jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Simone Manti | Alessandro Lucantonio
Source: arXiv (Cornell University) (preprint, 2023-10-10)
Field: Materials Science — Materials Chemistry

Discovering Interpretable Physical Models using Symbolic Regression and Discrete Exterior Calculus Computational modeling is a key resource to gather insight into physical systems in modern scientific research and engineering. While access to large amount of data has fueled the use of Machine Learning (ML) to recover physical models from experiments and increase the accuracy of physical simulations, purely data-driven models have limited generalization and interpretability. To overcome these limitations, we propose a framework that combines Symbolic Regression (SR) and Discrete Exterior Calculus (DEC) for the automated discovery of physical models starting from experimental data. Since these models consist of mathematical expressions, they are interpretable and amenable to analysis, and the use of a natural, general-purpose discrete mathematical language for physics favors generalization with limited input data. Importantly, DEC provides building blocks for the discrete analogue of field theories, which are beyond the state-of-the-art applications of SR to physical problems. Further, we show that DEC allows to implement a strongly-typed SR procedure that guarantees the mathematical consistency of the recovered models and reduces the search space of symbolic expressions. Finally, we prove the effectiveness of our methodology by re-discovering three models of Continuum Physics from synthetic experimental data: Poisson equation, the Euler's Elastica and the equations of Linear Elasticity. Thanks to their general-purpose nature, the methods developed in this paper may be applied to diverse contexts of physical modeling. Interpretability ; Physical system ; Computer science ; Generalization ; Symbolic regression ; Consistency (knowledge bases) ; Theoretical computer science ; Mathematical model ; Artificial intelligence ; Machine learning ; Mathematics Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Explainable Artificial Intelligence (XAI)
Authors: R. Pasquier | M. Alouani
Source: arXiv (Cornell University) (preprint, 2023-09-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Calculated iron $L_{2,3}$ x-ray absorption and XMCD of spin-crossover Fe(phen)$_{2}$(NCS)$_{2}$ molecule adsorbed on Cu(001) surface The PAW method has been used to compute the iron L$_{2,3}$ edges of x-ray absorption spectra (XAS) and x-ray magnetic circular dichroism (XMCD) of the spin-crossover Fe(phen)$_{2}$(NCS)$_{2}$ molecule when adsorbed on Cu(001) surface and in the gas phase, for both the high spin (HS) and low spin (LS) states. It is found that the calculated XAS and XMCD with the static core hole or the Slater transition state half hole are in less good agreement with experiment than those using the so called initial state. This disagreement is due to the reduction of the iron spin magnetic moment caused by the static screening of the core hole by the photo-electron. The L$_{2,3}$ XAS formula is found to be directly related to the unoccupied $3d$ density of states (DOS), and hence the symmetry broken $e_g$ and the $t_{2g}$ iron DOS are used to explain the XAS and XMCD results. It is demonstrated that the dependence of the HS XMCD on the direction of incident x-ray circularly polarized light with respect to the magnetization direction can be used to determine the iron octahedron deformation, while the XMCD for various magnetization directions is directly related to the anisotropy of the orbital magnetic moment and the magneto-crystalline energy. It is also shown that the magnetic dipole moment $T_z$ is very large due to the strong distortion of the iron octahedron and is necessary for an accurate determination of the sum rule computed spin magnetic moment. Magnetic circular dichroism ; X-ray magnetic circular dichroism ; Magnetization ; Magnetic moment ; Condensed matter physics ; X-ray absorption spectroscopy ; Spin crossover ; Chemistry ; Octahedron ; Spin states ; Magnetic anisotropy ; Absorption spectroscopy ; Physics ; Crystallography ; Spectral line ; Magnetic field ; Optics Magnetism in coordination complexes ; Electron Spin Resonance Studies ; Porphyrin and Phthalocyanine Chemistry
Authors: Yougui Liao | C. P. Lee
Source: Microscopy and Microanalysis (article, 2023-07-22)
Field: Materials Science — Surfaces, Coatings and Films

