jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Behnam Foroozani | Hossein Bagherpour | K H Zaboli
Source: Food Science and Technology (article, 2020-04-01)
Field: Chemistry — Analytical Chemistry

Feasibility of Using NIR and PLSR Model for Prediction of Wheat Grains protein and moisture content and Mapping Quality Yield Map &amp;lt;p&amp;gt;&amp;lt;span&amp;gt;Protein as an important ingredient in wheat plays main role in the production of wheat&amp;rsquo;s products. &amp;lt;/span&amp;gt;&amp;lt;span&amp;gt;&amp;lt;span&amp;gt;Because of the production of various products from wheat&amp;lt;/span&amp;gt;&amp;lt;/span&amp;gt;&amp;lt;span&amp;gt;, fast and online measuring of wheat grain quality is very important&amp;lt;span&amp;gt;&amp;nbsp;&amp;nbsp; &amp;lt;/span&amp;gt;to control of flour production process and choosing an appropriate variety. Also in precision farming, combination of quantity and quality maps lets farmers to evaluate and control the plant production, well. Therefore, the purpose of this study was to evaluate the use of infrared spectroscopy in reflectance mode to predict protein and moisture content of wheat grain.&amp;lt;/span&amp;gt;&amp;lt;span&amp;gt; &amp;lt;/span&amp;gt;&amp;lt;span&amp;gt;In this study about 108 samples were collected from three varieties&amp;lt;span&amp;gt;&amp;nbsp;&amp;nbsp; &amp;lt;/span&amp;gt;namely Mihan, Gazkojhen and Pishgam in the region near Hamedan province in Iran. Grain proteins content were measured with a DA7200 near infrared spectroscopy apparatus. This spectroscopy collects reflectance over a wavelength range of 650-1650 nm in 5 nm increments. Results show that the best models were obtained using the PLSR method and its preprocessing &amp;lt;/span&amp;gt;&amp;lt;span&amp;gt;SG+SNV+D1&amp;lt;/span&amp;gt;&amp;lt;span&amp;gt; and MA+D2+SNV for protein and moisture content, respectively. The correlation coefficient (R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;), root mean square error of prediction (RMSEP) and Standard Deviation Ratio (SDR) were obtained 0.84, 0.835 and 2.54 for protein content, whereas 0.96, 0.994 and 5.34 for moisture content, respectively. Results showed that there are no significant differences among proteins of three varieties. But the sampling places have a significant effect on the protein content at the significant level of 5%. These results indicated that the infrared spectroscopy method is an efficient method and has a strong potential for rapid detection of protein and moisture content of wheat grains &amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt;\n&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;&amp;lt;span&amp;gt;Keywords: &amp;lt;/span&amp;gt;&amp;lt;/strong&amp;gt;&amp;lt;span&amp;gt;Non-destructive methods, Wheat, Near infrared, Protein&amp;lt;/span&amp;gt;&amp;lt;span&amp;gt;.&amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt; Yield (engineering) ; Water content ; Moisture ; Quality (philosophy) ; Environmental science ; Mathematics ; Chemistry ; Materials science ; Engineering ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Nitin Bhardwaj | P. K. Muhammed Jaslam | Jagriti Bhatia | Bhupender Parashar | Salinder -
Source: The Journal of Animal and Plant Sciences (article, 2020-12-15)
Field: Chemistry — Analytical Chemistry

NEURAL NETWORK AUTOREGRESSION AND CLASSICAL TIME SERIES APPROACHES FOR RICE YIELD FORECASTING This study deals with the application of classical time series models such as double moving average method, exponential smoothing (Brown’s double exponential smoothing method and Holt’s double exponential smoothing method) along with neural network autoregression model for rice yield prediction in Karnal district of Haryana (India). The district annual time series data on rice productivity were divided into the training data set from 1980-81 to 2013-14 and the test data set from 2014-15 to 2019-20. Among the models fitted, the neural network model had a significantly lower absolute error and the root mean square error. During the test period, a markedly low mean absolute percent deviation was observed for the moving average (7x7) model. However, Future forecast values by this model surpass a fair threshold for rice yield in the country. Considering both the error analysis and model validation results it is found that neural network forecasting models are best fit for forecasting of rice yield in Karnal district followed by Holts double exponential method. Keywords: Artificial neural network, Exponential smoothing, Moving average, Crop yield forecasting Autoregressive model ; Artificial neural network ; Time series ; Series (stratigraphy) ; Yield (engineering) ; Bayesian vector autoregression ; Econometrics ; Mathematics ; Biological system ; Computer science ; Statistics ; Artificial intelligence ; Biology ; Physics ; Thermodynamics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: E. Martinez
Source: Boletín das Ciencias (article, 2020-10-31)
Field: Chemistry — Physical and Theoretical Chemistry

