jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xuanhe Zhao | Xin Pan | Weihong Yan | Shengwei Zhang
Source: Scientific Reports (article, 2022-05-30)
Field: Chemistry — Analytical Chemistry

Visible-NIR hyperspectral classification of grass based on multivariate smooth mapping and extreme active learning approach Grass community classification is the basis for the development of animal husbandry and dynamic monitoring of environment, which has become a critical problem to further strengthen the intelligent management of grassland. Compared with grass survey based on satellite remote sensing, the visible near infrared (NIR) hyperspectral not only monitor dynamically in a short distance, but also have high dimensions and detailed spectral information in each pixel. However, the hyperspectral labeled sample for classification is expensive and manual selection is more subjective. In order to solve above limitations, we proposed a visible-NIR hyperspectral classification model for grass based on multivariate smooth mapping and extreme active learning (MSM-EAL). Firstly, MSM is used to preprocess and reconstruct the spectrum. Secondly, by jointing XGBoost and active learning (AL), the advanced samples with the largest amount of information are actively selected to improve the performance of target classification. Innovation lies in: (1) MSM global enhanced preprocessing spectral reconstruction algorithm is proposed, in which isometric feature mapping is effectively applied to the grass hyperspectral for the first time. (2) EAL framework is constructed to solve the issue of high cost and small number for hyperspectral labeled samples, at the same time, enhance the physical essence behind spectral classification more intuitively. A field hyperspectral collection platform is assembled to establish nm resolution visible-NIR hyperspectral dataset of grass, Grass1, containing 750 samples, which to verify the effectiveness of the model. Experiments on the Grass1 dataset confirmed that compared with the full spectrum, the time consumption of MSM was reduced by 9.471 s with guaranteed overall accuracy (OA). Comparing EAL with AL, and other classification algorithms, EAL improves OA 22.2% over AL, and XAL has the best performance value on Kappa, Macro, Recall and F1-score, respectively. Altogether, the lightweight MSM-EAL model realizes intelligent and real-time classification, providing a new method for obtaining high-precision inter group classification of grass. Hyperspectral imaging ; Preprocessor ; Computer science ; Remote sensing ; Multivariate statistics ; Artificial intelligence ; Pattern recognition (psychology) ; Feature selection ; Machine learning ; Geography Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Roopa Jayasingh. J | B. Nagajayanthi | T. Mary Neebha | Martin Sagayam | Ahmed A. Elngar
Source: Research Square (preprint, 2022-06-28)
Field: Chemistry — Analytical Chemistry

New calculation of calorie content and determining nutritional level from day-to-day intake of food using Image Processing Abstract One of the most urgent necessities of all individuals is food. Nowadays people mainly focus on diet because of diabetes, and obesity problems. When we have a healthy diet, it leads to a healthy life span. So, this paper gives a solution for this type of common problem. In this paper, we calculate nutritionally and calorie content utilizing image processing, display in the food segmentation and classification process, etc. This is very useful for dieticians despite the reality Ordinary people can benefit from this as well by manipulating their routine food patterns. Calorie ; Low calorie ; Day to day ; Food science ; Food intake ; Animal science ; Computer science ; Medicine ; Biology ; Economics ; Operations management ; Endocrinology Spectroscopy and Chemometric Analyses
Authors: Ming Zhang | Fuju Ye | Hongrui Tan | Sisi Luo | Haoyang Cui | Lei Chen
Source: Micromachines (article, 2022-11-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Reprogrammable Metasurface Controlled by 2D Thermal Fields The combination of thermal field sensing and microwave operation is an innovative topic in metamaterials. Although there exists research on modulating electromagnetic waves by controlling each column of the metasurface elements for programmable metasurfaces, the regulation is not flexible. In view of this, this paper proposes a metasurface based on distributed thermal sensing that can be independently modulated by each element. In this paper, the metasurface adopts a 1-bit coding metasurface, which is combined with PIN diodes to modulate the phase response. The voltage control circuit feeds back the change in the thermistors to the switching state of the PIN diode. Each metasurface unit contains thermistors, which are used to sense thermal stimulation and can be independently modulated. The metasurface composed of these elements can feel the field generated via heat energy. We can control electromagnetic waves by controlling this field. In order to prove the feasibility of this scheme, a metasurface sample of 8 × 8 elements was designed. Three patterns were used for the design, fabrication, and measurement of the samples. Meanwhile, printed circuit board (PCB) technology was applied. The results show that the simulated results are highly consistent with the experimental results, which verifies that this scheme is practicable. Thermistor ; Diode ; Microwave ; Metamaterial ; PIN diode ; Thermal ; Materials science ; Optoelectronics ; Fabrication ; Voltage ; Printed circuit board ; Electronic engineering ; Electrical engineering ; Computer science ; Engineering ; Physics ; Telecommunications Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Kazushige Murayama | Hikaru Kobori | Yoichi Kojima | Kenji Aoki | Shigehiko Suzuki
Source: Journal of Wood Science (article, 2022-01-22)
Field: Materials Science — Biomaterials