Review of Practical Problem Solving for Advanced Semiconductor Industry Journal Article Review of Practical Problem Solving for Advanced Semiconductor Industry Get access Yougui Liao, Yougui Liao R&D, Micron Technology, Inc., Boise, Idaho, USA Search for other works by this author on: Oxford Academic Google Scholar Che-chi Lee Che-chi Lee R&D, Micron Technology, Inc., Boise, Idaho, USA Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 29, Issue Supplement_1, 1 August 2023, Page 104, https://doi.org/10.1093/micmic/ozad067.044 Published: 22 July 2023 Semiconductor industry ; Library science ; Engineering ; Engineering physics ; Management ; Political science ; Computer science ; Economics ; Manufacturing engineering Electron and X-Ray Spectroscopy Techniques ; Advanced Materials Characterization Techniques ; Ion-surface interactions and analysis
Authors: Tetsuya Fukuda | Aya Nakayama | Yoko Chikaoka | Yoshio Bando | Takeshi Kawamura
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-08-24)
Field: Chemistry — Spectroscopy

Deep proteome analysis of time-series human plasma samples applied to Covid-19 antibody test Summary For the purpose of Covid-19 antibody testing, the human plasma samples acquired over a period of 310 days from August 18, 2021, to June 22, 2022, were subjected to DIA LC-MS proteome analysis. This process led to the acquisition of a quantitative protein profile and allowed for the validation of the temporal behavior of expressed proteins within the samples. A total of 1502 proteins were identified from the plasma samples. Before vaccination, after the first dose, after the second dose, and during the period after contracting the novel coronavirus, protein quantification values during each event interval were compared. Despite minimal changes observed before and after Covid-19 vaccination, notable proteins exhibiting distinct high-expression and low-expression patterns were identified after contracting the novel coronavirus infection. Proteome ; Coronavirus disease 2019 (COVID-19) ; Antibody ; Coronavirus ; Vaccination ; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ; 2019-20 coronavirus outbreak ; Human plasma ; Virology ; Biology ; Immunology ; Andrology ; Molecular biology ; Medicine ; Chemistry ; Bioinformatics ; Chromatography ; Pathology Advanced Proteomics Techniques and Applications ; Biosensors and Analytical Detection ; SARS-CoV-2 detection and testing
Authors: Luocheng Huang | Zheyi Han | Anna Wirth-Singh | Vishwanath Saragadam | Saswata Mukherjee | Johannes E. Fröch | Tanguy, Quentin A. A. | Joshua Rollag | Ricky Gibson | Joshua R. Hendrickson | Phillip W. C. Hon | Orrin Kigner | Zachary Coppens | K. F. Böhringer | Ashok Veeraraghavan | Arka Majumdar
Source: arXiv (Cornell University) (preprint, 2023-07-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Broadband Thermal Imaging using Meta-Optics Subwavelength diffractive optics known as meta-optics have demonstrated the potential to significantly miniaturize imaging systems. However, despite impressive demonstrations, most meta-optical imaging systems suffer from strong chromatic aberrations, limiting their utilities. Here, we employ inverse-design to create broadband meta-optics operating in the long-wave infrared (LWIR) regime (8 - 12 $μ$m). Via a deep-learning assisted multi-scale differentiable framework that links meta-atoms to the phase, we maximize the wavelength-averaged volume under the modulation transfer function (MTF) of the meta-optics. Our design framework merges local phase-engineering via meta-atoms and global engineering of the scatterer within a single pipeline. We corroborate our design by fabricating and experimentally characterizing all-silicon LWIR meta-optics. Our engineered meta-optic is complemented by a simple computational backend that dramatically improves the quality of the captured image. We experimentally demonstrate a six-fold improvement of the wavelength-averaged Strehl ratio over the traditional hyperboloid metalens for broadband imaging. Strehl ratio ; Optics ; Broadband ; Optical transfer function ; Apodization ; Physics ; Optoelectronics ; Adaptive optics Metamaterials and Metasurfaces Applications ; Photonic and Optical Devices ; Thermal Radiation and Cooling Technologies
Authors: Arman Kulyyassov | Saya Makhsatova
Source: Eurasian journal of applied biotechnology (article, 2023-12-20)
Field: Chemistry — Spectroscopy

