jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: G. D. A. Wood | D. D. Khalyavin | D. A. Mayoh | Juba Bouaziz | A. E. Hall | S. J. R. Holt | F. Orlando | P. Manuel | Stefan Blügel | J. B. Staunton | O. A. Petrenko | M. R. Lees | G. Balakrishnan
Source: arXiv (Cornell University) (preprint, 2023-04-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The Double-$Q$ Ground State with Topological Charge Stripes in the Skyrmion Candidate $\text{GdRu}_{\text{2}}\text{Si}_{\text{2}}$ $\text{GdRu}_{\text{2}}\text{Si}_{\text{2}}$ is a centrosymmetric magnet in which a skyrmion lattice has recently been discovered. Here, we investigate the magnetic structure of the zero field ground state using neutron diffraction on single crystal and polycrystalline $^{\text{160}}\text{GdRu}_{\text{2}}\text{Si}_{\text{2}}$. In addition to observing the principal propagation vectors $\mathbf{q}_{1}$ and $\mathbf{q}_{2}$, we discover higher order magnetic satellites, notably $\mathbf{q}_{1} + 2\mathbf{q}_{2}$. The appearance of these satellites are explained within the framework of a new double-$Q$ constant-moment solution. Using powder diffraction we implement a quantitative refinement of this model. This structure, which contains vortexlike motifs, is shown to have a one-dimensional topological charge density. Skyrmion ; Physics ; Ground state ; Order (exchange) ; Neutron diffraction ; Charge (physics) ; Lattice (music) ; Lattice constant ; Condensed matter physics ; Magnetic moment ; Crystallography ; Diffraction ; Topology (electrical circuits) ; Atomic physics ; Chemistry ; Combinatorics ; Quantum mechanics ; Mathematics Magnetic Properties of Alloys ; Advanced Condensed Matter Physics ; Rare-earth and actinide compounds
Authors: Andrés Felipe Rodríguez Acevedo | Lina Tatiana Velandia Torres | J. Rincón | Yulieth Catherine Reyes Roa
Source: Ingeniería Investigación y Desarrollo (article, 2023-12-27)
Field: Chemistry — Analytical Chemistry

Colorimetric analysis of leaves of plants exposed to cadmium. Cadmium is a toxic heavy metal which is absorbed, internalized, and translocated in plants in its divalent form Cd2+. The accumulation of cadmium in plant tissues at different concentrations represent significant phenotypic changes in plants and are initially expressed by leaves. In this study, morphological changes were evaluated by means of an analysis of variance for the colorimetric parameters of chroma, hue and gloss in leaves of Basil (Ocimum Basilicum), Peppermint (Mentha spicata), Lettuce (Lactuca sativa) and Spinach (Spinacia oleracea) plants grown under the floating root method in solutions containing cadmium at 100 µg/L. The results obtained indicate significant changes in each of the parameters considering the exposure time. The methodology used in this work can contribute significantly to the food safety problem by identifying and monitoring the effects of cadmium on food safety. Cadmium ; Botany ; Biology ; Horticulture ; Chemistry Heavy Metals in Plants
Authors: Woonghee Lee | Mehdi Rahimi | Adilakshmi Dwarasala | Andrea E. Lopez | Yeongjoon Lee | Abigail Chiu | Zowie Werner
Source: Biophysical Journal (article, 2023-02-01)
Field: Chemistry — Spectroscopy

The POKY suite: The revolutionized platform for biomolecular studies Suite ; Computer science ; Automation ; Engineering ; Geography Advanced NMR Techniques and Applications ; Enzyme Structure and Function ; Protein Structure and Dynamics
Authors: Mingquan Wei | Jinglei Xu | Xusheng Xing
Source: Highlights in Science Engineering and Technology (article, 2023-07-04)
Field: Chemistry — Inorganic Chemistry

