jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Minjie Chen
Source:  (preprint, 2022-10-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Quantifying the Complexity of Modeling Power Magnetic Material Characteristics &lt;p&gt;Core losses and hysteresis loops are critical information in the design process of power magnetics, yet the physics behind core loss and hysteresis loops are not fully understood. Core losses and hysteresis loops change for each magnetic material, are highly nonlinear, and depend heavily on the electrical operating conditions (e.g., waveform, frequency, amplitude, dc bias) and the mechanical properties (e.g. pressure, vibration), as well as temperature, and geometry of the magnetic components. Understanding the complexity of these factors is important for the development of accurate models, in addition to addressing the applicability and limitations of each model. Existing studies on power magnetics are usually developed based on a small amount of data and do not reveal the full magnetic behavior across a wide range of operating conditions. In this paper, based on a recently developed large-scale open-source database - MagNet - the core losses and hysteresis loops of Mn-Zn ferrites are analyzed in a wide range of amplitudes, frequencies, waveform shapes, dc bias, and temperatures, to quantify the complexity of modeling magnetic core losses and hysteresis loops and provide guidelines for modeling power magnetics with data-driven methods.&lt;/p&gt; Hysteresis ; Amplitude ; Magnet ; Waveform ; Magnetic hysteresis ; Core (optical fiber) ; Vibration ; Magnetic core ; Power (physics) ; Materials science ; Nonlinear system ; Range (aeronautics) ; Magnetic field ; Acoustics ; Mechanics ; Mechanical engineering ; Electrical engineering ; Condensed matter physics ; Physics ; Voltage ; Engineering ; Magnetization ; Thermodynamics ; Optics Magnetic Properties and Applications ; Magnetic properties of thin films ; Electric Motor Design and Analysis
Authors: Sangjin Byun
Source: Micromachines (article, 2022-03-26)
Field: Chemistry — Analytical Chemistry

Design of an Integrated Near-Infrared Spectroscopy Module for Sugar Content Estimation of Apples An integrated near-infrared (NIR) spectroscopy prototype module for sugar content estimation of apples is presented. Since this is the first attempt to design an integrated NIR spectroscopy module, we followed the design process as follows. First, we estimated the sugar content of apples using a tungsten halogen light source and a 700 nm-1000 nm NIR spectrometer with a 10 nm wavelength resolution and a 16b analog-to-digital converter (ADC) resolution. Second, we determined the most effective wavelengths among 31 evenly distributed wavelengths while observing the correlation coefficient, R<sup>2</sup>, and then we reduced the ADC resolution 1b by 1b starting from 16b while also observing the R<sup>2</sup>. Lastly, we designed an integrated NIR spectroscopy module with the selected eight wavelengths and a 13 ADC resolution. The module implemented in a 0.18 μm 1P6M CMOS process occupies a die area of 0.84 mm<sup>2</sup>. By utilizing this module with eight off-chip light emitting diodes (LED) and one photo diode (PD), the measured R<sup>2</sup> and the standard error of calibration (SEC) were 0.365 and 0.686 brix, respectively. Near-infrared spectroscopy ; Spectrometer ; Spectroscopy ; Materials science ; Light-emitting diode ; Calibration ; Resolution (logic) ; Wavelength ; Halogen lamp ; Diode ; Optoelectronics ; Analytical Chemistry (journal) ; Optics ; Chemistry ; Mathematics ; Computer science ; Physics ; Chromatography Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Juana-Valentina García-Ariza | Marco Javier Suárez Barón | Edmundo-Arturo Junco-Orduz | Juan-Sebastián González-Sanabria
Source: Inge CUC (article, 2022-09-20)
Field: Chemistry — Analytical Chemistry

