jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Julio Barón Velándia | Camilo Enrique Rocha Calderón | Daniel David Leal Lara
Source: International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering (article, 2021-04-29)
Field: Chemistry — Analytical Chemistry

Optimization techniques on fuzzy inference systems to detect Xanthomonas campestris disease &lt;span&gt;This paper shows the outcomes for four optimization models based on fuzzy inference systems, intervened using Quasi-Newton and genetic algorithms, to early assess&lt;/span&gt;&lt;span&gt; bean plants’ leaves for Xanthomonas campestris&lt;em&gt; &lt;/em&gt;disease. The assessment on the status of the plant (sane or ill) is defined through the intensity of the color in the RGB scale for the data-sets and images to analyze the implementation of the models. The best model performance is 99.68% when compared with the training data and a 94% effectiveness rate on the detection of Xanthomonas campestris in a bean leave image. Therefore, these results would allow farmers to take early measures to reduce the impact of the disease on the look and performance of green bean crops.&lt;/span&gt; Xanthomonas campestris ; Inference ; Fuzzy inference ; Xanthomonas ; Span (engineering) ; RGB color model ; Computer science ; Fuzzy logic ; Artificial intelligence ; Biology ; Adaptive neuro fuzzy inference system ; Fuzzy control system ; Engineering ; Bacteria Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Rodrigo P. Alzola | Seyed Mohammad Siadat | Anuj Gajjar | Rickard Stureborg | Jeffrey W. Ruberti | José Delpiano | Charles A. DiMarzio
Source: Journal of Biomedical Optics (article, 2021-07-08)
Field: Materials Science — Biomaterials

Method for measurement of collagen monomer orientation in fluorescence microscopy These findings can contribute to our understanding of collagen monomers interaction with collagen fibrils surface during fibril formation and remodeling. Monomer ; Fluorescence microscope ; Microscopy ; Orientation (vector space) ; Materials science ; Fibril ; Fluorescence ; Biophysics ; Collagen fibril ; Chemistry ; Biological system ; Optics ; Polymer ; Composite material ; Biology ; Mathematics ; Geometry Collagen: Extraction and Characterization ; Osteoarthritis Treatment and Mechanisms ; Spectroscopy and Chemometric Analyses
Authors: Hao Fang | Yuxuan Du | Peixuan Li | Youchuan Mao | De-Ye Lin | Jun Wang | Xingyu Gao | Kaixuan Wang | Xianghong Liu | Haifeng Song | Yong Feng | Jinshan Li | William Yi Wang
Source: Materials (article, 2021-04-19)
Field: Materials Science — Materials Chemistry

Effect of High Strain Rate on Adiabatic Shearing of α+β Dual-Phase Ti Alloy In the present work, the localized features of adiabatic shear bands (ASBs) of our recently designed damage tolerance α+β dual-phase Ti alloy are investigated by the integration of electron backscattering diffraction and experimental and theoretical Schmid factor analysis. At the strain rate of 1.8 × 10<sup>4</sup> s<sup>-1</sup> induced by a split Hopkinson pressure bar, the shear stress reaches a maximum of 1951 MPa with the shear strain of 1.27. It is found that the α+β dual-phase colony structures mediate the extensive plastic deformations along α/β phase boundaries, contributing to the formations of ASBs, microvoids, and cracks, and resulting in stable and unstable softening behaviors. Moreover, the dynamic recrystallization yields the dispersion of a great amount of fine α grains along the shearing paths and in the ASBs, promoting the softening and shear localization. On the contrary, low-angle grain boundaries present good resistance to the formation of cracks and the thermal softening, while the non-basal slipping dramatically contributes to the strain hardening, supporting the promising approaches to fabricate the advanced damage tolerance dual-phase Ti alloy. Adiabatic shear band ; Materials science ; Alloy ; Shearing (physics) ; Split-Hopkinson pressure bar ; Composite material ; Softening ; Work hardening ; Strain rate ; Dynamic recrystallization ; Strain hardening exponent ; Shear (geology) ; Metallurgy ; Microstructure ; Hot working High-Velocity Impact and Material Behavior ; Titanium Alloys Microstructure and Properties ; Nuclear Materials and Properties
Authors: Xuan Hao | Jinfeng Liu | Imran Ali | Hongyuan Luo | Yanqiang Han | Wenxin Hu | Jinyun Liu | Xiao He | Jinjin Li
Source: Scientific Reports (article, 2021-03-29)
Field: Chemistry — Physical and Theoretical Chemistry

