jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mohamad Hushnie Haron
Source: International Journal of Emerging Trends in Engineering Research (article, 2020-07-25)
Field: Chemistry — Organic Chemistry

Grading Of Agarwood Oil Quality Based On Its Chemical Compounds Using Self Organizing Map (SOM) Agarwood oil is derived from resin saturated heartwood of Agarwood tree using solvent extraction, hydro-distillation and etc. The oil consists ofcomplex mixtures of chromone derivatives, oxygenated sesquiterpenes and sesquiterpene hydrocarbonsand they are significant in determining the quality of Agarwood oil. In this paper, a study focusing on the determination of high and low grades of Agarwood oil quality using SOMwas carried out. Three chemical compounds namely -agarofuran, -agarofuran and 10-epi--eudesmol were used as inputs for SOM. In addition, KNN was used to confirm the Agarwood oil grades. This technique wasthen validated using independent samples. The results show that SOM was able to determine the high and low gradesof Agarwood oil with average silhouette indexes between reasonable and excellent, and KNN was able to confirmthese grades with 100% accuracies. In conclusion, the Agarwood oil quality gradingtechnique based on SOM was successfulin determining the Agarwood oil quality into high and low. Agarwood ; Self-organizing map ; Grading (engineering) ; Artificial intelligence ; Computer science ; Engineering ; Artificial neural network ; Civil engineering ; Medicine Wood and Agarwood Research ; Image Processing and 3D Reconstruction
Authors: Mahmoud Darwish | P. Neumann | J. Mizsei | L. Pohl
Source: Energies (article, 2020-07-03)
Field: Materials Science — Polymers and Plastics

Electro-Thermal Simulation of Vertical VO2 Thermal-Electronic Circuit Elements Advancement of classical silicon-based circuit technology is approaching maturity and saturation. The worldwide research is now focusing wide range of potential technologies for the “More than Moore” era. One of these technologies is thermal-electronic logic circuits based on the semiconductor-to-metal phase transition of vanadium dioxide, a possible future logic circuits to replace the conventional circuits. In thermal-electronic circuits, information flows in a combination of thermal and electronic signals. Design of these circuits will be possible once appropriate device models become available. Characteristics of vanadium dioxide are under research by preparing structures in laboratory and their validation by simulation models. Modeling and simulation of these devices is challenging due to several nonlinearities, discussed in this article. Introduction of custom finite volumes method simulator has however improved handling of special properties of vanadium dioxide. This paper presents modeling and electro-thermal simulation of vertically structured devices of different dimensions, 10 nm to 300 nm layer thicknesses and 200 nm to 30 μm radii. Results of this research will facilitate determination of sample sizes in the next phase of device modeling. Electronic circuit ; Vanadium dioxide ; Thermal ; Integrated circuit ; Electronic engineering ; Electronics ; Computer science ; Materials science ; Electrical engineering ; Nanotechnology ; Engineering ; Optoelectronics ; Physics Transition Metal Oxide Nanomaterials ; Advanced Memory and Neural Computing ; Thin-Film Transistor Technologies
Authors: Yi Guan | Minghe Chi | Jinfeng Zhang | Qian Wang | Xinlao Wei | Qingguo Chen
Source: IEEE Access (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Effect of Moistures on Space Charge and Trap Level Characteristics of Oil-Impregnated Pressboards Oil-paper insulation as an irreplaceable insulation system in the electrical power transformer is inevitably suffering the water intrusion on working condition, which will make dielectric performance degraded and insulation aging accelerated. In high interest of explaining the deterioration effect of absorbing moisture on the dielectric properties of oil-paper insulation system and corresponding mechanism, the space charge and trap level characteristics are investigated for the specially prepared oil-impregnated pressboards with different moisture contents. The Pulsed Electro Acoustic and Isothermal Surface Potential Decay experiments are performed to test the space charge distributions and evaluate the trap level depths, respectively. It is indicated that the polarity of injected space charges is alternated with the decaying rate being raised when the absorbing moisture is aggravated in oil-impregnated pressboards. The charge carriers are more prone to be captured by the shallow-level traps that have been introduced by water permeation near pressboard surface than the intrinsic deep-level traps inside pressboard. The captured charge carries accounts for the significantly exacerbated space charge accumulation as manifested by the surface charges of oil-impregnated pressboards. Pressboard ; Space charge ; Materials science ; Transformer oil ; Dielectric ; Moisture ; Composite material ; Insulation system ; Isothermal process ; Surface charge ; Transformer ; Electrical engineering ; Voltage ; Optoelectronics ; Chemistry ; Thermodynamics ; Physics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Lightning and Electromagnetic Phenomena
Authors: William H. Miller | Gayla M. Neumeyer | Matthew Schmidt | David H. Jonassen
Source:  (article, 2020-09-03)
Field: Materials Science — Materials Chemistry