QUANTIFICATION OF SODIUM DEOXYCHOLATE IN PROTEOMICS SAMPLE PREPARATION USING METHYLENE BLUE Anionic detergents like SDC are commonly used in biochemical and molecular biology research for solubilizing and separating proteins and nucleic acids. The detection method using methylene blue (MB) can be employed to determine the concentration of anionic detergents in these experimental preparations. The quantification protocol for surface active substances was optimized, with the consideration of factors such as pH, salts and the spectrophotometer wavelength. Several extraction methods were employed to remove the detergent from the sample, namely, ethyl acetate extraction, mineral oil and acid precipitation. All of the methods have about the same efficiency. The MB method was used as a standard technique for the precise quantification of detergent amounts which allowed as achieving an approximate concentration range between the lower limit of ca. 0.02% and 0.1% of the upper limit. Consequently, we weren’t able to detect the concentration of the detergent lower than 0.02%. In the presence of anionic detergents, the electrostatic interaction between positively charged MB and the anions forms a complex that can be extracted into chloroform, allowing for quantification. Without the formation of this complex, water-soluble MB remains immiscible in chloroform. Chloroform ; Chemistry ; Methylene blue ; Chromatography ; Extraction (chemistry) ; Detection limit ; Sample preparation ; Nucleic acid ; Coomassie Brilliant Blue ; Precipitation ; Protein precipitation ; Organic chemistry ; Staining ; Biochemistry Mass Spectrometry Techniques and Applications
Authors: Sevcan Ünlütürk | Pınar Akdoğan Şirin | Mehmet S. Unluturk
Source: Agriculture & Food (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

ESTIMATING VISCOSITY OF LOW SUGAR APPLE MARMALADE USING BACKPROPAGATION NEURAL NETWORK In this paper, a backpropagation neural network (BPNN) model was developed for the prediction of viscosity values of apple marmalade using experimental data collected from several measurements.Stevioside or Sucralose sweetener was used instead of sugar (sucrose) in some of the formulations.In the BPNN architecture, the shear stress, and shear strain with mass concentrations of the Stevioside, Sucrolase, and Sucrose were utilized as input, whereas the viscosity value of apple marmalade was used as an output to be estimated.The Stochastic gradient descent algorithm (SGD) was used to minimize the loss of the BPNN based on the experimental data set.The Mean squared error (MSE), and the coefficient ( 2 ) were employed to assess the performance of the BPNN.The number of hidden neurons was found to be 20 using the adaptive hidden neuron algorithm.With 20 hidden neurons, the least MSE and the highest R 2 value were attained.Furthermore, the predicted viscosity values were found to be within 1% of the experimental viscosity values.The developed BPNN model can, therefore, be effectively utilized to predict the viscosity of any fruit marmalade using the same input and output parameters in the data range where the new data is normalized with the experimental data used in this paper. Sugar ; Backpropagation ; Artificial neural network ; Viscosity ; Food science ; Biological system ; Mathematics ; Computer science ; Biology ; Artificial intelligence ; Materials science ; Composite material Spectroscopy and Chemometric Analyses
Authors: 真之 岸野 | Masayuki Kishino
Source: Institutional Repositories DataBase (IRDB) (article, 2023-03-01)
Field: Materials Science — Polymers and Plastics

Fatigue Analysis and Internal Strain Measurement in Bending Polymer Films Bending ; Strain (injury) ; Polymer ; Deformation (meteorology) ; Internal pressure Polymer crystallization and properties ; Elasticity and Material Modeling ; Rheology and Fluid Dynamics Studies
Authors: Bert Debusschere | Caitlin Curry | Daniel Seidl | Kyung Won Chang | Paul Mariner
Source:  (article, 2023-07-01)
Field: Materials Science — Materials Chemistry

Machine Learning Surrogates for Fuel Degradation Processes in Nuclear Waste Repository Simulations Degradation (telecommunications) ; Radioactive waste ; Computer science ; Waste management ; Nuclear engineering ; Engineering Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear and radioactivity studies
Authors: Federico Ferraccioli | Alessandro Casa | Marco Stefanucci
Source: arXiv (Cornell University) (preprint, 2023-09-13)
Field: Chemistry — Analytical Chemistry