Ab initio determination of crystal stability of di-p-tolyl disulfide With the rapid growth of energy demand and the depletion of existing energy resources, the new materials with superior performances, low costs and environmental friendliness for energy production and storage are explored. Di-p-tolyl disulfide (p-Tol<sub>2</sub>S<sub>2</sub>) is a typical lubricating material, which has been applied in the field of energy storage. The conformational properties and phase transformations of p-Tol<sub>2</sub>S<sub>2</sub> have been studied by pioneers, but their polymorphs and the polymorphism induced crystal structure changes require further analysis. In this study, we perform the crystal structural screening, prediction and optimization of p-Tol<sub>2</sub>S<sub>2</sub> crystal with quantum mechanical calculations, i.e., density functional theory (DFT) and second-order Møller-Plesset perturbation (MP2) methods. A series of crystal structures with different molecular arrangements are generated based on the crystal structure screening. As compared to long-established lattice energy calculation, we take an advantage of using more accurate technique, which is Gibbs free energy calculation. It considers the effects of entropy and temperature to predict the crystal structures and energy landscape. By comparing the Gibbs free energies between predicted and experimental structures, we found that phase α is the most stable structure for p-Tol<sub>2</sub>S<sub>2</sub> crystal at ambient temperature and standard atmospheric pressure. Furthermore, we provide an efficient method to discriminate different polymorphs that are otherwise difficult to be identified based on the Raman/IR spectra. The proposed work enable us to evaluate the quality of various crystal polymorphs rapidly. Crystal structure prediction ; Ab initio ; Gibbs free energy ; Crystal structure ; Lattice energy ; Crystal (programming language) ; Density functional theory ; Materials science ; Thermodynamics ; Raman spectroscopy ; Polymorphism (computer science) ; Crystallography ; Chemistry ; Computational chemistry ; Physics ; Computer science ; Quantum mechanics ; Organic chemistry Crystallography and molecular interactions ; Nonlinear Optical Materials Research ; Organometallic Compounds Synthesis and Characterization
Authors: Madlen Ullmann | Kristina Kittner | Ulrich Prahl
Source: Metals (article, 2021-11-03)
Field: Materials Science — Biomaterials

Hot Rolling of the Twin-Roll Cast and Homogenized Mg-6.8Y-2.5Zn (WZ73) Magnesium Alloy Containing LPSO Structures In this study, hot rolling trials were conducted on a twin-roll cast and homogenized magnesium alloy Mg-6.8Y-2.5Zn (WZ73). The WZ73 contains long period stacking ordered (LPSO) structures due to the ratio of Y and Zn content. Microstructural and texture evolution depending on the different strain and strain rates were investigated, and the resulting mechanical properties were also considered. Therefore, samples were quenched in water directly after hot rolling. The results revealed that the rolling parameters significantly influence dynamic recrystallization (DRX), while continuous and twin-induced dynamic recrystallization are assumed to be the main DRX mechanisms. It was also found that high strains and strain rates are required to proceed the DRX. The resulting textures revealed that the non-basal slip of &lt;a&gt;-dislocations and &lt;c+a&gt;-dislocations is activated during hot rolling. Hot rolling results in increased strength and ductility compared to the initial twin-roll cast and homogenized state. Materials science ; Dynamic recrystallization ; Metallurgy ; Magnesium alloy ; Alloy ; Recrystallization (geology) ; Strain rate ; Slip (aerodynamics) ; Hot working Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Aluminum Alloy Microstructure Properties
Authors: Benedikt Weger | Satyapriya Gupta | Thomas Hochrainer
Source: Comptes Rendus Physique (article, 2021-05-03)
Field: Materials Science — Materials Chemistry