Exploration And Analysis of Weathering Factors of Glass Artefacts Based on Cluster Analysis and Grey Correlation Analysis The aim of this paper is to investigate the relationship between the chemical composition of ancient glass artefacts after weathering and to construct a model for classifying and predicting the class of artefacts based on some of the characteristics of the ancient glass artefacts obtained. Cardinality tests, information entropy, cluster analysis, support vector machines and grey correlation are mainly used to conduct the study. Through chi-square test, the relationship between weathering of glass relics and glass type, glass color and glass pattern is explained. Finally, the relationship between weathering and glass type is closer. Information entropy was used to explain the relationship between the weathering of cultural relics and the content of chemical components contained in cultural relics. Among them, potassium oxide ( O) and calcium oxide (CaO) had the largest information gain. The effective information gain of lead barium is silicon dioxide (Si ), potassium oxide (PbO), copper oxide (CuO) and sodium oxide ( O). By using cluster analysis, the law analysis and corresponding subcategories of high-potassium cultural relics and lead-barium cultural relics are made, and 3-5 subcategories are formed. Support vector machine was used to judge the type of glass relics, and the final result was that A1, A6 and A7 were high potassium. A2, A3, A4, A5, A8 are lead barium; The correlation coefficient matrix was used to analyze the correlation between the chemical components in different kinds of cultural relics, and the differences were explained based on the results of grey correlation degree analysis. Finally, the conclusion was drawn: in the firing process of high-potassium glass, the substance with high potassium content as the co-solvent did not have an essential effect on the formation of high-potassium glass. However, lead barium glass is different. As lead ore (high content of lead oxide and barium oxide) is added as co-solvent in the firing process, lead barium glass produced by firing contains a large amount of lead oxide and barium oxide. Weathering ; Barium ; Mineralogy ; Potassium ; Oxide ; Materials science ; Chemistry ; Geology ; Metallurgy ; Geochemistry Pigment Synthesis and Properties ; Industrial Vision Systems and Defect Detection ; Color Science and Applications
Authors: John R. Yates | Ahrum Son | H. S. Kim | Jolene K. Diedrich | Casimir Bamberger | Daniel B. McClatchy
Source: Research Square (preprint, 2023-07-03)
Field: Chemistry — Spectroscopy

Using In Vivo Intact Structure for System-wide Quantitative Analysis of Changes in Proteins In vivo ; Computational biology ; Chemistry ; Cell biology ; Biophysics ; Biology ; Biotechnology Advanced Proteomics Techniques and Applications ; RNA and protein synthesis mechanisms ; Protein Structure and Dynamics
Authors: Jingyao Meng | Runyu Ma | Tianxing Ma | Hai-Qing Lin
Source: arXiv (Cornell University) (preprint, 2023-11-06)
Field: Materials Science — Materials Chemistry

Flat bands and superconductivity induced by periodic strain in monolayer graphene Superconductivity in single-layer graphene has attracted considerable interest. Here, using the determinant quantum Monte Carlo method, we study transitions of superconductivity and magnetism in a monolayer graphene with a special periodic strain. Consistent with experiments, the deformation accumulates a series of flat bands, whose robustness under interaction is verified through electron localization in real space. During the reconstruction of the band structure, the superconductivity appears in flat band range with next-nearest neighbor $d+id$ pairing symmetry dominating other modes and is accompanied by ferromagnetism caused by symmetry breaking. We also demonstrate that the strain-induced symmetry breaking would accumulate an energy-gap antiferromagnetic insulating phase at half filling even under the limitation of not strong enough interaction, which shows its potential as a platform that exhibits strongly correlated phenomena. Condensed matter physics ; Superconductivity ; Pairing ; Symmetry breaking ; Physics ; Magnetism ; Antiferromagnetism ; Symmetry (geometry) ; Ferromagnetism ; Band gap ; Graphene ; Quantum mechanics ; Geometry Graphene research and applications ; Superconductivity in MgB2 and Alloys ; Physics of Superconductivity and Magnetism
Authors: N. N. Kiseleva | V. A. Dudarev | A. V. Stolyarenko | A. A. Dokukin | O. V. Sen’ko | Yu. O. Kuznetsova
Source: Журнал неорганической химии (article, 2023-05-01)
Field: Materials Science — Materials Chemistry