Prediction of cellulose nanofibril (CNF) amount of CNF/polypropylene composite using near infrared spectroscopy Abstract The final goal of this study is to establish a classification method of cellulose nanofibril (CNF)/plastic composites such as their CNF amount, CNF types, and resin types, which are expected to progress the commercialization in the future, using near infrared (NIR) spectroscopy. To achieve this goal, NIR spectra of injection and film samples with different types and addition ratios of CNFs in CNF/polypropylene (PP) composites were measured and analyzed in the range of 1000–2200 nm. The results of the principal component analysis using all samples suggest that CNF addition ratio and sample shape could be expressed by principal component (PC) 1 and PC2 scores, which relate to the chemical components of PP and CNF complexly. Furthermore, the partial least-squares (PLS) regression model was able to predict the CNF addition ratio with about 2.0% accuracy, regardless of CNF type and sample shape. To develop an easier model compared to the PLS model, it was calculated to the simple linear regression model, which used the absorbance quotient of optimum wavelengths combination (OWC). Although this model did not have the accuracy to use the quality control, it is able to discriminate CNF addition ratio of CNF/PP composites with almost the same accuracy as the PLS model. However, if it is possible to separate the sample shapes before the analysis, it is suggested that the OWC regression model is able to predict CNF addition ratio of CNF/PP composites with less than 1% accuracy. Polypropylene ; Materials science ; Principal component analysis ; Partial least squares regression ; Composite number ; Absorbance ; Cellulose ; Near-infrared spectroscopy ; Composite material ; Analytical Chemistry (journal) ; Mathematics ; Chemistry ; Chromatography ; Statistics ; Optics ; Organic chemistry ; Physics Advanced Cellulose Research Studies ; Nanocomposite Films for Food Packaging ; Electrospun Nanofibers in Biomedical Applications
Authors: Jovana Stepanović | Tatjana Šarac | Dušan Trajković | Jovan Stepanović
Source: Industria Textila (article, 2022-08-31)
Field: Materials Science — Polymers and Plastics

Investigation of deformation properties of textured multifilament PES yarns In the process of texturing, smooth filaments are formed into crimpy yarns. By combining thermal and mechanical action, thermoplastic fibres acquire a permanent wavy shape. Since textured multifilament yarns, formed from POY PES filaments, produced on machines with high-temperature heaters, are insufficiently studied, this paper analyses the deformation characteristics of yarns produced in industrial conditions using different process parameters (primary heater temperature, yarn speed, yarn stretching, peripheral speed of friction discs). Special attention is paid to the characteristics at the elastic limit, then at the creep limit, yield and breaking of multifilament textured yarns. A method is proposed which can determine the key points of deformation in the process of stretching textured polyester multifilament yarn, as well as the relationship between the values of force and elongation at the limits of elasticity, creep, end of creep zone, yield and break Materials science ; Composite material ; Yarn ; Creep ; Elongation ; Polyester ; Thermoplastic ; Yield (engineering) ; Deformation (meteorology) ; Ultimate tensile strength Textile materials and evaluations ; Radiative Heat Transfer Studies
Authors: Weixiao Hou | Yufei Yao | Yaojin Li | Bin Peng | Keqing Shi | Ziyao Zhou | Jingye Pan | Ming Liu | Jifan Hu
Source: Frontiers of Materials Science (article, 2022-02-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Linearly shifting ferromagnetic resonance response of La0.7Sr0.3MnO3 thin film for body temperature sensors Materials science ; Electron paramagnetic resonance ; Ferromagnetic resonance ; Condensed matter physics ; Ferromagnetism ; Atmospheric temperature range ; Magnetization ; Resonance (particle physics) ; Paramagnetism ; Field (mathematics) ; Nuclear magnetic resonance ; Magnetic field ; Atomic physics ; Thermodynamics Magnetic and transport properties of perovskites and related materials ; Atomic and Subatomic Physics Research ; Perovskite Materials and Applications
Authors: Kuldeep Godiyal
Source: International Journal on Recent and Innovation Trends in Computing and Communication (article, 2022-12-09)
Field: Chemistry — Analytical Chemistry