Analysing discrete dislocation data using alignment and curvature tensors Analysis of large scale discrete dislocation data requires the characterisation of complex dislocation networks by suitable average quantities. In the current work, we suggest dislocation alignment tensors and closely related curvature tensors as easily extractable and intelligible measures of geometrical and topological characteristics of dislocation distributions. We provide formulae for extracting these measures from discrete dislocation data based on straight segments. Examples for interpreting and visualising these measures are provided for a simple configuration and two more involved results from discrete dislocation simulations. We suggest the alignment and curvature tensors for wider use in plasticity research. Dislocation ; Curvature ; Scale (ratio) ; Plasticity ; Statistical physics ; Physics ; Computer science ; Topology (electrical circuits) ; Geometry ; Mathematics ; Combinatorics ; Condensed matter physics Microstructure and mechanical properties ; Rock Mechanics and Modeling ; Elasticity and Material Modeling
Authors: Tim Cofala | Oliver Krämer
Source: ESANN 2021 proceedings (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Transformers for Molecular Graph Generation This work introduces an autoregressive generative model for graphs which is based on the transformer architecture and applied to the domain of molecular graph generation. Utilizing the multi-head self-attention mechanism to directly model distributions over atoms and bonds, it can sample new molecular graphs in an autoregressive manner. The benchmark framework MOSES is used to compare the proposed approach to other state-of-the-art molecule generation models. It is shown that the model is capable of generalizing from the training data to generate novel and realistic molecules. Autoregressive model ; Transformer ; Computer science ; Generative grammar ; Benchmark (surveying) ; Graph ; Bond graph ; Generative model ; Theoretical computer science ; Algorithm ; Artificial intelligence ; Mathematics ; Engineering ; Electrical engineering ; Voltage ; Econometrics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Text Analysis Techniques
Authors: Simon Appeltans | Orly Enrique Apolo-Apolo | Jaime Nolasco Rodríguez-Vázquez | Manuel Pérez Ruiz | Jan Pieters | Abdul Mounem Mouazen
Source: Remote Sensing (article, 2021-11-23)
Field: Chemistry — Analytical Chemistry

The Automation of Hyperspectral Training Library Construction: A Case Study for Wheat and Potato Crops The potential of hyperspectral measurements for early disease detection has been investigated by many experts over the last 5 years. One of the difficulties is obtaining enough data for training and building a hyperspectral training library. When the goal is to detect disease at a previsible stage, before the pathogen has manifested either its first symptoms or in the area surrounding the existing symptoms, it is impossible to objectively delineate the regions of interest containing the previsible pathogen growth from the areas without the pathogen growth. To overcome this, we propose an image labelling and segmentation algorithm that is able to (a) more objectively label the visible symptoms for the construction of a training library and (b) extend this labelling to the pre-visible symptoms. This algorithm is used to create hyperspectral training libraries for late blight disease (Phytophthora infestans) in potatoes and two types of leaf rust (Puccinia triticina and Puccinia striiformis) in wheat. The model training accuracies were compared between the automatic labelling algorithm and the classic visual delineation of regions of interest using a logistic regression machine learning approach. The modelling accuracies of the automatically labelled datasets were higher than those of the manually labelled ones for both potatoes and wheat, at 98.80% for P. infestans in potato, 97.69% for P. striiformis in soft wheat, and 96.66% for P. triticina in durum wheat. Phytophthora infestans ; Hyperspectral imaging ; Blight ; Computer science ; Labelling ; Artificial intelligence ; Automation ; Pattern recognition (psychology) ; Agricultural engineering ; Horticulture ; Biology ; Engineering Spectroscopy and Chemometric Analyses ; Plant Pathogens and Resistance ; Remote Sensing in Agriculture
Authors: Afsar Jahan | Brij Sharma | V D Sharma
Source: GSC Biological and Pharmaceutical Sciences (article, 2021-07-30)
Field: Chemistry — Organic Chemistry

