jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mariana Belgiu | Michael Marshall | Mirco Boschetti | Monica Pepe | Alfred Stein | Caroline Lievens
Source:  (article, 2021-07-11)
Field: Chemistry — Analytical Chemistry

HyNutri: Estimating the Nutritional Composition of Wheat from Multi-Temporal Prisma Data The goal of this work is to investigate the potential of PRecursore IperSpettrale della Missione Applicativa (PRISMA) hyperspectral data to predict the concentration of four macronutrients (K, P, N, S) and four micronutrients (Ca, Fe, Mg, Zn) in final wheat production. All investigated nutrients are essential to improving human nutrition. The initial findings indicate accurate predictions for Zn, P, Mg, S, K, Ca and Fe (R2 ranging from 0.57 to 0.74). N was less accurately estimated (R2 of 0.49). We conclude that the foliar chemical properties and temporal dynamics as detected by hyperspectral data translate successfully to the target micro- and macronutrients composition of the wheat production. Hyperspectral imaging ; Micronutrient ; Nutrient ; Composition (language) ; Chemistry ; Environmental science ; Environmental chemistry ; Computer science ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Leaf Properties and Growth Measurement
Authors: VR Sampath
Source: Current Trends in Fashion Technology & Textile Engineering (article, 2021-03-18)
Field: Materials Science — Polymers and Plastics

Investigation on Mechanical and Comfort Properties of Denim Woven Fabrics Made from Different Spandex Percentage for Sports Wear Fashion Technology & Textile Engineering introduces pioneering technologies, and improved understanding of textile materials, processes, chemistry and systems. It will encourage interdisciplinary research which will share newly developed technology, theory and techniques in the fashion and textile Denim ; Textile ; Engineering ; Manufacturing engineering ; Architectural engineering ; Mechanical engineering ; Materials science ; Composite material Textile materials and evaluations
Authors: Johannes Anastasiadis | Michael Heizmann
Source:  (article, 2021-03-17)
Field: Chemistry — Analytical Chemistry

Generation of artificial training data for spectral unmixing by modelling spectral variability using Gaussian random variables A stochastic method how artificial training data for spectral unmixing can be generated from real pure spectra is presented. Since the pure spectra are modelled as Gaussian random vectors, spectral variability is also considered. These training data can in turn be used to train an artificial neural network for spectral unmixing. Non-negativity and sum-to-one constraints are enforced by the network architecture. The approach is evaluated using real mixed spectra and achieves promising results with the used datasets. Training (meteorology) ; Computer science ; Gaussian ; Gaussian process ; Training set ; Artificial intelligence ; Data modeling ; Pattern recognition (psychology) ; Machine learning ; Geography ; Meteorology ; Physics Spectroscopy and Chemometric Analyses
Authors: Vojislav V. Mitić | Branislav Randjelović | Srdjan Ribar | Maria Čebela | Markus Mohr | Branislav Vlahović | H.‐J. Fecht
Source: Thermal Science (article, 2021-10-04)
Field: Materials Science — Materials Chemistry

Thermal parameters defined with graph theory approach in synthetized diamonds The Nanocrystaline diamonds are very important biomedical material with variety of applications. The experimental procedures and results have been done in the Institute of Functional Nanosystems at the University Ulm, Germany. There is an existing biocompatibility of the diamond layers, selectively improved by biomimetic 3-D patterns structuring. Based on that, we have been inspired to apply the graph theory approach in analysing and defining the physical parameters within the structure of materials structure samples. Instead the parameters values, characteristic at the samples surface, we penetrate the graphs deeply in the bulk structure. These values could be only, with some probability, distributed through the micro-structure what defines not enough precious parameters values between the micro-structure constituents, grains and pores. So, we originally applied the graph theory to get defined the physical parameters at the grains and pores levels. This novelty, in our paper, we applied for thermophysical parameters, like thermoconductiviy. By graph approach we open new frontiers in controlling and defining the processes at micro-structure relations. In this way, we can easily predict and design the structure with proposed parameters. Structuring ; Novelty ; Graph ; Diamond ; Computer science ; Graph theory ; Biological system ; Statistical physics ; Materials science ; Theoretical computer science ; Mathematics ; Physics ; Combinatorics Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Phase Equilibria and Thermodynamics
Authors: Seyed Hamed Javadi | Abdul Mounem Mouazen
Source: Ghent University Academic Bibliography (Ghent University) (article, 2021-01-01)
Field: Chemistry — Analytical Chemistry

