jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yinglai Huang | Shiyu Meng | Sung‐Wook Hwang | Kayoko Kobayashi | Junji Sugiyama
Source: BioResources (article, 2019-11-11)
Field: Chemistry — Analytical Chemistry

Neural network for classification of Chinese zither panel wood via near-infrared spectroscopy The wood grade used for Chinese zither panels is primarily determined through an artificial experience method, and the number of related practitioners is decreasing annually. In this study, a method using an improved BP neural network is proposed to assess the wood grade for Chinese zither panels. Abnormal spectral samples were first removed based on the Mahalanobis distance method. Normalization and Savitzky Golay second derivatization were applied to the remaining data set. According to the spectral peak, the spectral data were divided into three bands, which were applied to the model proposed in this paper, and the most critical spectral region for judging the wood grade of Chinese zither panels was obtained. Through principal component analysis, the appropriate feature variables were selected and applied to the experimental model for an analysis to reduce the calculated amount in the experiment. When the number of principal components was 6, the classification accuracy of unknown samples was 96.7%. Compared with the PLS model, the proposed model is more robust and accurate and has fewer losses. The experimental results indicated that the proposed method effectively identifies the wood grade used in Chinese zither panels. Mahalanobis distance ; Principal component analysis ; Normalization (sociology) ; Pattern recognition (psychology) ; Artificial neural network ; Near-infrared spectroscopy ; Biological system ; Feature (linguistics) ; Spectroscopy ; Computer science ; Materials science ; Artificial intelligence ; Physics ; Biology ; Optics Spectroscopy and Chemometric Analyses ; Cultural Heritage Materials Analysis ; Wood and Agarwood Research
Authors: Sang-Yun Yang | Hyung Gu Lee | Yonggun Park | Hyunwoo Chung | Hyunbin Kim | Se‐Yeong Park | In-Gyu Choi | Ohkyung Kwon | Hwanmyeong Yeo
Source: Journal of the Korean Wood Science and Technology (article, 2019-07-01)
Field: Chemistry — Organic Chemistry

Wood Species Classification Utilizing Ensembles of Convolutional Neural Networks Established by Near-Infrared Spectra and Images Acquired from Korean Softwood Lumber In our previous study, we investigated the use of ensemble models based on LeNet and MiniVGGNet to classify the images of transverse and longitudinal surfaces of five Korean softwoods (cedar, cypress, Korean pine, Korean red pine, and larch). It had accomplished an average F1 score of more than 98%; the classification performance of the longitudinal surface image was still less than that of the transverse surface image. In this study, ensemble methods of two different convolutional neural network models (LeNet3 for smartphone camera images and NIRNet for NIR spectra) were applied to lumber species classification. Experimentally, the best classification performance was obtained by the averaging ensemble method of LeNet3 and NIRNet. The average F1 scores of the individual LeNet3 model and the individual NIRNet model were 91.98% and 85.94%, respectively. By the averaging ensemble method of LeNet3 and NIRNet, an average F1 score was increased to 95.31%. Softwood ; Convolutional neural network ; Larch ; Pattern recognition (psychology) ; Artificial intelligence ; Artificial neural network ; Mathematics ; Computer science ; Materials science ; Botany ; Composite material Wood and Agarwood Research ; Industrial Vision Systems and Defect Detection ; Spectroscopy and Chemometric Analyses
Authors: Marzena Adamczyk | Małgorzata Grabarczyk
Source: Ionics (article, 2019-02-21)
Field: Chemistry — Electrochemistry