Quantitative structure-activity relationship study on the MMP-13 inhibitory activity of fused pyrimidine derivatives possessing a 1,2,4-Triazol-3-yl group as a ZBG QSAR study has been carried out on the MMP-13 inhibitory activity of fused pyrimidine derivatives possessing a1,2,4-triazol-3-yl group as a ZBG in 0D- to 2D-Dragon descriptors. The derived QSAR models have revealed that the number of Sulfur atoms (descriptor nS), Balaban mean square distance index (descriptor MSD), molecular electrotopological variation (descriptor DELS), structural information content index of neighborhood symmetry of 2nd and 3rd order (descriptors SIC2 and SIC3), average valence connectivity index chi-4 (descriptor X4Av) in addition to 1st order Galvez topological charge index (descriptor JGI1) and global topological charge index (descriptor JGT) played a pivotal role in rationalization of MMP-13 inhibition activity of titled compounds. Atomic properties such as mass and volume in terms of atomic properties weighted descriptors MATS5m and MATS3v, and certain atom centred fragments such as CH2RX (descriptor C-006), X--CX--X (descriptor C-044), H attached to heteroatom (descriptor H-050) and H attached to C0(sp3) with 1X attached to next C (descriptor H-052) are also predominant to explain MMP-13 inhibition actions of fused pyrimidines. PLS analysis has also corroborated the dominance of CP-MLR identified descriptors. Applicability domain analysis revealed that the suggested model matches the high-quality parameters with good fitting power and the capability of assessing external data and all of the compounds was within the applicability domain of the proposed model and were evaluated correctly. Quantitative structure–activity relationship ; Topological index ; Pyrimidine ; Molecular descriptor ; Chemistry ; Heteroatom ; Applicability domain ; Mathematics ; Stereochemistry ; Computational chemistry ; Organic chemistry Synthesis and biological activity ; Computational Drug Discovery Methods ; Multicomponent Synthesis of Heterocycles
Authors: Suryati Suryati | Meriatna Meriatna | Sulhatun Sulhatun | Dwi Lestari
Source: International Journal of Engineering Science and Information Technology (article, 2021-11-07)
Field: Materials Science — Biomaterials

Preparation and Characterization of Chitosan-Gelatin-Glycerol Biocomposite for Primary Wound Dressing Rice straw is one of material containing cellulose to produce Carboxymethyl Cellulose (CMC). CMC is a non toxic polysaccharide that produces from cellulose that widely used in the pharmaceutical, food, textile, detergent, and cosmetic products industries. There are two stages usually use to produce CMC which are mercerization and esterification processes. Rice straw waste is one of the materials to produce CMC, it has a cellulose content of 37.7%, hemi-cellulose 21.99%, and lignin 16.62 %. BPS Aceh shown that the total rice harvested area was 310.01 hectares, with a total production of 1.71 million tons, and rice produced at 982.57 thousand ton. This study aims to reduce waste and environmental pollution caused by rice straw and collects information of rice straw as a basic material to produce of carboxymethyl cellulose and to increase the economic value of rice straw. The effects of various wieght parameters sodium monochloroacetate on chemical properties of CMC that produce from rice straw were investigated in this research. Rice straw was collected from a rice field in Nisam, North Aceh. The research conducted by synthesizing 5 grams rice straw for 5.5 hours using NaOH and Sodium Monochloroacetate solutions. With variations weight of sodium monochloroacetate are 5,6,7,8 and 9 grams. The characterization of CMC was carried out by Fourier Transform Infrared (FTIR), CMC yield, DS, Viscosity, water content, pH. The result shows that addition of sodium monochloroacetate was significant factors influence the chemical properties on CMC. The CMC that produced in this study achieved to National Indonesia Standard (SNI). Carboxymethyl cellulose ; Cellulose ; Straw ; Chemistry ; Pulp and paper industry ; Lignin ; Nuclear chemistry ; Sodium ; Food science ; Organic chemistry Advanced Cellulose Research Studies ; Nanocomposite Films for Food Packaging ; Polymer Science and PVC
Authors: Marat I. Latypov | Jonathan M. Hestroffer | Jean‐Charles Stinville | Jason R. Mayeur | Tresa M. Pollock | Irene J. Beyerlein
Source: Extreme Mechanics Letters (article, 2021-08-22)
Field: Materials Science — Materials Chemistry

