jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anna Kamenskih | Anastasia Krysina | Anastasia P. Pankova
Source: Applied Mathematics and Control Sciences (article, 2023-07-10)
Field: Materials Science — General Materials Science

The effect analysis of the finite element approximation order on the protective polymer layer contact deformation Problems implementation using the finite element method (FEM) is primarily associated with the proper selection of approximation order of the shape functions, material behavior models, the coupling nature analysis and the implementation techniques choice. The choice of numerical solution parameters affects the computational procedures. The paper solves problem of the FEM parameters selection and its realization methods in order to achieve a reasonable balance between the counting time and the solution quality. Simplex elements have shape functions with linear dependences on the coordinates. The FEM implementation with the use of simplex elements has proven itself during the last decades. However, one can note the description inaccuracy of curvilinear boundaries of research objects, which can introduce additional inaccuracies in the problem numerical solution. Second-order elements will allow describing curvilinear boundaries, but require a large expenditure of computational power especially for nonlinear mechanics problems. The development of numerical application software packages allows us to evaluate the influence of the FEM approximation degree on the problem solution with the use of grid methods and computer resources at the present time. The paper found that an increase in the numerical solution error is observed at the point of initial steel - polymer contact. The study considers the elements of order 1 and 2 to analyze the influence of the FEM approximation order on the problem numerical solution. The study is carried out within the framework of the classical Hertz problem. In addition the paper investigate obtained solutions for modeling different characters of contact surface coupling for the problem of contact between a spherical die and a steel half-space through a protective polymer coating 4 mm to 12 mm thick. The study reveal that the elements order has the least influence on the problem solution at ideal contact and the greatest one at frictional contact. Finite element method ; Curvilinear coordinates ; Numerical analysis ; Computer science ; Applied mathematics ; Mathematical optimization ; Mathematics ; Mathematical analysis ; Structural engineering ; Geometry ; Engineering Material Properties and Applications ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry ; Mechanical Systems and Engineering
Authors: Jia Lu | Luhan Hao | Pengfei Wang | Huagui Huang | Xu Li | Changchun Hua | Lihong Su | Guanyu Deng
Source: Research Square (preprint, 2023-08-23)
Field: Materials Science — Materials Chemistry

Wear prediction model of hot rolling backup roll based on FEM+ML algorithm Abstract The wear of backup rolls will have a great impact on the quality of hot rolled strip sheet shape. In order to overcome the limitations of the finite element method (FEM) in calculating the wear of backup rolls in terms of efficiency and accuracy, this paper proposes a FEM + ML tandem hybrid model to optimise the prediction effect of the finite element method (FEM) on the wear of backup rolls. Firstly, a backup roller wear model is established based on FEM. Secondly, in order to select the optimal machine learning (ML) algorithm as the finite element error compensation model, three types of finite element error compensation models were established based on the random forest (RF) algorithm, radial basis neural network (RBF) algorithm, and particle swarm optimisation support vector machine (PSO-SVM) algorithm. Finally, the three types of finite element error compensation models were connected in series with the FEM model to compare the prediction performance of the three types of FEM + ML models on the wear of backup rolls. The numerical experimental results show that the FEM + PSO-SVM model can predict the wear of the backup roll better, and the PSO-SVM algorithm is most suitable for establishing the finite element error compensation model. It is proved that the FEM + ML model proposed in this paper can effectively improve the accuracy and computational efficiency of the FEM model for predicting the wear of the backup rolls without adding microelements. In addition, among the hot rolling parameters, the rolling force has the greatest influence on the wear of the backup rolls, and excessive rolling force should be avoided for a single pass in order to slow down the wear of the backup rolls. Finite element method ; Backup ; Compensation (psychology) ; Particle swarm optimization ; Algorithm ; Engineering ; Computer science ; Structural engineering ; Mechanical engineering Metal Alloys Wear and Properties ; Mechanical stress and fatigue analysis ; Metallurgy and Material Forming
Authors: N. Lutska | Lidiia Vlasenko | Nataliia Zaiets
Source: Scientific Works of National University of Food Technologies (article, 2023-06-01)
Field: Chemistry — Analytical Chemistry

