jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Florent Langenfeld | Απόστολος Αξενόπουλος | Anargyros Chatzitofis | Daniela Crăciun | Petros Daras | Bowen Du | Andrea Giachetti | Yu‐Kun Lai | Haisheng Li | Yingbin Li | Majid Masoumi | Yuxu Peng | Paul L. Rosin | Jeremy Sirugue | Li Sun | Spyridon Thermos | Matthew Toews | Yang Wei | Yujuan Wu | Yujia Zhai | Tianyu Zhao | Yanping Zheng | Matthieu Montès
Source: Espace ÉTS (ETS) (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

SHREC 2018 - Protein Shape Retrieval &amp;lt;p&amp;gt;Proteins are macromolecules central to biological processes that display a dynamic and complex surface. They display mul- tiple conformations differing by local (residue side-chain) or global (loop or domain) structural changes which can impact drastically their global and local shape. Since the structure of proteins is linked to their function and the disruption of their interactions can lead to a disease state, it is of major importance to characterize their shape. In the present work, we report the performance in enrichment of six shape-retrieval methods (3D-FusionNet, GSGW, HAPT, DEM, SIWKS and WKS) on a 2 267 protein structures dataset generated for this protein shape retrieval track of SHREC&amp;rsquo;18.&amp;lt;/p&amp;gt; Computer science ; Protein structure ; Protein function ; Domain (mathematical analysis) ; Computational biology ; Biological system ; Biology ; Mathematics ; Genetics ; Biochemistry Enzyme Structure and Function ; Image Processing and 3D Reconstruction ; Genomics and Phylogenetic Studies
Authors: Wataru Tanaka | Masanori Arita
Source: Mass Spectrometry (article, 2018-06-13)
Field: Chemistry — Spectroscopy

Physicochemical Prediction of Metabolite Fragmentation in Tandem Mass Spectrometry Current bottleneck of comprehensive non-target metabolite identification is insufficient spectral library. Many research groups have tried to build a theoretical product ion spectral library independent of measurement condition or settings, but mechanisms of metabolite fragmentation are not fully clarified. To achieve the mechanistic prediction of metabolite fragmentation which covers a wide range of metabolites, we will discuss utilization of physicochemical calculation. We introduce bonding patterns, which include two bound atoms and chemical groups adjacent to the bond. Cleavage of each bonding pattern is simulated and its activation energy is precisely calculated with quantum chemistry and assigned on metabolites. By tracing low-energy bond cleavages, fragmentation of a dipeptide molecule is successfully predicted. Prediction on another metabolite requires some additional features to fully reproduce its experimentally observed product ions. Physicochemical calculation shows its promising ability to predict fragmentation pathways only from metabolite structures, while required improvements suggested by comparison between our prediction and standard spectra stored in database are also discussed. Moreover, to construct a prediction strategy which withstands the vast metabolite space, we have to build a comprehensive list of bonding patterns and their activation energy. As theoretically possible bonding patterns are huge in number, proper simplification of the patterns must be implemented. We will discuss how to achieve it in addition to the prediction results. Metabolite ; Fragmentation (computing) ; Metabolomics ; Chemistry ; Tandem mass spectrometry ; Biological system ; Bottleneck ; Mass spectrometry ; Computer science ; Computational chemistry ; Computational biology ; Biology ; Biochemistry ; Chromatography Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: Dora Domajnko | Dejan Križaj
Source: Sensors (article, 2018-07-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Lagging-Domain Model for Compensation of Hysteresis of xMR Sensors in Positioning Applications The hysteresis of magnetoresistive sensors remains a considerable cause of inaccuracy of positioning applications. The phenomena itself has been well studied and described by different physical and phenomenological models. Various biasing techniques have been proposed. However, the increased fabrication and computational price they require is undesirable. In this paper, a computational algorithm for the compensation of hysteresis of linear and rotary encoders is proposed. A lagging-domain model based on play operators is presented for prediction of hysteresis. The outlined procedure for the calibration of parameters allows the use of the algorithm for various types of encoders without knowing their exact material properties. The method was tested on different anisotropic magnetoresistive and tunneling magnetoresistive sensors. Results show that the impact of hysteresis was reduced by up to 90% without a significant increase of computational time or production costs. Hysteresis ; Lagging ; Compensation (psychology) ; Magnetoresistance ; Encoder ; Computer science ; Electronic engineering ; Biasing ; Magnetic hysteresis ; Materials science ; Voltage ; Engineering ; Electrical engineering ; Condensed matter physics ; Physics ; Mathematics ; Magnetic field ; Statistics Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Electric Motor Design and Analysis
Authors: Long Chen | Yingwen Wu | Tianjun Li | Zhuo Chen
Source: Journal of Analytical Methods in Chemistry (article, 2018-08-29)
Field: Chemistry — Analytical Chemistry