Application of Unsupervised Learning in the Early Detection of Late Blight in Potato Crops Using Image Processing Introduction. Automatic detection can be useful in the search of large crop fields by simply detecting the disease with the symptoms appearing on the leaf. Objective: This paper presents the application of machine learning techniques aimed at detecting late blight disease using unsupervised learning methods such as K-Means and hierarchical clustering. Method: The methodology used is composed by the following phases: acquisition of the dataset, image processing, feature extraction, feature selection, implementation of the learning model, performance measurement of the algorithm, finally a 68.24% hit rate was obtained being this the best result of the unsupervised learning algorithms implemented, using 3 clusters for clustering. Results: According to the results obtained, the performance of the K-Means algorithm can be evaluated, i.e. 202 hits and 116 misses. Conclusions: Unsupervised learning algorithms are very efficient when processing a large amount of data, in this case a large amount of images without the need for predefined labels, its use to solve local problems such as late blight affectations in potato crops are novel, Blight ; Image processing ; Agronomy ; Artificial intelligence ; Computer science ; Biology ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Potato Plant Research
Authors: F.-J. Granados-Ortiz | A. López | J. Ortega-Casanova | Chih‐Chun Lai
Source: International Journal of Mechanical Sciences (article, 2022-12-01)
Field: Materials Science — Polymers and Plastics

Semi-analytical calculation of pore-related parameters of wire/woven screens Discretization ; Porosity ; Polygon mesh ; Porous medium ; Materials science ; Range (aeronautics) ; Boundary value problem ; Mechanics ; Computer science ; Geometry ; Structural engineering ; Composite material ; Engineering ; Mathematics ; Mathematical analysis ; Physics Textile materials and evaluations ; Wind and Air Flow Studies ; Urban Heat Island Mitigation
Authors: Kim Brettschneider | Viktoria Zettel | Pegah Sadeghi Vasafi | Darius Hummel | Jörg Hinrichs | Bernd Hitzmann
Source: Food and Bioprocess Technology (article, 2022-05-11)
Field: Chemistry — Analytical Chemistry

Spectroscopic-Based Prediction of Milk Foam Properties for Barista Applications Abstract The important quality parameters of cow’s milk for barista applications are frothability and foam stability. In the past, quality assessment was very time-consuming and could only be carried out after milk treatment had been completed. Since spectroscopy is already established in dairies, it could be advantageous to develop a spectrometer-based measurement method for quality control for barista applications. By integrating online spectroscopy to the processing of UHT (ultra-high temperature processing) milk before filling, it can be checked whether the currently processed product is suitable for barista applications. To test this hypothesis, a feasibility study was conducted. For this purpose, seasonal UHT whole milk samples were measured every 2 months over a period of more than 1 year, resulting in a total of 269 milk samples that were foamed. Samples were frothed using a self-designed laboratory frother. Frothability at the beginning and foam loss after 15 min describe the frothing characteristics of the milk and are predicted from the spectra. Near-infrared, Raman, and fluorescence spectra were recorded from each milk sample. These spectra were preprocessed using 15 different mathematical methods. For each spectrometer, 85% of the resulting spectral dataset was analyzed using partial least squares (PLS) regression and nine different variable selection (VS) algorithms. Using the remaining 15% of the spectral dataset, a prediction error was determined for each model and used to compare the models. Using spectroscopy and PLS modeling, the best results show a prediction error for milk frothability of 3% and foam stability of 2%. Partial least squares regression ; Spectrometer ; Chemometrics ; Calibration ; Near-infrared spectroscopy ; Raman spectroscopy ; Analytical Chemistry (journal) ; Mathematics ; Environmental science ; Biological system ; Materials science ; Process engineering ; Chemistry ; Chromatography ; Statistics ; Engineering ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Meat and Animal Product Quality
Authors: Tianye Xing | Yidan Wang | Yingxue Liu | Qi Wu | Rong Ma | Xiaoling Shang
Source: Applied Bionics and Biomechanics (article, 2022-08-18)
Field: Materials Science — Materials Chemistry