An adaptive functional regression framework for spatially heterogeneous signals in spectroscopy The attention towards food products characteristics, such as nutritional properties and traceability, has risen substantially in the recent years. Consequently, we are witnessing an increased demand for the development of modern tools to monitor, analyse and assess food quality and authenticity. Within this framework, an essential set of data collection techniques is provided by vibrational spectroscopy. In fact, methods such as Fourier near infrared and mid infrared spectroscopy have been often exploited to analyze different foodstuffs. Nonetheless, existing statistical methods often struggle to deal with the challenges presented by spectral data, such as their high dimensionality, paired with strong relationships among the wavelengths. Therefore, the definition of proper statistical procedures accounting for the peculiarities of spectroscopy data is paramount. In this work, motivated by two dairy science applications, we propose an adaptive functional regression framework for spectroscopy data. The method stems from the trend filtering literature, allowing the definition of a highly flexible and adaptive estimator able to handle different degrees of smoothness. We provide a fast optimization procedure that is suitable for both Gaussian and non Gaussian scalar responses, and allows for the inclusion of scalar covariates. Moreover, we develop inferential procedures for both the functional and the scalar component thus enhancing not only the interpretability of the results, but also their usability in real world scenarios. The method is applied to two sets of MIR spectroscopy data, providing excellent results when predicting milk chemical composition and cows' dietary treatments. Moreover, the developed inferential routine provides relevant insights, potentially paving the way for a richer interpretation and a better understanding of the impact of specific wavelengths on milk features. Interpretability ; Computer science ; Curse of dimensionality ; Gaussian ; Estimator ; Traceability ; Scalar (mathematics) ; Data mining ; Machine learning ; Mathematics ; Statistics ; Chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Danny Wicaksono | Waluyo Musiono Bintoro
Source: Jurnal Rekayasa Mesin (article, 2023-12-22)
Field: Materials Science — Materials Chemistry

Analisis Pengaruh Kecepatan Potong Terhadap Keausan Alat Potong pada Proses Pembubutan Material S50C Menggunakan Persamaan Taylor &lt;p&gt;Umur alat potong sangat bergantung pada parameter pemotongan. Terdapat beberapa parameter pemotongan seperti kecepatan potong (&lt;em&gt;Vc), &lt;/em&gt;pemakanan&lt;em&gt; (fz)&lt;/em&gt;, kedalaman pemakanan &lt;em&gt;(a)&lt;/em&gt;, dan beberapa kondisi lingkungan yang dapat berpengaruh terhadap proses pemotongan. Umur alat potong dapat dibuat panjang dengan menurunkan kecepatan potong, pemakanan, dan kedalaman pemakanan, tetapi hal tersebut dapat memengaruhi produktifitas dari proses pemesinan. Kecepatan potong sangat memengaruhi umur alat potong berdasarkan persamaan Vc.T&lt;sup&gt;n &lt;/sup&gt;= C. Nilai n pada persamaan Taylor menunjukan material alat potong yang digunakan. Material alat potong yang digunakan pada penelitian ini adalah kabida tungsten (WC) dengan pelapisan titanium karbonitrida (TiCN), benda keda kerja yang digunakan adalah S50C. Tujuan dari penelitian ini adalah menentukan persamaan Taylor untuk proses bubut. Proses pembubutan dilakukan dengan memvariasikan kecepatan potong, sedangakan parameter lain seperti kecepatan pemakanan dan kedalaman pemakanan dibuat konstan. Hasil eksperimen tersebut kemudian dibuat grafik kecepatan potong terhadap dimensi keausan alat potong. Hasil dari penelitian ini dapat digunakan sebagai referensi untuk menentukan parameter pemesinan dengan laju keausan alat potong yang diinginkan, persamaan Taylor dari hasil penelitian ini adalah Vc.T&lt;sup&gt;0,48&lt;/sup&gt;=847,09.&lt;/p&gt; Physics ; Chemistry Material Selection and Properties ; Manufacturing Process and Optimization ; Metal Forming Simulation Techniques
Authors: Yao Lu | Jun Wang | Di Pan | Jian Han | Lisong Zhu | Chenglei Diao | Jingtao Han | Zhengyi Jiang
Source: Materials (article, 2023-07-10)
Field: Materials Science — Materials Chemistry

