jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: С.В. Умбетов | С.П. Пронин
Source: Южно-Сибирский научный вестник (article, 2022-12-20)
Field: Materials Science — Materials Chemistry

COLOR IMAGE PROCESSING ALGORITHM METAL SURFACES AND DEPTH CALCULATION PENETRATION OF CORROSION INTO METAL BY RGB COMPONENTS В связи с высокой сложностью проведения неразрушающего контроля магистрального трубопровода и актуальностью данной задачи для поддержания магистралей в рабочей состоянии был предложен способ поиска коррозийных повреждений. В статье представлен алгоритм обработки изображения и поиска на нем глубины проникновения коррозии в металл по соотношение цветовых компонент. Проведено исследование и анализ факторов, влияющих на расчет остаточной глубины металла. Проведены результаты экспериментальных исследований образца стали марки СТ-3 в лабораторных условиях. Применяя низкочастотную пространственную фильтрацию и алгоритмы выделения сегментов на изображении выделяется область для исследования. Для анализа выбранного сегмента используется гистограммы распределения количества фоточувствительных элементов в различные сигналы, которые характеризуют яркость на исследуемы поверхности для каждой цветовой компоненты. Результатом выполнения стало разработки алгоритма обработки изображения, который базируется на построении гистограмм распределения цветовых компонентов на снимке. Результатом работы алгоритма является расчет остаточный толщены металла подверженного коррозии. The article presents an algorithm for processing an image and searching for the depth of penetration of corrosion into the metal on it by the ratio of color components. A study and analysis of the factors influencing the calculation of the residual depth of the metal was carried out. The results of experimental studies of a sample of steel grade ST-3 in laboratory conditions have been carried out. Using low-frequency spatial filtering and algorithms for selecting segments, an area for research is selected on the image. To analyze the selected segment, distribution histograms of the number of photosensitive elements in various signals are used, which characterize the brightness on the surface under study for each color component. The result of the implementation was the development of an image processing algorithm, which is based on the construction of histograms of the distribution of color components in the image. The result of the algorithm is the calculation of the residual thickness of the metal subject to corrosion. RGB color model ; Brightness ; Histogram ; Residual ; Image processing ; Artificial intelligence ; Algorithm ; Computer vision ; Color image ; Computer science ; Histogram equalization ; Corrosion ; Materials science ; Image (mathematics) ; Optics ; Metallurgy Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Industrial Engineering and Technologies
Authors: Razman Ayop | Chee Wei Tan | Abba Lawan Bukar | Awang Jusoh | Nik Din Muhamad | Syed Norazizul Syed Nasir
Source: International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering (article, 2022-07-21)
Field: Materials Science — Materials Chemistry

The design of energy storage based on thermoelectric generator and bidirectional converter &lt;span lang="EN-US"&gt;Energy storage plays an important role in the future of the power system. There are a lot of energy storage system (ESS) available and thermal energy storage shows a promising future. Conventionally, this system is based on a steam generator that converts heat energy to electrical energy and an electrical heater to convert electrical energy to heat energy. Nonetheless, there is still no proper ESS based on the thermoelectric generator (TEG), which can convert directly heat energy to electrical energy and vice versa. This paper proposed a power converter with a new controller for the thermal ESS based on the TEG. The bidirectional converter and the modified perturb and observe method are used to manage the energy transfer at the TEG. The thermal energy storage is based on the sensible approach, where the heat energy causes the temperature to increase. The results show that the thermal ESS based on the TEG is feasible since the energy can be stored and released from the proposed system.&lt;/span&gt; Energy storage ; Thermal energy storage ; Thermoelectric generator ; Thermal energy ; Electric potential energy ; Generator (circuit theory) ; Energy recovery ; Computer science ; Energy (signal processing) ; Electrical engineering ; Electric power ; Nuclear engineering ; Power (physics) ; Process engineering ; Automotive engineering ; Thermoelectric effect ; Engineering ; Physics ; Thermodynamics Advanced Thermoelectric Materials and Devices
Authors: Xiaodong Li | Xianzhao Shao | Fei Xie
Source: Applied Ecology and Environmental Research (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

