jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wenze Li | Chao Fang | Jia Liu | Jingxia Cui | Hongzhi Li | Ting Gao | Hui Li | Lihong Hu | Yinghua Lu
Source: Journal of Chemometrics (article, 2019-01-16)
Field: Chemistry — Analytical Chemistry

HSPXY: A hybrid‐correlation and diversity‐distances based data partition method Abstract A representative dataset is crucial to build a robust and generalized machine learning model, especially for small databases. Correlation is not usually considered in distance‐based set partition methods; therefore, distant yet correlated samples might be incorrectly assigned. An improved sample subset partition method based on joint hybrid correlation and diversity x ‐ y distances (HSPXY) is proposed in the framework of the sample set partition based on joint x ‐ y distances (SPXY). Therein, a hybrid distance consisting of both cosine angle distance and Euclidean distance in variable spaces cooperates the correlation of samples in the distance‐based set partition method. To compare with some existing partition methods, partial least squares (PLS) regression models are built on four set partition methods, random sampling (RS), Kennard‐Stone (KS), SPXY, and HSPXY. Upon the applications on small chemical databases, the proposed HSPXY algorithm‐based models achieved smaller root mean square errors and better coefficients of determination than other tested set partition methods, which indicates the training set is well represented. This suggests the proposed algorithm provides a new option to obtain a representative calibration set. Sample subset partition is widely considered in machine learning modeling. An improved sample subset partition method based on a hybrid correlation and diversity x ‐ y distance (HSPXY) is proposed in the framework of SPXY. Cosine angle distance and Euclidean distance in variable spaces are used to represent the correlation and diversity of samples, respectively. To explore the effectiveness of HSPXY, PLS models are built on four set partition methods, RS, KS, SPXY, and HSPXY. The models based on the proposed HSPXY algorithm carried the overall best result among all regression models, which suggests the proposed algorithm may be taken as an alternative to other existing data partition methods. Partition (number theory) ; Mathematics ; Euclidean distance ; Correlation ; Correlation coefficient ; Algorithm ; Pattern recognition (psychology) ; Statistics ; Artificial intelligence ; Computer science ; Combinatorics ; Geometry Spectroscopy and Chemometric Analyses ; Analytical Chemistry and Chromatography ; Advanced Statistical Methods and Models
Authors: Alexey Boubnov | Janis Timoshenko | Cody J. Wrasman | Adam S. Hoffman | Matteo Cargnello | Anatoly I. Frenkel | Simon R. Bare
Source: Radiation Physics and Chemistry (article, 2019-04-29)
Field: Materials Science — Materials Chemistry

Insight into restructuring of Pd-Au nanoparticles using EXAFS Extended X-ray absorption fine structure ; Palladium ; Catalysis ; Nanoparticle ; Materials science ; Coordination number ; Scattering ; Particle (ecology) ; Spectral line ; Particle size ; Analytical Chemistry (journal) ; Physical chemistry ; Crystallography ; Chemistry ; Absorption spectroscopy ; Nanotechnology ; Physics ; Organic chemistry Catalytic Processes in Materials Science ; X-ray Spectroscopy and Fluorescence Analysis ; Electrocatalysts for Energy Conversion
Authors: Kun Li | Honghui Shang | Yunquan Zhang | Shigang Li | Baodong Wu | Dong Wang | Libo Zhang | Fang Li | Dexun Chen | Zhiqiang Wei
Source:  (article, 2019-11-07)
Field: Materials Science — Materials Chemistry

OpenKMC With more attention attached to nuclear energy, the formation mechanism of the solute clusters precipitation within complex alloys becomes intriguing research in the embrittlement of nuclear reactor pressure vessel (RPV) steels. Such phenomenon can be simulated with atomic kinetic Monte Carlo (AKMC) software, which evaluates the interactions of solute atoms with point defects in metal alloys. In this paper, we propose OpenKMC to accelerate large-scale KMC simulations on Sunway many-core architecture. To overcome the constraints caused by complex many-core architecture, we employ six levels of optimization in OpenKMC: (1) a new efficient potential computation model; (2) a group reaction strategy for fast event selection; (3) a software cache strategy; (4) combined communication optimizations; (5) a Transcription-Translation-Transmission algorithm for many-core optimization; (6) vectorization acceleration. Experiments illustrate that our OpenKMC has high accuracy and good scalability of applying hundred-billion-atom simulation over 5.2 million cores with a performance of over 80.1% parallel efficiency. Computer science ; Scalability ; Computational science ; Embrittlement ; Software ; Parallel computing ; Nuclear physics ; Physics ; Operating system Machine Learning in Materials Science ; Nuclear Materials and Properties ; X-ray Diffraction in Crystallography
Authors: Edward Price | Andre J. Gesquiere
Source: Scientific Reports (article, 2019-09-26)
Field: Materials Science — Materials Chemistry