Simple, insensitive to environmental matrix interferences method of trace cadmium determination in natural water samples A simple, fast, and inexpensive anodic stripping voltammetric procedure for trace determination of cadmium in natural water samples containing high concentrations of surface-active substances was described. It was proved that interferences from the organic matrix such as surface-active substances could be removed by the addition of Amberlite XAD-7 resin to the analyzed sample prior to the voltammetric measurement. A renewable mercury film silver-based electrode (Hg(Ag)FE) was used as the working electrode. The following optimum conditions were found: 0.1 mol L−1 acetic acid, accumulation potential of − 0.8 V, and accumulation time of 30 s. The linear range of Cd(II) was observed over the concentration range from 5 × 10−8 mol L−1 to 6 × 10−5 mol L−1, with a correlation coefficient equal to 0.997. The detection limit of Cd(II) for preconcentration time of 30 s was 1.3 × 10−8 mol L−1. The proposed procedure was successfully tested for the detection of cadmium in different non-pretreated and non-deaerated real water samples. Cadmium ; Amberlite ; Detection limit ; Chemistry ; Anodic stripping voltammetry ; Matrix (chemical analysis) ; Mercury (programming language) ; Analytical Chemistry (journal) ; Trace Amounts ; Acetic acid ; Chromatography ; Electrode ; Electrochemistry Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Water Quality Monitoring and Analysis
Authors: Yongqiang Wang | Xia Zhang | Xiaolei Jiang | Yunjia Wang
Source: BioResources (article, 2019-01-07)
Field: Materials Science — Materials Chemistry

Effect of aging on material properties and partial discharge characteristics of insulating pressboard Pressboard samples with different aging degrees were prepared to study the material and discharge characteristics of pressboards with a shielding electrode. The step-up method was used for the subsequent experiment. The partial discharges of the pressboards with different aging degrees were compared and combined using Fourier transform infrared spectra, as well as electric field simulations and analysis. The microstructure of the aged pressboards was observed with scanning electron microscopy. The results showed that pre-discharge occurred mainly in the oil gap between the hemispherical electrode edge and pressboard. Aging the pressboard had no obvious effect on the discharge characteristics at the initial partial discharge stage. As the partial discharge developed, aging the pressboard resulted in a higher electric field intensity acting over a greater area, which led to a more intense discharge. Microscopic observation showed that aging and discharge destroyed the fibre, reduced the fibre width, and caused holes and fractures, which promoted further development of the discharge. The spectroscopic analysis showed that aging destroyed O-H functional groups and reduced the intermolecular forces and mechanical properties of the pressboard. Pressboard ; Partial discharge ; Materials science ; Composite material ; Microstructure ; Scanning electron microscope ; Electric field ; Electrode ; Fourier transform infrared spectroscopy ; Electrical engineering ; Voltage ; Chemistry ; Transformer ; Optics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Material Properties and Processing
Authors: Majid Gholmohammadi | Omid Sofalian | Mahdi Taheri | Alireza Ghanbari | Valiollah Rasoli
Source: Cellular and Molecular Biology (article, 2019-09-30)
Field: Chemistry — Organic Chemistry

Effect of drought stress on physiological traits and antioxidant activities in some olive cultivars Drought is important abiotic stress that negatively influences the growth and development of plants. Strong efforts are currently ongoing worldwide to improve olive production under adverse environmental conditions by extending genetic diversity to improve key agro-physiological and biochemical features through various breeding programs. This research was performed to evaluate the effect of drought stress on the changes of some physiological and biochemical traits in 20 commercial and promising olive genotypes under field conditions during 2015-2017. Fruit oil content as well as some of physiological traits and antioxidant activities under control and drought stress conditions were evaluated. The results of combined analysis of variance (ANOVA) for fruit yield and other measured traits showed that year, irrigation treatments, genotype main effects and their interactions were highly significant. In general, fruit yield, relative water content (RWC), oil content and total soluble proteins (TPs) showed a decreasing trend, whereas the electrolyte leakage, H2O2 content and activity of catalase (CAT), ascorbate peroxidase (APX) and peroxidase (POX) displayed an increasing trend in the tested olive genotypes during drought stress. A Principal component analysis (PCA)-based biplot demonstrated that stress tolerance index (STI) positively correlated with POX and TPs. Results also revealed a high level of genetic diversity in the tested olive genotypes, and among them, two commercial (Abou-satl) and promising genotypes (T2) responded better to drought by marinating a good balance for fruit yield and some of the antioxidant activities. These genotypes could be used in future programs to develop new olive cultivars with beneficial stress-adaptive traits. Cultivar ; Biplot ; Biology ; Horticulture ; Olea ; APX ; Irrigation ; Drought tolerance ; Abiotic stress ; Catalase ; Agronomy ; Genotype ; Antioxidant Edible Oils Quality and Analysis ; Essential Oils and Antimicrobial Activity ; Sunflower and Safflower Cultivation
Authors: M. C. Rao | Rashmi Laxmikant Malghan | Mervin A. Herbert | Shrikantha S. Rao
Source: Data in Brief (article, 2019-08-26)
Field: Materials Science — Materials Chemistry