Artificial Neural Network based Model for Fruit Grade Empirical Thresholding and Feature Extraction based Back Propagation This study details a novel attribute retrieval method for use in pre-processing images, and then applies it to the development of an "artificial neural network" system based on back propagation for identifying fruits in photographs. The “Scale Conjugate Gradient” (SCG) technique is used For back propagation. In this paper, there are three stages to the process. First, MATLAB was used to process a variety of external image-based apple properties. Since merely colour is insufficient to judge the quality, size and weight characteristics were also taken into consideration. Second, features extraction was carried out during picture pre-processing to simplify the method by concentrating only on important features. The Support Vector Machine (SVM) algorithm is a favourite for creating classification models that are relatively small in weight. The classification in this work is done using the MATLAB-ANN (Artificial Neural Network) toolkit. A single hidden layer BP-ANN (Back propagation- artificial neural network) was employed with sigmoid activation functions,. The outcome was determined by the appropriate output variables, which is the apple's quality class, which was determined to be Class A, Class B, Class C, and Class D, respectively. The modeling result indicates the tremendous match between the data used in training and assumed output values. It also has shorter calculation time due to the SCG algorithm. It is also possible for apple producers and distributors to classify their fruit using this model and reduce the cost by avoiding manual classification. Artificial neural network ; Computer science ; Thresholding ; MATLAB ; Artificial intelligence ; Backpropagation ; Support vector machine ; Pattern recognition (psychology) ; Sigmoid function ; Conjugate gradient method ; Process (computing) ; Class (philosophy) ; Machine learning ; Data mining ; Image (mathematics) ; Algorithm Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Thomas De Kerf | Arthur Gestels | Koen Janssens | Gunther Steenackers | Paul Scheunders | Steve Vanlanduit
Source: ChemRxiv (preprint, 2022-10-19)
Field: Chemistry — Analytical Chemistry

Quantitative detection of corrosion minerals in carbon steel using shortwave infrared hyperspectral imaging. This study presents a novel method for the detection and quantification of atmospheric corrosion products on carbon steel. Using hyperspectral imaging (HSI) in the short-wave infrared range (SWIR) (900-1700 nm), we are able to identify the most common corrosion minerals such as: α FeO(OH) (goethite), γ FeO(OH) (lepidocrocite), and γ Fe2O3 (maghemite). Six carbon steel samples were artificially corroded in a salt spray chamber, each sample with a different duration (between 1h and 120 hours). These samples were analysed by scanning X-ray diffraction (XRD) and also using a SWIR HSI sytem. The XRD data is used as baseline data. A random forest regression algorithm is used for training on the combined XRD and HSI data set. Using the trained model, we can predict the abundance map based on the HSI images alone. Several image correlation metrics are used to assess the similarity between the original XRD images and the HSI images. The overall abundance is also calculated and compared for XRD and HSI images. The analysis results show that we are able to obtain visually similar images, with error rates ranging from 3.27 to 13.37 %. This suggests that hyperspectral imaging could be a viable tool for the study of corrosion minerals. Goethite ; Hyperspectral imaging ; Lepidocrocite ; Maghemite ; Materials science ; Corrosion ; Carbon fibers ; Mineralogy ; Remote sensing ; Environmental science ; Magnetite ; Metallurgy ; Geology ; Chemistry ; Composite material Spectroscopy and Chemometric Analyses ; Cultural Heritage Materials Analysis ; Thermography and Photoacoustic Techniques
Authors: H. Kemper | Sebastian Schmidt | T. Keller
Source: ISPRS annals of the photogrammetry, remote sensing and spatial information sciences (article, 2022-05-17)
Field: Chemistry — Analytical Chemistry

CLIMATIC EFFECTS ON ARGENTINA’S TRADITIONAL DRINK – A MACHINE LEARNING BASED YERBA MATE PRODUCTIVITY PREDICTION Abstract. Yerba mate belongs to the most important agricultural products of Argentina. Due to climate change, together with El Niño and droughts, yields are negatively affected. Drought propagation from precipitation deficits to plant water stress, its impacts and especially its predictability are becoming an emerging field of research. This paper explores how the future yerba mate production in the states of Misiones and Corrientes, north-eastern Argentina, can be projected by using climate variables and satellite data. The applied methodology focuses on a machine learning approach based on multiple linear regression and random forest regression. The results indicate a significant relationship between yerba mate and the NDVI as well as the SOI. The highest yerba mate productivity is expected during weak La Niña events. The methodology of this analysis can successfully predict mate productivity based on satellite and climate data and can also easily be used for further research areas. Predictability ; Productivity ; Agricultural productivity ; Climate change ; Precipitation ; Agriculture ; Geography ; Forestry ; Environmental science ; Ecology ; Statistics ; Mathematics ; Meteorology ; Biology ; Economics Heavy Metals in Plants
Authors: Maeve Eunicia | Richie Skyszygfrid | Tiara Vitri | Vicky Caren
Source: Formosa Journal of Multidisciplinary Research (article, 2022-05-21)
Field: Chemistry — Analytical Chemistry

