jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mohammad Vahedi Torshizi | Mohsen Azadbakht | Mahdi Kashaninejad | M Azadbakht | M Torshizi | A Ziaratban | B Khoshnevisan | Sh | M Rafiee | M Omid | L Cappato | M Ferreira | J Guimaraes | J Portela | A Costa | M Freitas | A Cruz | I Castro | J Teixeira | S Salengke | S Sastry | A Vicente | G Chegini | J Khazaei | B Ghobadian | A Goudarzi | H Darvishi | A Hosainpour | F Nargesi | A Fadavi | M Khostaghaza | G Najafi | A Hosu | V Cristea | C Cimpoiu | K Kanjanapongkul | H Lu | H Zheng | H Lou | L Jiang | Y Chen | S Fang | H Mattar | L Minim | J Coimbra | V Minim | S Saraiva | J Telis-Romero | S Mazloumzadeh | S Alavi | M Nouri | M zdemir | M Akta | S evik | A Khanlari | F Salehi | A Ardabili | A Nemati | R Latifi Darab | S Razavi | B Soleimanzadeh | L Hemati | M Yolmeh | S Srivastav | S Roy | A Taheri-Garavand | F Karimi | M Karimi | V Lotfi | G Khoobbakht
Source: Carpathian Journal of Food Science and Technology (article, 2019-09-29)
Field: Chemistry — Analytical Chemistry

INVESTIGATION OF EXPERIMENT DATA AND SENSITIVITYCOEFFICIENT DATA WITH ARTIFICIAL NEURAL NETWORK IN THEOHMIC HEATING PROCESS FOR SOUR ORANGE JUICE In this investigation, an ohmic heating system was constructed and applied to the heating process at three voltage gradient inputs (8.33, 10.83, 13.33 V/ cm) and three percent weight loss sample (10, 20 and 30%) compared to total weight was selected. During the thermal process, the power consumption, electrical conductivity and coefficient performance system were calculated. All experiments were performed in three replications. An artificial neural network was used to predict experimental data. In this study multi-layer perceptron were selected and radial basic function artificial neural network by 1 hidden layers and 4, 8 and 12 neurons hidden layers, and with two activation function (hyperbolic tangent and sigmoid). The highest R values were for power consumption (0.998), electrical conductivity (0.996) and Coefficient performance systems (0.999) in a MLP network with 8 neuron in hidden layer and sigmoid activation. Also the fastest network with lowest EPOCH was in a network of 12 neuron. According to the results obtained for R, MSE and learning cycle, it can be said that the neural network has ability to predict power consumption, electrical conductivity and coefficient performance systems to an acceptable level for ohmic processing. Artificial neural network ; Orange juice ; Orange (colour) ; Sensitivity (control systems) ; Computer science ; Artificial intelligence ; Chemistry ; Food science ; Engineering ; Electronic engineering Spectroscopy and Chemometric Analyses ; Microbial Inactivation Methods ; Food Drying and Modeling
Authors: Yahya S. Al‐Degs | Amjad H. El‐Sheikh | Eman A. Abu Saaleek | Reema A. Omeir | Musab A. Al-ghodran
Source: ACTA Pharmaceutica Sciencia (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Combination of cumulative area pre- processing and partial least squares for handling intensely overlapping binary and ternary drug systems The analytical performance of cumulative area pre-processing (CAP), a recently developed signal filtering method, along with multivariate calibration for quantification of spectrally overlying drugs was outlined. The drug combinations containing high level of paracetamol (PAR) in the presence of caffeine (CAF), chlorpheniramine maleate (CHL), pseudoephedrine hydrochloride (PSE), phenylephrine hydrochloride (PHE), and diphenhydramine hydrochloride (DPH). The tested formulations were: PAR-CAF-PHE, PAR-CAF-PHE, and PAR-DRH. Based on netanalyte signal calculations, the formulations exhibited intense overlapping 53-68% for PAR-PSE-CHL, 55-95% for PAR-CAF-PHE, and 44% for PAR-DRH. For each system, PLS latent variables were estimated using cross-validation technique and more factors were needed for highly overlapping systems. PLS-CAP was found applicable for drugs quantification in all systems with excellent performance regardless the size of spectral overlapping and ratios of components in the formulation. For PAR-PSE-CHL (ratio 300:30:2 mg/tablet), the ingredients were quantified by CAP-PLS with satisfactorily recoveries (RSD, n = 3) 89.9% (3.1%), 104.6% (2.7%), and 99.0% (1.5%) for PAR, PSE, and CHL, respectively. Both PLS and CAP-PLS were demonstrated the same performance for binary system of modest overlapping and no component available in low concentration. Ternary operation ; Partial least squares regression ; Binary number ; Computer science ; Statistics ; Mathematics ; Arithmetic Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Suraj Gampa
Source: Insecta mundi (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