Dataset on flank wear, cutting force and cutting temperature assessment of austenitic stainless steel AISI316 under dry, wet and cryogenic during face milling operation The contemporaneous examination focuses on the impact of spindle speed as well as an eco-pleasing cooling strategy in the midst of processing of AISI316. Investigations were executed at three different machining approaches viz. dry, wet and cryogenic (LN<sub>2</sub>) using cemented carbide inserts. Water dissolvable oil was used as a cutting fluid for flood cooling approach. The workpiece was processed under three distinctive cutting speeds i.e. 1000, 2000 and 3000 rpm however feed rate and depth of cut were kept consistent at 450 mm/min and 1mm separately. An exhaustive investigation on the cooling impacts of LN<sub>2</sub> technique on a segment of the significant machinability perspective, for instance, cutting force (Fx), insert wear, surface quality and processing temperature is delineated. The beforehand specified processing responses were documented and contrasted all together to demonstrate the reasonableness and achievability of LN<sub>2</sub> approach in examination with dry and wet machining methodology. The outcomes accomplished in the midst of the examination obviously settled the commonness of realizing LN<sub>2</sub> for achieving upgraded machinability inside a predetermined scope of process parameters. Machinability ; Machining ; Tool wear ; Cemented carbide ; Materials science ; Metallurgy ; Austenite ; Insert (composites) ; Mechanical engineering ; Drilling ; Carbide ; Composite material ; Engineering Metal Alloys Wear and Properties ; Advanced machining processes and optimization ; High Entropy Alloys Studies
Authors: Vali Rasooli Sharabiani | Araz Soltani Nazarloo | Ebrahim Taghinezhad
Source: Yüzüncü Yıl Üniversitesi Tarım Bilimleri Dergisi (article, 2019-03-28)
Field: Chemistry — Analytical Chemistry

Prediction of Protein Content of Winter Wheat by Canopy of Near Infrared Spectroscopy (NIRS), Using Partial Least Squares Regression (PLSR) and Artificial Neural Network (ANN) Models In this study to predict amount of protein in wheat, near infrared spectroscopy technique (NIRS) was used that is a non-destructive and fast observing method. Partial Least Squares Regression (PLSR) and Artificial Neural Network (ANN) methods were used to choose the spectral bands and the best models, respectively. To compare the efficiency of models Root-mean-square error (RMSE) and R2 were applied. The finest consequence by cascade forward back propagation (CFBP) was related to network structure of 8-8-1 with Levenberg-Marquardt (LM), and function of TANSIG-TANSIG-PURELIN (TANSIG-TANSIG-PURELIN (R𝑀𝑆𝐸=0.0289 and 𝑅2=0.9881 at 14 epochs). The consequences of estimation for ANN model (𝑅2=0.9881) was better than the PLSR model (𝑅2=0.9783). Therefore, according to the results, it can be said that NIRS has a high potential for predicting the amount of protein in wheat. Partial least squares regression ; Mean squared error ; Artificial neural network ; Biological system ; Canopy ; Mathematics ; Pattern recognition (psychology) ; Artificial intelligence ; Statistics ; Computer science ; Botany ; Biology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Christian Ieritano | J. Larry Campbell | W. Scott Hopkins
Source: International Journal of Mass Spectrometry (article, 2019-07-11)
Field: Chemistry — Spectroscopy

Unravelling the factors that drive separation in differential mobility spectrometry: A case study of regioisomeric phosphatidylcholine adducts POPC ; Chemistry ; Lipidomics ; Ion-mobility spectrometry ; Structural isomer ; Mass spectrometry ; Lipidome ; Adduct ; Molecular dynamics ; Analytical Chemistry (journal) ; Chemical physics ; Computational chemistry ; Chromatography ; Stereochemistry ; Organic chemistry ; Biochemistry ; Lipid bilayer ; Membrane Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies
Authors: Guangzhao Tian | Xiaona Li | Baohua Zhang | Jun Zhou | Baoxing Gu
Source: IEEE Access (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Comparative Study of Two Different Strategies for Determination of Soluble Solids Content of Apples From Multiple Geographical Regions by Using FT-NIR Spectroscopy Apple is one of the most popular fresh fruits with an extensive scope of regions owing to its nutrition and sweet in flavor. There is a large difference in the composition of the fruits growing in varying regions because of the variation in the growing regions, such as temperature, soil nutrients, etc. As a result, it is of significance to decrease the impact of region variability on the measurement of soluble solids content (SSC) in apples. To lessen the impact of region variability and enhance the predictive ability of on the model, our manuscript compared the performance of the two multi-region prediction models for the estimation of SSC in apples from multiple geographical regions. One multi-region prediction model was developed by merging SSC values and spectral data of all samples from multiple regions. The other multi-region prediction model was built for the determination of SSC in combination with region discriminant, model search strategy, and single-region models. Support vector machine (SVM) was applied to establish the model for discriminating the apples from multiple geographical regions. It was found that the region discriminant model achieved great results, with the classification accuracy of 99.52%. By comparing and analyzing the two multi-region prediction models, the optimal multi-region prediction model was obtained. Finally, to decrease the irrelevant spectral information and reduce the computational cost, the multi-region SSC prediction model was optimized in combination with various spectral preprocessing methods (multiple scatter correction (MSC), standard normal variate (SNV), and first derivative (FD) correction) and variable selection methods (Monte Carlo uninformative variables elimination (MC-UVE), competitive adaptive reweighted sampling (CARS), and random frog (RF)). The overall results denoted that it was more accurate to estimate SSC in apples from the different geographical regions by using the multi-region models based on the region discriminant model in combination with SNV preprocessing algorithm and MC-UVE variable selection algorithm, and the prediction accuracy preceded the single-region models. Support vector machine ; Preprocessor ; Computer science ; Discriminant ; Mathematics ; Pattern recognition (psychology) ; Biological system ; Artificial intelligence ; Biology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Phytochemicals and Antioxidant Activities
Authors: Laima Česonienė | Gediminas Masaitis | Gintautas Mozgeris | Sébastien Gadal | Daiva Šileikienė | R. Karklelienė
Source: Journal of Elementology (article, 2019-01-25)
Field: Chemistry — Analytical Chemistry

Visible and near-infrared hyperspectral imaging to describe properties of conventionally and organically grown carrots This paper discusses the potential of visible and near-infrared hyperspectral imaging to describe properties of conventionally and organically grown carrots. 140 samples of four Lithuanian carrot cultivars were scanned using a VNIR400H hyperspectral camera, capable of covering the spectral range of 400-1000 nm with a sampling interval of 0.6 nm. Half of the samples were grown under organic farming conditions and the remainder under conventional conditions. Chemical and electro-chemical properties, i.e. nitrate content, acidity, reduction potential and electrical conductivity, were determined for the carrot root samples using conventional methods of chemical investigations. The ability to separate organically and conventionally grown samples on the basis of spectral data was examined by applying estimations of Jeffries-Matusita distances and linear discriminant analysis. Opportunities to predict the chemical and electro-chemical properties of samples applying the partial least squares regression and the spectral data as predictors were also investigated. The overall classification accuracy of samples of organically and conventionally grown carrot cultivars when applying linear discriminant analysis was in the range of 94.4-100% and the Jeffries-Matusita distances were in the range of 1.98-2.00. There was good prediction potential using the partial least squares regression for electrical conductivity (R 2 = 0.88) and reduction potential (R 2 = 0.81), better than moderate for nitrate content (R 2 = 0.77) and moderate for acidity (R 2 = 0.68) using hyperspectral reflectance data of carrot captured under laboratory conditions. Both the separation ability and prediction potential were higher if taking into account the cultivar. Partial least squares regression ; Hyperspectral imaging ; Linear discriminant analysis ; Chemistry ; Analytical Chemistry (journal) ; Linear regression ; Mathematics ; Remote sensing ; Environmental chemistry ; Statistics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Julie Cautereels | Nils Van Hee | Sneha Chatterjee | Christian Van Alsenoy | Filip Lemière | Frank Blockhuys
Source: Journal of Mass Spectrometry (article, 2019-10-25)
Field: Chemistry — Spectroscopy