A Táboa Periódica na era do Machine Learning Cando se me propuxo escribir este capítulo acerca do que significa para min, un químico que desenvolve modelos e traballa con computadores, a táboa periódica, non sabía moi ben por onde empezar. Con todo, pronto descubrín que Mendeleev, deseñador da táboa periódica no ano 1869 o que fixo foi racionalizar, ordenar e mesmo predicir os elementos químicos, accións que tamén leva a cabo un químico teórico cando pretende entender por exemplo as reaccións químicas, como é o meu caso. Por outra banda, a táboa periódica é como unha ecuación matemática que condensa moito do coñecemento existente sobre os elementos químicos. A maioría dos químicos e físicos teóricos que traballan con átomos ou moléculas non fan nin máis (nin menos) que iso: constrúen modelos (escritos na linguaxe matemática) que codifican coñecemento e teñen a capacidade de predicir comportamentos futuros. Humanities ; Philosophy ; Physics ; Psychology History and advancements in chemistry
Authors: Chun Ye | Jizhong Liu | Ying Liu | Li Yanda | Zhongsheng Cao | Huang Huang
Source: Research Square (preprint, 2020-08-25)
Field: Chemistry — Analytical Chemistry

Monitoring the Nitrogen Nutrition Status of Rice Plants Using Spectral and Image Technologies Abstract Background: We aimed to investigate methods to estimate the nitrogen (N) nutrition status of rice plants using data obtained using a digital camera and a spectroradiometer. The overall aim was to compare the advantages and potential of image technology and spectral technology to monitor rice N indexes accurately, inexpensively, and in real time to optimize fertilization strategies. We conducted field trials of rice plants grown with different levels of N fertilizer in 2018 to 2019. Spectral information and images of the rice canopy were obtained at the jointing stage and the booting stage. Various image and spectral characteristic parameters were selected to construct models to estimate rice N status. Results: The determination coefficients of the models constructed using the ratio vegetation index (RVI[800,720]) and cover canopy (CC) as dependent variables were highly significant. The jointing stage was the best observation period. Among the models using spectral parameters, those constructed using RVI[800,720] to estimate rice N indexes had the highest coefficient of determination (R2) values (0.79, 0.60, and 0.61 for the models to estimate leaf area index(LAI), aboveground biomass(AGB), and plant N accumulation(PNA), respectively) (P &lt; 0.01). Among the models using image data, those using CC to predict rice N indexes showed the highest R2 values (0.76, 0.66, and 0.73 for the models to estimate LAI, AGB, and PNA, respectively) (P &lt; 0.01). The model using the spectral parameter RVI[800,720] had a good fit and stability in estimating leaf area index (LAI; R2 = 0.7989, root mean square error (RMSE) = 0.4427, relative RMSE (RRMSE) = 11.45), and the model using the image parameter CC had a good fit in predicting plant nitrogen accumulation (PNA; R2 = 0.6768, RMSE = 11.6925 g·m-2, RRMSE = 121.78). Conclusions: Spectral and image parameters can be used as technical parameters to estimate biomass. The spectral parameter RVI[800,720] can be used to accurately estimate LAI, and the image parameter CC can be used to accurately estimate PNA, although its stability is poor. These technical parameters can be used for fast and non-destructive monitoring of rice plant biomass. Nitrogen ; Environmental science ; Image (mathematics) ; Agricultural engineering ; Agronomy ; Business ; Computer science ; Computer vision ; Chemistry ; Biology ; Engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Kohei Arai | Eisuke Kisu | Kazuhiro Nagafuchi
Source: International Journal of Advanced Computer Science and Applications (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

Barley Quality Estimation Method with UAV Mounted NIR Camera Data based on Regressive Analysis Barley quality estimation method with Unmanned Aerial Vehicle: UAV based Near Infrared: NIR camera data based on regressive analysis is proposed. The proposed method allows to predict barley quality, anthocyanin, β-glucan and water contents in the harvested “Daishimochi” of barley grains before the harvest. The prediction method proposed here is based on regression analysis with the Near Infrared: NIR camera data mounted on UAV which allows to estimate barley quality, anthocyanin, β-glucan and water contents in the harvested “Daishimochi” of barley grains before the harvest.. This is the first original attempt for the prediction in the world. Through experiment, it is found that water content (%), Anthocyanin content (mg Cy3G/100 g), Anthocyanin content (mg Cy3G/100 g: which corresponds to dry matter), and barley β-glucan (%) can be predicted before the harvest with high R2 value (more than 0.99). Therefore, farmers can control fertilizer and water supply for improvement of the Daishimochi barley grain quality. Anthocyanin ; Computer science ; Near-infrared spectroscopy ; Quality (philosophy) ; Environmental science ; Agricultural engineering ; Horticulture ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Н. П. Супрун | М. Л. Рябчиков | І. О. Іванов
Source: Bulletin of the Kyiv National University of Technologies and Design Technical Science Series (article, 2020-10-14)
Field: Materials Science — Polymers and Plastics

МОДЕЛЮВАННЯ ПРОЦЕСУ ТЕРТЯ В ТЕКСТИЛЬНИХ МАТЕРІАЛАХ Create a model for determining the coefficient of friction of textile materials to identify the main factors influencing the process of friction, taking into account the structural and mechanical properties of materials. Modeling of friction process in textile materials as a combination of adhesive and elastic phenomena. Roughness of solid bodies and the main parameters of roughness, such as the height of micro-irregularities, their pitch, sharpening, etc. described in many standards and scientific papers. However, the modeling of the friction process in such systems is very complicated due to the irregularity of distribution of microroughness. The analysis of literature data showed that the surface roughness of textile materials is an important and effective factor in predicting the tactile properties of products for various purposes. Estimation of surface roughness is usually carried out using subjective and objective methods, and the latter can be contact and non-contact. The paper develops a model for determining the coefficient of friction of textile materials to identify the main factors influencing the friction process, taking into account the structural and mechanical properties of materials. Friction force is presented as a combination of two main factors. The first is the elastic resistance to deformation, the second is the adhesive resistance to compression of the structural elements of the material. The main parameters influencing the coefficient of friction of textile fabrics - modulus of elasticity of structural elements, their geometrical parameters - surface density of textile material, linear density of structural elements are established. The obtained results allow to qualitatively predict the friction forces of a textile material with known parameters of its structural elements, as well as to normalize these parameters to create materials with specified friction indices. The obtained results make it possible to select the threads that form the textile material, according to the values of the modulus of elasticity, thickness, location density to ensure the minimum friction force. Materials science ; Surface finish ; Surface roughness ; Composite material ; Contact area ; Elastic modulus ; Deformation (meteorology) ; Textile ; Material properties ; Elasticity (physics) ; Adhesive Textile materials and evaluations
Authors: Zachary Spielman | Johanna Oxstrand | Stacey Whitmore
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Smart Planning Support for Nuclear Power Plant Work Planners Operation and Maintenance costs of a nuclear power plant account for 60% of overall plant costs due to the nuclear industry?s risk-adverse culture (World Nuclear Association, 2020). The industry relies on highly conservative estimates to avoid risk and brute force reactions to manage the unexpected and keep a plant running safely. This approach has kept plants operating safer than any other power source available over the last six decades (Ritchie, 2020). However, these conservative methods have cost the nuclear industry in market competitiveness. The solution? Transform the conservative approaches to accurate, efficient, and still safe data-driven approaches that leverage advanced technologies available in the digital age of operation. This project endeavored to identify two key aspects to successfully incorporate historical work execution data. First, where in the work management process is that information most pertinent? Next, what variables are key to informing the best strategy for organizing the upcoming work? The Smart Planner uses data collected by dynamic work instructions to provide a basis for suggesting requirements of similar, upcoming work packages. Further, it curbs inefficiencies of the work management process by analyzing the likelihood of discovery work, offering suggestions to build a contingency package. All this taking place as the work planner builds work packages that meet the predetermined scope of work to be performed. Work (physics) ; Nuclear power plant ; Nuclear power ; Computer science ; Architectural engineering ; Systems engineering ; Engineering ; Mechanical engineering ; Physics ; Biology ; Ecology Graphite, nuclear technology, radiation studies ; Advanced Data Processing Techniques ; Risk and Safety Analysis
Authors: Marcel Polling | Chen Li | Lu Cao | Fons J. Verbeek | Letty A. de Weger | Joost Willemse | Hugo de Boer | Barbara Gravendeel
Source:  (preprint, 2020-09-11)
Field: Chemistry — Analytical Chemistry

