jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shuozhi Xu | Justin Y. Cheng | Zezhou Li | Nathan A. Mara | Irene J. Beyerlein
Source: Computer Methods in Applied Mechanics and Engineering (article, 2021-12-24)
Field: Materials Science — Materials Chemistry

Phase-field modeling of the interactions between an edge dislocation and an array of obstacles Materials science ; Dislocation ; Glide plane ; Amorphous solid ; Condensed matter physics ; Crystallography ; Phase (matter) ; Anisotropy ; Alloy ; Dislocation creep ; Composite material ; Physics ; Chemistry ; Optics Microstructure and mechanical properties ; Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties
Authors: Zhongyu Zheng | Ran Ni | Yuren Wang | Yilong Han
Source: Science Advances (article, 2021-01-15)
Field: Materials Science — Materials Chemistry

Translational and rotational critical-like behaviors in the glass transition of colloidal ellipsoid monolayers Critical-like behaviors have been found in translational degrees of freedom near the glass transition of spherical particle systems mainly with local polycrystalline structures, but it is not clear if criticality exists in more general glassy systems composed of nonspherical particles without crystalline structures. Here, through experiments and simulations, we show critical-like behaviors in both translational and rotational degrees of freedom in monolayers of monodisperse colloidal ellipsoids in the absence of crystalline orders. We find rich features of the Ising-like criticality in structure and slow dynamics at the ideal glass transition point ϕ<sub>0</sub>, showing the thermodynamic nature of glass transition at ϕ<sub>0</sub> A dynamic criticality is found at the mode-coupling critical point ϕ<sub>c</sub> for the fast-moving clusters whose critical exponents increase linearly with fragility, reflecting a dynamic glass transition. These results cast light on the glass transition and explain the mystery that the dynamic correlation lengths diverge at two different temperatures. Ellipsoid ; Glass transition ; Monolayer ; Colloid ; Relaxation (psychology) ; Materials science ; Chemical physics ; Rotational dynamics ; Condensed matter physics ; Nanotechnology ; Chemistry ; Physics ; Molecule ; Physical chemistry ; Composite material ; Polymer ; Biology Material Dynamics and Properties ; Liquid Crystal Research Advancements ; Surfactants and Colloidal Systems
Authors: Patricia L.A. Leighton | José Segura | Stephanie Lam | M. Marcoux | Xinyi Wei | Ó. López-Campos | Olugbenga P. Soladoye | M. E. R. Dugan | M. Juárez | N. Prieto
Source: Meat and Muscle Biology (review, 2021-09-14)
Field: Chemistry — Analytical Chemistry

Prediction of Carcass Composition and Meat and Fat Quality Using Sensing Technologies: A Review Consumer demand for high-quality healthy food is increasing; therefore, meat processors require the means toassess their products rapidly, accurately, and inexpensively. Traditional methods for quality assessments are time-consum-ing, expensive, and invasive and have potential to negatively impact the environment. Consequently, emphasis has been puton finding nondestructive, fast, and accurate technologies for product composition and quality evaluation. Research in thisarea is advancing rapidly through recent developments in the areas of portability, accuracy, and machine learning.Therefore, the present review critically evaluates and summarizes developments of popular noninvasive technologies(i.e., from imaging to spectroscopic sensing technologies) for estimating beef, pork, and lamb composition and quality,which will hopefully assist in the implementation of these technologies for rapid evaluation/real-time grading of livestockproducts in the near future. Software portability ; Computer science ; Quality (philosophy) ; Emerging technologies ; Grading (engineering) ; Engineering ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: Борис Б. Аверкиев
Source: Digital Commons - USU (Utah State University) (article, 2021-09-30)
Field: Chemistry — Inorganic Chemistry