An Intelligent Health Monitoring Model Based on Fuzzy Deep Neural Network An intelligent health detection model is a new technology developed under an artificial intelligence environment, which is of great significance to the care of the elderly and other people who cannot take care of themselves. This paper comprehensively reviews the structural health monitoring method based on an intelligent algorithm, introduces the application model of neural networks in structural health monitoring in detail, and points out the shortcomings of using neural network technology alone. On the basis of previous work, the genetic algorithm and fuzzy theory were introduced as optimization tools, and a new neural network training algorithm was constructed by combining genetic algorithm, fuzzy theory, and neural network technology for structural health monitoring research. Aimed at the shortcoming of insufficient samples for training neural networks based on experimental data, this paper proposes to use the finite element method to construct a genetic fuzzy RBF neural network after corresponding processing of the first six-order bending modal frequencies of the structure, so as to realize the localization and detection of delamination damage of composite beams. <i>Injury Assessment</i>. The experimental results of this paper show that the finite element method proposed in this paper can effectively carry out damage localization and damage assessment; compared with the traditional algorithm, the localization accuracy of this algorithm is improved by 20%, and the damage assessment performance is improved by 10%. Structural health monitoring ; Artificial neural network ; Genetic algorithm ; Fuzzy logic ; Computer science ; Artificial intelligence ; Construct (python library) ; Machine learning ; Data mining ; Engineering ; Structural engineering Structural mechanics and materials ; Occupational Health and Safety Research ; Structural Health Monitoring Techniques
Authors: Anqi Hu | Lei Zhang | Zhenxin Wang | Chunyan Yuan | Ling Lin | Jiayi Zhang | Xia Gao | Xuguang Chen | Wei Guo | Pengyuan Yang | Huali Shen
Source: medRxiv (preprint, 2022-08-10)
Field: Chemistry — Spectroscopy

Cancer Serum Atlas supported precise pan-targeted proteomics enable multi-cancer detection Abstract The wide dynamic range of serum proteome restrained discovery of the clinically interested proteins in large cohort studies. Herein, we presented a high-sensitivity, high-throughput and precise pan-targeted serum proteomic strategy for high-efficient cancer serum proteomic research and biomarker discovery. We constructed a resource of over 2000 cancer-secreted proteins and the standard MS assays and spectra of at least one synthetic unique peptide per protein were acquired and documented (Cancer Serum Atlas, www.cancerserumatlas.com ). Then, the standard peptides anchored parallel reaction monitoring (SPA-PRM) method was developed with support of Cancer Serum Atlas, achieving precise quantification of cancer-secreted proteins with high throughput and sensitivity. We directly quantified 325 cancer-related serum proteins in 288 serum of four cancer types (liver, stomach, lung, breast) and controls with the pan-targeted strategy, and discovered considerable potential biomarkers benefit for early detection of cancer. Finally, a proteomics based multi-cancer detection model was built, demonstrating high sensitivity (87.2%), specificity (100%), with 73.8% localization accuracy for an independent test set. In conclusion, the Cancer Serum Atlas provides a wide range of potential biomarkers that serve as targets and standard assays for systematic and high-efficient serological studies of cancer, and the Cancer Serum Atlas supported pan-targeted proteomic strategy enables high-efficient biomarker discovery and multi-cancer detection, thus can be a powerful tool for liquid biopsy. Biomarker discovery ; Cancer ; Cancer biomarkers ; Proteomics ; Biomarker ; Proteome ; Computational biology ; Breast cancer ; Human Protein Atlas ; Cancer research ; Bioinformatics ; Biology ; Medicine ; Internal medicine ; Biochemistry ; Protein expression ; Gene Advanced Proteomics Techniques and Applications ; Genomics and Phylogenetic Studies ; vaccines and immunoinformatics approaches
Authors: Damian Marcinkowski | Kamil Czwartkowski | Marta Kołodziej | Monika Wereńska | Paweł Krzaczek
Source: Sustainability (article, 2022-10-12)
Field: Chemistry — Organic Chemistry