Data fusion modelling of visible-near-infrared and mid-infrared spectra Spectroscopy has emerged as a solution to estimate key soil attributes in precision agriculture (PA) during recent decades. Chemometrics and machine-learning methods are used in order to extract useful information out of the spectra. In this paper, the performance of visible-near-infrared (Vis-NIR) and mid-infrared (MIR) spectrophotometers for the prediction of pH, organic carbon (OC), phosphorous (P), potassium (K), magnesium (Mg), calcium (Ca), sodium (Na), moisture content (MC), and cation exchange capacity (CEC) were evaluated. Using 267soil samples measured with a CompactSensspectrometer (tec5 technology, Germany) with 350-1700nm spectral range and a 4300-FTIR (Agilent, US) with 650-4000cm-1spectral range, we compared single-sensor partial least squares (PLS) regression after feature selection. To take advantage of both sensors, the combined use of them were evaluated in three fusion scenarios: 1. Spectral concatenation (SC) in which the raw vis-NIR and MIR spectra are concatenated; 2. Feature fusion (FF) wherein the features (i.e., selected spectral ranges) of vis-NIR and MIR are concatenated; and 3. Fusion of the predictions given by vis-NIR and MIR PLS-basedmodels by linear regression (LR). The validation results showed that the vis-NIR model outperforms the MIR model in the prediction of all studied attributes, except for pH, Ca, and CEC. Furthermore, the single-sensor accuracies were improved in all cases by LRwhile SC and FF enhanced the single-sensor accuracies just in cases of OC, Ca, and CEC with FF being superior to SC. However, the improvement achieved by fusion was not significant. Accordingly, it is suggested to use just vis-NIR for prediction of the studied soil attributes since it showed more robustness than MIR. Partial least squares regression ; Chemometrics ; Near-infrared spectroscopy ; Analytical Chemistry (journal) ; Chemistry ; Spectroscopy ; Fusion ; Cation-exchange capacity ; Infrared ; Artificial intelligence ; Mathematics ; Computer science ; Environmental science ; Statistics ; Soil science ; Chromatography ; Optics ; Physics ; Soil water Spectroscopy and Chemometric Analyses ; Soil Geostatistics and Mapping ; Geochemistry and Geologic Mapping
Authors: Abdelazeem S Abdelazeem | Ahmed H. Ibrahim | Mohamed S. Mohamed
Source: INTERNATIONAL JOURNAL OF CIVIL ENGINEERING AND TECHNOLOGY (IJCIET) (article, 2021-01-19)
Field: Materials Science — Materials Chemistry

A FRAMEWORK FOR CALCULATING THE RELATIVE WEIGHT OF EDUCATIONAL BUILDINGS’ CRITERIA problems that have bigger number of criterions. This paper proposes AHP method to define the judgment matrix of related and unrelated criterions that is used to calculate the relative weights (RWs) of each criterion. Analytic network process (ANP) was also used to calculate the RWs of the interrelated building categories (architectural, mechanical, and electrical). Such RWs can be used by managers for physical condition assessment of an educational building. The educational building hierarchy are divided into five levels. Each level is then defined in a judgment matrix to calculate its RWs. Then. The consistency of such judgment matrix has been evaluated to ensure that the RWs' results are acceptable. As concluded in the space level of the building hierarchy, the classroom space type that includes most of the education processes had the highest relative weight (RW=26.76%) followed by the bathrooms (RW=26.14%) and corridors and stairs (RW=25.77%). On the other hand, the cafeteria space type was the least relative weight (RW=1.58%) among all other space types. AHP technique provided an improved approach to calculate the RWs of each criterion affecting physical condition of educational building in Egypt. Architectural engineering ; Computer science ; Mathematics ; Engineering Structural mechanics and materials
Authors: A S Lukovenko | I.V. Zenkov
Source: iPolytech Journal (article, 2021-03-20)
Field: Materials Science — Materials Chemistry