Geometry and Electronic Structure of Doped Clusters via the Coalescence Kick Method Developing chemical bonding models in clusters is one of the most challenging tasks of modern theoretical chemistry. There are two reasons for this. The first one is that clusters are relatively new objects in chemistry and have been extensively studied since the middle of the 20th century. The second reason is that clusters require high-level quantum-chemical calculations; while for many classical molecules their geometry and properties can be reasonably predicted by simpler methods. The aim of this dissertation was to study doped clusters and explain their chemical bonding. The research was focused on three classes of compounds: aluminum clusters doped with one nitrogen atom, planar compounds with hypercoordinate central atom, partially mixed carbon-boron clusters, and transition metal clusters. The geometry of the two latter classes of compounds was explained using the concept of aromaticity, previously developed in our group. Geometry ; Doping ; Coalescence (physics) ; Physics ; Materials science ; Mathematics ; Condensed matter physics ; Astrobiology Radioactive element chemistry and processing ; Railway Systems and Materials Science ; Engineering and Material Science Research
Authors: Peng Wang | Jiang Liu | Lijia Xu | Peng Huang | Xiong Luo | Yan Hu | Zhiliang Kang
Source: Agriculture (article, 2021-04-26)
Field: Chemistry — Analytical Chemistry

Classification of Amanita Species Based on Bilinear Networks with Attention Mechanism The accurate classification of Amanita is helpful to its research on biological control and medical value, and it can also prevent mushroom poisoning incidents. In this paper, we constructed the Bilinear convolutional neural networks (B-CNN) with attention mechanism model based on transfer learning to realize the classification of Amanita. When the model is trained, the weight on ImageNet is used for pre-training, and the Adam optimizer is used to update network parameters. In the test process, images of Amanita at different growth stages were used to further test the generalization ability of the model. After comparing our model with other models, the results show that our model greatly reduces the number of parameters while achieving high accuracy (95.2%) and has good generalization ability. It is an efficient classification model, which provides a new option for mushroom classification in areas with limited computing resources. Generalization ; Amanita ; Artificial intelligence ; Computer science ; Mechanism (biology) ; Machine learning ; Mushroom ; Convolutional neural network ; Transfer of learning ; Bilinear interpolation ; Pattern recognition (psychology) ; Mathematics ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote-Sensing Image Classification
Authors: Kazuma Ito | Hideaki Sawada | Shigenobu Ogata
Source: MATERIALS TRANSACTIONS (article, 2021-03-25)
Field: Materials Science — Materials Chemistry

Theoretical Prediction of Grain Boundary Segregation Using Nano-Polycrystalline Grain Boundary Model The importance of controlling grain boundary (GB) segregation is increasing, especially with the strengthening of steel nowadays. In this study, a theoretical prediction method for the amount of GB segregation for a solute element in polycrystals is established. This prediction method entails the development of a nano-polycrystalline GB model for simulating GBs in polycrystals, and the segregation energy of a solute element is comprehensively calculated for all atomic sites constituting the GB model by using an interatomic potential. From the obtained segregation energies, the segregation amount of the solute element at each atomic site is determined. Subsequently, each atomic site is classified based on its distance from the GB center and averaged to determine the segregation profile of the solute element for that distance from the GB center. By applying this method to the GB segregation of P in bcc-Fe and comparing the results with experimental findings, it is determined that this prediction method can deliver excellent prediction accuracies. Grain boundary ; Crystallite ; Materials science ; Boundary element method ; Nano- ; Interatomic potential ; Molecular dynamics ; Metallurgy ; Finite element method ; Thermodynamics ; Microstructure ; Composite material ; Computational chemistry ; Physics ; Chemistry Microstructure and mechanical properties ; Microstructure and Mechanical Properties of Steels ; Magnetic Properties and Applications
Authors: Puneet Mishra | Menno Sytsma | Aneesh Chauhan | G. Polder | E.J. Pekkeriet
Source: Analytica Chimica Acta (article, 2021-11-02)
Field: Chemistry — Analytical Chemistry