An in vitro assay and artificial intelligence approach to determine rate constants of nanomaterial-cell interactions In vitro assays and simulation technologies are powerful methodologies that can inform scientists of nanomaterial (NM) distribution and fate in humans or pre-clinical species. For small molecules, less animal data is often needed because there are a multitude of in vitro screening tools and simulation-based approaches to quantify uptake and deliver data that makes extrapolation to in vivo studies feasible. Small molecule simulations work because these materials often diffuse quickly and partition after reaching equilibrium shortly after dosing, but this cannot be applied to NMs. NMs interact with cells through energy dependent pathways, often taking hours or days to become fully internalized within the cellular environment. In vitro screening tools must capture these phenomena so that cell simulations built on mechanism-based models can deliver relationships between exposure dose and mechanistic biology, that is biology representative of fundamental processes involved in NM transport by cells (e.g. membrane adsorption and subsequent internalization). Here, we developed, validated, and applied the FORECAST method, a combination of a calibrated fluorescence assay (CF) with an artificial intelligence-based cell simulation to quantify rates descriptive of the time-dependent mechanistic biological interactions between NMs and individual cells. This work is expected to provide a means of extrapolation to pre-clinical or human biodistribution with cellular level resolution for NMs starting only from in vitro data. Extrapolation ; Computer science ; Biodistribution ; Computational biology ; In vitro ; In vivo ; Biological system ; Chemistry ; Biochemical engineering ; Nanotechnology ; Biophysics ; Biology ; Materials science ; Mathematics ; Biochemistry ; Genetics Nanoparticles: synthesis and applications ; Nanoparticle-Based Drug Delivery ; Cell Image Analysis Techniques
Authors: Dário Passos | Daniela Rodrigues | Ana M. Cavaco | M. D. Carlos Antunes | Rui Guerra
Source: Sensors (article, 2019-11-26)
Field: Chemistry — Analytical Chemistry

Non-Destructive Soluble Solids Content Determination for ‘Rocha’ Pear Based on VIS-SWNIR Spectroscopy under ‘Real World’ Sorting Facility Conditions In this paper we report a method to determine the soluble solids content (SSC) of 'Rocha' pear (<i>Pyrus communis</i> L. cv. Rocha) based on their short-wave NIR reflectance spectra (500-1100 nm) measured in conditions similar to those found in packinghouse fruit sorting facilities. We obtained 3300 reflectance spectra from pears acquired from different lots, producers and with diverse storage times and ripening stages. The macroscopic properties of the pears, such as size, temperature and SSC were measured under controlled laboratory conditions. For the spectral analysis, we implemented a computational pipeline that incorporates multiple pre-processing techniques including a feature selection procedure, various multivariate regression models and three different validation strategies. This benchmark allowed us to find the best model/preproccesing procedure for SSC prediction from our data. From the several calibration models tested, we have found that Support Vector Machines provides the best predictions metrics with an RMSEP of around 0.82 ∘ Brix and 1.09 ∘ Brix for internal and external validation strategies respectively. The latter validation was implemented to assess the prediction accuracy of this calibration method under more 'real world-like' conditions. We also show that incorporating information about the fruit temperature and size to the calibration models improves SSC predictability. Our results indicate that the methodology presented here could be implemented in existing packinghouse facilities for single fruit SSC characterization. Calibration ; PEAR ; Sorting ; Pipeline (software) ; Feature selection ; Benchmark (surveying) ; Support vector machine ; Pyrus communis ; Computer science ; Biological system ; Artificial intelligence ; Mathematics ; Algorithm ; Statistics ; Geography Spectroscopy and Chemometric Analyses ; Postharvest Quality and Shelf Life Management ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Kevin Smith | Matthew Getzin | Josephine J. Garfield | Sanika Suvarnapathaki | Gulden Camci‐Unal | Ge Wang | Manos Gkikas
Source: Nanomaterials (article, 2019-07-30)
Field: Materials Science — Materials Chemistry

