jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: A.S. Momot | R.M. Galagan
Source: Електронний архів наукових та освітніх матеріалів КПІ ім. Ігоря Сікорського (КПІ ім. Ігоря Сікорського) (article, 2019-10-01)
Field: Materials Science — Materials Chemistry

Influence of architecture and training dataset parameters on the neural networks efficiency in thermal nondestructive testing Describes the perspective of the use of artificial neural networks in automated thermal non-destructive testing and defectometry systems. The influence of backpropagation neural networks architecture on the efficiency of defect classification and accuracy of determining their depth and thickness are analyzed. Considered the influence of volume and quality of training dataset on the efficiency of defect classification and accuracy of defectometry. Performance of neural networks is evaluated by quantitative indicators, such as MSE, relative error and Tanimoto criterion. The optimal neural network architecture for using in active thermal testing was established on the basis of experimental researches. Artificial neural network ; Training (meteorology) ; Nondestructive testing ; Artificial intelligence ; Computer science ; Architecture ; Machine learning ; Geography ; Physics ; Meteorology Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Thermography and Photoacoustic Techniques
Authors: Julie Tang | Paul R. Petrie | Mark Whitty
Source: IFAC-PapersOnLine (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Low-Cost Filter Selection from Spectrometer Data for Multispectral Imaging Applications Imaging beyond RGB bands has the ability to provide solutions for non-destructive detection of objects or scene properties. Methods often used in research include hyperspectral cameras and spectrometers; the former is expensive and the latter can only provide point measurements. Multispectral imaging can provide low-cost solutions in instances where RGB does not suffice, however, evaluation of filters used in multispectral sensors or the use of off-the-shelf filters for user specific applications have not been explicitly analysed. This paper proposes a novel method for using spectrometer data by firstly limiting the search space to existing off-the-shelf filters, modelling those filters, and then applying the desired model for classification or regression to more appropriately model multispectral imaging performance. The results indicate that point measurements produced by spectrometers may not correspond with the response achieved when using off-the-shelf filters; particularly in the instance where the spectral profile of the measured object varies significantly within the transmission regions of the filter. The method presented focuses on the practical estimation of filter performance when selecting filters. Multispectral image ; Hyperspectral imaging ; RGB color model ; Spectrometer ; Imaging spectrometer ; Computer science ; Filter (signal processing) ; Computer vision ; Artificial intelligence ; Remote sensing ; Point (geometry) ; Transmission (telecommunications) ; Multispectral pattern recognition ; Optics ; Mathematics ; Geography ; Physics ; Telecommunications Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Remote Sensing in Agriculture
Authors: Yu. S. Fedorova | А. С. Сухих | Н. И. Суслов | Yu. V. Zakhar | Olga Soboleva
Source: Fundamental and Clinical Medicine (article, 2019-10-02)
Field: Chemistry — Analytical Chemistry

Chromatography of Hedysarum extracts on modified Sepharose CL sorbents Aim . To study the chromatographic efficiency of modified Sepharose CL-4B sorbents for separation, purification, and preparation of major components from Hedysarum theinum Krasnob. and Hedysarum neglectum Ledeb. root extracts. Materials and Methods . Sorption capacity of the native and modified sepharose was evaluated in both isocratic and gradient elution modes. 4-pyridinecarboxylic acid derivative [(4-hydroxy-3-methoxyphenyl)methylene]hydrazide (ftivazide) was used as a sorbent modifier ligand. We studied chromatographic properties of Sepharose CL-4B, Sepharose CL-6B, and azoepoxyadsorbent Sepharose CL-4B-DEP-p-nitrobenzohydrazide-ftivazide, a chemically modified analogue of Sepharose CL-4B. Results . Sepharose CL-4B and Sepharose CL-4B-DEP-p-nitrobenzohydrazide-ftivazide were found efficient carriers for liquid column chromatography of water-ethanol Hedysarum extracts. Complete elution of bioactive substances has been achieved exclusively by using sodium tetraborate as an eluent. We were able to identify flavonoids and tannins of different condensation in chromatographic fractions. Conclusion . Comparative chromatography of bioactive substances from Hedysarum root extracts is more efficient if employing Sepharose CL-4B instead of Sepharose CL-6B as a sorbent. Sepharose CL-4B-DEP-p-nitrobenzohydrazide-ftivazide demonstrates excellent separation capacity for tannins and flavonoids. Sepharose ; Chromatography ; Chemistry ; Elution ; Sorbent ; Adsorption ; Organic chemistry Chromatography in Natural Products ; Natural product bioactivities and synthesis ; Phytochemicals and Antioxidant Activities
Authors: Shinnosuke TAKANO | Hiromasa Kaneko
Source: Journal of Computer Chemistry Japan (article, 2019-01-01)
Field: Chemistry — Spectroscopy