PHYTOEXTRACTION OF HEAVY METALS FROM A DECOMMISSIONED TANNERY WASTE DISPOSAL AREA BY PIONEER HERBACEOUS PLANTS A field survey of pioneer herbaceous plants growing on a decommissioned tannery waste disposal area in Hubei Province, China, was conducted to identify the species extracting heavy metals especially accumulating Cr in their tissues. The results show that the soil in the area was extremely contaminated with soil Cr range of 1300-3100 mg/kg, which was 13-30 times higher than that of unpolluted soil. Besides, 18 herbaceous species belonging to 11 families were found in the polluted area, among which 8 species, were identified as the dominant adaptive species with Cr > 200 mg/kg in their shoots and occupying dominant biomass. Cynodon dactylon L. was found to have the highest Cr concentration 774 mg/kg and 2335 mg/kg respectively in its shoot and root. The Cr bioaccumulation coefficients and translocation factors of dominant herbaceous plants both were lower than 1, indicating that the plants were possibly tolerant-stratified to the high Cr environment. Cynodon dactylon L. was considered as the suitable candidate for bioremediation of Cr-contaminated soils in the area. Phytoremediation ; Phytoextraction process ; Heavy metals ; Herbaceous plant ; Environmental science ; Waste management ; Hyperaccumulator ; Engineering ; Agronomy ; Environmental chemistry ; Chemistry ; Biology Heavy Metals in Plants ; Soil and Environmental Studies
Authors: Reinaldo Corrêa Leite | Filipe M. do Rosário Menezes | Kalil Lucas da Silva Corrêa
Source: Anais do ... Simpósio Brasileiro de Sistemas Elétricos (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Analysis of Leakage Current on Polluted Polymeric Insulator Using Finite Element Method This paper develops a Finite Element Model (FEM) for a 138 kV High Temperature Vulcanized (HTV) Silicone Rubber (SiR) transmission insulator in which, in a first step, leakage current is calculated for different non-uniform continuous and discontinuous pollution deposits and then features based on the phase shift between the leakage current and the applied voltage are extracted for each kind of pollution layer to identify the existence of dry bands on the insulator surface. A phase shift of 84.75° lag between the leakage current and applied voltage was found for a pollution layer with dry bands inserted. This results evidence that the phase angle can be used to detect the dry band formation by measuring the leakage current and its phase angle. Silicone rubber ; Insulator (electricity) ; Materials science ; Finite element method ; Voltage ; Leakage (economics) ; Composite material ; Vulcanization ; Natural rubber ; Electrical engineering ; Structural engineering ; Engineering High voltage insulation and dielectric phenomena ; Electrical Fault Detection and Protection ; Power Transformer Diagnostics and Insulation
Authors: Abdelkrim Alileche
Source: F1000Research (preprint, 2022-03-07)
Field: Chemistry — Physical and Theoretical Chemistry

The IC-50-time evolution is a new model to improve drug responses consistency of large scale studies <ns3:p><ns3:bold>Abstract: </ns3:bold>Large-scale studies combining hundreds of cancer cell lines and many cancer drugs, with their promises and challenges, represent a new development in the <ns3:italic>in vitro</ns3:italic> screening of cancer drugs. However, drugs sensitivity results of the same cancer cell lines exposed to the same cancer drugs generated different IC50s by these studies as noticed by Haibe-Kains B <ns3:italic>et al</ns3:italic> (1). These inconsistencies are due to many factors: the experimental conditions and the use of the Four Parameter Logistic (4PL) regression model to analyze drugs sensitivity results. A new model based on the Levasseur LM <ns3:italic>et al</ns3:italic> model, the Gompertzian growth model of <ns3:italic>in vitro</ns3:italic> monolayer culture, and the IC-50 time course evolution is more appropriate to improve the accuracy of these large scale studies.</ns3:p> Medicine ; Scale (ratio) ; Computational biology ; Biology ; Physics thermodynamics and calorimetric analyses ; Mathematical Biology Tumor Growth ; Gene Regulatory Network Analysis
Authors: Tae-Hyeong Kim | Dae-Hyun Lee | Seungwoo Kang | Soo-Hyun Cho | Kyoung-Chul Kim
Source: Korean Journal of Agricultural Science (article, 2022-12-01)
Field: Chemistry — Analytical Chemistry