Prognostication resource efficiency of the sugar factory based on neuron network models Sugar factory ; Factory (object-oriented programming) ; Sugar ; Resource (disambiguation) ; Computer science ; Business ; Chemistry ; Food science ; Computer network Spectroscopy and Chemometric Analyses
Authors: B. Venkates | G. Krishna Haneesha | Reddi Sai Satya Keerthi | K. Jyothsna | Y. Vyshnavi
Source: International Journal of Research Publication and Reviews (article, 2023-11-23)
Field: Chemistry — Analytical Chemistry

Detection of Quality of Medicinal Leaves Using ML The demand for high-quality medicinal plants and their derived products has increased significantly due to their potential health benefits and therapeutic applications. Ensuring the authenticity and quality of medicinal leaves is crucial to maintain the efficacy and safety of herbal remedies. However, manual inspection of medicinal leaves is time-consuming, subjective, and prone to errors.This research proposes a novel approach for the automated detection of medicinal leaf quality using Machine Learning (ML) techniques. The features are fed into ML Deep Learning model with Transfer learning Algorithm. This study presents a pioneering effort in employing ML techniques for detecting the quality of medicinal leaves, making significant strides towards enhancing the overall standardization and authentication of medicinal plant-based products in the healthcare industry. Quality (philosophy) ; Environmental science ; Philosophy Spectroscopy and Chemometric Analyses
Authors: Francisco de Assis Toti | Gustavo Teixeira Morais | José Josimar de Oliveira | Marcio Silva de Macedo | AMILTON JOAQUIM CORDEIRO DE FREITAS
Source: Anais do Congresso Internacional de Engenharia Mecânica e Industrial (article, 2023-01-01)
Field: Materials Science — General Materials Science

ANÁLISE DA RUGOSIDADE SUPERFICIAL EM POLÍMEROS SUBMETIDOS AO PROCESSO DE FURAÇÃO A SECO EM FUNÇÃO DE PARÂMETROS E FERRAMENTAS DE CORTE Anais do Congresso Internacional de Engenharia Mecânica e Industrial (2764-4294) - Análise Da Rugosidade Superficial Em Polímeros Submetidos Ao Processo De Furação A Seco Em Função De Parâmetros E Ferramentas De Corte Physics ; Materials science ; Humanities ; Mathematics ; Art Engineering and Material Science Research ; Structural Behavior of Reinforced Concrete ; Innovative concrete reinforcement materials
Source:  (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Fruit Quality Detection Using Medical Image Processing The equilibrium between the soil, plants, animals, and human health has been disrupted by the increased use of chemicals in intensive farming.Those who are concerned about their health have been encouraged to learn more about and support organic farming because of the significant usage of pesticides and antibiotics in inorganic food production systems.The study found that compared to other types of food, food grown organically tastes better and has a higher ratio of vitamins and minerals.The danger of heart attacks, colon cancer, and other ailments is significantly decreased by eating organic food.Due to its environmentally friendly practices and rising consumer awareness of food safety, organic farming has gained popularity.The government is vital in encouraging farmers to switch from inorganic to organic agriculture systems since organic farming is economically viable in the country.The government must also take the necessary steps, including establishing a separate market for organic products, announcing a support price, increasing awareness-raising efforts through more programs, subsidizing suppliers of organic inputs, encouraging organic farmers with subsidies, accrediting farms, and boosting investment in research and development of organic farming methods. Quality (philosophy) ; Computer science ; Image processing ; Image quality ; Computer vision ; Artificial intelligence ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Hyeon-Kyu Park | Sang‐Koog Kim
Source: Scientific Reports (article, 2023-09-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Magnetization reversals in core–shell sphere clusters: finite-element micromagnetic simulation and machine learning analysis Recently developed permanent magnets, featuring specially engineered microstructures of inhomogeneous magnetic phases, are being considered as cost-effective alternatives to homogeneous single-main-phase hard magnets composed of Nd<sub>2</sub>Fe<sub>14</sub>B, without compromising performance. In this study, we conducted a comprehensive examination of a core-shell sphere cluster model of Ce-substituted inhomogeneous Nd<sub>2-δ</sub>Ce<sub>δ</sub>Fe<sub>14</sub>B phases versus homogeneous magnetic phases, utilizing finite-element micromagnetic simulation and machine learning methods. This involved a meticulous, sphere-by-sphere analysis of individual demagnetization curves calculated from the cluster model. The grain-by-grain analyses unveiled that these individual demagnetization curves can elucidate the overall magnetization reversal in terms of the nucleation and coercive fields for each sphere. Furthermore, it was observed that Nd-rich spheres exhibited much broader ranges of nucleation and coercive field distributions, while Nd-lean spheres showed relatively narrower ranges. To identify the key parameter responsible for the notable differences in the nucleation fields, we constructed a machine learning regression model. The model utilized numerous hyperparameter sets, optimized through the very fast simulated annealing algorithm, to ensure reliable training. Using the kernel SHapley Additive eXplanation (SHAP) technique, we inferred that stray fields among the 11 parameters were closely related to coercivity. We further substantiated the machine learning models' inference by establishing an analytical model based on the eigenvalue problem in classical micromagnetic theory. Our grain-by-grain interpretation can guide the optimal design of granular hard magnets from Nd<sub>2</sub>Fe<sub>14</sub>B and other abundant rare earth transition elements, focusing on extraordinary performance through the careful adjustment of microstructures and elemental compositions. Coercivity ; Magnet ; Nucleation ; Demagnetizing field ; Condensed matter physics ; Magnetization ; Materials science ; Artificial intelligence ; Algorithm ; Computer science ; Statistical physics ; Machine learning ; Physics ; Magnetic field ; Thermodynamics ; Quantum mechanics Magnetic Properties of Alloys ; Geomagnetism and Paleomagnetism Studies ; Magnetic Properties and Applications
Authors: James D. Green | Timothy J. H. Hele
Source: arXiv (Cornell University) (preprint, 2023-12-14)
Field: Chemistry — Physical and Theoretical Chemistry