Modeling lattice rotation fields from discrete crystallographic slip bands in superalloys Slip (aerodynamics) ; Lattice (music) ; Finite element method ; Crystal plasticity ; Lüders band ; Boundary value problem ; Materials science ; Geometry ; Physics ; Stress field ; Rotation (mathematics) ; Plasticity ; Optics ; Condensed matter physics ; Mechanics ; Mathematics ; Dislocation ; Acoustics ; Composite material ; Quantum mechanics Microstructure and mechanical properties ; Electromagnetic Effects on Materials ; High-Velocity Impact and Material Behavior
Authors: Carla Kirschbaum | Kim Greis | Maike Lettow | Sandy Gewinner | Wieland Schöllkopf | Gerard Meijer | Gert von Helden | Kevin Pagel
Source: Analytical and Bioanalytical Chemistry (article, 2021-05-06)
Field: Chemistry — Spectroscopy

Non-covalent double bond sensors for gas-phase infrared spectroscopy of unsaturated fatty acids The position and configuration of carbon-carbon double bonds in unsaturated fatty acids is crucial for their biological functions and influences health and disease. However, double bond isomers are not routinely distinguished by classical mass spectrometry workflows. Instead, they require sophisticated analytical approaches usually based on chemical derivatization and/or instrument modification. In this work, a novel strategy to investigate fatty acid double bond isomers (18:1) without prior chemical treatment or modification of the ion source was implemented by non-covalent adduct formation in the gas phase. Fatty acid adducts with sodium, pyridinium, trimethylammonium, dimethylammonium, and ammonium cations were characterized by a combination of cryogenic gas-phase infrared spectroscopy, ion mobility-mass spectrometry, and computational modeling. The results reveal subtle differences between double bond isomers and confirm three-dimensional geometries constrained by non-covalent ion-molecule interactions. Overall, this study on fatty acid adducts in the gas phase explores new avenues for the distinction of lipid double bond isomers and paves the way for further investigations of coordinating cations to increase resolution. Chemistry ; Double bond ; Adduct ; Covalent bond ; Derivatization ; Mass spectrometry ; Infrared spectroscopy ; Molecule ; Fatty acid ; Ionic bonding ; Spectroscopy ; Organic chemistry ; Ion ; Chromatography Mass Spectrometry Techniques and Applications ; Advanced Chemical Sensor Technologies ; Metabolomics and Mass Spectrometry Studies
Authors: Kai Chen | Twan van Laarhoven | Elena Marchiori
Source: Machine Learning (article, 2021-07-12)
Field: Chemistry — Analytical Chemistry

Gaussian processes with skewed Laplace spectral mixture kernels for long-term forecasting Abstract Long-term forecasting involves predicting a horizon that is far ahead of the last observation. It is a problem of high practical relevance, for instance for companies in order to decide upon expensive long-term investments. Despite the recent progress and success of Gaussian processes (GPs) based on spectral mixture kernels, long-term forecasting remains a challenging problem for these kernels because they decay exponentially at large horizons. This is mainly due to their use of a mixture of Gaussians to model spectral densities. Characteristics of the signal important for long-term forecasting can be unravelled by investigating the distribution of the Fourier coefficients of (the training part of) the signal, which is non-smooth, heavy-tailed, sparse, and skewed. The heavy tail and skewness characteristics of such distributions in the spectral domain allow to capture long-range covariance of the signal in the time domain. Motivated by these observations, we propose to model spectral densities using a skewed Laplace spectral mixture (SLSM) due to the skewness of its peaks, sparsity, non-smoothness, and heavy tail characteristics. By applying the inverse Fourier Transform to this spectral density we obtain a new GP kernel for long-term forecasting. In addition, we adapt the lottery ticket method, originally developed to prune weights of a neural network, to GPs in order to automatically select the number of kernel components. Results of extensive experiments, including a multivariate time series, show the beneficial effect of the proposed SLSM kernel for long-term extrapolation and robustness to the choice of the number of mixture components. Skewness ; Kurtosis ; Gaussian process ; Gaussian ; Mathematics ; Laplace transform ; Kernel (algebra) ; Fourier transform ; Term (time) ; Computer science ; Algorithm ; Econometrics ; Statistics ; Physics Spectroscopy and Chemometric Analyses ; Gaussian Processes and Bayesian Inference ; Air Quality Monitoring and Forecasting