Reuse of Bleaching Earth: The Green Solution for Rapeseed Oil Producers The main purpose of the presented research is to characterize the relationship between the amount of bleaching earth used in the bleaching process of rapeseed oil and the efficiency of this process. Changes in the content of chlorophyll and carotenoid pigments were examined using spectrophotometric and colorimetric methods. The process was carried out on a plate filter with different thicknesses of bleaching earth (BE) layers (1–3% in relation to the amount of oil). On the basis of the conducted research, a decrease in the content of chlorophylls (from 59 to 97%) and carotenoids (from 55 to 78%) was found, which affected the overall color of the oil. Based on the research results, it was found that the bleaching earth does not lose all of its adsorption properties after reaching the maximum, but they decrease in the next cycle. For some time, the sorbent can be used successfully, but to a lesser extent (40–45% of the original properties). This information is crucial as it will reduce BE consumption by up to 30% and shorten the oil bleaching cycle, as it will reduce the setup time of the machine. Rapeseed ; Reuse ; Sorbent ; Pulp and paper industry ; Earth (classical element) ; Chemistry ; Environmental science ; Process engineering ; Carotenoid ; Process (computing) ; Adsorption ; Chlorophyll ; Pigment ; Waste management ; Mathematics ; Computer science ; Food science ; Engineering ; Organic chemistry Edible Oils Quality and Analysis ; Biodiesel Production and Applications ; Spectroscopy and Chemometric Analyses
Authors: Ian Jones | Lucas Dent | Tomoaki Higo | Theodoros I. Roumeliotis | Mar Arias-García | Hansa Shree | Jyoti S. Choudhary | Malin Pedersen | Chris Bakal
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-06-24)
Field: Chemistry — Spectroscopy

Characterization of proteome-size scaling by integrative omics reveals mechanisms of proliferation control in cancer Abstract Almost all living cells maintain size uniformity through successive divisions. Proteins that sub- or super-scale with size act as rheostats which regulate cell progression. A comprehensive atlas of these proteins is lacking; particularly in cancer cells where both mitogen and growth signalling are dysregulated. Utilising a multi-omic strategy, that integrates quantitative single cell imaging, phosphoproteomic and transcriptomic datasets, we leverage the inherent size heterogeneity of melanoma cells to investigate how peptides, post-translational modifications, and mRNAs scale with cell size to regulate proliferation. We find melanoma cells have different mean sizes, but all retain uniformity. Across the proteome, we identify proteins and phosphorylation events that ‘sub’ and ‘super’ scale with cell size. In particular, G2/M, biosynthetic, and cytoskeletal regulators sub- and super-scale with size. In small cells growth and proliferation processes are tightly coupled by translation which promotes CCND1 accumulation and anabolic increases in mass. Counter intuitively, anabolic growth pathways and translational process are low in large cells, which throttles the expression of factors such as CCND1 and thereby coupling proliferation from anabolic growth. Strikingly, these cells exhibit increased growth and comparable proliferation rates. Mathematical modelling suggests that decoupling growth and proliferative signalling fosters proliferation under mitogenic inhibition. As factors which promote adhesion and actin reorganization super-scale with size or are enriched in large cells, we suggest that growth/proliferation in these cells may be decoupled by cell spreading and mechanics. This study provides one of the first demonstrations of size-scaling phenomena in cancer and how morphology determines the chemistry of the cell. Cell growth ; Anabolism ; Cell biology ; Biology ; Proteome ; Cancer cell ; Cell ; Computational biology ; Bioinformatics ; Cancer ; Biochemistry ; Genetics Advanced Proteomics Techniques and Applications ; RNA Research and Splicing ; Bioinformatics and Genomic Networks
Authors: Sergei V. Kalinin | Yongtao Liu | Rama K. Vasudevan | Kyle P. Kelley | Maxim Ziatdinov
Source: Microscopy and Microanalysis (article, 2022-07-22)
Field: Materials Science — Materials Chemistry