ExROPPP: Fast, Accurate and Spin-Pure Calculation of the Electronically Excited States of Organic Hydrocarbon Radicals Recent years have seen an explosion of interest in organic radicals due to their promise for highly efficient organic light-emitting diodes (OLEDs) and molecular qubits. However, accurately and inexpensively computing their electronic structure has been challenging, especially for excited states, due to the spin-contamination problem. Furthermore, while alternacy or `pseudoparity' rules have guided the interpretation and prediction of the excited states of closed-shell hydrocarbons since the 1950s, similarly general rules for hydrocarbon radicals have not to our knowledge yet been found. In this article we present solutions to both of these challenges. We firstly combine the extended configuration interaction singles (XCIS) method with Pariser-Parr-Pople (PPP) theory to obtain a method which we call ExROPPP (Extended Restricted Open-shell PPP theory). We find that ExROPPP computes spin-pure excited states of hydrocarbon radicals with comparable accuracy to experiment as high-level GMC-QDPT calculations but at a computational cost that is at least two orders of magnitude lower. We then use ExROPPP to derive widely-applicable rules for the spectra of alternant hydrocarbon radicals which are completely consistent with our computed results. These findings pave the way for the highly accurate and efficient computation and prediction of the excited states of organic radicals. Radical ; Excited state ; Hydrocarbon ; Spin (aerodynamics) ; Lying ; Photochemistry ; Chemistry ; Computational chemistry ; Atomic physics ; Physics ; Organic chemistry ; Thermodynamics Photochemistry and Electron Transfer Studies ; Organic Light-Emitting Diodes Research ; Molecular Junctions and Nanostructures
Authors: Steven J. Kregel | Thomas F. Derrah | Seokjin Moon | David T. Limmer | Gilbert M. Nathanson | Timothy H. Bertram
Source: ChemRxiv (preprint, 2023-02-07)
Field: Chemistry — Electrochemistry