A Data-driven Approach for Detecting Stress in Plants Using Hyperspectral Imagery A phenotype is an observable characteristic of an individual and is a function of its genotype and its growth environment. Individuals with different genotypes are impacted differently by exposure to the same environment. Therefore, phenotypes are often used to understand morphological and physiological changes in plants as a function of genotype and biotic and abiotic stress conditions. Phenotypes that measure the level of stress can help mitigate the adverse impacts on the growth cycle of the plant. Image-based plant phenotyping has the potential for early stress detection by means of computing responsive phenotypes in a non-intrusive manner. A large number of plants grown and imaged under a controlled environment in a high-throughput plant phenotyping (HTPP) system are increasingly becoming accessible to research communities. They can be useful to compute novel phenotypes for early stress detection. In early stages of stress induction, plants manifest responses in terms of physiological changes rather than morphological, making it difficult to detect using visible spectrum cameras which use only three wide spectral bands in the 380nm - 740 nm range. In contrast, hyperspectral imaging can capture a broad range of wavelengths (350nm - 2500nm) with narrow spectral bands (5nm). Hyperspectral imagery (HSI), therefore, provides rich spectral information which can help identify and track even small changes in plant physiology in response to stress. In this research, a data-driven approach has been developed to identify regions in plants that manifest abnormal reflectance patterns after stress induction. Reflectance patterns of age-matched unstressed plants are first characterized. The normal and stressed reflectance patterns are used to train a classifier that can predict if a point in the plant is stressed or not. Stress maps of a plant can be generated from its hyperspectral image and can be used to track the temporal propagation of stress. These stress maps are used to compute novel phenotypes that represent the level of stress in a plant and the stress trajectory over time. The data-driven approach is validated using a dataset of sorghum plants exposed to drought stress in a LemnaTec Scanalyzer 3D HTPP system. Advisers: Ashok Samal and Sruti Das Choudhury Hyperspectral imaging ; Remote sensing ; Environmental science ; Computer science ; Artificial intelligence ; Geology Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Mariya Georgieva-Nikolova | Zlatin Zlatev
Source: Applied Researches in Technics Technologies and Education (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

APPLICATION OF CLASSIFICATION TECHNIQUES IN THE ANALYSIS OF EXTERNAL CHARACTERISTICS OF EGGS In this article a comparative analysis is made to determine the influence of vectors of selected features derived from geometric, optical and dielectric characteristics of eggs on the accuracy of classification, depending on their weight. Suitable for classification are the principal components and latent variables that reduce feature vectors containing shape indices (D, A, V), spectral indices (TVI, GLI), dielectric characteristics (C, k), selected by four methods (CORR, SFCPP, RELIEFF, FSRNCA). By comparative studies it is found that the use of classification methods (DT, DA, SVM) are more effective in predicting weight of hen eggs than in quail eggs. The proposed egg analysis methods take precedence over the known solutions in this field as it takes into account changes in the internal properties of quail and hen eggs when stored. Quail ; Pattern recognition (psychology) ; Support vector machine ; Principal component analysis ; Artificial intelligence ; Mathematics ; Statistics ; Computer science ; Biology ; Ecology Spectroscopy and Chemometric Analyses ; Advanced Scientific Research Methods ; Animal Nutrition and Health
Authors: Brett Tully
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2019-05-07)
Field: Chemistry — Spectroscopy

