jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Weiwei Sun | Ze Shi | BingCheng Chen | Jun Feng
Source: Civil Engineering Journal (article, 2020-04-01)
Field: Materials Science — Materials Chemistry

Numerical Study on RC Multilayer Perforation with Application to GA-BP Neural Network Investigation The finite element model of projectile penetrating multi-layered reinforced concrete target was established via LS-DYNA solver. The penetration model was validated with the test data in terms of residual velocity and deflection angle. Parametric analyses were carried out through the verified penetration model. Seven influential factors for penetration conditions, including the initial velocity of projectile, initial angle of attack of projectile, initial dip angle of projectile, the first layer thickness of concrete target, the residual layer thickness of concrete target, target distance and the layer number of concrete target, were put emphasis on further analysis. Furthermore, the influence of foregoing factors on residual velocity and deflection angle of projectile were numerically obtained and discussed. Based on genetic algorithm, the BP neural network model was trained by 263 sets of data obtained from the parametric analyses, whereby the prediction models of residual velocity and attitude angle of projectile under different penetration conditions were achieved. The error between the prediction data obtained by this model and the reserved 13 sets of test data is found to be negligible. Projectile ; Residual ; Deflection (physics) ; Parametric statistics ; Penetration (warfare) ; Structural engineering ; Materials science ; Artificial neural network ; Test data ; Solver ; LS-DYNA ; Finite element method ; Mechanics ; Engineering ; Computer science ; Mathematics ; Algorithm ; Optics ; Physics ; Statistics ; Artificial intelligence High-Velocity Impact and Material Behavior ; Geophysical Methods and Applications ; Rock Mechanics and Modeling
Authors: Ling Jing | Jie Xu | Haijun Lin | Jinyuan Lin
Source: International journal of agricultural and biological engineering (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

Prediction and fusion algorithm for meat moisture content measurement based on loss-on-drying method The loss-on-drying method has been widely used as a standard approach for measuring the moisture content of high-moisture materials such as solid and semi-solid foods. Loss-on-drying method provides reliable results, whilst usually labor-intensive and time-consuming. This paper presents a novel algorithm for predicting the moisture content of meats based on the loss-on drying method. The proposed approach developed a drying kinetics model of meats based on Fick’s Second Law and designed a prediction algorithm for meat moisture content using the least-squares method. The predicted results were compared with the official method recommended by the Association of Official Analytical Chemists (AOAC). When the moisture content of meat samples (beef and pork) was varied from 69.46% to 74.21%, the relative error of the meat moisture content (MMC) calculated by the proposed algorithm was 0.0017-0.0117, the absolute errors were less than 1%. The testing time was about 40.18%-56.87% less than the standard detection procedure. Keywords: meat moisture content, loss-on-drying method, Fick’s Second Law, fusion algorithm, measurement, prediction DOI: 10.25165/j.ijabe.20201304.5729 Citation: Ling J, Xu J, Lin H J, Lin J Y. Prediction and fusion algorithm for meat moisture content measurement based on loss-on-drying method. Int J Agric & Biol Eng, 2020; 13(4): 198–204. Water content ; Algorithm ; Moisture ; Mathematics ; Materials science ; Engineering ; Composite material ; Geotechnical engineering Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Advanced Measurement and Detection Methods
Authors: Dejana Herceg | Krzysztof Chwastek
Source: Energies (article, 2020-12-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The Use of Hypergeometric Functions in Hysteresis Modeling Accurate hysteresis models are necessary for modeling of magnetic components of devices such as transformers and motors. This study presents a hysteresis model with a convenient analytical form, based on hypergeometric functions with one free parameter, built upon a class of parameterized curves. The aim of this work is to explore suitability of the presented model for describing major and minor loops, as well as to demonstrate improved agreement between experimental and modeled hysteresis loops. The procedure for generating first order reversal curves is also discussed. The added parameter, introduced into the model, controls the shape of the model curve, especially near saturation. It can be adjusted to provide better agreement between measured and model curves. The model parameters are nonlinearly dependent; therefore, they are determined in a nonlinear curve fitting procedure. The choice of the initial approximation and a suitable set of constraints for the optimization procedure are discussed. The inverse of the model function, required to generate first order reversal curves, cannot be obtained in analytical form. The procedure to calculate the inverse numerically is presented. Performance of the model is demonstrated and verified on experimental data obtained from measurements on construction steel sheets and grain-oriented electrical steel samples. Parameterized complexity ; Inverse ; Hysteresis ; Nonlinear system ; Applied mathematics ; Parametric statistics ; Mathematics ; Algorithm ; Physics ; Geometry Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels ; Non-Destructive Testing Techniques
Authors: Clarisse Gotti | Florence Roux‐Dalvai | Charles Joly-Beauparlant | Loïc Mangnier | Mickaël Leclercq | Arnaud Droit
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-11-04)
Field: Chemistry — Spectroscopy

Extensive and accurate benchmarking of DIA acquisition methods and software tools using a complex proteomic standard ABSTRACT Over the past decade, the data-independent acquisition mode has gained popularity for broad coverage of complex proteomes by LC-MS/MS and quantification of low-abundance proteins. However, there is no consensus in the literature on the best data acquisition parameters and processing tools to use for this specific application. Here, we present the most comprehensive comparison of DIA workflows on Orbitrap instruments published so far in the field of proteomics. Using a standard human 48 proteins mixture (UPS1 – Sigma) at 8 different concentrations in an E. coli proteome background, we tested 36 workflows including 4 different DIA window acquisition schemes and 6 different software tools (DIA-NN, DIA-Umpire, OpenSWATH, ScaffoldDIA, Skyline and Spectronaut) with or without the use of a DDA spectral library. Based on the number of proteins identified, quantification linearity and reproducibility, as well as sensitivity and specificity in 28 pairwise comparisons of different UPS1 concentrations, we summarize the major considerations and propose guidelines for choosing the DIA workflow best suited for LC-MS/MS proteomic analyses. Our 96 DIA raw files and software outputs have been deposited on ProteomeXchange for testing or developing new DIA processing tools. Computer science ; Workflow ; Software ; Benchmarking ; Proteome ; Data mining ; Proteomics ; Mascot ; Bioinformatics ; Database ; Chemistry ; Biology ; Programming language Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications
Authors: Philip Donald C. Sanchez | Norhashila Hashim | Rosnah Shamsudin | Mohd Zuhair Mohd Nor
Source: Advances in Agricultural and Food Research Journal (review, 2020-11-02)
Field: Chemistry — Analytical Chemistry

Potential application of laser-based imaging technology in the quality evaluation of agricultural products : A review Non-destructive quality evaluation of agricultural products particularly during postharvest stage has been a primary concern in recent years. The laser-based imaging technology is one of the most promising non-invasive tools which demonstrate potential ability to replace the conventional methods of quality monitoring that are time-consuming, expensive, laborious, inaccurate and most of all destructive. Hence, in this paper, we briefly reviewed the potential application of laser-light backscattering imaging technique (LLBI) as a non-destructive quality evaluation tool applied in agricultural products. This review mainly reports the current knowledge on the successful implementations of the LLBI in measuring the various quality-related attributes of agricultural products under different postharvest conditions such as in drying, storage, sorting, maturity identification, defect detection, etc. The basic components, uses and considerations of the technique are highlighted in this paper. Moreover, the advantages, drawbacks, measurement methods, data analysis applied as well as the accuracies obtained are briefly summarized. Postharvest ; Quality (philosophy) ; Computer science ; Sorting ; Identification (biology) ; Agriculture ; Implementation ; Process engineering ; Risk analysis (engineering) ; Systems engineering ; Agricultural engineering ; Biochemical engineering ; Engineering ; Business Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Yuki Nakamura | Seiki Baba | Nobuhiro Mizuno | Takaki Irie | Go Ueno | Kunio Hirata | Shô Itô | Kazuya Hasegawa | Masaki Yamamoto | Takashi Kumasaka
Source: Acta Crystallographica Section F Structural Biology Communications (article, 2020-11-25)
Field: Materials Science — Materials Chemistry

Computer-controlled liquid-nitrogen drizzling device for removing frost from cryopreserved crystals Cryocrystallography is a technique that is used more often than room-temperature data collection in macromolecular crystallography. One of its advantages is the significant reduction in radiation damage, which is especially useful in synchrotron experiments. Another advantage is that cryopreservation provides simple storage of crystals and easy transportation to a synchrotron. However, this technique sometimes results in the undesirable adhesion of frost to mounted crystals. The frost produces noisy diffraction images and reduces the optical visibility of crystals, which is crucial for aligning the crystal position with the incident X-ray position. To resolve these issues, a computer-controlled device has been developed that drizzles liquid nitrogen over a crystal to remove frost. It was confirmed that the device works properly, reduces noise from ice rings in diffraction images and enables the centering of crystals with low visibility owing to frost adhesion. Frost (temperature) ; Liquid nitrogen ; Materials science ; Visibility ; Diffraction ; Ice crystals ; Optics ; Synchrotron radiation ; Synchrotron ; Crystal (programming language) ; Crystallography ; Composite material ; Computer science ; Chemistry ; Physics Enzyme Structure and Function ; Freezing and Crystallization Processes
Authors: Mingyan He | Jing Hu | Tingting Fang | Wenqing Tang | Bei Lv | Biwei Yang | Jinglin Xia
Source: Research Square (Research Square) (preprint, 2020-06-10)
Field: Materials Science — Materials Chemistry

Protein convertase subtilisin/Kexin type 9 inhibits hepatocellular carcinoma growth by interacting with GSTP1 and suppressing the JNK signaling pathway <title>Abstract</title> Background PCSK9 has been found to be closely related to the occurrence and development of a variety of tumors. However, the concrete role of PCSK9 and its relationship with HCC development is largely unknown. Methods The expression levels of PCSK9 in HCC tissues and HCC cell lines were determined by quantitative real-time polymerase chain reaction (qRT-PCR), western blot and immunohistochemistry assays. The effects of PCSK9 expression on HCC biological traits were investigated by overexpressing and downregulating PCSK9 protein in vivo and in vitro. The mechanism by which PCSK9 mediates the depolymerization of the GSTP1 dimer and the phosphorylation of the JNK signaling pathway was further investigated. Results PCSK9 is downregulated in human HCC tissues. HCC cell proliferation, cell cycle phase distribution and apoptosis were inhibited by PCSK9 in vitro. PCSK9 suppresses tumor growth and metastasis of HCC in vivo. PCSK9 interacts with GSTP1 and promotes the depolymerization of the GSTP1 dimer and inactivation of the JNK signaling pathway. Furthermore, low PCSK9 protein expression in primary HCC tissues correlated with worse clinical outcomes. Conclusions PCSK9 inhibits HCC cell proliferation, cell cycle phase distribution and apoptosis by interacting with GSTP1 and suppressing the JNK signaling pathway, suggesting that PCSK9 might act as a tumor suppressor and may serve as a therapeutic target for the treatment of HCC. Kexin ; Proprotein convertase ; Cancer research ; Signal transduction ; Cell growth ; Cell cycle ; Apoptosis ; Biology ; Cell cycle checkpoint ; PCSK9 ; Chemistry ; Cell biology ; LDL receptor ; Endocrinology ; Biochemistry Enzyme Structure and Function ; Protein Kinase Regulation and GTPase Signaling ; Advanced Proteomics Techniques and Applications
Authors: Li Liu | Xindong Shi | Shuo Zhang | Yinggang Shi | Yan Long
Source: International Journal of Design & Nature and Ecodynamics (article, 2020-02-29)
Field: Chemistry — Analytical Chemistry

Saccharinity Test on Cherry Tomatoes Based on Hyperspectral Imaging Based on the hyperspectral imaging (HSI) technique, this paper attempts to test the saccharinity of three varieties of cherry tomatoes in a nondestructive manner. The cherry tomato samples of the three varieties were collected, and kept at room temperature for 12h. Then, the spectral curves of the samples were obtained between the wavelengths of 914.91nm and 1,661.91nm. After that, the feature bands were extracted by three algorithms, namely, competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA) and SPA-CARS. The samples were divided into a correction dataset and a prediction dataset at the ratio of 2:1. Next, the feature bands extracted by the three algorithms were combined with the partial least squares (PLS) and least squares-support vector machine (LS-SVM) into six saccharinity prediction models. Finally, the prediction results of the six models were compared, revealing that the CARS-LS-SVM achieved the best performance with a prediction accuracy of >92%. The evaluation indices of this model are as follows: the correlation coefficient of correction dataset (R ), 0.9696; the correlation coefficient of prediction dataset (R ), 0.9220; the root mean square error of correction dataset (RMSEC), 0.2768; the root mean square error of prediction dataset (RMSEP), 0.4390. The research results lay the basis for industrial grading of saccharinity of cherry tomatoes in a nondestructive manner. Hyperspectral imaging ; Test (biology) ; Cherry tomato ; Remote sensing ; Artificial intelligence ; Computer science ; Horticulture ; Biology ; Geography ; Botany Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: Wolfgang Lechner
Source: IEEE Transactions on Quantum Engineering (preprint, 2020-01-01)
Field: Materials Science — Materials Chemistry

Quantum Approximate Optimization With Parallelizable Gates The quantum approximate optimization algorithm (QAOA) has been introduced as a heuristic digital quantum computing scheme to find approximate solutions of combinatorial optimization problems. We present a scheme to parallelize this approach for arbitrary all-to-all connected problem graphs in a layout of quantum bits (qubits) with nearest-neighbor interactions. The protocol consists of single qubit operations that encode the optimization problem, whereas interactions are problem-independent pairwise CNOT gates among nearest neighbors. This allows for a parallelizable implementation in quantum devices with a planar lattice geometry. The basis of this proposal is a lattice gauge model, which also introduces additional parameters and protocols for QAOA to improve the efficiency. Parallelizable manifold ; Quantum Fourier transform ; Controlled NOT gate ; Qubit ; Quantum computer ; Quantum algorithm ; Quantum gate ; Computer science ; ENCODE ; Quantum ; Topology (electrical circuits) ; Mathematics ; Algorithm ; Theoretical computer science ; Quantum mechanics ; Physics ; Combinatorics Chemical and Physical Properties of Materials ; Quantum Computing Algorithms and Architecture ; Quantum-Dot Cellular Automata
Authors: Carlos A. Platero | José A. Sánchez | Konstantinos N. Gyftakis | Francisco Cuadros Blázquez | Ricardo Granizo
Source: Sensors (article, 2020-05-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Performance Problems of Non-Toroidal Shaped Current Transformers Nowadays, non-toroidal shape primary pass-through current transformers are commonly used for large current machines with several cables per phase. As these transformers exhibit no radial symmetry, it is not clear if they can be tested using the indirect test described in the IEC 61869 standard. In order to answer this question, two non-toroidal shaped current transformers of different secondary winding designs have been tested and simulated. One transformer has a uniformly distributed secondary winding and the other has a partially distributed secondary winding. Both transformers have the same nameplate characteristics. Both perform correctly in the indirect test. However, only the transformer with the uniformly distributed secondary winding performs correctly in a direct test. A finite element simulation shows that the iron core of the partially distributed secondary winding transformer was saturated, while the iron core of the uniformly distributed one was not. This result explains their different performance. The main conclusion is that the indirect test is not sensitive enough to cover all cases and therefore under doubtful situations, the transformers should be tested using the direct test. Transformer ; Current transformer ; Electromagnetic coil ; Toroid ; Finite element method ; Delta-wye transformer ; Engineering ; Rotary variable differential transformer ; Magnetic core ; Electrical engineering ; Structural engineering ; Physics ; Voltage Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Electrical Fault Detection and Protection
Authors: Hamidreza Taghvaee | Sergi Abadal | Alexandros Pitilakis | Odysseas Tsilipakos | Anna C. Tasolamprou | Christos Liaskos | Maria Kafesaki | Nikolaos V. Kantartzis | Albert Cabellos‐Aparicio | Eduard Alarcón
Source: IEEE Access (preprint, 2020-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Scalability Analysis of Programmable Metasurfaces for Beam Steering Programmable metasurfaces have garnered significant attention as they confer unprecedented control over the electromagnetic (EM) response of any surface. Such feature has given rise to novel design paradigms such as Software-Defined Metamaterials (SDM) and Reconfigurable Intelligent Surfaces (RIS) with multiple groundbreaking applications. However, the development of programmable metasurfaces tailored to the particularities of a potentially large application pool becomes a daunting task because the design space becomes remarkably large. This paper aims to ease the design process by proposing a methodology that employs a semi-analytical formulation to model the response of a metasurface and, then, derives performance scaling trends as functions of a representative set of design and application-specific variables. Although the methodology is amenable to any EM functionality, this paper explores its use for the case of beam steering at 26 GHz for 5G applications. Conventional beam steering metrics are evaluated as functions of the unit cell size, number of unit cell states, and metasurface size for different incidence and reflection angles. It is shown that metasurfaces $5\lambda \times 5 \lambda $ or larger with unit cells of $\lambda /3$ and four unit cell states ensure good performance overall. Further, it is demonstrated that performance degrades significantly for angles larger than $\theta > 60^{o}$ and that, to combat this, extra effort is needed in the development of the unit cell. These performance trends, when combined with power and cost models, will pave the way to optimal metasurface dimensioning. Scalability ; Beam steering ; Computer science ; Software ; Process (computing) ; Dimensioning ; Metamaterial ; Computer architecture ; Reflection (computer programming) ; Beam (structure) ; Computer hardware ; Electronic engineering ; Computer engineering ; Physics ; Engineering ; Optics ; Antenna (radio) ; Aerospace engineering ; Telecommunications Metamaterials and Metasurfaces Applications ; Advanced Wireless Communication Technologies ; Advanced Antenna and Metasurface Technologies
Authors: Ali Raza | Faaiq Waqar | Árni Sturluson | Cory M. Simon | Xiaoli Z. Fern
Source: arXiv (Cornell University) (preprint, 2020-12-02)
Field: Chemistry — Inorganic Chemistry

Towards explainable message passing networks for predicting carbon\n dioxide adsorption in metal-organic frameworks Metal-organic framework (MOFs) are nanoporous materials that could be used to\ncapture carbon dioxide from the exhaust gas of fossil fuel power plants to\nmitigate climate change. In this work, we design and train a message passing\nneural network (MPNN) to predict simulated CO$_2$ adsorption in MOFs. Towards\nproviding insights into what substructures of the MOFs are important for the\nprediction, we introduce a soft attention mechanism into the readout function\nthat quantifies the contributions of the node representations towards the graph\nrepresentations. We investigate different mechanisms for sparse attention to\nensure only the most relevant substructures are identified.\n Metal-organic framework ; Nanoporous ; Fossil fuel ; Computer science ; Adsorption ; Message passing ; Artificial neural network ; Node (physics) ; Graph ; Function (biology) ; Distributed computing ; Materials science ; Nanotechnology ; Artificial intelligence ; Chemistry ; Theoretical computer science ; Engineering ; Waste management Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis