mn-Cu Cast Iron Results of a study on influence of Cr, Mo and Al on the microstructure, abrasive wear and corrosion resistance of Ni-Mn-Cu cast iron in the as-cast and heat-treated conditions are presented. Because of the chilling effect of first two elements (tendency to create hard spots), graphitising Al was added to the alloys, with the highest concentration of Cr and Mo. All castings in the as-cast condition showed an austenitic matrix, guaranteeing good machinability. Heat treatment of raw castings, consisting in annealing at 500 °C for 4 h, resulted in partial transformation of austenite. As a result the carbon-supersaturated acicular ferrite, morphologically similar to bainitic ferrite was formed. The degree of this transformation increased with increasing concentrations of Cr and Mo, which successively decreased the thermodynamic stability of austenite. A change of matrix structure made it possible to significantly increase hardness and abrasive-wear resistance of castings. The largest increment of hardness and abrasion resistance was demonstrated by the castings with the highest total concentration of Cr and Mo with an addition of 0.4% Al. Introduction of Cr and Mo also resulted in an increase of corrosion resistance. In the heat-treated specimens, increasing the concentration of Cr and Mo resulted in a successive decrease of the depth of corrosion pits, with an increase in their number at the same time. This is very favourable from the viewpoint of corrosion resistance. Materials science ; Metallurgy ; Corrosion ; Microstructure ; Acicular ferrite ; Austenite ; Machinability ; Abrasive ; Cast iron ; Abrasion (mechanical) ; Ferrite (magnet) ; Composite material ; Machining ; Bainite Metal Alloys Wear and Properties ; Microstructure and Mechanical Properties of Steels ; Surface Treatment and Coatings
Authors: Guohao Li | Guocheng Qian | Itzel C. Delgadillo | Matthias Müller | Ali Thabet | Bernard Ghanem
Source: arXiv (Cornell University) (preprint, 2019-11-30)
Field: Materials Science — Materials Chemistry

SGAS: Sequential Greedy Architecture Search Architecture design has become a crucial component of successful deep learning. Recent progress in automatic neural architecture search (NAS) shows a lot of promise. However, discovered architectures often fail to generalize in the final evaluation. Architectures with a higher validation accuracy during the search phase may perform worse in the evaluation. Aiming to alleviate this common issue, we introduce sequential greedy architecture search (SGAS), an efficient method for neural architecture search. By dividing the search procedure into sub-problems, SGAS chooses and prunes candidate operations in a greedy fashion. We apply SGAS to search architectures for Convolutional Neural Networks (CNN) and Graph Convolutional Networks (GCN). Extensive experiments show that SGAS is able to find state-of-the-art architectures for tasks such as image classification, point cloud classification and node classification in protein-protein interaction graphs with minimal computational cost. Please visit https://www.deepgcns.org/auto/sgas for more information about SGAS. Computer science ; Convolutional neural network ; Artificial intelligence ; Architecture ; Graph ; Greedy algorithm ; Machine learning ; Theoretical computer science ; Algorithm ; Geography Machine Learning in Materials Science ; Advanced Neural Network Applications ; Advanced Graph Neural Networks
Authors: Siva Chiriki | Mads-Peter Verner Christiansen | Bjørk Hammer
Source: Physical review. B./Physical review. B (article, 2019-12-19)
Field: Materials Science — Materials Chemistry

Constructing convex energy landscapes for atomistic structure optimization We propose a global optimization strategy for atomistic structure determination based on two new concepts: a few-atom complementary energy landscape and atomic role models. Global optimization of costly energy expressions may be aided by performing some of the optimization on model energy landscapes. These are often based on a sum-of-atomic-contributions form that accurately reproduces every local energy minimum of the true energy expression. However, we propose that, by not including all atomic contributions, the resulting energy landscapes may become more convex, making the search for the global optimum more facile. A role model is someone we aspire to be more like; in the same vein we define the role model of an atom to be another atom whose local environment the first atom seeks to obtain itself. Basing a complementary energy landscape on the distance of some atoms from their role models in a feature space, we arrive at a useful few-atom complementary energy landscape. We show that relaxation in this landscape is an effective mutation when employed in an evolutionary algorithm used to identify the bulk cristobalite structure of ${\mathrm{SiO}}_{2}$ and the $(1\ifmmode\times\else\texttimes\fi{}4)$ surface reconstruction of anatase ${\mathrm{TiO}}_{2}$(001). Regular polygon ; Energy (signal processing) ; Computer science ; Mathematical optimization ; Statistical physics ; Materials science ; Mathematics ; Physics ; Geometry ; Statistics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: Jiejie Dai | Yingbing Teng | Zhaoqi Zhang | Zhongmin Yu | Gehao Sheng | Xiuchen Jiang
Source: Energies (article, 2019-09-26)
Field: Materials Science — Materials Chemistry