Automatic pollen image identification using neural networks increases accuracy for hay fever forecasts Hay fever ; Identification (biology) ; Artificial neural network ; Artificial intelligence ; Pollen ; Image (mathematics) ; Computer science ; Hay ; Pattern recognition (psychology) ; Machine learning ; Computer vision ; Biology ; Botany ; Immunology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Junior Souza | Vanessa Weber | Ariadne Barbosa Gonçalves | Marco Álvarez | Marney Pascoli Cereda | Wesley Nunes Gonçalves | Valguima Odakura | Hemerson Pistori
Source:  (article, 2020-10-07)
Field: Chemistry — Analytical Chemistry

Viable Yeast Identification using Bag of Visual Words in Colored images In this research it is reported a system to automate the process of identification of viable yeasts whose population control is a crucial task in the ethanol production process. The identification and counting of yeasts made by human vision under a light microscope, is repetitive and susceptible to errors. We used computer vision techniques such as BoVW, Color Coherence Vectors (CCV), Color Moments (CM), Bag-of-Color (BoC) and Opponent Color (OpC) were applied for extracting characteristics that were classified by the Naive Bayes, KNN, SVM and J48 algorithms in 2614 images of yeasts separated into three classes: viable, non-viable and background. The results were analyzed using software R, which in the ANOVA test resulted in a p value equal to 2e􀀀16 indicating a significant difference between the techniques. The OPC with SVM classifier showed the highest performance using the PCC Percent Correct Classification metric, about 95% compared to other techniques. Artificial intelligence ; Naive Bayes classifier ; Support vector machine ; Pattern recognition (psychology) ; Computer science ; Colored ; Computer vision ; Population ; C4.5 algorithm Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: Md. Khalilur Rahman Khan
Source: Saudi Journal of Engineering and Technology (article, 2020-11-09)
Field: Materials Science — Polymers and Plastics

Investigating the Mass Variation of Jute Spun Yarn Produced by Modified Spinning Frame Evenness is one of the main criteria characterizing quality for any staple spun yarn. Variation in yarn mass have significant influences like the other quality parameters of jute spun yarn for being applied in the diversified items. However, jute flyer spinning frame had been modified in the drafting zone to improve the quality parameters of jute yarn. In this study, mass variation (unevenness) results of jute yarn had been investigated by producing two types of jute yarn counts (i.e., 6.5 and 10 lbs/spy) for both the existing and the modified spinning frame. Hence, the results of jute yarn unevenness values had been compared between the modified and the existing spinning frame. Results revealed that mass variations of jute spun yarn had been decreased for the modified spinning frame for the both counts (i.e., 6.5 lbs/spy and 10 lbs/spy). Finally, it was remarked that the modified spinning frame produced a better quality of jute yarn in terms of mass variation compared to that of the existing spinning frame. Yarn ; Spinning ; Variation (astronomy) ; Frame (networking) ; Mathematics ; Composite material ; Materials science ; Pulp and paper industry ; Engineering ; Physics ; Mechanical engineering ; Astrophysics Textile materials and evaluations
Authors: Gabriela Daniels | Slobodanka Tamburić | Sergio Benini | Mattia Sarvardi | Tracey Sanderson | Jane Randall
Source: Institutional Research Information System (Università degli Studi di Brescia) (article, 2020-10-23)
Field: Materials Science — Polymers and Plastics