Optimising Two-Stage Vacuum Heat Treatment for a High-Strength Micro-Alloyed Steel in Railway Spring Clip Application: Impact on Microstructure and Mechanical Performance The heat treatment process is a vital step for manufacturing high-speed railway spring fasteners. In this study, orthogonal experiments were carried out to obtain reliable optimised heat treatment parameters through a streamlined number of experiments. Results revealed that a better comprehensive mechanical performance could be obtained under the following combination of heat treatment parameters: quenching temperature of 850 °C, holding time of 35 min, medium of 12% polyalkylene glycol (PAG) aqueous solution, tempering temperature of 460 °C, and holding time of 60 min. As one of the most important testing criteria, fatigue performance would be improved with increasing strength. Additionally, a high ratio of martensite to ferrite is proven to improve the fatigue limit more significantly. After this heat treatment process, the metallographic microstructure and mechanical properties satisfy the technical requirements for the high-speed railway practical operation. These findings provide a valuable reference for the practical forming process of spring fasteners. Materials science ; Tempering ; Microstructure ; Spring steel ; Ferrite (magnet) ; Martensite ; Quenching (fluorescence) ; Fatigue limit ; Spring (device) ; Composite material ; Metallurgy ; Structural engineering ; Engineering Metal Alloys Wear and Properties ; Microstructure and Mechanical Properties of Steels ; High Entropy Alloys Studies
Authors: Adeyemi Adegbenjo | Li Liu | Michael Ngadi
Source: Preprints.org (preprint, 2023-08-10)
Field: Chemistry — Analytical Chemistry

An Adaptive Partial Least Square Regression Approach for Classifying Chicken Egg Fertility by Hyperspectral Imaging Partial least square (PLS) regression is a well-known chemometric method used for predictive modelling, especially in the presence of many variables. Although PLS was not initially developed as a technique for classification tasks, scientists have reportedly used this approach successfully for discrimination purposes. Whereas some non-supervised learning approaches including but not limited to PCA, and k-means clustering do well in identifying/understanding grouping and clustering patterns in multidimensional data, they are limited when the end target is discrimination, making PLS a preferable alternative. A total of fertilized 672 chicken egg hyperspectral imaging data, consisting of 336 white eggs and 336 brown eggs were used in this study. Hyperspectral images in the NIR region of 900-1700 nm wavelength range were captured prior to incubation on day 0 and on days 1-4 after incubation. Eggs were candled on incubation day 5 and broken out on day 10 to confirm fertility. While a total number of 312 and 314 eggs were found to be fertile in the brown and white egg batches respectively, total numbers of non-fertile eggs in the same set of batches were 23 and 21 respectively. Spectral information was extracted from a segmented region of interest (ROI) of each hyperspectral image and spectral transmission characteristics were obtained by averaging the spectral information. A moving-thresholding technique was implemented for discrimination based on PLS regression results on the calibration set. With true positive rates (TPR) of up to 100% obtained at selected threshold values of between 0.50-0.85 and on different days of incubation, the results indicated that the proposed PLS technique can accurately discriminate between fertile and non-fertile eggs. The adaptive PLS approach was thereby presented as suitable for handling hyperspectral imaging-based chicken egg fertility data Hyperspectral imaging ; Partial least squares regression ; Thresholding ; Cluster analysis ; Pattern recognition (psychology) ; Mathematics ; Artificial intelligence ; Statistics ; Regression ; Data set ; Range (aeronautics) ; k-means clustering ; Computer science ; Image (mathematics) ; Engineering Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Animal Nutrition and Physiology
Authors: S. Sivaranjani | V. Geethalakshmi | S Pazhanivelan | JS Kennedy | S. Ramanathan | R. Gowtham | K. Pugazenthi
Source: Current Science (article, 2023-05-25)
Field: Chemistry — Analytical Chemistry

Effect of temperature on brown planthopper Infestation in rice using hyperspectral remote Sensing Hyperspectral imaging ; Brown planthopper ; Infestation ; Remote sensing ; Environmental science ; Planthopper ; Biology ; Agronomy ; Geography ; Botany ; Hemiptera Spectroscopy and Chemometric Analyses