Prediction of the Melting Points of Double Halides The atmospheric pressure melting points of binary halides ABHal3, ABHal4, A2BHal4, A2BHal5, and A3BHal6 (A and B are various elements; Hal = F, Cl, Br, or I) were predicted. Calculations were made using our developed system of machine learning. Computer models were searched by analyzing information on the already known melting points of halides. The unknown melting points of the halides were predicted using only the values of the properties of the elements A, B, and Hal. It was shown that the programs based on the methodology of ensembles of machine learning algorithms make the most accurate estimates of melting points (the mean absolute errors determined by the cross-validation method in the leave-one-out cross validation mode were in the range of 29–52 K, depending on the halide composition and the chosen algorithm). The coefficient of multiple determination for the models used for prediction was no lower than 0.7. Computer science Machine Learning in Materials Science
Authors: Yoshiaki Sofue
Source: Research Square (preprint, 2023-07-03)
Field: Chemistry — Spectroscopy

Face-on Molecular Map of the Milky Way and the 3-kpc Expanding Ring based on the High-accuracy Rotation Curve Abstract We analyze the longitude-velocity diagram (LVD) of 12 CO line emission from archival data and use the most accurate rotation curve (RC) of the Milky Way to transform radial velocity to face-on position in the galactic plane. We find that, especially in the inner Milky Way, the face-on transformation is highly sensitive to the adopted RC in the sense that deviations of the RC from the true rotation velocity lead to artifact holes or overcrowded concentrations along the tangent circle for overestimated or underestimated RC, respectively. Even if the RC is sufficiently accurate, non-circular motion such as with the 3 kpc expanding ring introduces significant artifacts in the resulting face-on-map, as long as a circular rotation is assumed. On the other hand, if we properly take into account the non-circular motion, it can be used to solve the near-far degeneracy problem of determination of kinematic distance. We thus propose a new method to solve the degeneracy problem by incorporating the expanding motion of a ring. We apply the method to the LVD of the 3-kpc expanding ring and present a face-on map projected onto the galactic plane for the first time. Milky Way ; Rotation (mathematics) ; Degeneracy (biology) ; Physics ; Ring (chemistry) ; Tangent ; Galaxy rotation curve ; Galactic plane ; Position (finance) ; Face (sociological concept) ; Geometry ; Plane (geometry) ; Line (geometry) ; Circular motion ; Astrophysics ; Galaxy ; Mathematics ; Classical mechanics Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Advanced Chemical Physics Studies
Authors: Mrs. Anupama Singh
Source: Journal for ReAttach Therapy and Developmental Diversities (article, 2023-01-02)
Field: Chemistry — Analytical Chemistry

GLCM Features And Hybrid Manhattan Distance With SVM For Identification Of The Leaves Of Indonesian Herbal Medicines Herbal plants are those plants, which can be considered as a substitute for naturally healing diseases. The public still does not have the awareness about the presence of herbal plants. It is owing to the fact that there are several kinds of medicinal plants available and therefore, specialized knowledge is required for their identification. To get over this, an intelligent and precise herbal leaf recognition system is necessary. In recent work introduces one enhanced model for Indonesian Herbal Medicines identification. In which there are three techniques introduced. The first one includes the novel leaf feature framework. There are 16 distances between perimeter points and leaf centroid points, as well as seven median line connectors. The second project entails the creation of leaf feature extraction techniques and algorithms that allow for the production of 23 leaf shape attributes for any type of herbal crop. Thirdly, forming a prototype identification system or introducing herbal plants on the basis of morphological features of the leaf. The procedure of recognition is performed with the help of two mechanisms, which include MDs (Manhattan Distances) and Artificial Neural Networks. However in existing work foreground segmentation was not done based on any particular algorithm and its leads to poor segmentation results. Also ANN is about the executing with parallel processing, and therefore processors providing support to parallel processing is required, and therefore the ANNs exhibit hardware dependency. To avoid those problems in this work introduced an improved model for herbal medicines plant leaf identification. In this pre-processing is done by using Image segmentation based on k means clustering to segment the foreground object, Image Filtering and Translation of RGB (Red, Green, Blue) images into Grayscale. Features are extracted using GLCMs (Grey Level Co-Occurrence Matrices) and Centre point based model. Finally the Indonesian Herbal Medicines plant leafs are identified based on those extracted features using Hybrid MDs with SVMs (Support vector machines). Experimental results shows that this proposed model produces better leaf identification accuracy than other state of the art models. Indonesian ; Traditional medicine ; Identification (biology) ; Support vector machine ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Medicine ; Biology ; Botany Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Machine Learning in Bioinformatics
Authors: Sinem Öztürk | Sadiye İnceoğlu | Adem Deniz Kurt
Source: Orclever Proceedings of Research and Development (article, 2023-12-31)
Field: Materials Science — Polymers and Plastics