Monomer Design of Polymer Materials with High RefractiveIndex and High Glass Transition Temperature 本研究ではポリマー物性として屈折率 (refractive index, RI) およびガラス転移温度 (glass transition temperature, Tg) を対象にして，モノマーの化学構造から計算される構造記述子 X とそのモノマーを重合して得られるポリマーの物性 y との間で，高分子データベースを用いて物性推定モデルを構築し，構築されたモデルを用いて高い RI かつ高い Tg をもつポリマーを重合できると考えられる新規モノマー構造の設計を行った．モノマー構造の前処理方法・構造記述子・回帰分析手法を検討したところ，RDKit で構造記述子を計算し，support vector regression で回帰モデルを構築したときに，推定性能の良好な RI および Tg 推定モデルが得られた．RI と Tg がともに高いポリマーを達成するモノマーを設計するため，breaking of retrosynthetically interesting chemical substructure (BRICS) により仮想的な化学構造を生成し，モデルの適用範囲により推定値の信頼性を評価した後に，モデルを用いて RI および Tg を推定した．その結果，多様な化学構造が得られ，中には良好な RI および Tg の推定値をもつ化学構造が存在することを確認した．提案手法により複数の目標物性のある高分子材料の開発が促進することを期待する． Glass transition ; Polymer ; Monomer ; Materials science ; Chemical engineering ; Polymer science ; Engineering physics ; Composite material ; Engineering Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Spectroscopy and Chemometric Analyses
Authors: Andrew C. Strikwerda | Timothy Sleasman | William S. Anderson | Ra’id S. Awadallah
Source: Sensors (article, 2019-10-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Sub-Wavelength Focusing in Inhomogeneous Media with a Metasurface Near Field Plate Overcoming the diffraction limit, which enables focusing much less than the wavelength, requires tailoring the evanescent spectrum of an aperture’s field distribution. We model and simulate a corrugated near field plate, which can generate a sub-wavelength focus in inhomogeneous background media. All reactive coupling, between the metasurface near field plate and the focusing domain and among the corrugations in the metasurface, is taken into consideration with the finite element method, which we solve in combination with a constraint to generate a desired focus. Various geometries for the near field plate are considered and we demonstrate that the proposed method can effectively create a deeply sub-wavelength focus within a layered medium having properties resembling brain tissue. Such a device could find use as a detector of biological signals or for hyperthermic treatment near the skin surface. Focus (optics) ; Optics ; Wavelength ; Near and far field ; Aperture (computer memory) ; Diffraction ; Detector ; Physics ; Field (mathematics) ; Finite element method ; Coupling (piping) ; Limit (mathematics) ; Long wavelength limit ; Optoelectronics ; Materials science ; Acoustics Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Advanced Antenna and Metasurface Technologies
Authors: Najwa Abu Nassif
Source: التصميم الدولية (article, 2019-10-01)
Field: Materials Science — Polymers and Plastics

Dimensional Stability, Aesthetic and Functional Properties of Cotton:Polyester Blended Knitted Fabrics with Different Structures Throughout this study, functional, aesthetic, and dimensional stability properties of knitted blended fabrics were experimentally presented. An experimental work was introduced to examine. In order to investigate the loop length of the knitted fabrics and the polyester blending ratio on bursting, air permeability, pilling resistance and shrinkage ratio of knitted fabrics, thirty fabric samples were knitted with three loop length, five levels of polyester ratios for single jersey and rib knit structures. Analysis of variance and regression analysis were used to detect the influence of loop length and polyester content on knitted fabric properties. The findings of this study revealed that independent variables under study have a massive influence on the functional and aesthetic properties of the knitted blended fabrics. Polyester ; Shrinkage ; Materials science ; Composite material ; Air permeability specific surface ; Fabric structure ; Yarn Textile materials and evaluations ; Dyeing and Modifying Textile Fibers ; Mechanical Behavior of Composites
Authors: Kévin Jacq | Yves Perrette | Bernard Fanget | Pierre Sabatier | Didier Coquin | Martinez-Lamas Ruth | Debret Maxime | Arnaud Fabien
Source:  (preprint, 2019-02-15)
Field: Chemistry — Analytical Chemistry