All-in-one: A spectral imaging laboratory system for standardised automated image acquisition and real-time spectral model deployment Spectral imaging (SI) in analytical chemistry is widely used for the assessment of spatially distributed physicochemical properties of samples. Although massive development in instrument and chemometrics modelling has taken place in the recent years, the main challenge with SI is that available sensors require extensive system integration and calibration modelling before their use for routine analysis. Further, the models developed during one experiment are rarely useful once the system is reintegrated for a new experiment. To avoid system reintegration and reuse calibrated models, this study presents an intelligent All-In-One SI (ASI) laboratory system allowing standardised automated data acquisition and real-time spectral model deployment. The ASI system supplies a controlled standardised illumination environment, an in-built computing system, embedded software for automated image acquisition, and model deployment to predict the spatial distribution of sample properties in real-time. To show the capability of the ASI framework, exemplary cases of fruit property prediction in different fruits are presented. Furthermore, ASI is also benchmarked in performance against the current commercially available portable as well as high-end laboratory spectrometers. Software deployment ; Chemometrics ; Reuse ; Data acquisition ; Software ; Calibration ; Multispectral image ; Sample (material) ; Spectral imaging ; Imaging spectrometer ; Computer science ; Systems engineering ; Spectrometer ; Artificial intelligence ; Real-time computing ; Remote sensing ; Chemistry ; Machine learning ; Software engineering ; Engineering Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Muhammad Raza Khowja | Gulrukh Turabee | Paolo Giangrande | Vincenzo Madonna | Georgina Cosma | Gaurang Vakil | Chris Gerada | Michael Galea
Source: IEEE Access (article, 2021-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Lifetime Estimation of Enameled Wires Under Accelerated Thermal Aging Using Curve Fitting Methods Estimating the lifetime of enameled wires using the conventional/standard test method requires a significant amount of time that can endure up to thousands of testing hours, which could considerably delay the time-to-market of a new product. This paper presents a new approach that estimates the insulation lifetime of enameled wire, employed in electrical machines, using curve fitting models whose computation is rapid and accurate. Three curve fit models are adopted to predict the insulation resistance of double-coated enameled magnet wire samples, with respect to their aging time. The samples' mean time-to-failure is estimated, and performance of the models is apprised through a comparison against the conventional `standard method' of lifetime estimation of the enameled wires. The best prediction accuracy is achieved by a logarithmic curve fit approach, which gives an error of 0.95% and 1.62% when its thermal index is compared with the conventional method and manufacturer claim respectively. The proposed approach provides a time-saving of 67% (83 days) when its computation time is compared with respect to the `standard method' of lifetime estimation. Computation ; Logarithm ; Computer science ; Curve fitting ; Cooling curve ; Thermal ; Materials science ; Algorithm ; Mathematics ; Mathematical analysis ; Physics Magnetic Properties and Applications ; Thermal Analysis in Power Transmission ; Non-Destructive Testing Techniques
Authors: Ewa Ropelewska | Jan Piecko
Source: European Food Research and Technology (article, 2021-11-28)
Field: Chemistry — Analytical Chemistry