Development of a Multi-Use Personal Protective Isolation Suit Suitable for Repeated Washing - Drying Process Personal protective equipment (PPE) corresponds to any device, tool or material designed to be worn, placed or carried by person for protection against one or more health and safety hazards. Nowadays, when personal protective insulation coveralls are considered, they are widely used in medicine, painting, maintenance, food processing, agriculture, chemistry, mechanical works, automotive, mining, woodworking and insulation works; which are among the sectors and areas where protective overalls are the most used. Researchers continue to work on raw materials, chemicals and fabric, taking the needs and demonds into account in these areas. Moreover, the ability of the produced textile material to perform several functions simultaneously instead of a single function is also among the studies that have been carried out. In addition, the ability of the textile material produced to fulfill a single function instead of a single function is also among the studies carried out. Within the scope of this study, three-layer laminated fabrics will be produced in order to develop personal protective coveralls. While producing layered structures with the hot-melt lamination production method, breathability as well as high waterproofness is provided. Thanks to the special membrane used in layered structure production, it is aimed to develop a reusable insulation coverall fabric that has higher performance and comfort compared to it disposable insulation coverall counterparts, has less environmental effect, and is more breathable and suitable for reusing. Personal protective equipment ; Woodworking ; Automotive industry ; Production (economics) ; Reuse ; Process (computing) ; Textile ; Process engineering ; Raw material ; Materials science ; Computer science ; Engineering ; Mechanical engineering ; Composite material ; Waste management Textile materials and evaluations ; Microplastics and Plastic Pollution ; Additive Manufacturing and 3D Printing Technologies
Authors: Christopher Beckham | Christopher Pal
Source: arXiv (Cornell University) (preprint, 2023-04-07)
Field: Materials Science — Materials Chemistry

Conservative objective models are a special kind of contrastive divergence-based energy model In this work we theoretically show that conservative objective models (COMs) for offline model-based optimisation (MBO) are a special kind of contrastive divergence-based energy model, one where the energy function represents both the unconditional probability of the input and the conditional probability of the reward variable. While the initial formulation only samples modes from its learned distribution, we propose a simple fix that replaces its gradient ascent sampler with a Langevin MCMC sampler. This gives rise to a special probabilistic model where the probability of sampling an input is proportional to its predicted reward. Lastly, we show that better samples can be obtained if the model is decoupled so that the unconditional and conditional probabilities are modelled separately. Divergence (linguistics) ; Probabilistic logic ; Conditional probability distribution ; Probability distribution ; Conditional probability ; Simple (philosophy) ; Applied mathematics ; Random variable ; Energy (signal processing) ; Computer science ; Markov chain Monte Carlo ; Mathematics ; Bayesian probability ; Statistics Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Fault Detection and Control Systems
Authors: Dionissios T. Hristopulos
Source: arXiv (Cornell University) (preprint, 2023-02-19)
Field: Chemistry — Analytical Chemistry

Non-separable Covariance Kernels for Spatiotemporal Gaussian Processes based on a Hybrid Spectral Method and the Harmonic Oscillator Gaussian processes provide a flexible, non-parametric framework for the approximation of functions in high-dimensional spaces. The covariance kernel is the main engine of Gaussian processes, incorporating correlations that underpin the predictive distribution. For applications with spatiotemporal datasets, suitable kernels should model joint spatial and temporal dependence. Separable space-time covariance kernels offer simplicity and computational efficiency. However, non-separable kernels include space-time interactions that better capture observed correlations. Most non-separable kernels that admit explicit expressions are based on mathematical considerations (admissibility conditions) rather than first-principles derivations. We present a hybrid spectral approach for generating covariance kernels which is based on physical arguments. We use this approach to derive a new class of physically motivated, non-separable covariance kernels which have their roots in the stochastic, linear, damped, harmonic oscillator (LDHO). The new kernels incorporate functions with both monotonic and oscillatory decay of space-time correlations. The LDHO covariance kernels involve space-time interactions which are introduced by dispersion relations that modulate the oscillator coefficients. We derive explicit relations for the spatiotemporal covariance kernels in the three oscillator regimes (underdamping, critical damping, overdamping) and investigate their properties. We further illustrate the hybrid spectral method by deriving covariance kernels that are based on the Ornstein-Uhlenbeck model. Covariance ; Covariance function ; Mathematics ; Separable space ; Gaussian ; Kernel (algebra) ; Applied mathematics ; Gaussian process ; Matérn covariance function ; Harmonic oscillator ; Parametric statistics ; Rational quadratic covariance function ; Statistical physics ; Mathematical analysis ; Covariance intersection ; Physics ; Pure mathematics ; Statistics ; Quantum mechanics Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Air Quality Monitoring and Forecasting
Authors: Maxim Ziatdinov
Source: Microscopy and Microanalysis (article, 2023-07-22)
Field: Materials Science — Materials Chemistry