High-resolution prediction of organic matter concentration with hyperspectral imaging on a sediment core In the case of environmental samples, the use of a chemometrics-based prediction model is highly challenging because of the difficulty in experimentally creating a well-ranged reference sample set. In this study, we present a methodology using short wave infrared hyperspectral imaging to create a partial least squares regression model on a cored sediment sample. It was applied to a sediment core of the well-known Lake Bourget (Western Alps, France) to develop and validate a model for downcore high resolution LOI550 measurements used as a proxy of the organic matter. In lake and marine sediment, the organic matter content is widely used, for example, to reconstruct carbon flux variations through time. Organic matter analysis through routine analysis methods is time- and material-consuming, as well as not spatially resolved. A new instrument based on hyperspectral imaging allows high spatial and spectral resolutions to be acquired all along a sediment core. In this study, we obtain a model characterized by a 0.95 r prediction, with 0.77 wt% of model uncertainty based on 27 relevant wavelengths. The concentration map shows the variation inside each laminae and flood deposit. LOI550 reference values obtained with the loss on ignition are highly correlated to the inc/coh ratio used as a proxy of the organic matter in X-ray fluorescence with a correlation coefficient of 0.81. This ratio is also correlated with the averaged subsampled hyperspectral prediction with a r of 0.65. Hyperspectral imaging ; Partial least squares regression ; Organic matter ; Sediment ; Environmental science ; Total organic carbon ; Correlation coefficient ; Loss on ignition ; Soil science ; Geology ; Remote sensing ; Environmental chemistry ; Chemistry ; Geomorphology ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Isotope Analysis in Ecology
Authors: Ahmad Mohamad Zaki | Ragaa Abdelkader Ramadan | Pacint Moez | Hala Ghareeb | Ahmad Elkarmouty
Source: Asian Pacific Journal of Cancer Prevention (article, 2019-01-01)
Field: Chemistry — Spectroscopy

Plasma Peptidome Pattern of Breast Cancer Using Magnetic Beads-Based Plasma Fractionation and MALDI-TOF MS: A Case Control Study in Egypt Objective: The present study aimed to determine peptidome patterns in breast cancer (BC). Methods: We analyzed the plasma proteomic proﬁling of 80 BC patients and 50 healthy controls, using hydrophobic interaction chromatography magnetic beads (MB-HIC8) separation followed by Matrix assisted laser desorption ionization/ time of flight mass spectrometry (MALDI-TOF MS). Results: ClinProTools software identiﬁed 92 peaks that differed among the analyzed groups, 33 peaks were signiﬁcantly different (P < 0.05). Of those, 22 peaks were up-regulated while 11 peaks were down-regulated in BC patients compared with the healthy controls. Three peptide ion signatures (m/z 1,570.31, 1,897.4 and 2,568.17) were provided by the Quick Classifier model to discriminate BC patients from healthy control subjects with 96.4% accuracy. External validation was performed by an independent group and this achieved a sensitivity of 100% and a specificity of 76.9%. Conclusion: MALDI-TOF MS has good analytical performance in distinguishing BC patients from healthy controls. Breast cancer ; Fractionation ; Plasma ; Chemistry ; Chromatography ; Medicine ; Cancer ; Internal medicine ; Physics Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Molecular Biology Techniques and Applications
Authors: Samantha Rudinsky | Yu Yuan | Raynald Gauvin | Nicolas Piché | Mike Marsh
Source: Microscopy and Microanalysis (article, 2019-08-01)
Field: Materials Science — Surfaces, Coatings and Films