Methods for calculating the reliability of power supply systems The aim was to determine the reliability indicators of a power supply system using an artificial neural network model. A model for calculating technical reliability was developed using the following methods: an algorithm for calculating reliability indicators of power supply systems, the method of failure rate of a power supply system and a forecasting model using artificial neural networks. It was established that a power supply system is formed by an open radial power supply circuit. The failure rate of the power supply subsystem was determined by calculating the failure rate of i-th element of the subsystem. As a result of calculating the probability of failure-free operation of the subsystem for various conditions (5 time intervals), it was found that with an increase in the operating time from 100 to 500 h, a linear increase in the rate of system failures occurs from 0.0051 to 0.0073 1/h. A comparison of the obtained mean-to-failure values of the main and the same backup subsystem in the unloaded mode with an absolutely reliable switch (269.62 h) with the main and the same backup subsystem in the loaded mode (202.21 h) was carried out. The results differ by 67.41 h, which indicates a higher degree of reliability of the first method. The software package Prognoz_INS_2020 was developed. An acceptable accuracy of no more than 2.17% was obtained by comparing the results of the conventional calculation of the failure rate of power supply systems and using the Prognoz_INS_2020 software package. This indicates the efficiency of the proposed software package in reliability calculations at operating energy enterprises. The proposed methods for assessing technical reliability both using the conventional model and a model based on an artificial neural network made it possible to assess the state of power supply systems, which helps to prevent dangerous emergencies. Backup ; Failure rate ; Reliability (semiconductor) ; Reliability engineering ; Power (physics) ; Artificial neural network ; Electric power system ; Failure mode and effects analysis ; Computer science ; Control theory (sociology) ; Engineering ; Physics Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Power System Reliability and Maintenance
Authors: JC Boulet | Marion Brandolini-Bunlon | Gilles Chaix | Benoît Jaillais | Éric Latrille | Matthieu Lesnoff | Anthony I. Mallet | Sílvia Mas Garcia | Maxime Metz | J.M. Roger | Virginie Rossard | DN Rutledge | Rémi Servien
Source: NIR news (article, 2021-11-14)
Field: Chemistry — Analytical Chemistry

ChemHouse: A research and development centre for chemometrics International audience Chemometrics ; Computer science ; Biochemical engineering ; Engineering ; Machine learning Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Metabolomics and Mass Spectrometry Studies
Authors: James P. Horwath | Peter W. Voorhees | Eric A. Stach
Source: Microscopy and Microanalysis (article, 2021-07-30)
Field: Materials Science — Surfaces, Coatings and Films

In situ Transmission Electron Microscopy for Data-driven Modeling of Nanoparticle Evolution Supported metal nanoparticles are important for industrial catalysis. Their high surface-to-volume ratio produces highly active heterogeneous catalysts, however this high surface energy also provides a driving force for particle growth to stabilize the nanoparticles. Though several mechanisms are expected to contribute to particle growth, and previous experiments have verified their existence in real systems, our understanding of the evolution of supported nanoparticles is based on mean-field theories which cannot describe the behavior of real, non-ideal systems. In situ Transmission Electron Microscopy (TEM), paired with high-framerate direct electron detection cameras, is particularly well suited to studying dynamic processes as it allows us to observe changes with high resolution in both space and time. Transmission electron microscopy ; In situ ; Transmission (telecommunications) ; Materials science ; Nanoparticle ; Nanotechnology ; Content (measure theory) ; Computer science ; Chemistry ; Telecommunications Electron and X-Ray Spectroscopy Techniques ; nanoparticles nucleation surface interactions ; Machine Learning in Materials Science
Authors: Ali Punjani | David J. Fleet
Source: Acta Crystallographica Section A Foundations and Advances (article, 2021-07-30)
Field: Materials Science — Surfaces, Coatings and Films