Temperature Dependence of the Relative Rates of Chlorination and Hydrolysis of N2O5 in NaCl-Water Solutions. We have measured the temperature dependence of the ClNO2 product yield in competition with hydrolysis following N2O5 uptake to aqueous NaCl solutions. For NaCl-D2O solutions spanning 0.0054 M to 0.21 M, the ClNO2 product yield decreases on average by only 43 % from 5 to 25 C. Less reproducible measurements at 0.54 M and 2.4 M NaCl also fall within this range. The ratio of the rate constants for chlorination and hydrolysis of N2O5 in D2O is determined on average to be 1150±90 at 25 C up to 0.21 M NaCl, favoring chlorination. This ratio is observed to decrease significantly at the two highest concentrations. An Arrhenius analysis reveals that the activation energy for hydrolysis is just 3.0±1.5 kJ/mol larger than for chlorination up to 0.21 M, indicating that Cl- and D2O attack on N2O5 have similar energetic barriers despite the differences in charge and complexity of these reactants. In combination with the measured pre-exponential ratio favoring chlorination of {300\ }_{-200}^{+400}, we conclude that the strong preference of N2O5 to undergo chlorination over hydrolysis is driven by dynamic and entropic, rather than enthalpic, factors. Molecular dynamics simulations elucidate the distinct solvation between strongly hydrated Cl- and the hydrophobically solvated N2O5. Combining this molecular picture with the Arrhenius analysis implicates the role of water in mediating interactions between such distinctly solvated species and suggests a role for diffusion limitations on the chlorination reaction. Hydrolysis ; Chemistry ; Aqueous solution ; Arrhenius equation ; Solvation ; Activation energy ; Yield (engineering) ; Reaction rate constant ; Arrhenius plot ; Kinetics ; Thermodynamics ; Physical chemistry ; Molecule ; Organic chemistry Electrochemical Analysis and Applications ; Water Treatment and Disinfection ; Environmental Toxicology and Ecotoxicology
Authors: Yicheng Ma | Ying Li | Xinkai Peng | Congyu Chen | Hengkai Li | Xinping Wang | Weilong Wang | Xiaozhen Lan | Jixuan Wang | Zhiyong Pei
Source: Forests (article, 2023-12-19)
Field: Chemistry — Analytical Chemistry

QPWS Feature Selection and CAE Fusion of Visible/Near-Infrared Spectroscopy Data for the Identification of Salix psammophila Origin Salix psammophila, classified under the Salicaceae family, is a deciduous, densely branched, and erect shrub. As a leading pioneer tree species in windbreak and sand stabilization, it has played a crucial role in combating desertification in northwestern China. However, different genetic sources of Salix psammophila exhibit significant variations in their effectiveness for windbreak and sand stabilization. Therefore, it is essential to establish a rapid and reliable method for identifying different Salix psammophila varieties. Visible and near-infrared (Vis-NIR) spectroscopy is currently a reliable non-destructive solution for origin traceability. This study introduced a novel feature selection strategy, called qualitative percentile weighted sampling (QPWS), based on the principle of the long tail effect for Vis-NIR spectroscopy. The core idea of QPWS combines weighted sampling and percentage wavelength selection to identify key wavelengths. By employing a multi-threaded parallel execution of multiple QPWS instances, we aimed to search for the optimal feature bands to address the instability issues that can arise during the feature selection process. To address the problem of reduced prediction performance in one-dimensional convolutional neural network (1D-CNN) models after feature selection, we have introduced convolutional autoencoders (CAEs) to reduce the dimensions of wavelengths that are discarded during feature selection. Subsequently, these reduced dimensions are fused with the selected wavelengths, thereby enhancing the model’s performance. With our completed model, we selected outstanding models for model fusion and established a decision system for Salix psammophila. It is worth noting that all 1D-CNN models in this study were developed using Bayesian optimization methods. In comparison with principal component analysis (PCA) and full spectrum methods, QPWS exhibits superior predictive performance in the field of machine learning. In the realm of deep learning, the fusion of data combining QPWS with CAE demonstrated even greater potential with an improvement of average accuracy of approximately 2.13% when compared to QPWS alone and a 228% increase in operational speed compared to a model with full spectra. These results indicated that the combination of CAE with QPWS can be an effective tool for identifying the origin of Salix psammophila. Feature selection ; Pattern recognition (psychology) ; Feature (linguistics) ; Selection (genetic algorithm) ; Artificial intelligence ; Random forest ; Computer science Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Remote Sensing in Agriculture
Authors: Endang Juliastuti | Irwan Setiawan | Vebi Nadhira | Deddy Kurniadi
Source: Journal of Engineering and Technological Sciences (article, 2023-12-31)
Field: Materials Science — Polymers and Plastics