Extending Monte Carlo Simulations of Electron Microscopy Images and Hyperspectral Images in a User-Friendly Framework Characterization by electron microscopy plays a crucial role in research across a variety of materials research applications because it can be used to discern the physical and chemical properties of the studied materials. However, in order to make large scale assessments of new products and structures, such techniques need to be automated and optimized so that large amounts of data may be processed efficiently. Optimization can sometimes be done empirically by varying imaging conditions on an available microscope, but when those imaging conditions can be varied through simulation, it is easier to test more systematically a wider variety of conditions. MC X-ray [1] is such a simulation tool which can compute the complete backscattered electron (BSE) images and x-ray spectra from the simulation of electron scattering in solids of various geometries. Until recently, the flexibility of simulated sample domains was constrained by limits in the MC X-ray user interface. We present here the integration of the simulation technology of MC X-ray in a feature-rich graphical image processing and image visualization platform, enabling greater user flexibility and the capability to use previously collected images as the initial spatial domain of simulation. Library science ; Object (grammar) ; Computer science ; Art history ; Sociology ; History ; Artificial intelligence Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Zhuo Li | Bin Li
Source: Journal of Synchrotron Radiation (article, 2019-06-04)
Field: Materials Science — Surfaces, Coatings and Films

Towards an extremely high resolution broad-band flat-field spectrometer in the `water window' The optical design of a novel spectrometer is presented, combining a cylindrically convex pre-mirror with a cylindrically concave varied-line-spacing grating (both in the meridional) to deliver a resolving power of 100000–200000 in the `water window' (2–5 nm). Most remarkably, the extremely high spectral resolution is achieved for an effective meridional source size of 50 µm (r.m.s.); this property could potentially be applied to diagnose SASE-FEL and well resolve individual single spikes in its radiation spectrum. The overall optical aberrations of the system are well analysed and compensated, providing an excellent flat-field at the detector domain throughout the whole spectral range. Also, a machine-learning scheme – SVM – is introduced to explore and reconstruct the optimal system with high efficiency. Spectrometer ; Optics ; Grating ; Physics ; Detector ; Spectral resolution ; Resolution (logic) ; Radiation ; Window (computing) ; Computer science ; Spectral line Optical Coatings and Gratings ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Johannes Anastasiadis | Fernando Puente León
Source: tm - Technisches Messen (article, 2019-08-27)
Field: Chemistry — Analytical Chemistry

Ortsaufgelöste spektrale Entmischung mit Hilfe von konvolutionalen neuronalen Netzen / Spatially resolved spectral unmixing using convolutional neural networks Zusammenfassung In diesem Beitrag wird ein modellfreier Ansatz, basierend auf einem konvolutionalen neuronalen Netz, für die spektrale Entmischung von Hyperspektralbildern vorgestellt. Dieser wird anhand von Beispieldatensätzen evaluiert und mit Methoden, denen das lineare Mischmodell zugrunde liegt, verglichen. Außerdem werden Möglichkeiten vorgestellt, wie die physikalische Interpretierbarkeit der geschätzten Materialanteile gewährleistet werden kann. Bei dem hier vorgestellten Ansatz geschieht dies durch Ausgangsschichten, die erzwingen, dass die geschätzten Materialanteile alle positiv sind und in der Summe eins ergeben. Physics Spectroscopy and Chemometric Analyses ; Thermography and Photoacoustic Techniques ; Remote-Sensing Image Classification
Authors: Leonel Pablo Tedesco | Adriano da C. de Freitas | Rolf Fredi Molz | Jacques Nelson Corleta Schreiber
Source: Revista Brasileira de Engenharia Agrícola e Ambiental (article, 2019-09-07)
Field: Chemistry — Analytical Chemistry

Proposal of automated computational method to support Virginia tobacco classification ABSTRACT This article proposes an automatic method for classification of cured tobacco leaves. Typically this process is performed manually, allowing the occurrence of human errors. In addition, the existence of an automated comparative procedure, helping to perform the classification, can make this process faster and more transparent. In order to implement the method, non-invasive to the agricultural product, 250 samples of Virginia tobacco digital images in the RGB and HSV color models were analyzed. The validation of the method was carried out using partial least squares (PLS) and artificial neural network (ANN), presenting a qualitative and quantitative analysis of both tools. It has been verified that the PLS can be applied to this method, as it has a shorter computational time, better suiting a real-time process. It can be verified that the ANN obtained better prediction results. Both methods employed had better results when adopting the RGB color model, reaching coefficient of determinations of 68 and 96% for the PLS and ANN methods, respectively. RGB color model ; Artificial neural network ; Computer science ; Artificial intelligence ; Process (computing) ; Partial least squares regression ; HSL and HSV ; Pattern recognition (psychology) ; Machine learning ; Data mining Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Water Quality Monitoring and Analysis
Authors: Alvaro Sebastian Vaca Jácome | Ryan Peckner | Nicholas Shulman | Karsten Krug | Katherine C. DeRuff | Adam Officer | Brendan MacLean | Michael J. MacCoss | Steven A. Carr | Jacob D. Jaffe
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2019-03-01)
Field: Chemistry — Spectroscopy