Collaborative Penalized Least Squares for Background Correction of Multiple Raman Spectra Although Raman spectroscopy has been widely used as a noninvasive analytical tool in various applications, backgrounds in Raman spectra impair its performance in quantitative analysis. Many algorithms have been proposed to separately correct the background spectrum by spectrum. However, in real applications, there are commonly multiple spectra collected from the close locations of a sample or from the same analyte with different concentrations. These spectra are strongly correlated and provide valuable information for more robust background correction. Herein, we propose two new strategies to remove background for a set of related spectra collaboratively. Based on weighted penalized least squares, the new approaches will use the fused weights from multiple spectra or the weights from the average spectrum to estimate the background of each spectrum in the set. Background correction results from both simulated and real experimental data demonstrate that the proposed collaborative approaches outperform traditional algorithms which process spectra individually. Raman spectroscopy ; Spectral line ; Set (abstract data type) ; Least-squares function approximation ; Analyte ; Computer science ; Pattern recognition (psychology) ; Algorithm ; Artificial intelligence ; Mathematics ; Statistics ; Chemistry ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Optical Imaging and Spectroscopy Techniques
Authors: Koichi Fujiwara | Manabu Kano
Source: Frontiers in Chemistry (article, 2018-05-22)
Field: Chemistry — Analytical Chemistry

Nearest Correlation-Based Input Variable Weighting for Soft-Sensor Design In recent years, soft-sensors have been widely used for estimating product quality or other important variables when online analyzers are not available. In order to construct a highly accurate soft-sensor, appropriate data preprocessing is required. In particular, the selection of input variables or input features is one of the most important techniques for improving estimation performance. Fujiwara et al. proposed a variable selection method, in which variables are clustered into variable groups based on the correlation between variables by nearest correlation spectral clustering (NCSC), and each variable group is examined as to whether or not it should be used as input variables. This method is called NCSC-based variable selection (NCSC-VS). However, these NCSC-based methods have a lot of parameters to be tuned, and their joint optimization is burdensome. The present work proposes an effective input variable weighting method to be used instead of variable selection to conserve labor required for parameter tuning. The proposed method, referred to herein as NC-based variable weighting (NCVW), searches input variables that have the correlation with the output variable by using the NC method and calculates the correlation similarity between the input variables and output variable. The input variables are weighted based on the calculated correlation similarities, and the weighted input variables are used for model construction. There is only one parameter in the proposed NCVW since the NC method has one tuning parameter. Thus, it is easy for NCVW to develop a soft-sensor. The usefulness of the proposed NCVW is demonstrated through an application to calibration model design in a pharmaceutical process. Soft sensor ; Weighting ; Variable (mathematics) ; Feature selection ; Cluster analysis ; Computer science ; Correlation ; Mathematics ; Preprocessor ; Mathematical optimization ; Data mining ; Algorithm ; Statistics ; Pattern recognition (psychology) ; Artificial intelligence ; Process (computing) Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Advanced Chemical Sensor Technologies
Authors: Marzieh Ayati | Danica Wiredja | Daniela Schlatzer | Sean Maxwell | Ming Li | Mehmet Koyutürk | Mark R. Chance
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2018-03-04)
Field: Chemistry — Spectroscopy

CoPhosK: A Method for Comprehensive Kinase Substrate Annotation Using Co-phosphorylation Analysis Abstract We present CoPhosK to predict kinase-substrate associations for phosphopeptide substrates detected by mass spectrometry (MS). The tool utilizes a Naïve Bayes framework with priors of known kinase-substrate associations (KSAs) to generate its predictions. Through the mining of MS data for the collective dynamic signatures of the kinases’ substrates revealed by correlation analysis of phosphopeptide intensity data, the tool infers KSAs in the data for the considerable body of substrates lacking such annotations. We benchmarked the tool against existing approaches for predicting KSAs that rely on static information (e.g. sequences, structures and interactions) using publically available MS data, including breast, colon, and ovarian cancer models. The benchmarking reveals that co-phosphorylation analysis can significantly improve prediction performance when static information is available (about 35% of sites) while providing reliable predictions for the remainder, thus tripling the KSAs available from the experimental MS data providing a to comprehensive and reliable characterization of the landscape of kinase-substrate interactions well beyond current limitations. Author Summary Kinases play an important role in cellular regulation and have emerged as an important class of drug targets for many diseases, particularly cancers. Comprehensive identification of the links between kinases and their substrates enhances our ability to understand the underlying mechanism of diseases and signalling networks to drive drug discovery. Most of the current computational methods for prediction of kinase-substrate associations use static information such as sequence motifs and physical interactions to generate predictions. However, phosphorylation is a dynamic process and these static predictions may overlook unique features of cellular context, where kinases may be rewired. In this manuscript, we propose a computational method, CoPhosK, which uses the mass spectrometry based phosphoproteomics data to predict the kinase for all identified phosphosites in the experiment. We show that our approach complements and extends existing approaches. Kinase ; Computer science ; Computational biology ; Annotation ; Data mining ; Chemistry ; Biology ; Biochemistry ; Artificial intelligence Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics
Authors: DEMİRYÜREK OĞUZ | KILIÇ AYÇA
Source: Industria Textila (article, 2018-07-01)
Field: Materials Science — Polymers and Plastics

An investigation on the unevenness, hairiness and friction coefficientproperties of cotton-bamboo blended ring-spun yarns This study aims to investigate the frictional and other important yarn properties such as unevenness and hairiness of cotton/bamboo blended yarns. For this purpose, %100 cotton, %50/%50 cotton/bamboo and %100 bamboo ring spun yarns were produced in five different twist coefficients (ae: 3.5; 3.7; 4.0; 4.2; 4.5) and in four different linear densities (Ne 16/1; 20/1; 24/1; 28/1). General factorial design was used for analyzing the data. In conclusion, the highest friction coefficient and the lowest hairiness were obtained for the pure bamboo blends; whereas, the highest unevenness values were observed in pure cotton Bamboo ; Materials science ; Yarn ; Composite material ; Friction coefficient ; Coefficient of friction ; Factorial experiment ; Mathematics ; Statistics Textile materials and evaluations ; Mechanical Engineering and Vibrations Research
Authors: R. Rizal Isnanto | Munawar Agus Riyadi | Muhammad Fahmi Awaj
Source: International Journal of Electrical and Computer Engineering (IJECE) (article, 2018-06-01)
Field: Chemistry — Analytical Chemistry

Herb Leaves Recognition using Gray Level Co-occurrence Matrix and Five Distance-based Similarity Measures Herb medicinal products derived from plants have long been considered as an alternative option for treating various diseases. In this paper, the feature extraction method used is Gray Level Co-occurrence Matrix (GLCM), while for its recognition using the metric calculations of Chebyshev, Cityblock, Minkowski, Canberra, and Euclidean distances. The method of determining the GLCM Analysis based on the texture analysis resulting from the extraction of this feature is Angular Second Moment, Contrast, Inverse Different Moment, Entropy as well as its Correlation. The recognition system used 10 leaf test images with GLCM method and Canberra distance resulted in the highest accuracy of 92.00%. While the use of 20 and 30 test data resulted in a recognition rate of 50.67% and 60.00%. Co-occurrence matrix ; Pattern recognition (psychology) ; Artificial intelligence ; Gray level ; Euclidean distance ; Minkowski distance ; Mathematics ; Feature extraction ; Computer science ; Inverse ; Distance matrix ; Image processing ; Image texture ; Combinatorics ; Image (mathematics) ; Geometry Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Computer Science and Engineering
Authors: Enzhe Song | Guofeng Zhao | Chong Yao | Zi-Kun Ma | Shun-Liang Ding | Xiuzhen Ma
Source: Mathematical Problems in Engineering (article, 2018-09-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Study of Nonlinear Characteristics and Model Based Control for Proportional Electromagnet The nonlinear characteristics of proportional electromagnet caused by hysteresis bring great difficulties on its accurate position tracking control by current. In order to enhance the practicability and reliability of long stroke electromagnet in case of position sensor fault and improve the position tracking performance during current closed-loop control, experimental investigations on the electromagnet actuator hysteresis characteristics of diesel engine governor are carried out to analyze the system dynamic features and the effects of hysteresis on actuator position tracking performance. It is clear that hysteresis can significantly hinder the accurate position control of the electromagnet actuator. Consequently, the fuel injection will be delayed, which will lead to hysteresis of engine speed control as well as deterioration of engine performance. In this paper, the hysteresis phenomenon of an actuator and its influence on control performance of engine are investigated. The model of proportional electromagnet actuator (PEA) is established and the hysteresis principle is analyzed. Then the inverse model control strategy based on neural network (NN) is proposed to linearize the transfer behavior of electromagnet and compensate for the magnet hysteresis. Rapid control prototyping (RCP) experiment based on MicroAutoBox is further implemented to validate the real-time performance of the proposed control strategy in D6114 diesel engine. The results show that the speed fluctuation (SF) under steady-state conditions (especially under idle speed condition) and the recovery time as well as the overshoot under transient conditions are significantly improved. This makes it possible to develop redundant electromagnet driving control strategy. Electromagnet ; Control theory (sociology) ; Overshoot (microwave communication) ; Actuator ; Engineering ; Hysteresis ; Diesel engine ; Nonlinear system ; Magnet ; Control engineering ; Automotive engineering ; Computer science ; Mechanical engineering ; Control (management) ; Physics ; Electrical engineering Magnetic Properties and Applications ; Electric Motor Design and Analysis ; Piezoelectric Actuators and Control
Authors: Zaleha Mohamad | Al Emran Ismail | Muhd Hafeez Zainulabidin | Fazimah Mat Noor | Hanani Abd Wahab | Maznan Ismon | Mohd Amran Madlan | Shahruddin Mahzan
Source: International Journal of Integrated Engineering (article, 2018-12-01)
Field: Materials Science — Polymers and Plastics

Classification of Damage Severity in Natural Fibre Composites Using Principal Component Analysis The present paper deals with an approach in predicting the classification of damage in natural fibre reinforced composites (NFC) panel using signal processing procedure as indicative parameters and principle component analysis as a learning tool. An impact event produced strain data and the response signal was investigated. An effective impact damage classification procedure is established using a principal component analysis approach. The system was trained to classify the damage class based on the input from the signal features. It has been observed that, the network can learn and classify effectively the damage size in the panel which is the combination features retained at about 84.5% of the variance Principal component analysis ; SIGNAL (programming language) ; Composite material ; Component (thermodynamics) ; Signal processing ; Pattern recognition (psychology) ; Computer science ; Materials science ; Artificial intelligence ; Structural engineering ; Engineering Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites ; Mechanical Engineering and Vibrations Research
Authors: Martin Jacob | Toby Sanders | Nicolas Bernier | Adeline Grenier | Rafael Bortolin Pinheiro | Frédéric Mazen | Pascale Bayle‐Guillemaud | Zineb Saghi
Source: Microscopy and Microanalysis (article, 2018-08-01)
Field: Materials Science — Surfaces, Coatings and Films