Modeling Red Wine Quality Based on Physicochemical Tests: A Data Mining Approach Classification of the quality of red wine is done in the hope of making it easier to assess the quality of red wine. Data used for this research is the wine quality data set with 4898 number of instances, obtained from UCI machine learning repository. Classification of the quality of red wine this study was carried out by comparing the three algorithms of data mining, that is random forest, naive bayes and generalized linear model. From the results of this study comparing the three algorithms, the generalized linear model showed the highest accuracy among the other algorithms. It was tested with a generalized linear model with 68.75% accuracy results, this algorithm is ideal for classifying the quality of red wine. In addition, a secondary random forest gives 67.81% accuracy results, while Naive Bayes gives 61.25% accuracy results. Studies conducted to classify the quality of red wine based on its composition use a generalized linear model for the optimal algorithm. Wine ; Random forest ; Naive Bayes classifier ; Quality (philosophy) ; Computer science ; Data set ; Generalized linear model ; Data mining ; Mathematics ; Artificial intelligence ; Machine learning ; Support vector machine ; Food science ; Chemistry Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Data Mining Algorithms and Applications
Authors: Isabell Bludau | Sander Willems | Wen‐Feng Zeng | Maximilian T. Strauss | Fynn M. Hansen | Maria C. Tanzer | Özge Karayel | Brenda A. Schulman | Matthias Mann
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-02-24)
Field: Chemistry — Spectroscopy

The structural context of PTMs at a proteome wide scale Abstract The recent revolution in computational protein structure prediction provides folding models for entire proteomes, which can now be integrated with large-scale experimental data. Mass spectrometry (MS)-based proteomics has identified and quantified tens of thousands of post-translational modifications (PTMs), most of them of uncertain functional relevance. In this study, we determine the structural context of these PTMs and investigate how this information can be leveraged to pinpoint potential regulatory sites. Our analysis uncovers global patterns of PTM occurrence across folded and intrinsically disordered regions. We found that this information can help to distinguish regulatory PTMs from those marking improperly folded proteins. Interestingly, the human proteome contains thousands of proteins that have large folded domains linked by short, unstructured regions that are strongly enriched in regulatory phosphosites. These include well-known kinase activation loops that induce protein conformational changes upon phosphorylation. This regulatory mechanism appears to be widespread in kinases but also occurs in other protein families such as solute carriers. It is not limited to phosphorylation but includes ubiquitination and acetylation sites as well. Furthermore, we performed three-dimensional proximity analysis which revealed examples of spatial co-regulation of different PTM types and potential PTM crosstalk. To enable the community to build upon these first analyses, we provide tools for 3D visualization of proteomics data and PTMs as well as python libraries for data accession and processing. Proteome ; Computational biology ; Proteomics ; Posttranslational modification ; Context (archaeology) ; Crosstalk ; Phosphorylation ; Human proteome project ; Acetylation ; Folding (DSP implementation) ; Chemistry ; Computer science ; Biology ; Bioinformatics ; Biochemistry ; Enzyme ; Gene Advanced Proteomics Techniques and Applications ; Bioinformatics and Genomic Networks ; Protein Structure and Dynamics
Authors: Lijie Geng | Zhikun Ji | Ruiliang Zhang | Zhifeng Zhang | Yusheng Zhai | Wenyan Zhang
Source: Emirates Journal of Food and Agriculture (article, 2022-06-27)
Field: Materials Science — Polymers and Plastics

A new method for lint percentage non-destructive detection based on optical penetration imaging Lint percentage of seed cotton is one of the important bases for pricing in the trading segment. Unfortunately, the conventional methods of lint percentage are manually operated, which relies on the abundant experience of experts, and restrained by personal, physical and environmental factors. Up to date, the calculation of the lint percentage of seed cotton has not fully automated. In this paper, we proposed a non-destructive detection method for automatically obtaining lint percentage of seed cotton based on optical penetration imaging and machine vision, for the first time to our knowledge. The cotton seed image was obtained by the penetration imaging setup with a LED white backlight source. To accurately identify the number of cotton seeds, the image features of the cotton seed was studied and three key features was been found, which are the circumference, area, and greyscale value, respectively. A calculation system based on the three key features was presented to process the images and then automatically calculate the lint percentage of seed cotton. The first step of the system is to segment the original image using adaptive thresholding followed by morphological operations. Afterwards, the number of cotton seed was obtained by the three key features of the cotton seed. Then, the lint percentage was achieved by a professional industry formula. The suggested lint percentage detection methods were verified by the experiments with two seed cotton varieties samples of H219 and ZHM19. The experimental results indicated that the detection average accuracy of the developed system for seed cotton varieties H219 and ZHM19 were 96.33% and 95.40% Lint ; Thresholding ; Artificial intelligence ; Computer science ; Penetration (warfare) ; Mathematics ; Agricultural engineering ; Agronomy ; Engineering ; Biology ; Image (mathematics) Textile materials and evaluations ; Industrial Vision Systems and Defect Detection