Partial Discharge Data Matching Method for GIS Case-Based Reasoning With the accumulation of partial discharge (PD) detection data from substation, case-based reasoning (CBR), which computes the match degree between detected PD data and historical case data provides new ideas for the interpretation and evaluation of partial discharge data. Aiming at the problem of partial discharge data matching, this paper proposes a data matching method based on a variational autoencoder (VAE). A VAE network model for partial discharge data is constructed to extract the deep eigenvalues. Cosine distance is then used to calculate the match degree between different partial discharge data. To verify the advantages of the proposed method, a partial discharge dataset was established through a partial discharge experiment and live detections on substation site. The proposed method was compared with other feature extraction methods and matching methods including statistical features, deep belief networks (DBN), deep convolutional neural networks (CNN), Euclidean distances, and correlation coefficients. The experimental results show that the cosine distance match degree based on the VAE feature vector can effectively detect similar partial discharge data compared with other data matching methods. Partial discharge ; Pattern recognition (psychology) ; Matching (statistics) ; Artificial intelligence ; Computer science ; Convolutional neural network ; Autoencoder ; Feature extraction ; Degree (music) ; Feature (linguistics) ; Data mining ; Artificial neural network ; Mathematics ; Statistics ; Engineering ; Voltage High voltage insulation and dielectric phenomena ; Power Systems Fault Detection ; High-Voltage Power Transmission Systems
Authors: Feng Zhao | Islem Rekik | Seong‐Whan Lee | Jing Liu | Junying Zhang | Dinggang Shen
Source: Complexity (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Two‐Phase Incremental Kernel PCA for Learning Massive or Online Datasets As a powerful nonlinear feature extractor, kernel principal component analysis (KPCA) has been widely adopted in many machine learning applications. However, KPCA is usually performed in a batch mode, leading to some potential problems when handling massive or online datasets. To overcome this drawback of KPCA, in this paper, we propose a two‐phase incremental KPCA (TP‐IKPCA) algorithm which can incorporate data into KPCA in an incremental fashion. In the first phase, an incremental algorithm is developed to explicitly express the data in the kernel space. In the second phase, we extend an incremental principal component analysis (IPCA) to estimate the kernel principal components. Extensive experimental results on both synthesized and real datasets showed that the proposed TP‐IKPCA produces similar principal components as conventional batch‐based KPCA but is computationally faster than KPCA and its several incremental variants. Therefore, our algorithm can be applied to massive or online datasets where the batch method is not available. Kernel principal component analysis ; Computer science ; Principal component analysis ; Incremental learning ; Pattern recognition (psychology) ; Artificial intelligence ; Kernel (algebra) ; Feature (linguistics) ; Feature vector ; Batch processing ; Data mining ; Kernel method ; Support vector machine ; Mathematics Spectroscopy and Chemometric Analyses ; Face and Expression Recognition ; Advanced Algorithms and Applications
Authors: Qiang Gao | Meili Wang | Yangyang Guo | Xiaoqiang Zhao | Dongjian He
Source: IEEE Access (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Comparative Analysis of Non-Destructive Prediction Model of Soluble Solids Content for <i>Malus micromalus Makino</i> Based on Near-Infrared Spectroscopy To investigate the feasibility of using near-infrared (NIR) spectral technology to detect the soluble solids content (SSC) of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Malus micromalus Makino</i> , rapid and non-destructive prediction models of SSC were studied using least-square support vector regression (LS-SVR), partial least squares regression (PLSR), and the error back propagation artificial neural network (BP-ANN). First, 110 samples of NIR diffuse reflectance spectra in the wavelength range of 400.41–1083.89 nm were obtained, and then were divided into the calibration set and prediction set by sample set partitioning based on the joint x–y distance (SPXY) algorithm. Second, we compared the prediction performance of the PLSR model after preprocessing by nine spectral preprocessing methods, and applied data dimension reduction methods (random frog, the successive projections algorithm (SPA), and principal component analysis) for variable selection. Finally, the effect of applying full spectrum and characteristic spectrum modeling on SSC prediction accuracy was compared and analyzed. The comparison studies confirmed that the optimal fusion model of SPA-LS-SVR had the best performance ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\text {R}}_{\mathrm {C}} =0.9629$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\text {R}}_{\mathrm {P}} =0.9029$ </tex-math></inline-formula> , RMSEC = 0.199, RMSEP = 0.271). The experimental results could provide a reference for future development of the internal component analysis system for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Malus micromalus Makino</i> based on NIR spectroscopy and its classification system using SSC as the classification standard. Partial least squares regression ; Artificial intelligence ; Mathematics ; Calibration ; Principal component analysis ; Artificial neural network ; Support vector machine ; Data set ; Computer science ; Pattern recognition (psychology) ; Algorithm ; Statistics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Olof Svensson | Maciej Gilski | Didier Nurizzo | Matthew W. Bowler
Source: IUCrJ (article, 2019-07-09)
Field: Materials Science — Materials Chemistry