<i>Avant-garde</i> : An automated data-driven DIA data curation tool Abstract Data-Independent Acquisition (DIA) is a technique that promises to comprehensively detect and quantify all peptides above an instrument’s limit of detection. Several software tools to analyze DIA data have been developed in recent years. However, several challenges still remain, like confidently identifying peptides, defining integration boundaries, dealing with interference for selected transitions, and scoring and filtering of peptide signals in order to control false discovery rates. In practice, a visual inspection of the signals is still required, which is impractical with large datasets. Avant-garde is a new tool to refine DIA (and PRM) by removing interfered transitions, adjusting integration boundaries and scoring peaks to control the FDR. Unlike other tools where MS runs are scored independently from each other, Avant-garde uses a novel data-driven scoring strategy. DIA signals are refined by learning from the data itself, using all measurements in all samples together to achieve the best optimization. We evaluated the performances of Avant-garde with a calibrated sample using spiked-in standards in a complex background, a phospho-enriched dataset (Abelin et al , 2016), and two complex hybrid proteome samples for benchmarking DIA software tools. The results clearly showed that Avant-garde is capable of improving the selectivity, accuracy, and reproducibility of the quantification results in very complex biological matrices. We have further shown that it can evaluate the suitability of a peak to be used for quantification reaching the same levels of selectivity, accuracy, and reproducibility obtained with manual validation. Benchmarking ; Computer science ; Software ; Reproducibility ; Data mining ; Artificial intelligence ; Machine learning ; Chemistry ; Chromatography Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Machine Learning in Bioinformatics
Authors: Qian Zhang | Chao Huang
Source: Journal of Mathematics (article, 2019-06-12)
Field: Materials Science — Polymers and Plastics

A Leap-Frog Finite Difference Method for Strongly Coupled System from Sweat Transport in Porous Textile Media In this paper, we present an uncoupled leap-frog finite difference method for the system of equations arising from sweat transport through porous textile media. Based on physical mechanisms, the sweat transport can be viewed as the multicomponent flow that coupled the heat and moisture transfer, such that the system is nonlinear and strongly coupled. The leap-frog method is proposed to solve this system, with the second order accuracy in both spatial and temporal directions. We prove the existence and uniqueness of the solution to the system with optimal error estimates in the discrete <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msup><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math> norm. Numerical simulations are presented and analyzed, respectively. Uniqueness ; Porous medium ; Algorithm ; Nonlinear system ; Computer science ; Mathematics ; Chemistry ; Biological system ; Porosity ; Mathematical analysis ; Physics ; Biology Textile materials and evaluations ; Composite Structure Analysis and Optimization ; Acoustic Wave Phenomena Research
Authors: Md. Reazuddin Repon | Md. Nura Alam Shiddique | Rajib Al Mamun
Source: Universal Journal of Engineering Science (article, 2019-07-01)
Field: Materials Science — Polymers and Plastics

Effect of 1×1, 2×1, 2×2, 3×1 and 3×3 Knit Structure on Different Properties of Rib Knitted Fabric This study investigates the effect of different knit structure on various properties of rib knitted fabric. The acrylic yarns were used to make 1×1, 2×1, 2×2, 3×1 and 3×3 rib structured fabrics using manual V-bed knitting machine in this research work. The effects of knit structure were measured concerning drape co-efficient percentage, bending length, flexural rigidity and tightness factor. The results disclosed that the fabric properties are greatly influenced by knit structures. 1×1 rib knit fabric showed the lowest value for all tested properties and 3×3 rib knit fabric showed the highest value except drape co-efficient. The bending length and flexural rigidity was investigated in both wale and course direction. At the same time, surface and back sides bending length were also calculated. It has been identified from the test results that the effect of knit structure on flexural rigidity in both wale and course direction were influenced in a same chronological way. Composite material ; Materials science ; Structural engineering ; Fabric structure ; Engineering Textile materials and evaluations ; Dyeing and Modifying Textile Fibers