jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Juerg Straubhaar | Alexandria K. D’Souza | Zachary Niziolek | Bogdan Budnik
Source: ChemRxiv (preprint, 2022-06-13)
Field: Chemistry — Spectroscopy

Single Cell Proteomics Analysis of Drug Response Shows its Potential as a Drug Discovery Platform Single-cell analysis has clearly established itself in biology and biomedical fields as an invaluable tool that allows one to comprehensively understand the relationship between cells, including their types, states, transitions, trajectories, and spatial position. Scientific methods such as fluorescence labeling, nanoscale super-resolution microscopy, advances in single cell RNAseq and proteomics technologies, provide more detailed information about biological processes which were not evident with the analysis of bulk material. This new era of single-cell biology provides a better understanding of such complex biological systems as cancer, inflammation, immunity mechanism and aging processes, and opens the door into the field of drug response heterogeneity. The latest discoveries of cellular heterogeneity gives us an unique understanding of complex biological processes, such as disease mechanism, and will lead to new strategies for better and personalized treatment strategies. Recently, single-cell proteomics techniques that allow quantification of thousands of proteins from single mammalian cells have been introduced. Here we present an improved single-cell mass spectrometry-based proteomics platform called SCREEN (Single Cell pRotEomE aNalysis) for deep and high-throughput single-cell proteome coverage with high efficiency, less turnaround time and with an improved ability for protein quantitation across more cells than previously achieved. We applied this new platform to analyze the single-cell proteomic landscape under different drug treatment over time to uncover heterogeneity in cancer cell response, which for the first time, to our knowledge, has been achieved by mass spectrometry based analytical methods. We discuss challenges in single-cell proteomics, future improvements and general trends with the goal to encourage forthcoming technical developments. Proteomics ; Proteome ; Computational biology ; Drug discovery ; Mass cytometry ; Single-cell analysis ; Systems biology ; Mechanism (biology) ; Computer science ; Drug response ; Cell ; Biology ; Nanotechnology ; Bioinformatics ; Drug ; Physics ; Genetics ; Materials science Advanced Proteomics Techniques and Applications ; Single-cell and spatial transcriptomics ; Cell Image Analysis Techniques
Authors: Hirotaka Naito | Riki Yoshida | Amano Takahiro | Yoshinari Morio | Katsusuke Murakami
Source: Journal of Science and Technology in Lighting (article, 2022-05-19)
Field: Chemistry — Analytical Chemistry

Application of Partial Least Squares (PLS) Regression to Control Light Source for &lt;i&gt;Solanum lycopersicum&lt;/i&gt; Seedling Growth in Plant Factory with Artificial Lighting The use of LEDs as a light source in plant factories with artificial lighting is expected to reduce costs, but it has been reported that some crops do not grow as expected due to differences in the wavelength of the light source. Therefore, it is necessary to design LEDs with the appropriate wavelength for each crop to be grown. On the other hand, there is research toward the realization of smart plant factories that utilize artificial intelligence, and artificial intelligence may contribute to the design of the light environment in plant factories. In this study, we selected mini-tomatoes as a model crop, and prepared fluorescent lamps and LEDs as the light environment during seedling growth, respectively, and searched for suitable light source wavelengths while investigating the relationship with growth conditions using statistical analysis methods, one of the artificial intelligence techniques. We investigated the relationship between multiple light environments, PPFD, R/B ratio, and spectrum of wavelengths respectively, using LEDs, fluorescent lamps, and dimming filters, and growth indices stem diameter in order to clarify the light environments that contribute to growth. The correlation between the measured light environment and crop growth results was objectively shown by PLS regression analysis, and the contributing wavelengths were explored by calculating the selectivity ratio and regression coefficient. As a result, it was suggested that stem diameter was promoted at around 550 nm and 630 nm, and suppressed at around 460 nm. Light-emitting diode ; Plant factory ; Artificial light ; Wavelength ; Seedling ; Optoelectronics ; Materials science ; Biological system ; Mathematics ; Optics ; Horticulture ; Biology ; Botany ; Physics ; Illuminance Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Greenhouse Technology and Climate Control
Authors: Ufuk Beyaztaş | Han Lin Shang
Source: arXiv (Cornell University) (preprint, 2022-03-09)
Field: Chemistry — Analytical Chemistry