Advances in modelling continuous heterogeneity from single-particle cryo-EM data Single particle cryo-EM excels in determining static structures of biological macromolecules such as proteins. However, many proteins are dynamic, with their motion inherently linked to their function. Recovering the continuous motion and detailed 3D structure of flexible proteins from cryo-EM data has remained an open challenge. In this talk, we describe two new algorithms that allow both motion and structure of flexible proteins to resolved from cryo-EM data. Particle (ecology) ; Materials science ; Geology Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Eman El-Sayed | Eman Yehia Abd-Elkawe
Source: European Journal of Agriculture and Food Sciences (article, 2021-10-08)
Field: Materials Science — Polymers and Plastics

Production of Fine Count Yarns from Some Extra-long Egyptian Cottons on Different Spinning Systems The current research was carried out to produce fine count yarns from extra-long Egyptian cotton varieties using compact and ring spinning systems.in addition, to compare between compact yarns and ring yarns in terms of their physical and mechanical properties. Three commercial extra-long staple Egyptian cottons Giza92, Giza93 and Giza 96 were used to produce four linear densities of 80, 100, 120 and 140 at 3.6 twist multiplier. Results obtained showed that Giza 92 was surpassed significantly other extra -long staple varieties. It recorded the highest mean values of yarn strength and yarn evenness While, the same variety recorded the lowest mean values of yarn hairiness and imperfections. Compact yarns were much better than yarns spun on the ring spinning in yarn strength, yarn elongation, evenness, yarn imperfections and yarn hairiness. Yarn count 80,s gave higher single yarn strength (20.89cN/tex), yarn elongation (5.03%) and yarn evenness (17.49%) and lower yarn hairiness (2.04) and imperfections than yarn count 140s. Single yarn strength, yarn elongation and yarn evenness were decreased with increasing yarn count. While the number of neps, hairiness, the number of thin and thick places were increased with increasing yarn count. Concerning, the effect of interaction between cotton varieties × yarn counts × spinning systems on yarn quality properties. Yarn count 80s recorded the highest mean values of yarn strength (23.14, 21.1 and 20.2 cN/tex) and yarn evenness (17.72, 16.53 and 16.79%) for varieties Giza92, Giza93 and Giza96, respectively for compact spinning system. Yarn strength at count 80, 100, 120 and 140 correlated negatively and highly significant with micronaire value and maturity ratio. Yarn ; Spinning ; Composite material ; Species evenness ; Elongation ; Mathematics ; Materials science ; Ultimate tensile strength Textile materials and evaluations ; Research in Cotton Cultivation
Authors: Ivan Kozii | Tetyana Zhylenko | Инна Александровна Трунова | Yevhen Volodymyrovych Bataltsev | Н П Макаренко
Source: Ecological Sciences (article, 2021-01-01)
Field: Materials Science — General Materials Science

Criteria for choosing environmental equipment for cleaning industrial emissions of enterprises . Gephi, - UMAP Business ; Environmental science ; Process engineering ; Engineering Material Properties and Applications ; Quality and Management Systems ; Engineering and Environmental Studies
Authors: Rodnellin Onesime Malouata | Gelin Chedly Louzayadio | Michel Koukouatikissa Diafouka | Léonard Niéré
Source: Journal of Advances in Mathematics and Computer Science (article, 2021-12-23)
Field: Chemistry — Analytical Chemistry

A Dual Method of Carroll's Generalized Canonical Correlation Analysis Aims/Objectives: We propose a dual method of Carroll's generalized canonical correlation analysis and we prove by means of the proposed criterion that the duality is formulated by exchanging the operators. It is an extension of Carroll's generalized canonical correlation analysis. The approach of analysis is illustrated on the basis of case study. Canonical correlation ; Dual (grammatical number) ; Canonical analysis ; Extension (predicate logic) ; Mathematics ; Duality (order theory) ; Correlation ; Canonical correspondence analysis ; Basis (linear algebra) ; Algebra over a field ; Applied mathematics ; Pure mathematics ; Computer science ; Statistics ; Linguistics ; Geometry Spectroscopy and Chemometric Analyses ; Neural Networks and Applications ; Face and Expression Recognition
Authors: Anjana Ghule | Ratnadeep R. Deshmukh
Source: Current Science (article, 2021-03-10)
Field: Chemistry — Analytical Chemistry