Discrimination of tomato seeds belonging to different cultivars using machine learning Abstract This study was aimed at developing the discriminant models for distinguishing the tomato seeds based on texture parameters of the outer surface of seeds calculated from the images (scans) converted to individual color channels R , G , B , L , a , b , X , Y , Z . The seeds of tomatoes ‘Green Zebra’, ‘Ożarowski’, ‘Pineapple’, Sacher F1 and Sandoline F1 were discriminated in pairs. The highest results were observed for models built based on sets of textures selected individually from color channels R , L and X and sets of textures selected from all color channels. In all cases, the tomato seeds ‘Green Zebra’ and ‘Ożarowski’ were discriminated with the highest average accuracy equal to 97% for the Multilayer Perceptron classifier and 96.25% for Random Forest for color channel R , 95.25% (Multilayer Perceptron) and 95% (Random Forest) for color channel L , 93% (Multilayer Perceptron) and 95% (Random Forest) for color channel X , 99.75% (Multilayer Perceptron) and 99.5% (Random Forest) for a set of textures selected from all color channels ( R , G , B , L , a , b , X , Y , X ). The highest average accuracies for other pairs of cultivars reached 98.25% for ‘Ożarowski’ vs. Sacher F1, 95.75% for ‘Pineapple’ vs. Sandoline F1, 97.5% for ‘Green Zebra’ vs. Sandoline F1, 97.25% for Sacher F1 vs. Sandoline F1 for models built based on textures selected from all color channels . The obtained results may be used in practice for the identification of cultivar of tomato seeds. The developed models allow to distinguish the tomato seed cultivars in an objective and fast way using digital image processing. The results confirmed the usefulness of texture parameters of the outer surface of tomato seeds for classification purposes. The discriminative models allow to obtain a very high probability and may be applied to authenticate and detect seed adulteration. Cultivar ; Mathematics ; Random forest ; Artificial intelligence ; Perceptron ; Linear discriminant analysis ; Horticulture ; Botany ; Pattern recognition (psychology) ; Statistics ; Biology ; Artificial neural network ; Computer science Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Leaf Properties and Growth Measurement
Authors: Devendra Kumar Pandey | Prabhjot Kaur | Vijay Kumar | Rathindra Mohan Banik | Tabarak Malik | Abhijit Dey
Source: BMC Plant Biology (article, 2021-02-05)
Field: Chemistry — Organic Chemistry

Screening the elite chemotypes of Gloriosa superba L. in India for the production of anticancer colchicine: simultaneous microwave-assisted extraction and HPTLC studies Therefore, this newly developed optimized MAE coupled with HPTLC densitometry methodology not only quantifies colchicine in order to identify elite chemotypes of G. superba, but it also encourages in selecting high yielding populations of the plants for industrial use and economic boost for the farmers. This validated, simple and reproducible HPTLC protocol is being used for the first time to estimate colchicine from natural populations of G. superba obtained from 32 different geographical regions of India. Chromatography ; Chemistry ; Chemotype ; Colchicine ; Extraction (chemistry) ; High performance thin layer chromatography ; Derivatization ; Traditional medicine ; Methanol ; Thin-layer chromatography ; High-performance liquid chromatography ; Biology ; Medicine ; Organic chemistry ; Essential oil Synthesis and biological activity ; Natural product bioactivities and synthesis ; Drug-Induced Hepatotoxicity and Protection
Authors: Luciano Ortenzi | Simone Figorilli | Corrado Costa | Federico Pallottino | Simona Violino | Mauro Pagano | Giancarlo Imperi | Rossella Manganiello | Barbara Lanza | Francesca Antonucci
Source: Sensors (article, 2021-04-22)
Field: Chemistry — Analytical Chemistry

A Machine Vision Rapid Method to Determine the Ripeness Degree of Olive Lots The degree of olive maturation is a very important factor to consider at harvest time, as it influences the organoleptic quality of the final product, for both oil and table use. The Jaén index, evaluated by measuring the average coloring of olive fruits (peel and pulp), is currently considered to be one of the most indicative methods to determine the olive ripening stage, but it is a slow assay and its results are not objective. The aim of this work is to identify the ripeness degree of olive lots through a real-time, repeatable, and objective machine vision method, which uses RGB image analysis based on a k-nearest neighbors classification algorithm. To overcome different lighting scenarios, pictures were subjected to an automatic colorimetric calibration method-an advanced 3D algorithm using known values. To check the performance of the automatic machine vision method, a comparison was made with two visual operator image evaluations. For 10 images, the number of black, green, and purple olives was also visually evaluated by these two operators. The accuracy of the method was 60%. The system could be easily implemented in a specific mobile app developed for the automatic assessment of olive ripeness directly in the field, for advanced georeferenced data analysis. Ripeness ; RGB color model ; Artificial intelligence ; Machine vision ; Ripening ; Computer vision ; Computer science ; Mathematics ; Food science Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Remote Sensing in Agriculture
Authors: María Belén Estevez | María Laura Casaux | Martín Fraga | Ricardo Faccio | Silvana Alborés
Source: Frontiers in Bioengineering and Biotechnology (article, 2021-04-26)
Field: Materials Science — Materials Chemistry