Photometric Stereo Method Used for Woven Fabric Density Measurement Based on 3D Surface Structure The measurement of the density of woven fabrics based on the vision method has been widely developed. This study used a photometric stereo method to measure the warp and weft density of woven fabrics based on the 3D surface structure. Six 2D images of the fabric were recorded, each with a different lighting direction. The six images were then reconstructed using the unbiased photometric stereo algorithm to produce the three-dimensional surface structure. The reconstructed image was used to detect and correct the skew angle with the Hough transform. For each image, a depth profile was made toward the x-axis to get the weft curve and towards the y-axis to get the warped curve. The two depth curves were filtered using a locally weighted smoothing (LOESS) filter. This study successfully measured the density of woven fabric with an average error for warp and weft of 0.64% and 0.45%, respectively. Surface (topology) ; Fabric structure ; Materials science ; Woven fabric ; Mathematics ; Composite material ; Geometry Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; 3D Shape Modeling and Analysis
Authors: Mohamed Ehab Bakr | Ahmed Farouk Deifalla
Source: Proceedings of the World Congress on Mechanical, Chemical, and Material Engineering (article, 2023-08-01)
Field: Materials Science — Materials Chemistry

Nuclear Radiation Shielding Capabilities of Fiber-Reinforced Concrete: a case study Hybrid-Polypropylene-Steel As Fiber reinforced concrete (FRC) have superior properties over the conventional concrete; however, this superiority depends on the types of fiber used inside the concrete mixture.Radiation attenuation parameters as linear attenuation coefficient (LAC), mass attenuation coefficients m (MAC), Half Value Layer (HVL), Tenth Value Layer (TVL) and Mean Free Path (MFP), were calculated for the studied Hybrid-Polypropylene-Steel, FRC, at photon energies 80, 356, 662 and 1333 KeV.For the findings of the study, the hybrid fiber-reinforced concrete is suitable for the nuclear shielding applications.In addition, the increase in the fiber contents improves the shielding efficiency. Electromagnetic shielding ; Polypropylene ; Materials science ; Radiation shielding ; Composite material ; Nuclear radiation ; Radiation ; Nuclear engineering ; Optics ; Engineering ; Physics Graphite, nuclear technology, radiation studies ; High-Velocity Impact and Material Behavior ; Nuclear and radioactivity studies
Authors: D.A.S. D.A.S. Udayanga | Ashan Serasinghe | Supun Dassanayake | Roshan Godaliyadda | Vijitha Herath | M. P. B. Ekanayake | H. L. P. Malshan
Source: arXiv (Cornell University) (preprint, 2023-10-04)
Field: Chemistry — Analytical Chemistry

Dual mode multispectral imaging system for food and agricultural product quality estimation Multispectral imaging coupled with Artificial Intelligence, Machine Learning and Signal Processing techniques work as a feasible alternative for laboratory testing, especially in food quality control. Most of the recent related research has been focused on reflectance multispectral imaging but a system with both reflectance, transmittance capabilities would be ideal for a wide array of specimen types including solid and liquid samples. In this paper, a device which includes a dedicated reflectance mode and a dedicated transmittance mode is proposed. Dual mode operation where fast switching between two modes is facilitated. An innovative merged mode is introduced in which both reflectance and transmittance information of a specimen are combined to form a higher dimensional dataset with more features. Spatial and temporal variations of measurements are analyzed to ensure the quality of measurements. The concept is validated using a standard color palette and specific case studies are done for standard food samples such as turmeric powder and coconut oil proving the validity of proposed contributions. The classification accuracy of standard color palette testing was over 90% and the accuracy of coconut oil adulteration was over 95%. while the merged mode was able to provide the best accuracy of 99% for the turmeric adulteration. A linear functional mapping was done for coconut oil adulteration with an R2 value of 0.9558. Multispectral image ; Palette (painting) ; Transmittance ; Computer science ; Reflectivity ; Artificial intelligence ; Mode (computer interface) ; Quality (philosophy) ; Remote sensing ; Computer vision ; Optics ; Process engineering ; Materials science ; Optoelectronics ; Geography ; Engineering Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research