Nanophosphor-Based Contrast Agents for Spectral X-ray Imaging Lanthanide-based nanophosphors (NPhs) are herein developed as contrast agents for spectral X-ray imaging, highlighting the chemical, macromolecular and structural differences derived from ligand exchange on computed tomography (CT) and solvent dispersibility. Taking advantage of the ability of spectral X-ray imaging with photon-counting detectors to perform image acquisition, analysis, and processing at different energy windows (bins), enhanced signal of our K-edge materials was derived, improving sensitivity of CT imaging, and differentiation between water, tumor-mimic phantoms, and contrast materials. Our results indicate that the most effective of our oleic acid-stabilized K-edge nanoparticles can achieve 2-4x higher contrast than the examined iodinated molecules, making them suitable for deep tissue imaging of tissues or tumors. On the other hand, ligand exchange yielding poly(acrylic acid)-stabilized K-edge nanoparticles allows for high dispersibility and homogeneity in water, but with a lower contrast due to the high density of the polymer grafted, unless further engineering is probed. This is the first well-defined study that manages to correlate NPh grafting density with CT numbers and water dispersibility, laying the groundwork for the development of the next generation CT-guided diagnostic and/or theranostic materials. Contrast (vision) ; X-ray ; Materials science ; Spectral imaging ; Optics ; Remote sensing ; Physics ; Geology Luminescence Properties of Advanced Materials ; Advanced X-ray and CT Imaging ; X-ray Diffraction in Crystallography
Authors: Carla Gabriela Silva Leite | Eli Jorge da Cruz | Mattia Lago | A. Zambon | Irene Calliari | Vicente Afonso Ventrella
Source: Materials (article, 2019-09-09)
Field: Materials Science — Metals and Alloys

Nd: YAG Pulsed Laser Dissimilar Welding of UNS S32750 Duplex with 316L Austenitic Stainless Steel Duplex stainless steels (DSSs), a particular category of stainless steels, are employed in all kinds of industrial applications where excellent corrosion resistance and high strength are necessary. These good properties are provided by their biphasic microstructure, consisting of ferrite and austenite in almost equal volume fractions of phases. In the present work, Nd: YAG pulsed laser dissimilar welding of UNS S32750 super duplex stainless steel (SDSS) with 316L austenitic stainless steel (ASS), with different heat inputs, was investigated. The results showed that the fusion zone microstructure observed consisted of a ferrite matrix with grain boundary austenite (GBA), Widmanstätten austenite (WA) and intragranular austenite (IA), with the same proportion of ferrite and austenite phases. Changes in the heat input (between 45, 90 and 120 J/mm) did not significantly affect the ferrite/austenite phase balance and the microhardness in the fusion zone. Austenite ; Materials science ; Metallurgy ; Ferrite (magnet) ; Microstructure ; Welding ; Composite material Hydrogen embrittlement and corrosion behaviors in metals ; Welding Techniques and Residual Stresses ; Microstructure and Mechanical Properties of Steels
Authors: Yeng Seng Lee | Fwen Hoon Wee | Kok Yeow You | Z. Liyana | C.Y Lee | Mas Elyna Azol | M. Hafifi Ramli | Hong‐Seng Gan | M. S. Shakhirul | Nurulbariah Idris | Ee Meng Cheng
Source: Indonesian Journal of Electrical Engineering and Computer Science (article, 2019-04-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Study of single layer microwave absorber based on rice husk Ash/CNTs composites &lt;span&gt;This paper presents the study of microwave absorption single layer microwave absorber based on rice husk ash (RHA) with additional carbon nanotubes (CNTs) filler loading into the composites. The relative permittivity of RHA and CNTs composites (RHA/CNTs) were measured by using Agilent high temperature probe and 85070E software. The CST-MWS software is used to design and evaluate the microwave absorption of RHA and RHA/CNTs with metal backed plate. The microwave absorption of simulated and measured results is compared. The RHA microwave absorber only absorbed maximum, -8 dB at 10.8 GHz of the incident electromagnetic radiation and the RHA/CNTs absorbed less than -15 dB with wider bandwidth over 10.8 GHz to 12.8 GHz compare with RHA composites single layer microwave absorber. The results indicated that the RHA/CNTs composites have enhanced the microwave absorption of RHA composites.&lt;/span&gt; Microwave ; Materials science ; Husk ; Carbon nanotube ; Composite material ; Absorption (acoustics) ; Telecommunications Electromagnetic wave absorption materials ; Microwave-Assisted Synthesis and Applications ; Advanced Antenna and Metasurface Technologies
Authors: Dorin Copaci | Luís Moreno | Dolores Blanco
Source: Frontiers in Robotics and AI (article, 2019-09-04)
Field: Materials Science — Materials Chemistry