A Problem Based Introduction To Nuclear Sciences Funded by the US Department of Energy (DE-FG07- Curriculum ; Problem-based learning ; Library science ; Computer science ; Medical education ; Mathematics education ; Engineering ethics ; Sociology ; Engineering ; Pedagogy ; Medicine ; Psychology Graphite, nuclear technology, radiation studies ; Biomedical and Engineering Education ; Nuclear and radioactivity studies
Authors: Yusuf Arslan
Source: Advances in Materials Science and Engineering (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

The Effects of Cryogenic Process on the AISI M2 Punch Materials and on the Hole Edge Geometry of the DIN EN 10111‐98 Sheet Metal Control Arm Parts In this study, not only the effects of cryogenic processing on the wear of AISI M2 punches but also the effects of punch wear on the hole edge geometry of DIN EN 10111‐98 sheet metal control arm parts were investigated. The hole geometry changes are generally associated with punch wear and process parameters. Piercing operations were performed using eccentric press on 2.5‐mm‐thick sheet metal control arm parts with circular and slot AISI M2 tool steel punches. The punches were traditionally heat treated. The others were cryogenically treated at −145°C in addition to the conventional heat treatment. Weight losses were measured for punch wear assessments; furthermore, SEM and OM images were analyzed. The hole edge geometries of the selected parts were measured with a contour measuring machine in the specified number of blanks. So, no damage was done to the products for measurements. The cryogenic process resulted in a significantly low amount of retained austenite and caused uniformly distributed thin carbide precipitates. Reduction of retained austenite and formation of fine carbide particles led to increase in hardness values. It was found that untreated circular punch wear weight losses were approximately 40% higher than those of cryogenically treated samples. The untreated slot punch change rate was about 106% higher than that of the cryogenically treated samples. The wear process during the punching was faster and greater for the untreated punches. Fatigue microcracks were more common at the cutting edge of the untreated punches. However, abrasive wear was generally observed in cryogenically treated punches. The edge geometry values in the circular holes were at least two times higher than those in the slot holes of untreated samples. At the end of the industrial piercing process, it was determined that the M2 tool steel punch wear rates were decreased by cryogenic treatment, and the size changes of the hole geometry of the punches of the DIN EN 10111‐98 control arm parts were more economic and with a better quality. Materials science ; Enhanced Data Rates for GSM Evolution ; Abrasive ; Punching ; Carbide ; Metallurgy ; Composite material ; Cryogenic treatment ; Sheet metal ; Metal ; Die (integrated circuit) ; Microstructure Metal Alloys Wear and Properties ; Metal and Thin Film Mechanics ; Tribology and Wear Analysis
Authors: Satoru SHIMOFUJI | Motoko Matsui | Yukari Muramoto | Hironori Moriyama | Reina Kato | Yoshiro HOKI | Haruhiko UEHIGASHI
Source: Japan Journal of Food Engineering (article, 2020-03-15)
Field: Chemistry — Analytical Chemistry

Machine Learning in Analyses of the Relationship between Japanese Sake Physicochemical Features and Comprehensive Evaluations We investigated the contributions of physicochemical features to a comprehensive evaluation of the Japanese sake known as ‘Junmai Ginjo’ by applying machine learning. We used 173 samples of the commercial Japanese sake. The sensory evaluation was conducted by 35 panelists. The panel conducted the evaluation of each sample using five statements for the comprehensive evaluation of the sample. General analysis, substance-related nucleic acid, volatile components and simplified analyses were measured as physicochemical analyses. We performed regression analyses using a multiple regression analysis (MRA), partial least squares regression (PLS) and machine learning employing a support vector machine (SVM), an artificial neural network (ANN), and random forest (RF). The results of these five analysis methods have demonstrated that machine learning (especially RF) provides comparable or higher prediction accuracy and better fitting than MRA. We also discuss the contribution of each physicochemical feature to the evaluation scores based on the regression coefficients obtained by MRA and the features’ importance obtained in RF. The analysis of the individual scores indicated that ethyl caproate and isoamyl acetate make large contributions to influence the sake evaluation. Artificial intelligence ; Psychology ; Computer science ; Machine learning Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Identification and Quantification in Food
Authors: Cátia Nara Tobaldini Frizon | Silvana Nisgoski
Source: Floresta e Ambiente (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

Color Parameters to Predict Moisture and Tannin Content in Yerba Mate Process This study evaluated color and reflectance spectra during industrial process of yerba mate leaves and tested the potential of these parameters to predict moisture and tannin content, to be applied as an auxiliary tool in quality control in industry. Two groups of yerba mate samples were evaluated: one grown under organic and another under conventional conditions. The results indicate an increase in lightness, loss of the green color, although less marked in the organic samples, an increase of yellow in both types of samples along processing due to moisture removal at high temperatures. For moisture content, reflectance curve can be applied with R2 ≥ 0.9, and for tannin content the sum of b* parameter and spectra in region 590-700 nm are recommended. Color parameters and reflectance spectra can be applied for a preliminary evaluation of moisture and tannin content in the yerba mate industry, contributing for the quality control of samples. Tannin ; Lightness ; Water content ; Reflectivity ; Moisture ; Food science ; Environmental science ; Mathematics ; Chemistry ; Artificial intelligence ; Computer science ; Optics ; Physics Heavy Metals in Plants ; Isotope Analysis in Ecology ; Agricultural and Food Sciences
Authors: Halime Ergün
Source: BioResources (article, 2020-12-07)
Field: Chemistry — Organic Chemistry

Segmentation of rays in wood microscopy images using the U-net model Rays are an important anatomical feature in tree species identification. They are found in certain proportions in trees, which vary for each tree. In this study, the U-Net model is adopted for the first time to detect wood rays. A dataset is created with images taken from the wood database. The resolution of microscopic wood images in tangential section is 640×400. The input image for training is divided into 32×32 image blocks. Each pixel in the dataset is labeled as belonging to the ray or the background. Then, the dataset is increased by applying scale, rotation, salt-and-pepper noise, circular mean filter, and gauss filter. The U-Net network created for ray segmentation is trained using the Adam optimization algorithm. The experimental results show that the ray segmentation accuracy in testing is 96.3%. Artificial intelligence ; Segmentation ; Pattern recognition (psychology) ; Filter (signal processing) ; Pixel ; Image segmentation ; Tree (set theory) ; Computer science ; Computer vision ; Scale (ratio) ; Feature (linguistics) ; Noise (video) ; Mathematics ; Image (mathematics) ; Physics Wood and Agarwood Research ; Industrial Vision Systems and Defect Detection ; Remote Sensing and LiDAR Applications
Authors: Jorge Daniel Mello-Román | Adolfo Hernández
Source: IEEE Access (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

KPLS Optimization With Nature-Inspired Metaheuristic Algorithms Kernel partial least squares regression (KPLS) is a technique used in several scientific areas because of its high predictive ability. This article proposes a methodology to simultaneously estimate both the parameters of the kernel function and the number of components of the KPLS regression to maximize its predictive ability. A metaheuristic optimization problem was proposed taking the cumulative cross-validation coefficient as an objective function to be maximized. It was solved using nature-inspired metaheuristic algorithms: the genetic algorithm, particle swarm optimization, grey wolf optimization and the firefly algorithm. To validate the results and have a reference measure of the efficiency of the nature-inspired metaheuristic algorithms, derivative-free optimization algorithms were also applied: Hooke-Jeeves and Nelder-Mead. The metaheuristic algorithms estimated optimal values of both of the kernel function parameters and the number of components in the KPLS regression. Metaheuristic ; Particle swarm optimization ; Firefly algorithm ; Computer science ; Kernel (algebra) ; Mathematical optimization ; Algorithm ; Parallel metaheuristic ; Artificial intelligence ; Meta-optimization ; Mathematics Spectroscopy and Chemometric Analyses ; Thermography and Photoacoustic Techniques ; Industrial Vision Systems and Defect Detection
Authors: Rémi Moulian | Johann Le Maître | Hélène Leroy | Ryan P. Rodgers | Brice Bouyssière | Carlos Afonso | Pierre Giusti | Caroline Barrère‐Mangote
Source: Processes (article, 2020-11-18)
Field: Chemistry — Analytical Chemistry

Chemical Characterization Using Different Analytical Techniques to Understand Processes: The Case of the Paraffinic Base Oil Production Line Mineral base oils are used to produce commercial lubricants and are obtained from refining vacuum residue. Lubricants are used to reduce friction in industry devices, so their viscosity is a key characteristic that needs to be optimized throughout the process. The purpose of this study is to show how global chemical characterization of samples from the base oil production chain can facilitate a better understanding of the molecular impacts of processing and their effect on macroscopic properties like viscosity. Eight different samples were characterized by different analytical techniques, including liquid chromatography and mass spectrometry techniques, to understand their chemical evolution through the different process units at the molecular level. Furthermore, a statistical treatment allowed for the identification of parameters that influence viscosity, mainly sulfur and polyaromatics content. This study demonstrates the importance and effectiveness of cross-checking results from different complementary analytical techniques to acquire valuable data on lubricating oil base samples. Base oil ; Characterization (materials science) ; Viscosity ; Process engineering ; Viscosity index ; Materials science ; Mass spectrometry ; Vacuum distillation ; Oil analysis ; Refining (metallurgy) ; Base (topology) ; Biochemical engineering ; Sulfur ; Residue (chemistry) ; Oil production ; Lubricant ; Chemistry ; Chromatography ; Petroleum engineering ; Nanotechnology ; Organic chemistry ; Metallurgy ; Mathematics ; Engineering ; Composite material Petroleum Processing and Analysis ; Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography
Authors: Chengli Guan | Yue Yang
Source: Food Science & Nutrition (article, 2020-01-08)
Field: Chemistry — Analytical Chemistry

Research of extraction behavior of heavy metal Cd in tea based on backpropagation neural network In order to meet the increasing demand for food and beverage safety and quality, this study focused on the application of a back propagation (BP) neural network to determine the leaching rate of heavy metal in tea to improve the scientific health of tea drinking. The evaluation index and target expectations have been determined based on the extraction experiment of heavy metal Cd in tea soaking, with 3 evaluation index values taken as input layer parameters and the heavy metal extraction rate taken as output layer parameter. Then, employ the sample data standardized by min-max linearization method to train and test the network model and get the satisfactory results, which showed that the constructed BP neural network expressed a fast convergence speed and the systematic error was as low as 0.0003509. Additionally, there was no significance between Cd leaching rate of experimental results and neural network model results by reliability testing with a correlation coefficient was .9895. These results revealed that the network model established possessed an outstanding training accuracy and generalization performance, which effectively reflected the extraction rate of heavy metal in tea soaking and improved the safety of tea drinking. Backpropagation ; Artificial neural network ; Extraction (chemistry) ; Artificial intelligence ; Computer science ; Chemistry ; Chromatography Heavy Metals in Plants ; Spectroscopy and Chemometric Analyses ; Geochemistry and Geologic Mapping
Authors: Jonah Flor V. Oraño | Elmer A. Maravillas | Chris Jordan G. Aliac
Source: International Journal on Advanced Science Engineering and Information Technology (article, 2020-09-20)
Field: Chemistry — Analytical Chemistry