Toffee – a highly efficient, lossless file format for DIA-MS ABSTRACT The closed nature of vendor file formats in mass spectrometry is a significant barrier to progress in developing robust bioinformatics software. In response, the community has developed the open mzML format, implemented in XML and based on controlled vocabularies. Widely adopted, mzML is an important step forward; however, it suffers from two challenges that are particularly apparent as the field moves to high-throughput proteomics: large increase in file size, and a largely sequential I/O access pattern. Described here is ‘toffee’, an open, random I/O format backed by HDF5, with lossless compression that gives file sizes similar to the original vendor format and can be reconverted back to mzML without penalty. It is shown that mzML and toffee are equivalent when processing data using OpenSWATH algorithms, in additional to novel applications that are enabled by new data access patterns. For instance, a peptide-centric deep-learning pipeline for peptide identification is proposed. Documentation and examples are available at https://toffee.readthedocs.io , and all code is MIT licensed at https://bitbucket.org/cmriprocan/toffee . Computer science ; File format ; Pipeline (software) ; XML ; XML framework ; Vendor ; Database ; Software ; Operating system Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Rennan Santos de Araujo | L.A.L. de Almeida | Felipe Sadami Oiwa da Costa
Source: Anais do 14º Simpósio Brasileiro de Automação Inteligente (article, 2019-01-01)
Field: Materials Science — Polymers and Plastics

Modelo de Neurônio Memristivo de Dióxido de Vanádio com Codificação de Impulsos PRBS-PWM Neuromorphic computing features a recent paradigm of computational architecture that, based on the structure of a biological neural system, aims to be a more efficient alternative for the large-scale information processing. Attending to neuromorphic computing main driving force, the energy economy, this work proposes a new neuron model, stimulated by pulses encoded by PRBS (Pseudorandom Binary Sequence) and modulated by PWM (Pulse Width Modulation), whose memristence and activation are based on the behavior of the thermal and nonlinear phase transition dynamics of the inorganic compound vanadium dioxide (V O 2 ). Based on the positive results obtained by the computational simulations in this work, it is expected that this new proposal of artificial neuron, when implemented in a spiking neural network, will be able to establish higher standards of neural plasticity and energy efficiency. Resumo: A computao neuromrfica caracteriza um recente paradigma de arquitetura computacional que, respaldando-se na estrutura de um sistema neural biolgico, almeja ser uma alternativa mais eficiente para o processamento de informaes em larga escala. Atendendo a principal fora motriz da computao neuromrica, a economia energtica, este trabalho prope um novo modelo de neurnio, estimulado por impulsos codificados por PRBS (Pseudorandom Binary Sequence) e modulados por PWM (Pulse Width Modulation), cuja memristncia e ativao so baseadas no comportamento das dinmicas trmicas e no lineares de transio de fase do composto inorgnico dixido de vandio (V O 2 ). Embasando-se nos resultados positivos obtidos pelas simulaes computacionais neste trabalho, esperado que esta nova proposta de neurnio artificial, quando implementada em uma rede neural pulsada, seja capaz de estabelecer maiores ndices de plasticidade neural e eficincia energtica. Computer science Conducting polymers and applications ; Electrochemical Analysis and Applications
Authors: Mahmoud Hassanin | I Sanaa | Amer | E Fathy | Ahmed Abdel-Kader | ; Abu | Mohammed Abdelmoneim | Shereen Kayed | Taie | Mohammed Selem | Sherine Fatehy | M Abd ; Enas | Anar Ahmed | Hady | M Sherine | A El-Kader | Khalil | Hanan Hussein | Mohamed Hanafy Radwan | Hussein Elsayed | Sherine | Ppf ; Shunt | A Harmonics | S Wahsh | M Badr | A | ; Magdy Kamal | N Maged | Nashed | H Hanaa | Mohamed Qamar | Asmaa El-Mashade | Farahat | F Khalid | Ahmed Hussein | Ahmed Abdeen | Attyia | W Ghz | Ahmed Hassan | Amr Sedki | Hussein Hasanin | Badr | H Fathy | Ahmed Awad | Mansour | I Mostafa | Ahmed Marei | Sattar Abdel | Aabdel-Aziz ; Peter Maha | Michal apk | Matej Kocr | Juraj Rbek | Matej | A Asmaa | Abd Alamin
Source:  (article, 2019-11-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Table of contents Table (database) ; Computer science ; Database Supercapacitor Materials and Fabrication ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications
Authors: J. Meynard
Source: theses.fr (ABES) (preprint, 2019-12-04)
Field: Materials Science — Materials Chemistry