Artificial intelligence and sensory assessment of hair assembly features: a combined approach An explorative comparison of the sensitivity of human perception with that of classification algorithms for machine learning when applied to human hair tresses. Artificial intelligence ; Computer science ; Sensory system ; Psychology ; Cognitive psychology Textile materials and evaluations ; 3D Shape Modeling and Analysis ; Computer Graphics and Visualization Techniques
Authors: Maohua Xiao | Anna Ignaczak | Kai Wen | Yue Zhu | Yilidaer Yiliyasi | D Wang | Q Miao | X Fan | W Yang | M Xiao | W Zhou | Y Lei | Z Liu | J Ouazri | W Wen | Z Fan | D Karg | V Bujoreanu | B Horga | Drgan | V Rai | A Mohanty | B Sreejith | A Verma | A Srividya | Z Feng | M Liang | F Chu | Z Peng | W Peter | L Law | J Kim | W Liew | D Yao | G Cai | H Liu | J Ali | N Fnaiech | L Saidi | N Huang | Z Shen | S Long | J Lin | Z He | J Dybaa | R Zimroz | J Zheng | J Cheng | Y Yang | X Zhang | J Zhou | J Zhang | R Yan | R Gao | Y Liang | X Xue | Y Xu | A Tabrizi | L Garibaldi | A Fasana | Z Chen | N Gao | W Sun | C Shen | Q He | F Kong | A Raj | N Murali | Q Chen | M Torres | M Colominas | G Schlotthauer
Source: International Journal of Mechatronics and Applied Mechanics (article, 2020-07-01)
Field: Materials Science — Polymers and Plastics

RESEARCH ON FAULT FEATURE EXTRACTION OF ROLLING BEARING BASED ON IMPROVED CEEMDAN As a core component of machinery, rolling bearings play an important role in modern industry. In order to avoid unpredictable accidents and undesired downtime costs, fault diagnosis techniques for rolling bearings are widely used in the industrial field. Although there are many troubleshooting methods, such as empirical mode decomposition (EMD), collective empirical mode decomposition (EEMD) and wavelet transform, their disadvantages are very obvious. For example, the eigenmode function (IMF) generated by EEMD always contains residual noise. Furthermore, adding different types of Gaussian noise to the signal will result in a different number of IMFs, which makes the average difficult. In order to eliminate these shortcomings, this paper proposes an improved combination of wavelet transform and full set empirical mode decomposition and adaptive noise (CEEMDAN). Simulation experiments were used to illustrate the effectiveness of CEEMDAN. To further illustrate the proposed method, the improved CEEMDAN is applied to fault diagnosis motor rolling bearings. In addition, two indicators, signal-to-noise ratio (SNR) and root mean square error (RMSE), are established to evaluate the fault diagnosis capability of the proposed method. The analysis results show that the method can improve the fault diagnosis ability of rolling bearings. Bearing (navigation) ; Fault (geology) ; Feature extraction ; Extraction (chemistry) ; Pattern recognition (psychology) ; Computer science ; Feature (linguistics) ; Artificial intelligence ; Geology ; Seismology ; Chemistry ; Philosophy Textile materials and evaluations
Authors: Варвара Алексеевна Егорова | Mikhail Evgenievich Zhukovsky | Олег Сергеевич Косарев | И. А. Тараканов
Source: Keldysh Institute Preprints (article, 2020-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

On modeling of the radiative electromagnetic field in polydisperse media of a closed-cell structure .. .. , .. , .. , .. - -2020 .. , .. , Radiative transfer ; Electromagnetic field ; Physics ; Field (mathematics) ; Classical mechanics ; Computational physics ; Optics ; Mathematics ; Quantum mechanics ; Pure mathematics Advanced Physical and Chemical Molecular Interactions ; Scientific Research and Discoveries ; Fusion and Plasma Physics Studies
Authors: Ayman Y. Al-Rawashdeh | Ali Dalabeeh | Abdallah Al-Zeyod | Ashraf Samarah | Ghazi Qaryouti | Omar Albarbarawi
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2020-08-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The tooth factor effect on the harmonics of large electrical machines In the current study, the general mathematical model for the calculation and analysis of asynchronous systems and transient cases in asynchronous synchronous large electrical machines was developed. The theory of magnetic fields in the teeth's circuits with a smooth surface of the rotor was used and at the same time, high harmonics of magnetic fields and its effect on the transient cases was also calculated. Performance curves were investigated using Matlab codes and evaluated under different values of factor. The results confirmed the possibility of improving the noise harmonics on the sinusoidal wave form, which is reflected on the machines starting. Harmonics ; Factor (programming language) ; Materials science ; Electrical engineering ; Computer science ; Engineering ; Voltage Magnetic Properties and Applications ; Electric Motor Design and Analysis ; Induction Heating and Inverter Technology