Wavelength Selection and Classification of Hyperspectral Non-Imagery Data to Discriminate Healthy and Unhealthy Vegetable Leaves Being the largest vegetarian population across the globe, vegetables are an integral part of Indian meals. The proposed research finds significant wavelengths to discriminate healthy and unhealthy vegetable plants. Spectral-reflectance (SR) and first-derivative (FD) in the visible, red edge and near infrared region (350-1000 nm) of three vegetables brinjal, cluster beans and long beans were used. The significant wavelengths were selected using ReliefF and Support-Vector-Machine (SVM). Random forest algorithm was used for classification. The binary classification was used for each vegetable separately, and multiclass classification was applied for all the samples. The most significant spectral wavelengths, for the prediction of diseased brinjal, correspond primarily to the red edge in SR. Long beans samples were classified accurately in the red-edge. In the case of cluster beans, SR is more effective than FD in the red-edge. The results substantiate the utility of HS data for discrimination of healthy and unhealthy vegetable plants and even vegetable types. Hyperspectral imaging ; Remote sensing ; Selection (genetic algorithm) ; Pattern recognition (psychology) ; Artificial intelligence ; Environmental science ; Computer science ; Geography Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Alessandro Barbiero | Asmerilda Hitaj
Source: Annals of Operations Research (article, 2021-03-18)
Field: Materials Science — Materials Chemistry

Approximation of continuous random variables for the evaluation of the reliability parameter of complex stress–strength models Abstract In many management science or economic applications, it is common to represent the key uncertain inputs as continuous random variables. However, when analytic techniques fail to provide a closed-form solution to a problem or when one needs to reduce the computational load, it is often necessary to resort to some problem-specific approximation technique or approximate each given continuous probability distribution by a discrete distribution. Many discretization methods have been proposed so far; in this work, we revise the most popular techniques, highlighting their strengths and weaknesses, and empirically investigate their performance through a comparative study applied to a well-known engineering problem, formulated as a stress–strength model, with the aim of weighting up their feasibility and accuracy in recovering the value of the reliability parameter, also with reference to the number of discrete points. The results overall reward a recently introduced method as the best performer, which derives the discrete approximation as the numerical solution of a constrained non-linear optimization, preserving the first two moments of the original distribution. This method provides more accurate results than an ad-hoc first-order approximation technique. However, it is the most computationally demanding as well and the computation time can get even larger than that required by Monte Carlo approximation if the number of discrete points exceeds a certain threshold. Reliability (semiconductor) ; Stress (linguistics) ; Random variable ; Mathematics ; Econometrics ; Statistics ; Reliability engineering ; Engineering ; Physics Material Properties and Failure Mechanisms ; Fatigue and fracture mechanics ; Engineering Diagnostics and Reliability
Authors: Lukas Rosenbauer | Johannes Maier | Dániel Gerber | Anthony Stein | Jörg Hähner
Source:  (article, 2021-01-01)
Field: Materials Science — Polymers and Plastics

An Evolutionary Calibration Approach for Touch Interface Filter Chains Touch interfaces are human machine interface (HMI) that can be found in a wide range of products ranging from mobile phones over cars to home appliances.Many of these HMIs measure digital signals which are used to detect touch events.These signals are processed using filters in order to decide whether there is a touch event or not.The filterchain must be functional even if the signal contains heavy noise.Thus a precise calibration of the individual filters is necessary.We employ a genetic algorithm (GA) to choose the filter parameters automatically.We evaluate our approach in a series of experiments which includes simulated as well as real data.We additionally compare our GA with manually calibrated parameters and thereby show the superiority of our method in terms of the accuracy of the calibration provided.A cost-intensive manual calibration can thus be avoided. Computer science ; Calibration ; Interface (matter) ; Filter (signal processing) ; Noise (video) ; Measure (data warehouse) ; Ranging ; Genetic algorithm ; Range (aeronautics) ; SIGNAL (programming language) ; Event (particle physics) ; Real-time computing ; Artificial intelligence ; Computer vision ; Data mining ; Engineering ; Machine learning ; Mathematics ; Telecommunications Textile materials and evaluations ; Color perception and design ; Animal Behavior and Welfare Studies