A comparative anatomy of protein crystals: lessons from the automatic processing of 56 000 samples The fully automatic processing of crystals of macromolecules has presented a unique opportunity to gather information on the samples that is not usually recorded. This has proved invaluable in improving sample-location, characterization and data-collection algorithms. After operating for four years, MASSIF-1 has now processed over 56 000 samples, gathering information at each stage, from the volume of the crystal to the unit-cell dimensions, the space group, the quality of the data collected and the reasoning behind the decisions made in data collection. This provides an unprecedented opportunity to analyse these data together, providing a detailed landscape of macromolecular crystals, intimate details of their contents and, importantly, how the two are related. The data show that mosaic spread is unrelated to the size or shape of crystals and demonstrate experimentally that diffraction intensities scale in proportion to crystal volume and molecular weight. It is also shown that crystal volume scales inversely with molecular weight. The results set the scene for the development of X-ray crystallography in a changing environment for structural biology. Volume (thermodynamics) ; Crystal (programming language) ; Computer science ; Set (abstract data type) ; Macromolecule ; Protein Data Bank ; Sample (material) ; Scale (ratio) ; Data set ; Data collection ; Data mining ; Crystallography ; Data science ; Biology ; Information retrieval ; Chemistry ; Mathematics ; Artificial intelligence ; Physics ; Statistics ; Geography ; Protein structure ; Cartography ; Biochemistry Enzyme Structure and Function ; Protein Structure and Dynamics ; Crystallization and Solubility Studies
Authors: Marion Janschitz | Natalie Romanov | Gina Varnavides | David M. Hollenstein | Gabriela Gérecová | Gustav Ammerer | Markus Hartl | Wolfgang Reiter
Source: Cell Communication and Signaling (article, 2019-06-17)
Field: Chemistry — Spectroscopy

Novel interconnections of HOG signaling revealed by combined use of two proteomic software packages Modern quantitative mass spectrometry (MS)-based proteomics enables researchers to unravel signaling networks by monitoring proteome-wide cellular responses to different stimuli. MS-based analysis of signaling systems usually requires an integration of multiple quantitative MS experiments, which remains challenging, given that the overlap between these datasets is not necessarily comprehensive. In a previous study we analyzed the impact of the yeast mitogen-activated protein kinase (MAPK) Hog1 on the hyperosmotic stress-affected phosphorylome. Using a combination of a series of hyperosmotic stress and kinase inhibition experiments, we identified a broad range of direct and indirect substrates of the MAPK. Here we re-evaluate this extensive MS dataset and demonstrate that a combined analysis based on two software packages, MaxQuant and Proteome Discoverer, increases the coverage of Hog1-target proteins by 30%. Using protein-protein proximity assays we show that the majority of new targets gained by this analysis are indeed Hog1-interactors. Additionally, kinetic profiles indicate differential trends of Hog1-dependent versus Hog1-independent phosphorylation sites. Our findings highlight a previously unrecognized interconnection between Hog1 signaling and the RAM signaling network, as well as sphingolipid homeostasis. Proteome ; MAPK/ERK pathway ; Proteomics ; Phosphorylation ; Biology ; Computational biology ; Signal transduction ; Protein kinase A ; Cell biology ; Kinase ; Cell signaling ; Sphingolipid ; Systems biology ; Bioinformatics ; Biochemistry Advanced Proteomics Techniques and Applications ; Fungal and yeast genetics research ; Endoplasmic Reticulum Stress and Disease
Authors: Mona Ibrahim | D. D. Parrish | Tim Brown | P. J. McDonald
Source: Sensors (article, 2019-07-17)
Field: Materials Science — Materials Chemistry