From Event Detection to Physical Hypothesis Learning via Automated and Autonomous Microscopy Electron and scanning probe microscopies are by now the mainstay of areas ranging from fundamental condensed matter physics and materials science to chemistry and biology. In many cases, these imaging methods provide quantitative or semiquantitative information on materials structure and functionalities and allow for the exploration of broad range of responses to global (temperature, chemistry) and local (beam exposure, probe bias and pressure) stimuli. However, the connection between microscopy imaging, spectral data, and specific mechanisms of physical and chemical phenomena often requires extensive studies including multiple human-driven stages. Event (particle physics) ; Computer science ; Action (physics) ; Content (measure theory) ; Artificial intelligence ; Physics ; Mathematics Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Cell Image Analysis Techniques
Authors: Thomas Hochrainer | Benedikt Weger | Satyapriya Gupta
Source: Materials Theory (article, 2022-02-22)
Field: Materials Science — Materials Chemistry

Making sense of dislocation correlations Abstract Since crystal plasticity is the result of moving and interacting dislocations, it seems self-evident that continuum plasticity should in principle be derivable as a statistical continuum theory of dislocations, though in practice we are still far from doing so. One key to any statistical continuum theory of interacting particles is the consideration of spatial correlations. However, because dislocations are extended one-dimensional defects, the classical definition of correlations for point particles is not readily applicable to dislocation systems: the line-like nature of dislocations entails that a scalar pair correlation function does not suffice for characterizing spatial correlations and a hierarchy of two-point tensors is required in general. The extended nature of dislocations as closed curves leads to strong self-correlations along the dislocation line. In the current contribution, we thoroughly introduce the concept of pair correlations for general averaged dislocation systems and illustrate self-correlations as well as the content of low order correlation tensors using a simple model system. We furthermore detail how pair correlation information may be obtained from three-dimensional discrete dislocation simulations and provide a first analysis of correlations from such simulations. We briefly discuss how the pair correlation information may be employed to improve existing continuum dislocation theories and why we think it is important for analyzing discrete dislocation data. Dislocation ; Statistical physics ; Physics ; Scalar (mathematics) ; Hierarchy ; Plasticity ; Theoretical physics ; Classical mechanics ; Mathematics ; Condensed matter physics ; Geometry Microstructure and mechanical properties ; High Temperature Alloys and Creep ; Fatigue and fracture mechanics
Authors: Hui Ma | Ming Chen | Siyu Zhang | Hongye Pan | Yong Chen | Yongjiang Wu
Source: Molecules (article, 2022-12-20)
Field: Chemistry — Analytical Chemistry

Rapid Determination of Geniposide and Baicalin in Lanqin Oral Solution by Near-Infrared Spectroscopy with Chemometric Algorithms during Alcohol Precipitation The selection of key variables is an important step that improves the prediction performance of a near-infrared (NIR) real-time monitoring system. Combined with chemometrics, NIR spectroscopy was employed to construct high predictive accuracy, interpretable models for the rapid detection of the alcohol precipitation process of Lanqin oral solution (LOS). The variable combination population analysis-iteratively retaining informative variables (VCPA-IRIV) was innovatively introduced into the variable screening process of the model of geniposide and baicalin. Compared with the commonly used synergy interval partial least squares regression, competitive adaptive reweighted sampling, and random frog, VCPA-IRIV achieved the maximum compression of variable space. VCPA-IRIV-partial least squares regression (PLSR) only needs to use about 1% of the number of variables of the original data set to construct models with Rp values greater than 0.95 and RMSEP values less than 10%. With the advantages of simplicity and strong interpretability, the prediction ability of the PLSR models had been significantly improved simultaneously. The VCPA-IRIV-PLSR models met the requirements of rapid quality detection. The real-time detection system can help researchers to understand the quality rules of geniposide and baicalin in the alcohol precipitation process of LOS and provide a reference for the optimization of a LOS quality control system. Partial least squares regression ; Interpretability ; Chemometrics ; Feature selection ; Baicalin ; Mathematics ; Statistics ; Algorithm ; Computer science ; Artificial intelligence ; Machine learning ; Chemistry ; Chromatography Spectroscopy and Chemometric Analyses ; Traditional Chinese Medicine Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research