Physics-Augmented Machine Learning for Automated and Autonomous Experiments in Microscopy Journal Article Physics-Augmented Machine Learning for Automated and Autonomous Experiments in Microscopy Get access Maxim Ziatdinov Maxim Ziatdinov Oak Ridge National Lab, Computational Sciences & Engineering Division, Oak Ridge, TN, United States Corresponding author: ziatdinovmax@gmail.com Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 29, Issue Supplement_1, 1 August 2023, Page 1929, https://doi.org/10.1093/micmic/ozad067.998 Published: 22 July 2023 Microscopy ; Physics ; Nanotechnology ; Artificial intelligence ; Materials science ; Computer science ; Optics Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Force Microscopy Techniques and Applications
Authors: Elias Anthony
Source:  (preprint, 2023-10-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Metamaterials and Metasurfaces: Revolutionizing Electromagnetic Wave Control at the Nanoscale Metamaterials and Metasurfaces: Revolutionizing Electromagnetic WaveControl at the Nanoscale Metamaterial ; Nanoscopic scale ; Electromagnetic radiation ; Physics ; Nanotechnology ; Optics ; Materials science Metamaterials and Metasurfaces Applications ; Molecular Communication and Nanonetworks ; Energy Harvesting in Wireless Networks
Authors: Ramon Gonçalves de Paula | Martha Freire Silva | Cibele Hummel do Amaral | Guilherme Sousa Paula | Laércio Junio da Silva | Herika Paula Pessoa | Felipe Lopes Silva
Source: Research Square (preprint, 2023-12-13)
Field: Chemistry — Analytical Chemistry

WITHDRAWN: Leveraging Leaf Spectroscopy to Identify Drought-Resistant Soybean Cultivars Cultivar ; Biology ; Abiotic component ; Photosynthesis ; Carotenoid ; Agronomy ; Chlorophyll ; Specific leaf area ; Horticulture ; Crop ; Botany ; Ecology Spectroscopy and Chemometric Analyses ; Soybean genetics and cultivation ; Leaf Properties and Growth Measurement
Authors: Jingqi Zhang | Zhiyuan Wu | Yi Lin
Source: Highlights in Science Engineering and Technology (article, 2023-11-06)
Field: Chemistry — Inorganic Chemistry

Subclassification Determination and Weathering Composition Prediction of Ancient Glass Artifact Compositions Based on Clustering Algorithms Ancient glass is susceptible to environmental weathering, and the content of chemical elements varies with the degree of weathering. Through the correlation analysis of weathering degree and color, pattern and composition by Chi-square test, it is found that the weathering degree of glass surface changes based on composition and color, and pattern has little correlation. The principal component analysis of the glass is based on different chemical compositions and whether the glass is weathered or not. Through the supervised learning k-means algorithm, four types are obtained: unweathered high potassium glass, weathered high potassium glass, unweathered lead barium glass, weathered lead barium glass; Further, the glass is classified into 9 subclasses based on the content of other elements to facilitate the study. The cloud model was used to predict the composition of cultural relics before weathering, and the relationship between the main chemical composition of high-potassium glass and lead-barium glass before weathering was obtained, which was convenient for subsequent research and restoration of cultural relics. Weathering ; Barium ; Chemical composition ; Mineralogy ; Degree (music) ; Composition (language) ; Artifact (error) ; Principal component analysis ; Materials science ; Geology ; Chemistry ; Artificial intelligence ; Geochemistry ; Metallurgy ; Computer science ; Physics ; Art Pigment Synthesis and Properties