Two-Stage Shape Memory Alloy Identification Based on the Hammerstein–Wiener Model Thanks to characteristics, such as high force and light weight, a good biocompatibility, noiseless operation and simplicity, and relatively low-cost compared with other conventional actuators, actuators based on shape memory alloy are currently one of the most interesting research topics. They have been introduced in applications such robotics, medicine, automation, and so on. For a good actuator integration of these types of applications, proper control is needed, which seems to be a difficult task due to the hysteresis, dilatory response, and non-linear behavior. This work presents a new form of modeling of this type of actuator based on Hammerstein-Wiener model. This has been identified in two stages of the operation. When the activation temperature for the actuator is obtained by the Joule effect, electrically energy is transformed into thermal energy. In the second stage, the thermal energy is transformed into mechanical work. To fulfill this objective, experimental data [e.g., the input signal (pulse-width modulation), temperature signal, and position signal] from the two stages was obtained for a specific shape memory alloy wire and for specific environmental conditions. This data was used in the modeling process. The final model consists of a combination of the models from the two stages, which represent the behavior of the shape memory alloy actuator where the input signal is the pulse-width modulation signal and the output signal are the position of the actuator. Our results indicate that our model has a very similar response to the behavior of the real actuator. This model can be used to tune different control algorithms, simulate the entry system before manufacture and test on real devices. Actuator ; Computer science ; Shape-memory alloy ; SIGNAL (programming language) ; Control theory (sociology) ; Hysteresis ; Artificial intelligence ; Physics Shape Memory Alloy Transformations
Authors: Dominique Büchele | Madlen Chao | Markus Ostermann | M. Leenen | Ilko Bald
Source: Scientific Reports (article, 2019-11-26)
Field: Chemistry — Analytical Chemistry

Multivariate chemometrics as a key tool for prediction of K and Fe in a diverse German agricultural soil-set using EDXRF Within the framework of precision agriculture, the determination of various soil properties is moving into focus, especially the demand for sensors suitable for in-situ measurements. Energy-dispersive X-ray fluorescence (EDXRF) can be a powerful tool for this purpose. In this study a huge diverse soil set (n = 598) from 12 different study sites in Germany was analysed with EDXRF. First, a principal component analysis (PCA) was performed to identify possible similarities among the sample set. Clustering was observed within the four texture classes clay, loam, silt and sand, as clay samples contain high and sandy soils low iron mass fractions. Furthermore, the potential of uni- and multivariate data evaluation with partial least squares regression (PLSR) was assessed for accurate determination of nutrients in German agricultural samples using two calibration sample sets. Potassium and iron were chosen for testing the performance of both models. Prediction of these nutrients in 598 German soil samples with EDXRF was more accurate using PLSR which is confirmed by a better overall averaged deviation and PLSR should therefore be preferred. Partial least squares regression ; Loam ; Multivariate statistics ; Chemometrics ; Principal component analysis ; Soil test ; Soil texture ; Environmental science ; Silt ; Soil science ; Sample (material) ; Soil water ; Calibration ; Environmental chemistry ; Mathematics ; Chemistry ; Computer science ; Statistics ; Geology ; Machine learning Spectroscopy and Chemometric Analyses ; Soil Geostatistics and Mapping ; Heavy Metals in Plants
Authors: Jay Liu | Alham Alipuly | Tomasz Bączek | Ming Wah Wong | Petar Žuvela
Source: International Journal of Molecular Sciences (article, 2019-07-12)
Field: Chemistry — Spectroscopy