Estimation of tomato maturity as a continuous index using deep neural networks In this study, tomato maturity was estimated based on deep learning for a harvesting robot.Tomato images were obtained using a RGB camera installed on a monitoring robot, which was developed previously, and the samples were cropped to 128 128 size images to generate a dataset for training the classification model.The classification model was constructed based on convolutional neural networks, and the mean-variance loss was used to learn implicitly the distribution of the data features by class.In the test stage, the tomato maturity was estimated as a continuous index, which has a range of 0 to 1, by calculating the expected class value.The results show that the F1-score of the classification was approximately 0.94, and the performance was similar to that of a deep learning-based classification task in the agriculture field.In addition, it was possible to estimate the distribution in each maturity stage.From the results, it was found that our approach can not only classify the discrete maturation stages of the tomatoes but also can estimate the continuous maturity. Index (typography) ; Artificial neural network ; Maturity (psychological) ; Estimation ; Deep neural networks ; Statistics ; Computer science ; Mathematics ; Artificial intelligence ; Data mining ; Geography ; Engineering ; World Wide Web Spectroscopy and Chemometric Analyses
Authors: Matthew White | D. Marc Jones | Joost de Folter | Simran Kaur Aulakh | Helen R. Flynn | Lynn Krüger | Vadim Demichev | Pinkus Tober‐Lau | Florian Kurth | Michael Mülleder | Véronique Blanchard | Christoph B. Messner | Markus Ralser
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-06-02)
Field: Chemistry — Spectroscopy

OxoScan-MS: Oxonium ion scanning mass spectrometry facilitates plasma glycoproteomics in large scale Abstract Protein glycosylation is a complex and heterogeneous post-translational modification. Specifically, the human plasma proteome is rich in glycoproteins, and as protein glycosylation is frequently dysregulated in disease, glycoproteomics is considered an underexplored resource for biomarker discovery. Here, we present OxoScan-MS, a data-independent mass spectrometric acquisition technology and data analysis software that facilitates sensitive, fast, and cost-effective glycoproteome profiling of plasma and serum samples in large cohort studies. OxoScan-MS quantifies glycosylated peptide features by exploiting a scanning quadrupole to assign precursors to oxonium ions, glycopeptide-specific fragments. OxoScan-MS reaches a high level of sensitivity and selectivity in untargeted glycopeptide profiling, such that it can be efficiently used with fast microflow chromatography without a need for experimental enrichment of glycopeptides from neat plasma. We apply OxoScan-MS to profile the plasma glycoproteomic in an inpatient cohort hospitalised due to severe COVID-19, and obtain precise quantities for 1,002 glycopeptide features. We reveal that severe COVID-19 induces differential glycosylation in disease-relevant plasma glycoproteins, including IgA, fibrinogen and alpha-1-antitrypsin. Thus, with OxoScan-MS we present a strategy for quantitatively mapping glycoproteomes that scales to hundreds and thousands of samples, and report glycoproteomic changes in severe COVID-19. Glycoproteomics ; Oxonium ion ; Glycosylation ; Glycoprotein ; Proteome ; Glycopeptide ; Chemistry ; Mass spectrometry ; Blood proteins ; Chromatography ; Biochemistry ; Glycan ; Ion Advanced Proteomics Techniques and Applications ; Glycosylation and Glycoproteins Research ; vaccines and immunoinformatics approaches
Authors: milton Medina | jenniffer Bustamante-Mejía
Source:  (article, 2022-07-08)
Field: Chemistry — Analytical Chemistry

Non-invasive analysis with dynamic infrared thermography to calculate dry matter in Hass avocado via neural networks By using dynamic infrared thermography was possible to obtain the percentage of dry matter in Hass avocado fruits. Two groups were worked on the fruits, one of them was stimulated with a high-power laser and the other one with halogen lamps. Were acquired more than 17000 images during the cooling stage in 300 fruits. An algorithm constructed previously in Matlab allowed segmenting the registered images in order to extract statistical characteristics with which a neural network model was trained to quantify the dry matter. Thermography ; Halogen lamp ; Dry matter ; Artificial neural network ; Infrared ; MATLAB ; Materials science ; Horticulture ; Botany ; Optics ; Artificial intelligence ; Biology ; Physics ; Computer science Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Advanced Chemical Sensor Technologies
Authors: Minjie Chen
Source:  (preprint, 2022-10-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

On the Complexity of Power Magnetics Modeling &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 ; Waveform ; Magnet ; Core (optical fiber) ; Magnetic hysteresis ; Vibration ; Magnetic core ; Power (physics) ; Nonlinear system ; Materials science ; Range (aeronautics) ; Electrical engineering ; Mechanics ; Acoustics ; Magnetic field ; Voltage ; Physics ; Engineering ; Condensed matter physics ; Magnetization ; Thermodynamics ; Optics ; Electromagnetic coil Magnetic Properties and Applications ; Electric Motor Design and Analysis ; Magnetic properties of thin films
Authors: Luis Arley Zúñiga Picado | Stefanny Campos-Boza | Juan Roberto Mora Chaves | Luis Barboza‐Barquero
Source: Agronomía Mesoamericana (article, 2022-11-30)
Field: Chemistry — Analytical Chemistry

Cuantificación del porcentaje de grano quebrado total en arroz (Oryza sativa L.) mediante análisis digital de imágenes Introduction. Digital image analysis (DIA) can be used to evaluate the quality parameters of rice grains, such as the percentage of whole grain. Objective. To quantify percentage of total broken grain by means of digital image analysis (DIA) applied to the quantification of rice (Oryza sativa L.) quality. Materials and methods. The present work was developed in facilities of the Centro para Investigaciones en Granos y Semillas (CIGRAS) of the Universidad de Costa Rica (UCR), Costa Rica, in 2021. The work was carried out with commercial rice samples. A sample plate was developed to facilitate the arrangement of the grains and to acquire the digital images. The length parameters established in the technical regulation of Costa Rica RTCR 202:1998 were used to quantify the percentages of small broken, broken grains, and whole grains, which were quantified conventionally and by means of digital images. The DIA included the segmentation and binarization process of the objects (small broken, broken grains, and whole grain) to quantify their areas and catalog the digitally identified elements in weight values. Results. It was possible to quantified the percentage of small broken and broken grain with DIA. The correlation between the variables quantified by DIA and those evaluated conventionally was greater than 0.93 for the small broken property and 0.98 for broken grain. Conventional analysis requires 1 to 2 hours to determine the percentage of total broken grain and other quality properties in each rice sample. The digital analysis requires 7 to 23 minutes per sample plate to analyze all the properties evaluated (small broken and broken grain). Conclusions. The digital analysis method applied allowed to determine the total broken grain properties in samples composed of hundreds of grains. Humanities ; Physics ; Oryza sativa ; Mathematics ; Biology ; Philosophy ; Gene ; Genetics Spectroscopy and Chemometric Analyses
Authors: Hsien‐Jung L. Lin | David H. Parkinson | J. Connor Holman | W. Chad Thompson | Christian N. K. Anderson | Marcus Hadfield | Stephen Ames | Nathan R. Zuniga Pina | Jared N. Bowden | Colette F. Quinn | Lee D. Hansen | John C. Price
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-06-23)
Field: Chemistry — Physical and Theoretical Chemistry