Effect of pore size, morphology and spatial distribution on the thermal conductivity of UO2ceramics Dans un réacteur nucléaire, le comportement des combustibles est piloté par des phénomènes thermiques. C'est pourquoi il est important de modéliser précisément leur comportement thermique.L’objectif de ces travaux est d’évaluer l’impact de la microstructure sur les propriétés thermiques d’un combustible UO2 à 50°C.Des céramiques UO2 ont été fabriquées. Deux familles de porosité ont été identifiées à l’aide de différentes techniques d’imagerie (microscopie optique, MEB-FIB et tomographie X) : la porosité occluse de forme quasi-sphérique et localisée dans les granulats d’UO2 et un réseau de porosité "d'assemblage" localisée à l’interface des granulats. Des paramètres descripteurs ont été déterminés par mesures par immersion et par analyse d’images. Des études par homogénéisation analytique et numérique (calculs par transformée de Fourier rapide) ont été réalisées afin d’évaluer l’impact de ces caractéristiques sur la conductivité thermique. Les calculs numériques ont été réalisés sur des images 2D et 3D obtenues par imagerie et générées avec un modèle morphologique original reproduisant les spécificités des structures poreuses observées. Ces études ont mis en évidence l’impact de la répartition spatiale et de l’interconnexion de la porosité d’assemblage sur la conductivité thermique des céramiques UO2. Finalement, le modèle proposé a été comparé à des mesures expérimentales de diffusivité thermique obtenues par méthode Flash. Cette comparaison a permis de montrer que le modèle reproduit les tendances associées à la dégradation de la conductivité thermique mesurée sur les céramiques étudiées. Morphology (biology) ; Thermal conductivity ; Materials science ; Spatial distribution ; Distribution (mathematics) ; Composite material ; Geology ; Mathematics ; Statistics ; Mathematical analysis Nuclear Materials and Properties ; Radioactive element chemistry and processing ; Advanced ceramic materials synthesis
Authors: Nadine Pereira Merlo | Moilton R. Franco | Samuel Chagas | Nattácia Rodrigues de Araújo Felipe Rocha | Warley Augusto Pereira | Gabriela Ciribelli Santos Pompêu | Lynicker Dourado
Source: Revista ION (article, 2019-08-30)
Field: Chemistry — Spectroscopy

Estimativa de densidade de álcoois biocombustíveis pelo segundo coeficiente da equação virial Os volumes molares ou densidades de seis álcoois puros pertencentes à indústria de biocombustíveis foram estimados para diferentes valores de temperaturas dentro da faixa entre 280 K e 333 K, utilizando um modelo desenvolvido a partir da equação de equilíbrio líquido-vapor ou Equação de Clausius- Clapeyron. O segundo coeficiente virial (B) foi calculado a partir de dados preditivos aplicados ao modelo. Como amplamente conhecido, variações no valor de B refletiriam nos volumes molares dos compostos com resultado semelhante. Neste trabalho, observou-se que os desvios foram positivos para todas as espécies estudadas e aumentaram entre os álcoois de longa cadeia de carbono. Quando disponível, foi feita uma comparação entre o volume molar experimental e o predito. Virial coefficient ; Chemistry ; Humanities ; Thermodynamics ; Physics ; Philosophy Adsorption, diffusion, and thermodynamic properties of materials ; Chemical Thermodynamics and Molecular Structure ; Chemistry and Stereochemistry Studies
Authors: Sebastian Ortiz | Luka Stanisic | Boris A. Rodríguez | Markus Rampp | Gerhard Hummer | Pilar Cossio
Source: arXiv (Cornell University) (preprint, 2019-08-02)
Field: Materials Science — Surfaces, Coatings and Films

Cross-validation tests for cryo-EM maps using an independent particle set Cryo-electron microscopy is a revolutionary technique that can provide 3D density maps at near-atomic resolution. However, map validation is still an open issue in the field. Despite several efforts from the community, it is possible to overfit the reconstructions to noisy data. Here, inspired by modern statistics, we develop a novel methodology that uses a small independent particle set to validate the 3D maps. The main idea is to monitor how the map probability evolves over the control set during the refinement. The method is complementary to the gold-standard procedure, which generates two reconstructions at each iteration. We low-pass filter the two reconstructions for different frequency cutoffs, and we calculate the probability of each filtered map given the control set. For high-quality maps, the probability should increase as a function of the frequency cutoff and of the refinement iteration. We also compute the similarity between the probability distributions of the two reconstructions. As higher frequencies are added to the maps, more dissimilar are the distributions. We optimized the BioEM software package to perform these calculations, and tested the method on several systems, some which were overfitted. Our results show that our method is able to discriminate the overfitted sets from the non-overfitted ones. We conclude that having a control particle set, not used for the refinement, is essential for cross-validating cryo-EM maps. Overfitting ; Computer science ; Set (abstract data type) ; Algorithm ; Probability density function ; Similarity (geometry) ; Software ; Data mining ; Artificial intelligence ; Mathematics ; Statistics ; Image (mathematics) Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Magnetic properties of thin films
Authors: Annick De Backer | Annelies De wael | Iván Lobato | Thomas Altantzis | Armand Béché | Lewys Jones | Peter D. Nellist | Sara Bals | Sandra Van Aert
Source: Microscopy and Microanalysis (article, 2019-08-01)
Field: Materials Science — Surfaces, Coatings and Films