Multivariate Analysis and Compressed Sensing Methods for Spectroscopic Electron Tomography of Semiconductor Devices An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button. Semiconductor ; Multivariate statistics ; Materials science ; Compressed sensing ; Electron ; Optoelectronics ; Computer science ; Physics ; Artificial intelligence ; Nuclear physics Electron and X-Ray Spectroscopy Techniques ; CCD and CMOS Imaging Sensors ; Geophysical and Geoelectrical Methods
Authors: Hassiba Benchekor | Abdelkader Iddou | Hafida Hentit | Abdallah Aziz | Jeferson Steffanello Piccin
Source: Key engineering materials (article, 2018-02-01)
Field: Chemistry — Organic Chemistry

Multilayer Adsorption of Purple NR5 Industrial Dye by &lt;i&gt;Aristeus antennautus&lt;/i&gt; Shell in Aqueous Solution In this work, chitin-based material has been proposed for removing the purple NR5 dye in aqueous solution. The material chosen is from shrimp shell: Aristeus antennautus. This biomaterial is used as a raw form. The adsorption isotherms obtained showed a behavior of L3a indicating the passage of monolayer to multilayer adsorption. The results are well correlated with the BET model, (R²&gt;0.99). And the maximum monolayer adsorption capacity was found to be 667.33 mg/g. The kinetic data were evaluated using pseudo first order, pseudo second order and the intraparticle diffusion models. The chemical nature of the biomaterial is confirmed by Fourier Transform infrared spectroscopy (FTIR). Monolayer ; Aqueous solution ; Adsorption ; Fourier transform infrared spectroscopy ; Biomaterial ; Nuclear chemistry ; Chemistry ; Materials science ; Chemical engineering ; Shell (structure) ; Chromatography ; Analytical Chemistry (journal) ; Organic chemistry ; Nanotechnology ; Composite material Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal ; Graphene and Nanomaterials Applications
Authors: Cheng Wang | Youping Tu | Zhikang Yuan | Han Jiang
Source:  (article, 2018-06-01)
Field: Materials Science — Materials Chemistry

Study on Breakdown of HTV Silicone Rubber After Water Invasion Decay-Like fracture of composite insulators had become a problem that has plagued the operating department in recent years. People had found that there were perforations on the silicone rubber house of the Decay-Like fracture insulators. It was speculated that the breakdown of the silicone rubber house during operation was the cause of perforation. The effect of water invasion on the breakdown characteristics of HTV silicone rubber was studied in this paper. Firstly, the water absorption test was carried out on HTV silicone rubber, and the water absorption characteristics of the material based on the Langmuir diffusion model were obtained. The change law of the content of free water and bound water during the water absorption process were studied. Secondly, we measured the breakdown voltage of silicone rubber under different water absorption degrees, and the relationship between free water content, as well as bound water, and the breakdown voltage of silicone rubber was obtained, respectively. We found that the free water content in the silicone rubber has a good correspondence with the breakdown voltage. We proposed that the breakdown voltage drop of HTV silicone rubber was caused by the formation of a water bridge under electric field. Silicone rubber ; Composite material ; Materials science ; Absorption of water ; Breakdown voltage ; Natural rubber ; Silicone ; Forensic engineering ; Voltage ; Electrical engineering High voltage insulation and dielectric phenomena