Decision Tree Pattern Recognition Model for Radio Frequency Interference Suppression in NQR Experiments Radio frequency interference places a major limitation on the in-situ use of unshielded nuclear quadrupole or nuclear magnetic resonance methods in industrial environments for quality control and assurance applications. In this work, we take the detection of contraband in an airport security-type application that is subject to burst mode radio frequency interference as a test case. We show that a machine learning decision tree model is ideally suited to the automated identification of interference bursts, and can be used in support of automated interference suppression algorithms. The usefulness of the data processed additionally by the new algorithm compared to traditional processing is shown in a receiver operating characteristic (ROC) analysis of a validation trial designed to mimic a security contraband detection application. The results show a highly significant increase in the area under the ROC curve from 0.580 to 0.906 for the proper identification of recovered data distorted by interfering bursts. Interference (communication) ; Computer science ; Decision tree ; Identification (biology) ; Radio frequency ; Artificial intelligence ; Pattern recognition (psychology) ; Telecommunications Solid-state spectroscopy and crystallography ; Nuclear Physics and Applications ; Geophysical Methods and Applications
Authors: Syed S. Ali | Sayyad Ajij
Source: Journal of Intelligent Systems (article, 2019-05-09)
Field: Chemistry — Analytical Chemistry

An Efficient Quality Inspection of Food Products Using Neural Network Classification Abstract Currently, there is a necessity for the expansion of precise, rapid, and intentional quality assurance with respect to the character of food and horticultural food items, because it is difficult to maintain and organize food products in an elevated quality and secure manner for the increasing population. In this article, we propose a procedure to resolve difficulties and to categorize food as either a broken or quality product. Therefore, the proposed process encompasses four segments, such as preprocessing, segmentation of broken division, feature extraction, and classification. At the first stage, the preprocessing method is used to remove all unnecessary noises. After that, modified region expansion-related segmentation is undertaken to segment the broken division of the food product. Then, feature extraction is used to remove the distinctive attributes of each food product to categorize their evaluation. Finally, the neural network classification procedure is used to examine the food quality. The proposed method is executed in the operational platform of MATLAB, and the consequences are examined by using obtainable methods. Categorization ; Preprocessor ; Computer science ; Segmentation ; Product (mathematics) ; Artificial neural network ; Artificial intelligence ; Division (mathematics) ; Quality (philosophy) ; Process (computing) ; Quality assurance ; Feature (linguistics) ; Market segmentation ; Feature extraction ; Pattern recognition (psychology) ; Population ; Data mining ; Machine learning ; Engineering ; Mathematics ; Operations management ; Marketing Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Food Supply Chain Traceability
Authors: Habiba Halepoto | Tao Gong | Kaleem Kashif
Source: Tekstilec (article, 2019-11-14)
Field: Materials Science — Polymers and Plastics

Real-Time Quality Assessment of Neppy Mélange Yarn Manufacturing Using Macropixel Analysis The aim of this paper is to provide a simple MATLAB-based model to determine the real-time homogeneity of neppy mlange yarn fabrics. Currently, the mlange yarn industry relies solely on visual assessment and experience. This algorithm, however, proposes a solution for the mlange yarn industry. The designed algorithm presented in this paper, which is based on kernel density function and macropixel analysis, was implemented for the real-time nep detection of neppy mlange yarns and calculated an inhomogeneity of neps of around 91%. This strategy would be useful for the mlange yarn industry and can also be used in other types of fashion yarns. Yarn ; Quality (philosophy) ; Materials science ; Composite material ; Physics Textile materials and evaluations ; Industrial Vision Systems and Defect Detection
Authors: Wenying Qu | Min Luo | Zhipeng Guo | Xiaogang Hu | Ang Zhang | Fan Zhang | Daquan Li | Yongzhong Zhang
Source: Materials (article, 2019-10-11)
Field: Materials Science — Materials Chemistry

Effect of Temperature Gradient on the Grain Size Homogeneity of SEED Produced Semi-Solid Slurries by Phase-Field Simulation The distribution homogeneity of grain size affects the fluidity of the semi-solid slurry, which in turn affects the properties of the casting. One key factor affecting grain size uniformity resides in the nucleation number, which has been studied thoroughly, while the other factor is temperature gradient which has not been investigated yet. In this study, the microstructure evolutions under certain temperature gradients are investigated by experiment and simulation using a two-dimensional quantitative phase-field (PF) model. A parallel and adaptive mesh refinement algorithm is adopted to solve the nonlinear phase-field equations. The results indicate that temperature gradient can affect the size distribution of microstructure in the semi-solid slurry prepared by the SEED process. A higher temperature gradient (in the range of 0.230~0.657 °C/mm) along the radial direction is beneficial to the homogeneity of the grain size in a slurry. Homogeneity (statistics) ; Slurry ; Materials science ; Nucleation ; Microstructure ; Grain size ; Temperature gradient ; Particle-size distribution ; Shape factor ; Composite material ; Thermodynamics ; Chemical engineering ; Particle size ; Geometry ; Mathematics ; Physics Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties ; Metallurgy and Material Forming