Classification of Jackfruit Fruit Damage Using Color Texture Features and Backpropagation Neural Network An accessible and cost-effective technology for plant pest and disease diagnosis could be beneficial for the farmers to be equipped with the technical know-how in producing high quality and quantity of crop yields. This study presents an implementation of image processing and machine learning techniques in building a predictive model for a computer-based and a mobile-based classification of jackfruit fruit damages caused by pests (fruit borer and fruit fly) and diseases (rhizopus fruit rot and sclerotium fruit rot). First, captured images of healthy, and infected fruit were split into two datasets: 60% for training and 40% for the testing phase, wherein each set contains five different classes. Then pre-processing methods such as cropping, scaling, and median filtering were applied that would make these images appropriate for information extraction. Next, 13 Haralick texture features were extracted from color co-occurrence matrices generated from Hue, Saturation, and Luminance color components of pre-processed images. Through Pearson’s correlation approach, texture features such as uniformity, variance, sum average, sum entropy, and entropy were selected as significant descriptors for training the classification model using a backpropagation learning algorithm. Lastly, basic evaluation metrics such as accuracy, precision, sensitivity, specificity, and Cohen’s kappa were computed to determine the performance of the model in recognizing the type of fruit damage on an unforeseen dataset. As a result, an overall accuracy rate of 93.42% and a kappa value of 0.9146 were obtained. In addition, the developed application displays suggestions on the proper pest control or disease management of the identified damage on the fruit surface of jackfruit. Artificial intelligence ; Hue ; Backpropagation ; Pattern recognition (psychology) ; Artificial neural network ; Mathematics ; Computer science Spectroscopy and Chemometric Analyses
Authors: Leon Bichmann | Shubham Gupta | George Rosenberger | Lèon Kuchenbecker | Timo Sachsenberg | Oliver Alka | Julianus Pfeuffer | Oliver Kohlbacher | Hannes Röst
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-12-09)
Field: Chemistry — Spectroscopy

DIAproteomics: A multi-functional data analysis pipeline for data-independent-acquisition proteomics and peptidomics ABSTRACT Data-independent acquisition (DIA) is becoming a leading analysis method in biomedical mass spectrometry. Main advantages include greater reproducibility, sensitivity and dynamic range compared to data-dependent acquisition (DDA). However, data analysis is complex and often requires expert knowledge when dealing with large-scale data sets. Here we present DIAproteomics a multi-functional, automated high-throughput pipeline implemented in Nextflow that allows to easily process proteomics and peptidomics DIA datasets on diverse compute infrastructures. Central components are well-established tools such as the OpenSwathWorkflow for DIA spectral library search and PyProphet for false discovery rate assessment. In addition, it provides options to generate spectral libraries from existing DDA data and carry out retention time and chromatogram alignment. The output includes annotated tables and diagnostic visualizations from statistical post-processing and computation of fold-changes across pairwise conditions, predefined in an experimental design. DIAproteomics is open-source software and available under a permissive license to the scientific community at https://www.openms.de/diaproteomics/ . Computer science ; Pipeline (software) ; Data mining ; Pairwise comparison ; Software ; Data acquisition ; False discovery rate ; MIT License ; Process (computing) ; Artificial intelligence Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies