jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Claudia Millán | Ronan M. Keegan | Joana Pereira | Massimo Sammito | Adam J. Simpkin | Airlie J. McCoy | Andrei N. Lupas | Marcus D. Hartmann | Daniel J. Rigden | Randy J. Read
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-06-21)
Field: Materials Science — Materials Chemistry

Assessing the utility of CASP14 models for molecular replacement Abstract The assessment of CASP models for utility in molecular replacement is a measure of their use in a valuable real-world application. In CASP7, the metric for molecular replacement assessment involved full likelihood-based molecular replacement searches; however, this restricted the assessable targets to crystal structures with only one copy of the target in the asymmetric unit, and to those where the search found the correct pose. In CASP10, full molecular replacement searches were replaced by likelihood-based rigid-body refinement of models superimposed on the target using the LGA algorithm, with the metric being the refined likelihood (LLG) score. This enabled multi-copy targets and very poor models to be evaluated, but a significant further issue remained: the requirement of diffraction data for assessment. We introduce here the relative-expected-LLG (reLLG), which is independent of diffraction data. This reLLG is also independent of any crystal form, and can be calculated regardless of the source of the target, be it X-ray, NMR or cryo-EM. We calibrate the reLLG against the LLG for targets in CASP14, showing that it is a robust measure of both model and group ranking. Like the LLG, the reLLG shows that accurate coordinate error estimates add substantial value to predicted models. We find that refinement by CASP groups can often convert an inadequate initial model into a successful MR search model. Consistent with findings from others, we show that the AlphaFold2 models are sufficiently good, and reliably so, to surpass other current model generation strategies for attempting molecular replacement phasing. CASP ; Molecular replacement ; Metric (unit) ; Ranking (information retrieval) ; Measure (data warehouse) ; Computer science ; Diffraction ; Algorithm ; Data mining ; Artificial intelligence ; Physics ; Protein structure prediction ; Optics ; Protein structure ; Nuclear magnetic resonance Machine Learning in Materials Science ; Advanced NMR Techniques and Applications ; Enzyme Structure and Function
Authors: Carole Wright | B.B. Wedding | S. Grauf | Olivia Whybird
Source: Marine and Freshwater Research (article, 2021-03-31)
Field: Chemistry — Analytical Chemistry

Age estimation of barramundi (Lates calcarifer) over multiple seasons from the southern Gulf of Carpentaria using FT-NIR spectroscopy The age of whole otoliths from barramundi (Lates calcarifer) obtained from the southern Gulf of Carpentaria were estimated using Fourier transform near-infrared (FT-NIR) spectroscopy. Otoliths from 1716 barramundi collected in 2006, 2009 and 2012–2015 were used in this study. Partial least-squares regression models (PLS-R) and multiple linear regression models (MLR) were developed from the diffuse reflectance spectra and the age was obtained from traditional sectioned otoliths. Calibration models were built up over consecutive years (2012–2015) by using a subset of the samples and used to predict the age of the remaining samples and samples from the following year. Results suggest that when seasonal (temporal) variability is incorporated into the calibration model, FT-NIR has the ability to predict barramundi age (validation R2 ranged from 0.73 to 0.78; RMSEP ranged from 6.92 to 7.64 months). The predicted age class was within 1 year of the reference age in over 96% of the samples. These models were also able to predict the age of otoliths from 2006 and 2009, which were retrieved from long-term storage (validation R2 ranged from 0.77 to 0.84; RMSEP ranged from 8.66 to 10.88 months). The results from this study have shown the potential for barramundi from the southern Gulf of Carpentaria to be aged quickly and accurately by using FT-NIR. Barramundi ; Carpentaria ; Lates ; Otolith ; Estuary ; Partial least squares regression ; Biology ; Calibration ; Fishery ; Fish <Actinopterygii> ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Isotope Analysis in Ecology
Authors: Zohir Tabet | Ahmed Belaadi | Messaouda Boumaaza | Mostefa Bourchak
Source: Research Square (preprint, 2021-05-12)
Field: Materials Science — Polymers and Plastics

Drilling of a Bidirectional Jute Fibre and Cork-Reinforced Polymer Biosandwich Structure: ANN and RSM approaches for Modelling and Optimization Abstract The present study examined the effects of drilling parameters such as spindle speed ( N ), feed rate ( f ), diameter of the tools ( d ) and drill geometry such as twist drills (HSS-TiN) and brad &amp; spur drills (BSD) used on delamination damage in a biosandwich structure consisting of an epoxy matrix reinforced with bidirectional jute fibres and cork (JFCE). Response surface methodology (RSM) and artificial neural networks (ANNs) were exploited to evaluate the influence and interaction of the cutting parameters on the delamination factor ( F d ) at the output during drilling. In addition, several optimization methods, such as desirability-based RSM, the genetic algorithm (GA) and the fmincon function, were applied to validate the optimal combination of cutting parameters ( f, N and d ) in the structures studied in biosandwiches during this research. According to the experimental results, severe damage was indeed observed with the BSD tool ( F d = 1.684) compared to the HSS-TiN tool ( F d = 1.555) for the same cutting conditions. To obtain the minimum F d , the optimum conditions obtained by GA were respectively 1397.54 rev/min, 51.162 mm/min and 5.981 mm for HSS-TiN for f , N and d . Tin ; Delamination (geology) ; Response surface methodology ; Drilling ; Drill ; Cork ; Epoxy ; Materials science ; Composite material ; Artificial neural network ; Structural engineering ; Mechanical engineering ; Computer science ; Engineering ; Metallurgy ; Geology ; Machine learning Natural Fiber Reinforced Composites ; Plant Surface Properties and Treatments ; Advanced machining processes and optimization
Authors: Fabio Corti | Abdelazeem Hassan Shehata | Antonino Laudani | E. Cardelli
Source: Energies (article, 2021-10-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Design and Comparison of the Performance of 12-Pulse Rectifiers for Aerospace Applications In this paper, a conventional 12-pulse transformer unit (CTU) and an autotransformer 12-pulse transformer unit (ATU) are compared in the view of the RTCA DO-160 standard for aircraft applications. The design of the magnetic components is proposed via a coupled FEM-circuital analysis in the time domain for an 800 Hz/2 kW system. Input AC distortion, power factor, and output DC ripple are evaluated through simulations. An accurate power loss analysis is carried out, taking into account copper losses, magnetic losses, and power losses due to power switches. The reduction in the size and weight of the ATU with respect to the CTU solution is discussed, including the need for filtering systems and the standard requirements. Autotransformer ; Ripple ; Transformer ; Electronic engineering ; Electrical engineering ; Aerospace ; Engineering ; Power factor ; Copper loss ; Automotive engineering ; Computer science ; Electromagnetic coil ; Distribution transformer ; Voltage Magnetic Properties and Applications ; Electromagnetic Launch and Propulsion Technology ; Induction Heating and Inverter Technology
Authors: Carter Rhea | Laurie Rousseau-Nepton | S. Prunet | Myriam Prasow-Émond | Julie Hlavacek-Larrondo | N. Vale Asari | Kathryn Grasha | Laurence Perreault-Levasseur
Source: The Astrophysical Journal (article, 2021-04-01)
Field: Chemistry — Analytical Chemistry

A Machine-learning Approach to Integral Field Unit Spectroscopy Observations. II. H ii Region Line Ratios Abstract In the first paper of this series, we demonstrated that neural networks can robustly and efficiently estimate kinematic parameters for optical emission-line spectra taken by SITELLE at the Canada–France–Hawaii Telescope. This paper expands upon this notion by developing an artificial neural network to estimate the line ratios of strong emission lines present in the SN1, SN2, and SN3 filters of SITELLE. We construct a set of 50,000 synthetic spectra using line ratios taken from the Mexican Million Model database replicating H ii regions. Residual analysis of the network on the test set reveals the network’s ability to apply tight constraints to the line ratios. We verified the network’s efficacy by constructing an activation map, checking the [ N ii ] doublet fixed ratio, and applying a standard k-fold cross-correlation. Additionally, we apply the network to SITELLE observations of M33; the residuals between the algorithm’s estimates and values calculated using standard fitting methods show general agreement. Moreover, the neural network reduces the computational costs by two orders of magnitude. Although standard fitting routines do consistently well depending on the signal-to-noise ratio of the spectral features, the neural network can also excels at predictions in the low signal-to-noise regime within the controlled environment of the training set as well as on observed data when the source spectral properties are well constrained by models. These results reinforce the power of machine learning in spectral analysis. Artificial neural network ; Algorithm ; Computer science ; Artificial intelligence ; Line (geometry) ; Spectral line ; Residual ; Noise (video) ; Machine learning ; Mathematics ; Physics ; Geometry Spectroscopy and Chemometric Analyses ; Spectroscopy and Laser Applications ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Alexander Scott Ditter | Danil E. Smiles | Daniel Lussier | Alison B. Altman | Mukesh Bachhav | Lingfeng He | Michael W. Mara | Claude Degueldre | Stefan G. Minasian | David K. Shuh
Source: Journal of Synchrotron Radiation (article, 2021-12-23)
Field: Materials Science — Materials Chemistry

Chemical and elemental mapping of spent nuclear fuel sections by soft X-ray spectromicroscopy Soft X-ray spectromicroscopy at the O K-edge, U N<sub>4,5</sub>-edges and Ce M<sub>4,5</sub>-edges has been performed on focused ion beam sections of spent nuclear fuel for the first time, yielding chemical information on the sub-micrometer scale. To analyze these data, a modification to non-negative matrix factorization (NMF) was developed, in which the data are no longer required to be non-negative, but the non-negativity of the spectral components and fit coefficients is largely preserved. The modified NMF method was utilized at the O K-edge to distinguish between two components, one present in the bulk of the sample similar to UO<sub>2</sub> and one present at the interface of the sample which is a hyperstoichiometric UO<sub>2+x</sub> species. The species maps are consistent with a model of a thin layer of UO<sub>2+x</sub> over the entire sample, which is likely explained by oxidation after focused ion beam (FIB) sectioning. In addition to the uranium oxide bulk of the sample, Ce measurements were also performed to investigate the oxidation state of that fission product, which is the subject of considerable interest. Analysis of the Ce spectra shows that Ce is in a predominantly trivalent state, with a possible contribution from tetravalent Ce. Atom probe analysis was performed to provide confirmation of the presence and localization of Ce in the spent fuel. Atom probe ; Analytical Chemistry (journal) ; Oxidation state ; Ion beam ; Uranium ; Ion ; Oxide ; Uranium oxide ; Nuclear fission product ; Beam (structure) Nuclear Materials and Properties ; Radioactive element chemistry and processing ; Nuclear reactor physics and engineering
Authors: Xiaoli Liu | Zhibin Li
Source: EURASIP Journal on Advances in Signal Processing (article, 2021-08-06)
Field: Materials Science — Polymers and Plastics

Influence mechanism of running sportswear fatigue based on BP neural network Abstract The fatigue of running sportswear is reflected in the fatigue caused by the tightness of the tights on the skin surface of the limbs, trunk, and other parts during long-term running sports. However, the current research on the fatigue of running sportswear is not deep enough. Therefore, the purpose of this study is to study the mechanism of the fatigue of running sportswear based on BP neural network. This article first takes sportswear as the starting point and uses the surface myoelectricity index as a physiological quantity as a means to combine clothing with sports medicine and sports physiology, breaking the traditional shackles of subjective assessment of fatigue, and giving play to the advantages of interdisciplinary to expand the new direction of the apparel industry and, secondly, use muscle fatigue evaluation method to analyze the muscles of the lower leg under the pressure of sportswear to analyze the strength of the EMG signal and the number of participating sports units and the frequency of discharge synchronization. Experimental data shows that the AUC is 0.756 when wearing sports tights, the sensitivity and specificity are 72% and 19%, and the accuracy is 65%. The experimental results show that clothing pressure affects the fatigue of running sportswear based on BP neural network. Clothing ; Mechanism (biology) ; Closing (real estate) ; Muscle fatigue ; Computer science ; Point (geometry) ; Artificial neural network ; Synchronization (alternating current) ; Physical medicine and rehabilitation ; Simulation ; Artificial intelligence ; Electromyography ; Medicine ; Mathematics ; Telecommunications ; Business Textile materials and evaluations ; Sports Performance and Training ; Thermoregulation and physiological responses
Authors: Iracema Caballero | Massimo Sammito | Pavel V. Afonine | Isabel Usón | Randy J. Read | Airlie J. McCoy
Source: Acta Crystallographica Section D Structural Biology (article, 2021-02-01)
Field: Materials Science — Materials Chemistry

Detection of translational noncrystallographic symmetry in Patterson functions Detection of translational noncrystallographic symmetry (TNCS) can be critical for success in crystallographic phasing, particularly when molecular-replacement models are poor or anomalous phasing information is weak. If the correct TNCS is detected then expected intensity factors for each reflection can be refined, so that the maximum-likelihood functions underlying molecular replacement and single-wavelength anomalous dispersion use appropriate structure-factor normalization and variance terms. Here, an analysis of a curated database of protein structures from the Protein Data Bank to investigate how TNCS manifests in the Patterson function is described. These studies informed an algorithm for the detection of TNCS, which includes a method for detecting the number of vectors involved in any commensurate modulation (the TNCS order). The algorithm generates a ranked list of possible TNCS associations in the asymmetric unit for exploration during structure solution. Phaser ; Normalization (sociology) ; Molecular replacement ; Symmetry (geometry) ; Reflection symmetry ; Algorithm ; Reflection (computer programming) ; Physics ; Computer science ; Variance (accounting) ; Function (biology) ; Optics ; Statistical physics ; Computational physics ; Protein structure ; Mathematics ; Biology ; Geometry ; Nuclear magnetic resonance ; Business Enzyme Structure and Function ; Protein Structure and Dynamics ; Crystallization and Solubility Studies
Authors: Sulaiman Abdulkareem | Joshua O. Ighalo | Adewale George Adeniyi
Source: Iranica Journal of Energy and Environment (article, 2021-01-01)
Field: Materials Science — Polymers and Plastics

Evaluation of the Electrical Characteristics of Recycled Iron Reinforced Polystyrene Composites The prospective reuse of iron fillings from the milling machine and polystyrene in the solid waste streams in the production of plastic composites were considered in this study. The preparation, electrical properties, density, void fraction and particle distributions of the solvated polystyrene filled composites were all investigated as a function of recycled iron fillings concentration. The composites were developed by hand layup technique and cured by casting under ambient conditions (25 ± 2oC) for 7 days. The compared micrographs confirmed well-dispersed recycled iron fillings in polystyrene matrix and decreasing void fraction as iron filling increases in the composites. The highest electrical conductivity and density values of the composites were obtained at the highest iron filling composition of 40 wt% as 5.91 × 10-07 S/cm and 1.31 g/cm3, respectively. The developed iron polystyrene composite has good electrical properties, making it suitable to be an alternative material for metals. Materials science ; Polystyrene ; Composite material ; Void (composites) ; Composite number ; Polymer Natural Fiber Reinforced Composites ; Recycling and Waste Management Techniques ; Innovations in Concrete and Construction Materials
Authors: Rola Houhou | Petra Rösch | Jürgen Popp | Thomas Bocklitz
Source: Analytical and Bioanalytical Chemistry (article, 2021-05-14)
Field: Chemistry — Analytical Chemistry

Comparison of functional and discrete data analysis regimes for Raman spectra Raman spectral data are best described by mathematical functions; however, due to the spectroscopic measurement setup, only discrete points of these functions are measured. Therefore, we investigated the Raman spectral data for the first time in the functional framework. First, we approximated the Raman spectra by using B-spline basis functions. Afterwards, we applied the functional principal component analysis followed by the linear discriminant analysis (FPCA-LDA) and compared the results with those of the classical principal component analysis followed by the linear discriminant analysis (PCA-LDA). In this context, simulation and experimental Raman spectra were used. In the simulated Raman spectra, normal and abnormal spectra were used for a classification model, where the abnormal spectra were built by shifting one peak position. We showed that the mean sensitivities of the FPCA-LDA method were higher than the mean sensitivities of the PCA-LDA method, especially when the signal-to-noise ratio is low and the shift of the peak position is small. However, for a higher signal-to-noise ratio, both methods performed equally. Additionally, a slight improvement of the mean sensitivity could be shown if the FPCA-LDA method was applied to experimental Raman data. Raman spectroscopy ; Principal component analysis ; Linear discriminant analysis ; Context (archaeology) ; Functional data analysis ; Spectral line ; Pattern recognition (psychology) ; Noise (video) ; Sensitivity (control systems) ; Position (finance) ; Analytical Chemistry (journal) ; Chemistry ; Mathematics ; Artificial intelligence ; Statistics ; Optics ; Computer science ; Physics ; Chromatography Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Mohammad Alkhedher
Source: Nanotechnology Reviews (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Hygrothermal environment effect on the critical buckling load of FGP microbeams with initial curvature integrated by CNT-reinforced skins considering the influence of thickness stretching Abstract Due to the need for structures with refined properties to bear against different loading conditions, recently, carbon nanotubes (CNTs) have been used widely to reinforce them. The extremely high stiffness of CNTs makes them significant as one of the best reinforcements to improve the mechanical behaviors of structures. This work focuses on microbeam buckling response with an initial curvature that includes three layers. The mid-layer that is known as the core is constituted of functionally graded porous (FGP) materials and two CNT-reinforced composite skins are bonded to the core to integrate it. The whole structure is affected by the hygrothermal environment and springs and shear layers are put below it. For the first time, for such a structure, a refined shear deformation theory (RSDT) as a higher-order theory that considers thickness stretching effect in polar coordinates is used that presents more accurate results, especially for deeply curved beams. Modified couple stress theory (MCST) in combination with the virtual displacement principle is utilized to establish the governing equations. The obtained results demonstrate the significance of porosity percentage and CNTs’ addition to the skins on the critical nanotubes buckling load. Also, the different behaviors of the microstructure at various temperatures are analyzed and discussed in detail. Materials science ; Buckling ; Composite material ; Curvature ; Carbon nanotube ; Stiffness ; Microbeam ; Microstructure ; Structural engineering ; Virtual work ; Porosity ; Finite element method ; Geometry ; Engineering ; Optics Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Carbon Nanotubes in Composites
Authors: Ji-Young Choi | Jiyoon Kim | Jungsoo Kim | Sung-Hyeon Jeong | Minhyun Kim | Sanghyeok Park | Kwang‐Deog Moon
Source: Korean Journal of Food Preservation (article, 2021-07-01)
Field: Chemistry — Analytical Chemistry

Hyperspectral imaging technigue for monitoring moisture content of blueberry during the drying process Changes in the moisture content (MC) of blueberries during drying was monitored by hyperspectral image analysis, and the degree of drying was determined using the partial least squares (PLS) model. Blueberries (n=820) were dried at 35°C for 0 (control), 3, 6, 9 and 12 days. The PLS discriminant analysis prediction accuracy of smoothing the pre-processed data was the highest. Regression coefficients were high at 706, 790, 827, 868, and 894 nm, corresponding to water molecules and carbohydrates (830-840 nm). To develop a prediction model for blueberry MC, 150 hyperspectral images were obtained from 30 samples per group. The MC of each group was also analyzed. The accuracy of the MC prediction model pretreated by the multiplicative scatter correction method was the highest at 0.9302. As indicated by Pearson’s correlation analysis, the blueberry MC showed a high correlation of 0.95 with the total soluble solid contents, brightness, and total flavonoid contents. These results suggest that hyperspectral imaging techniques can be used to predict and monitor various quality characteristics as well as the MC of blueberries during drying. Hyperspectral imaging ; Partial least squares regression ; Water content ; Linear discriminant analysis ; Smoothing ; Mathematics ; Correlation coefficient ; Chemistry ; Remote sensing ; Statistics ; Geography ; Engineering Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Food Drying and Modeling