Quantification of 3D Atomic Structures and Their Dynamics by Atom-Counting from an ADF STEM Image Atom (system on chip) ; Dynamics (music) ; Image (mathematics) ; Atomic physics ; Materials science ; Chemical physics ; Physics ; Computer science ; Artificial intelligence ; Parallel computing ; Acoustics Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Advanced Materials Characterization Techniques
Authors: Julianus Pfeuffer | Timo Sachsenberg | Tjeerd M. H. Dijkstra | Oliver Serang | Knut Reinert | Oliver Kohlbacher
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2019-08-15)
Field: Chemistry — Spectroscopy

EPIFANY – A method for efficient high-confidence protein inference Abstract Accurate protein inference under the presence of shared peptides is still one of the key problems in bottom-up proteomics. Most protein inference tools employing simple heuristic inference strategies are efficient, but exhibit reduced accuracy. More advanced probabilistic methods often exhibit better inference quality but tend to be too slow for large data sets. Here we present a novel protein inference method, EPIFANY, combining a loopy belief propagation algorithm with convolution trees for efficient processing of Bayesian networks. We demonstrate that EPIFANY combines the reliable protein inference of Bayesian methods with significantly shorter runtimes. On the 2016 iPRG protein inference benchmark data EPIFANY is the only tested method which finds all true-positive proteins at a 5% protein FDR without strict pre-filtering on PSM level, yielding an increase in identification performance (+10% in the number of true positives and +35% in partial AUC) compared to previous approaches. Even very large data sets with hundreds of thousands of spectra (which are intractable with other Bayesian and some non-Bayesian tools) can be processed with EPIFANY within minutes. The increased inference quality including shared peptides results in better protein inference results and thus increased robustness of the biological hypotheses generated. EPIFANY is available as open-source software for all major platforms at https://OpenMS.de/epifany . Inference ; Computer science ; Frequentist inference ; Bayesian inference ; Robustness (evolution) ; Machine learning ; Fiducial inference ; Artificial intelligence ; Benchmark (surveying) ; Bayesian probability ; Data mining ; Algorithm ; Biology Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Machine Learning in Bioinformatics
Authors: M.V. Nosikov
Source: Proceedings of Higher Educational Institutions Маchine Building (article, 2019-07-01)
Field: Materials Science — Materials Chemistry

Research and Educational Centre for Rocket Building, Miass Branch, South Ural State University The key aspect of radiation-proof manipulators used in nuclear industry is human presence in the manipulator control loop due to a wide variety of performed operations and the nondeterministic nature of the working environment. Relatively large distances between the control stations and the manipulators, imperfect visibility of the sealed chamber’s inner space from the control station necessitate the use of computer vision systems. They serve both for general chamber observation as well as local observation of the manipulator end-effector working area. High levels of radiation impose certain limitations on the primary video sensors with regard to radiation resistance and service life. This requires special technical solutions to ensure additional radiation protection as well as design and algorithmic ways of minimizing the influence of hazardous environment. Modern trends in digital manufacturing design are based on a system of unique identification of materials, package, etc. One of the technically viable ways is applying optical labels containing barcodes that are identified by algorithms. The article covers the design, structure, functional possibilities and experimental results of one of the radiation-proof manipulator vision systems. This system is designed for working together with radiation-proof robotic electromechanical manipulators installed in sealed chambers of nuclear plants. Proof of concept ; Key (lock) ; Computer science ; Variety (cybernetics) ; Systems engineering ; Engineering ; Mechanical engineering ; Control engineering ; Artificial intelligence ; Computer security Graphite, nuclear technology, radiation studies ; Nuclear and radioactivity studies ; Nuclear Issues and Defense