Biogenic Silver Nanoparticles as a Strategy in the Fight Against Multi-Resistant Salmonella enterica Isolated From Dairy Calves Infectious diseases are one of the most important health problems worldwide, one of the main causes being the development of multi-resistant microorganisms. Likewise, the zoonotic potential of some pathogens and their ability to transfer resistance mechanisms, reduce the therapeutic options in both humans and animals. <i>Salmonella enterica</i> is an important pathogen that affects a wide range of animal species and humans, being <i>Salmonella</i> Typhimurium one of the most frequent serotypes affecting cattle, causing enteritis, diarrhea, and septicemia. The search for alternative therapeutic approaches has gained importance since the emergence of multidrug resistance to antibiotics and periodic outbreaks of salmonellosis. In this sense, the discovery of new drugs and the development of new strategies, such as the use of nanoparticles with antimicrobial activity, are very promising. The aim of this work was the extracellular production of biogenic silver nanoparticles using fungal extracts and the evaluation of their antimicrobial activity against resistant and multi-resistant <i>Salmonella</i> Typhimurium strains. We here demonstrated the potential of the biogenic nanoparticles as effective bacteriostatic and bactericidal agents for use in biomedical applications. In addition, Confocal Raman Microscopy and Atomic Force Microscopy were used to advance the understanding of the antimicrobial mechanism of biogenic nanoparticles against these pathogenic strains, the results of which suggested that the nanoparticles produced damage in several bacterial cell structures. Salmonella enterica ; Antimicrobial ; Salmonella ; Antibiotic resistance ; Microbiology ; Silver nanoparticle ; Pathogen ; Biology ; Antibiotics ; Multiple drug resistance ; Serotype ; Microorganism ; Bacteria ; Chemistry ; Nanotechnology ; Nanoparticle ; Materials science Nanoparticles: synthesis and applications ; Bacterial biofilms and quorum sensing
Authors: Pamela Brunswick | Daniel J. Cuthbertson | Jeffrey Yan | Candice C. Chua | Isabelle Duchesne | Nathalie Isabel | Philip D. Evans | Peter Gasson | Geoffrey C. Kite | Joy Bruno | Graham van Aggelen | Dayue Shang
Source: Environmental Advances (article, 2021-07-28)
Field: Chemistry — Organic Chemistry

A practical study of CITES wood species identification by untargeted DART/QTOF, GC/QTOF and LC/QTOF together with machine learning processes and statistical analysis Illegal logging and trafficking of endangered timber species has attracted the world's major organized crime groups, with associated deforestation and serious social damage. The inability of traditional methodologies and DNA analysis to readily perform wood identification to the species level for monitoring has stimulated research on chemotyping techniques. In this study, simple wood extraction of endangered rosewoods (Dalbergia spp), amenable to use in the field, produced colorful hues that were suggestive of wood species. A more definitive study was conducted to develop wood species identification procedures using high-resolution quadrupole time-of-flight (QTOF) mass spectrometers interfaced with liquid chromatography (LC), gas chromatography (GC), and Direct Analysis in Real Time (DART). The time consuming process of extracting “identifying” mass spectral ions for species identification, contentious due to their ubiquitous nature, was supplanted by application of machine learning processes. The unbiased software mining of raw data from multiple analytical batches, followed by statistical Random Forest analysis, enabled discrimination between both anatomically and chemotypically similar Dalbergia species. Statistical Principal Component Analysis (PCA) scatterplots with 95% confidence ellipses were visually compelling in showing a differential clustering of Dalbergia from other commonly traded and lookalike wood species. The information rich raw data from GC or LC analyses offered a corroborative, legally defensible, and widely available confirmatory tool in the identification of timber species. Identification (biology) ; Endangered species ; Species identification ; Computer science ; DART ion source ; Artificial intelligence ; Chemistry ; Biology ; Ecology ; Evolutionary biology Wood and Agarwood Research ; Cultural Heritage Materials Analysis ; Identification and Quantification in Food