Structural stability of Human serum albumin is modified in rheumatoid arthritis ABSTRACT Differential scanning calorimetry (DSC) can interrogate changes in structure and/or concentration of the most abundant proteins in a biological sample via heat denaturation curves (HDCs). In blood serum for example, HDC changes are a result of either concentration or altered thermal stabilities for 7-10 proteins and has previously been shown capable of differentiating between sick and healthy human subjects. Here, we compare HDCs and proteomic profiles of 50 patients experiencing joint-inflammatory symptoms, 27 of which were clinically diagnosed with rheumatoid arthritis (RA). The HDC of all 50 subjects appeared significantly different from expected healthy curves, but comparison of additional differences between the RA the non-RA subjects allowed more specific understanding of RA samples. We used mass spectrometry (MS) to investigate the reasons behind the additional HDC changes in RA patients. The HDC differences do not appear to be directly related to differences in the concentrations of abundant serum proteins. Rather, the differences can be attributed to modified thermal stability of the most abundant protein, human serum albumin (HSA). By quantifying differences in the frequency of artificially induced post translational modifications (PTMs), we found that HSA in RA subjects had a much lower surface accessibility, indicating potential ligand or protein binding partners in certain regions that could explain the shift in HSA melting temperature in the RA HDCs. Several low abundance proteins were found to have significant changes in concentration in RA subjects and could be involved in or related to binding of HSA. Certain amino acid sites clusters were found to be less accessible in RA subjects, suggesting changes in HSA structure that may be related to changes in protein-protein interactions. These results all support a change in behavior of HSA which may give insight into mechanisms of RA pathology. Rheumatoid arthritis ; Human serum albumin ; Albumin ; Chemistry ; Denaturation (fissile materials) ; Differential scanning calorimetry ; Amino acid ; Serum albumin ; Human blood ; Internal medicine ; Biochemistry ; Medicine ; Physiology thermodynamics and calorimetric analyses ; Protein purification and stability ; Protein Interaction Studies and Fluorescence Analysis
Authors: Samira Savadi | Mohsen Vazifedoost | Zohre Didar | Mohammad Mehdi Nemat Shahi | Eisa Jahed
Source: Food Science and Technology (article, 2022-02-19)
Field: Chemistry — Organic Chemistry

Effect of Methanolic Extract obtained from Cynodon dactylon (L) Pers. Rhizomes on Oxidative Stability of Soybean Oil under Thermal Conditions Effect of Methanolic Extract obtained from Cynodon dactylon (L) Pers. Rhizomes on Oxidative Stability of Soybean Oil under Thermal Conditions Cynodon dactylon ; Rhizome ; Botany ; Essential oil ; Chemistry ; Biology Edible Oils Quality and Analysis ; Phytochemicals and Antioxidant Activities ; Food Chemistry and Fat Analysis
Authors: Amitava Akuli | Samikshan Das | Anil Kumar Bag | Suparna Parua | Alokesh Ghosh
Source: YMER Digital (article, 2022-06-15)
Field: Chemistry — Analytical Chemistry

Morphological Image Processing for Cocoa Bean Classification The purpose of this paper is to offer a machine vision approach for classifying cocoa beans based on their morphological properties. Using traditional machine learning approaches, the shape and size of cocoa beans were retrieved from photographs. A series of image processing techniques are used to extract the features from the photos. Finally, typical machine learning approaches such as KNN, SVM, Decision Tree, and Random Forest are used to divide the cocoa beans into four groups: large, medium, small, and rejected. A comparison of different methodologies is also carried out. Two optimization strategies, Univariate Selection and Feature Importance, are used to maximize retrieved features prior to training the model. For performance analysis, trained models are evaluated using stratified K-fold cross validations and the mean cross validation score is produced. The Random Forest Classifier has the greatest accuracy score of 0.75, according to the results of the experiments. Keywords: Cocoa beans, Classification, Image processing, Machine Learning, Feature Optimization. Artificial intelligence ; Random forest ; Univariate ; Support vector machine ; Pattern recognition (psychology) ; Feature selection ; Computer science ; Machine learning ; Image processing ; Classifier (UML) ; Decision tree ; Image (mathematics) ; Multivariate statistics Spectroscopy and Chemometric Analyses ; Food Chemistry and Fat Analysis ; Smart Agriculture and AI