QCMS<sup>2</sup> as a new method for providing insight into peptide fragmentation: The influence of the side‐chain and inter–side‐chain interactions The identification of peptides and proteins from tandem mass spectra is a difficult task and multiple tools have been developed to aid this identification. We present a new method called quantum chemical mass spectrometry for materials science (QCMS<sup>2</sup> ), which is based on quantum chemical calculations of bond orders, reaction, and transition-state energies at the DFT/B3LYP/6-311+G* level of theory. The method was used to describe the fragmentation pathways of five X-His-Ser tripeptides with X = Asn, Asp, Glu, Ser, and Trp, thereby focusing on the influence of the side chain and inter-side-chain interactions on the fragmentation. The main features in the mass spectra of the five tripeptides were correctly reproduced, and a number of fragments were assigned to fragmentations involving the side chain and the influence of inter-side-chain interactions. Product ion spectra were recorded to evaluate the capabilities and limitations of QCMS<sup>2</sup> and a number of conventional tools. Chemistry ; Side chain ; Tripeptide ; Fragmentation (computing) ; Tandem mass spectrometry ; Quantum chemical ; Mass spectrometry ; Collision-induced dissociation ; Mass spectrum ; Tandem ; Computational chemistry ; Peptide ; Molecule ; Chromatography ; Organic chemistry ; Polymer Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications
Authors: Darwison Darwison | Syukri Arief | Hairul Abral | Ariadi Hazmi | Mohd Hafizi Ahmad | Eka Putra Waldi | Rudy Fernandez
Source: Indonesian Journal of Electrical Engineering and Computer Science (article, 2019-12-01)
Field: Materials Science — Materials Chemistry

A leakage current estimation based on thermal image of polymer insulator Polymer insulators tend to fail because of the climatic and environmental conditions. The failure occurs when the surface of insulator is contaminated by sea salt or cement dust which lead to partial discharge (PD). Leakage currents will increase by PD that causes deterioration of insulation. To predict the insulation failures, an adaptive neurofuzzy inference system (ANFIS) method using initial color detection processes are proposed to estimate the leakage currents based on the polymer insulator thermal images (infrared signature). In this study, the sodium chloride and kaolin are used as pollutants of the polymer insulator according to IEC 60507 standards. Then, the insulator is tested in the laboratory using AC high voltage applied at 18 kV where the temperature detection is controlled at 26° C and 70% RH (relative humidity). The percentage of colors (Red, Yellow, and Blue) from the thermal image is measured using the color detection method. Correspond to the color percentage, the ANFIS method predicts leakage currents from polymer insulators. Furthermore, this system interprets measured data from insulators that need to be categorized as Safe, Need Maintenance or Harmful. The final application of the system can be a non-contact tool to predict the polymer insulators used by technicians in the field. Insulator (electricity) ; Leakage (economics) ; Materials science ; Polymer ; Relative humidity ; Composite material ; Thermal ; Environmental science ; Nuclear engineering ; Optoelectronics ; Engineering ; Meteorology ; Physics High voltage insulation and dielectric phenomena ; Image Enhancement Techniques ; Fire Detection and Safety Systems