A Robust Functional Partial Least Squares for Scalar-on-Multiple-Function Regression The scalar-on-function regression model has become a popular analysis tool to explore the relationship between a scalar response and multiple functional predictors. Most of the existing approaches to estimate this model are based on the least-squares estimator, which can be seriously affected by outliers in empirical datasets. When outliers are present in the data, it is known that the least-squares-based estimates may not be reliable. This paper proposes a robust functional partial least squares method, allowing a robust estimate of the regression coefficients in a scalar-on-multiple-function regression model. In our method, the functional partial least squares components are computed via the partial robust M-regression. The predictive performance of the proposed method is evaluated using several Monte Carlo experiments and two chemometric datasets: glucose concentration spectrometric data and sugar process data. The results produced by the proposed method are compared favorably with some of the classical functional or multivariate partial least squares and functional principal component analysis methods. Partial least squares regression ; Robust regression ; Least trimmed squares ; Outlier ; Mathematics ; Functional data analysis ; Estimator ; Regression analysis ; Scalar (mathematics) ; Principal component regression ; Regression ; Generalized least squares ; Total least squares ; Principal component analysis ; Applied mathematics ; Statistics Spectroscopy and Chemometric Analyses ; Advanced Statistical Methods and Models ; Fault Detection and Control Systems
Authors: Da Kuang | Lingfeng Duan | D Houle | D Govindaraju | S Omholt | R Mir | M Reynolds | F Pinto | M Khan | M Bhat | P Gerland | A Raftery | H Sevcikova | P Song | J Wang | X Guo | X Jin | P Zarco-Tejada | U Schmidhalter | C Zhao | Y Zhang | J Du | W Wen | S Gu | J Fan | H Zhang | H Zhou | J Zheng | Y Ge | Y Li
Source:  (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Global Patent Analysis on Computer Vision Powered Crop Phenotyping The objective of this work is to obtain the research trend and technical composition of computer vision powered crop phenotyping by analyzing related patents in a global scale. Based on the retrieved patent data, we analyze the global innovation distribution, research hotspots, major countries, and major patent applicants. It is revealed that the patents of computer vision powered crop phenotyping has been booming in the past two decades. With number of patent and patent applicant both rising steadily in recent years, this technology is still on a sharply growing stage. Results show that the total number of patents related to computer vision powered crop phenotyping is 3 765, of which 3 446 are invention patents. The top 3 countries by patent number are China, the United States and Japan. The vast majority of patents are owned by private corporations and universities, including several major applicants such as Pioneer Seed and Monsanto. Pattern recognition, image analysis, sensor application and computational models are among the research hotspots. This study presents a panoramic view of the global patent on crop phenotyping, and provides references for the research layouts and innovation decision-making organizations. Patent analysis ; Computer science ; Crop ; Computer vision ; Artificial intelligence ; Data science ; Biology ; Agronomy Metallurgy and Material Science ; Educational Robotics and Engineering
Authors: Xianglu Ding | Wei Shang
Source: Electronic Journal of Structural Engineering (article, 2022-08-11)
Field: Chemistry — Spectroscopy

The partial renovation of old factory buildings - Performance study of the inorganic vitrified micro-bubble external wall insulation system This paper took the partial renovation project of an old factory building in Wuhan as an example to study its external wall insulation system. A new insulation system was designed based on inorganic vitrified micro-bubbles. The material was briefly analyzed. The performance of the new insulation system was detected by designing test specimens. The results found that the specimens had good compressive, tensile and flexural properties, reaching the standard of A1 grade of construction materials, the specimens were non-combustible, and the temperature change of the concrete base was stable under the action of heating-cooling and heating-rain cycles, suggesting good thermal insulation performance. The results verify the effectiveness of the insulation system. The insulation system can be better applied in practice. Insulation system ; Thermal insulation ; Materials science ; Composite material ; Factory (object-oriented programming) ; Compressive strength ; Vacuum insulated panel ; Ultimate tensile strength ; Computer science Aerogels and thermal insulation
Authors: Avijeet Datta | Md Mutasim Uddiin | Md. Mahbubor Rahman | Md Mazharul Helal
Source: GUB Journal of Science and Engineering (article, 2022-11-16)
Field: Materials Science — Polymers and Plastics

The Benefits of using Computerized system over Manual System in Apparel Production Bangladesh's clothing industry is the backbone of the country's economy. One of the most crucial first phases in the apparel industry is to create and develop garment patterns based on the anticipated apparel's specifications, and then to plan the marker and cut the fabric according to the pattern pieces. These can be done manually or with the use of Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM) software (CAM). The garment business has been transformed by computer-aided design (CAD) and computer-aided manufacturing (CAM). Because fabric costs account for a large portion of the cost of garment manufacture, it is critical to properly utilize the fabric and reduce the percentage of fabric waste (percent). Additionally, minimizing production time will reduce lead time even more. Although CAD/CAM is expensive, it has a number of economic and technological advantages over other methods. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 8, Dec 2021 P 36-40 Clothing ; CAD ; Manufacturing engineering ; Computer Aided Design ; Production (economics) ; Computer-aided manufacturing ; Computer-aided ; Plan (archaeology) ; Engineering drawing ; Software ; Fast fashion ; Textile industry ; Computer science ; Engineering ; Operations management ; Industrial engineering ; Mechanical engineering ; Economics ; Operating system Textile materials and evaluations
Authors: Roman Kvit
Source: Technology audit and production reserves (article, 2022-04-30)
Field: Materials Science — Materials Chemistry

Development of the strength statistical characteristics of materials, which takes into account the features of their brittle fracture The object of the research is the algorithm for determining the finding of the most probable, mean value, dispersion and coefficient of failure loading variation of a stochastically defective plate under conditions of comprehensive tensile-compression. The material of the plate is considered as a continuous medium in which evenly distributed defects such as rectilinear cracks that do not interact with each other. It is isotropic and has the same crack resistance. Let’s believe that the plate consists of primary elements, each of which can be weakened by one defect. To predict the strength and failure conditions of plates made of such material, it is natural to use, on the one hand, the results of the theory of limit equilibrium of individual determined defects and their development, and on the other hand, probabilistic-statistical methods that take into account the randomness of defects. This comprehensive approach makes it possible to calculate the statistical characteristics of strength and fracture based on data on the structure of the material defect and its resistance to the emergence and development of cracks. The main content of this paper is the algorithm for calculating and research the strength statistical characteristics of stochastically defective plate structural elements taking into account some deterministic features of their brittle fracture. Based on the deterministic failure criterion, which takes into account the initial direction of crack propagation, the ratio is obtained to find the most probable, mean value, dispersion and coefficient of variation of failure loading. The dependences of the specified strength statistical characteristics on the type of applied loading, the number of defects (body size) and structural inhomogeneity of the material, as well as the effect of taking into account the initial direction of crack propagation are investigated. The obtained results allow to more adequately assessing the reliability of structural materials under conditions of complex stress state, taking into account the stochastic of their structure. This is due to the fact that the use of the approach to determine the limit applied stresses, which takes into account the initial direction of the crack propagation, improves the algorithm for finding strength statistical characteristics. Randomness ; Isotropy ; Fracture (geology) ; Brittleness ; Ultimate tensile strength ; Structural engineering ; Probabilistic logic ; Materials science ; Mathematics ; Composite material ; Engineering ; Statistics ; Physics Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Structural mechanics and materials
Authors: Celal Soyarslan | Jos Havinga | Léon Abelmann | A.H. van den Boogaard
Source: Key engineering materials (article, 2022-07-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Estimation of the Effective Magnetic Properties of Two-Phase Steels We investigate the predictive performance of specific analytical and numerical methods to determine the effective magnetic properties of two-phase steels at the macroscale. We utilize various mixture rules reported in the literature for the former, some of which correspond to rigorous bounds, e.g., Voigt (arithmetic) and Reuss (harmonic) averages. For the latter, we employ asymptotic homogenization together with the finite element method (FEM) and periodic boundary conditions (PBC). The voxel-based discretization of the representative volume element is conducted with digital image processing on the existing micrographs of DP600-grade steel. We show that unlike the considered isotropic mixture rules, which use only the phase volume fraction as the statistical microstructural descriptor, finite element method-based first-order asymptotic homogenization allows prediction of both phase content and directional dependence in the magnetic permeability by permitting an accurate consideration of the underlying phase geometry. Homogenization (climate) ; Discretization ; Isotropy ; Finite element method ; Volume fraction ; Mathematical analysis ; Voxel ; Mathematics ; Materials science ; Phase (matter) ; Representative elementary volume ; Applied mathematics ; Geometry ; Mechanics ; Computer science ; Physics ; Composite material ; Optics ; Thermodynamics ; Artificial intelligence Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels ; Non-Destructive Testing Techniques
Authors: Arulmurugan Senthilnathan | Pınar Acar
Source: AIAA SCITECH 2022 Forum (article, 2022-01-03)
Field: Materials Science — Materials Chemistry

Multi-Scale Modeling for Texture and Grain Topology of Polycrystalline Microstructures under Uncertainty View Video Presentation: https://doi.org/10.2514/6.2022-2106.vid The present work addresses multi-scale modeling for grain topology of polycrystalline microstructures under the effects of the microstructural uncertainties. The special focus is on the Titanium-7wt\%-Aluminum alloy (Ti-7Al), which is a candidate material for many aerospace systems owing to its outstanding mechanical performance in elevated temperatures. The electron backscatter diffraction (EBSD) samples of Ti-7Al in small scales are used to reconstruct the alloy microstructures in larger domains. The reconstruction provides a statistically-equivalent synthetic representation to the small-scale test samples while introducing epistemic uncertainty on microstructural features. Here, the grain topology of the polycrystalline microstructures is quantified using shape moment invariants. These invariant parameters can capture the shapes of physical objects with a numerical representation and they are invariant to shape transformations. To quantify the effects of the uncertainties on the homogenized properties of the microstructures, a surrogate model is developed as a function of shape moment invariants with Gaussian Process Regression (GPR) by utilizing the experiments and crystal plasticity simulations for training data. Electron backscatter diffraction ; Materials science ; Microstructure ; Crystallite ; Texture (cosmology) ; Topology (electrical circuits) ; Mathematics ; Computer science ; Composite material ; Metallurgy ; Artificial intelligence Machine Learning in Materials Science ; Microstructure and mechanical properties ; Enzyme Structure and Function
Authors: Mario Teixeira Parente | Georg Brandl | Christian Franz | U. Stuhr | Marina Ganeva | A. Schneidewind
Source: arXiv (Cornell University) (preprint, 2022-09-02)
Field: Materials Science — Materials Chemistry

Active learning-assisted neutron spectroscopy with log-Gaussian processes Neutron scattering experiments at three-axes spectrometers (TAS) investigate magnetic and lattice excitations by measuring intensity distributions to understand the origins of materials properties. The high demand and limited availability of beam time for TAS experiments however raise the natural question whether we can improve their efficiency and make better use of the experimenter's time. In fact, there are a number of scientific problems that require searching for signals, which may be time consuming and inefficient if done manually due to measurements in uninformative regions. Here, we describe a probabilistic active learning approach that not only runs autonomously, i.e., without human interference, but can also directly provide locations for informative measurements in a mathematically sound and methodologically robust way by exploiting log-Gaussian processes. Ultimately, the resulting benefits can be demonstrated on a real TAS experiment and a benchmark including numerous different excitations. Probabilistic logic ; Gaussian ; Computer science ; Benchmark (surveying) ; Neutron ; Spectrometer ; Lattice (music) ; Statistical physics ; Artificial intelligence ; Machine learning ; Algorithm ; Physics ; Nuclear physics ; Optics ; Acoustics ; Quantum mechanics Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Nuclear Physics and Applications
Authors: A. Krishna | Tran Doan Huan | Chaofan Huang | Rampi Ramprasad | V. Roshan Joseph
Source: arXiv (Cornell University) (preprint, 2022-12-02)
Field: Materials Science — Materials Chemistry

Adaptive Exploration and Optimization of Materials Crystal Structures A central problem of materials science is to determine whether a hypothetical material is stable without being synthesized, which is mathematically equivalent to a global optimization problem on a highly non-linear and multi-modal potential energy surface (PES). This optimization problem poses multiple outstanding challenges, including the exceedingly high dimensionality of the PES and that PES must be constructed from a reliable, sophisticated, parameters-free, and thus, very expensive computational method, for which density functional theory (DFT) is an example. DFT is a quantum mechanics based method that can predict, among other things, the total potential energy of a given configuration of atoms. DFT, while accurate, is computationally expensive. In this work, we propose a novel expansion-exploration-exploitation framework to find the global minimum of the PES. Starting from a few atomic configurations, this ``known'' space is expanded to construct a big candidate set. The expansion begins in a non-adaptive manner, where new configurations are added without considering their potential energy. A novel feature of this step is that it tends to generate a space-filling design without the knowledge of the boundaries of the domain space. If needed, the non-adaptive expansion of the space of configurations is followed by adaptive expansion, where ``promising regions'' of the domain space (those with low energy configurations) are further expanded. Once a candidate set of configurations is obtained, it is simultaneously explored and exploited using Bayesian optimization to find the global minimum. The methodology is demonstrated using a problem of finding the most stable crystal structure of Aluminum. Curse of dimensionality ; Computer science ; Mathematical optimization ; Space (punctuation) ; Domain (mathematical analysis) ; Optimization problem ; Set (abstract data type) ; Transformation (genetics) ; Potential energy surface ; Global optimization ; Algorithm ; Mathematics ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Chemical Physics Studies
Authors: Jean Henri Odendaal | Nitin Bhatia | Russell Wilson | Johan Potgieter
Source:  (article, 2022-11-16)
Field: Chemistry — Analytical Chemistry

Exploring non-destructive mechanical characterisation of hydrogels using hyperspectral imaging and machine vision Hydrogel polymer networks are non-covalently crosslinked soft materials that exhibit unique mechanical and excellent bio-characteristics such as adapting to environmental forces and strengthening to the conditions they experience. Quantifying these characteristics are important for their usage in multitude of applications especially where new formulations or composites of hydrogels being prepared such as for 3D printing. Often such characteristics are quantified using destruc-tive methods and/or using complex mathematical models, which can destroy samples, are limiting and time-consuming. Herein, we describe non-destructive hyperspectral imaging (HSI) based machine vision approach that enable faster characterisation of hydrogel's mechanical properties. Several measurements of hydrogel samples were collected using HSI to develop a database used to train a Gaussian Process Regression (GPR) based learning model. From the confusion matrix results, we observed the >90% accuracy, which we think can provide relatively good performance compared to other traditional methods. We conclude that HSI with machine vision could simplify the measurement procedure, allowing for a more straightforward characterisation of hydrogel samples, is fast and reproducible to provide serve as both an in-process and a quality control method. Hyperspectral imaging ; Self-healing hydrogels ; Computer science ; Process (computing) ; Artificial intelligence ; Machine vision ; Machine learning ; Gaussian process ; Limiting ; Confusion ; Kriging ; Materials science ; Gaussian ; Mechanical engineering ; Engineering ; Chemistry Spectroscopy and Chemometric Analyses ; Optical Imaging and Spectroscopy Techniques ; Spectroscopy Techniques in Biomedical and Chemical Research