Quantitative Structure–Retention Relationships with Non-Linear Programming for Prediction of Chromatographic Elution Order In this work, we employed a non-linear programming (NLP) approach via quantitative structure-retention relationships (QSRRs) modelling for prediction of elution order in reversed phase-liquid chromatography. With our rapid and efficient approach, error in prediction of retention time is sacrificed in favor of decreasing the error in elution order. Two case studies were evaluated: (i) analysis of 62 organic molecules on the Supelcosil LC-18 column; and (ii) analysis of 98 synthetic peptides on seven reversed phase-liquid chromatography (RP-LC) columns with varied gradients and column temperatures. On average across all the columns, all the chromatographic conditions and all the case studies, percentage root mean square error (%RMSE) of retention time exhibited a relative increase of 29.13%, while the %RMSE of elution order a relative decrease of 37.29%. Therefore, sacrificing %RMSE(<i>t</i><sub>R</sub>) led to a considerable increase in the elution order predictive ability of the QSRR models across all the case studies. Results of our preliminary study show that the real value of the developed NLP-based method lies in its ability to easily obtain better-performing QSRR models that can accurately predict both retention time and elution order, even for complex mixtures, such as proteomics and metabolomics mixtures. Elution ; Chromatography ; Mean squared error ; Chemistry ; High-performance liquid chromatography ; Reversed-phase chromatography ; Retention time ; Mean squared prediction error ; Gradient elution ; Analytical Chemistry (journal) ; Mathematics ; Statistics Analytical Chemistry and Chromatography ; Chromatography in Natural Products ; Computational Drug Discovery Methods
Authors: Rubén Martı́n-Clemente | Vicente Zarzoso
Source: IEEE Transactions on Signal Processing (article, 2019-11-25)
Field: Chemistry — Analytical Chemistry

LDA via L1-PCA of Whitened Data Principal component analysis (PCA) and Fisher's linear discriminant analysis (LDA) are widespread techniques in data analysis and pattern recognition. Recently, the L1-norm has been proposed as an alternative criterion to classical L2-norm in PCA, drawing considerable research interest on account of its increased robustness to outliers. The present work proves that, combined with a whitening preprocessing step, L1-PCA can perform LDA in an unsupervised manner, i.e., sparing the need for labelled data. Rigorous proof is given in the case of data drawn from a mixture of Gaussians. A number of numerical experiments on synthetic as well as real data confirm the theoretical findings. Principal component analysis ; Linear discriminant analysis ; Pattern recognition (psychology) ; Outlier ; Artificial intelligence ; Robustness (evolution) ; Computer science ; Preprocessor ; Mathematics Spectroscopy and Chemometric Analyses ; Face and Expression Recognition ; Advanced Statistical Methods and Models
Authors: Md. Khalilur Rahman Khan | Hosne Ara Begum | Md. Razib Sheikh
Source: Journal of Textile Science and Technology (article, 2019-12-24)
Field: Materials Science — Polymers and Plastics

An Overview on the Spinning Triangle Based Modifications of Ring Frame to Reduce the Staple Yarn Hairiness Traditional ring spinning system is associated with the several limitations, one of which is yarn hairiness. The awareness has been vibrantly growing among the spinning mills for reducing staple yarn hairiness because of its detrimental effects on the subsequent processes (i.e., weaving, knitting, dyeing etc.) and the quality of resultant textile products. As a result, modified form of ring frame has attracted a lot of attention among the researchers for making yarn with the lower level of hairiness value. Besides the raw material parameters and some processing variables, spinning triangle (ST) is the most critical region that has decisive influence on the distribution of fiber tensions and their spatial orientations and shapes in staple yarn body. Thus, using appropriate methods to control the spinning triangle geometry actively has been a vital issue in the field of yarn hairiness reduction. In this paper, it is attempted to make an overview on the spinning triangle based modifications of ring frame that have been designed and destined to reduce hairiness. However, agent aided ring spinning system has also been briefly discussed in this paper as well. Yarn ; Spinning ; Weaving ; Ring (chemistry) ; Dyeing ; Textile ; Frame (networking) ; Clothing ; Engineering ; Mathematics ; Engineering drawing ; Mechanical engineering ; Composite material ; Materials science ; Geography Textile materials and evaluations ; Dyeing and Modifying Textile Fibers ; Additive Manufacturing and 3D Printing Technologies