jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kehinde Aina
Source: BALTIC FORESTRY (article, 2022-05-12)
Field: Materials Science — Polymers and Plastics

Influences of cement brands and mixing ratios on the physico-mechanical properties of cement bonded particleboards made of wood particles of Gmelina arborea, Afzelia africana and Triplochiton scleroxylon Cement bonded particleboards (CBPs) were produced of wood particles of Gmelina arborea, Afzelia africana and Triplochiton scleroxylon using two brands of ordinary portland cement manufactured by two different domestic cement companies operating in Nigeria known as Dangote PLC and Lafarge Company. The admixture of wood particles and cement for production of the CBPs were done at mixing ratios of 1:1, 1:1.5 and 1:2 (weight to weight basis). The properties such as water absorption and thickness swelling at 24 hours, density, modulus of rupture (MOR), modulus of elasticity (MOE) and impact bending strength were determined. The results of the study reveal that the density ranged from 0.61 to 0.92 g/cm3. Meanwhile, water absorption and thickness swelling were found to be 30.34 to 72.44 % and 11.43 to 10.77 % at 24 hours, respectively. The bending properties obtained from the laboratory tests ranged from 115.77 to 826.55 N/mm2 and from 1157.70 to 4773.38 N/mm2 for MOR and MOE, respectively, while impact bending strength ranged from 41.70 to 238.49 N/mm2, respectively. As the cementious content increases from 50 % to 66.3 % and wood content decreases from 50 % to 33.4 %, the properties such as density, MOR, MOE and impact bending strength increased, while water absorption and thickness swelling decreases. The statistical results showed that such factors as wood species, cement type and mixing ratio had significant influence on the physical and mechanical properties of CBPs. The study reveals that CBPs made of wood particles of Triplochiton scleroxylon and Dangote cement brand at the ratio of 1:2 were more dimensionally stable, denser and stronger while CBPs of wood particles of Afzelia africana were stiffest and most rigid. As concluded, cement brand had significant importance in qualities of CBPs, which can be attributed to chemical compositions found in materials used. Keywords: cement, physical, mechanical, wood, particleboards, mixing ratio Gmelina ; Absorption of water ; Flexural strength ; Cement ; Composite material ; Young's modulus ; Portland cement ; Swelling ; Materials science ; Botany Natural Fiber Reinforced Composites ; Innovations in Concrete and Construction Materials
Authors: Murayama Kazushige | Kobori Hikaru | Kojima Yoichi | Aoki Kenji | Suzuki Shigehiko
Source: Institutional Repositories DataBase (IRDB) (article, 2022-01-22)
Field: Materials Science — Biomaterials

Prediction of cellulose nanofibril (CNF) amount of CNF/polypropylene composite using near infrared spectroscopy Composite number ; Near-infrared spectroscopy ; Cellulose ; Spectroscopy ; Fourier transform infrared spectroscopy Advanced Cellulose Research Studies ; Electrospun Nanofibers in Biomedical Applications ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Cody Walker | Vivek Agarwal | Pradeep Ramuhalli | Nancy Lybeck
Source: Annual Conference of the PHM Society (article, 2022-10-28)
Field: Materials Science — Materials Chemistry

Development of an End State Vision to Implement Digital Monitoring in Nuclear Plants Nuclear plant sites collect and store large volumes of data gathered from various equipment and systems. These datasets typically include plant process parameters, maintenance records, technical logs, online monitoring data, and equipment failure data. The collection of such data affords an opportunity to leverage data-driven machine learning (ML) and artificial intelligence (AI) technologies to provide diagnostic and prognostic capabilities within the nuclear power industry, thus reducing operations and maintenance (O&amp;M) costs. In this way, nuclear energy can become more economically competitive with other energy sources, and premature plant closures can be avoided. From a maintenance standpoint, savings can be achieved by leveraging ML and AI technologies to develop data-driven algorithms that better diagnose and predict potential faults within the system. Improved model accuracy can help reduce unnecessary maintenance and foster more efficient planning of future maintenance, thereby lowering the costs associated with parts, labor, and costly planned, forced, or extended outages. From an operations perspective, cost savings can be generated by shifting from routine-based monitoring to online monitoring by taking advantage of advancements in sensors and wireless communication technologies. Advancements in data storage, mapping, management, and analytics would inform the transition from onsite- to cloud-based computing and storage services. Online monitoring would reduce the number of operator manhours required for taking routine measurements, while cloud computing services would generate cost savings by reducing the amount of hardware needing to be purchased and maintained all while scaling to both computational and storage demands. This paper summarizes an end state vision of how to shift from costly, labor-intensive preventative maintenance to cost-effective predictive maintenance. State (computer science) ; Computer science ; Environmental science ; Computer vision Graphite, nuclear technology, radiation studies ; Knowledge Management and Technology
Authors: Saad Bin Ahmed | Ammar H. Al‐Dujaili
Source: Research Square (preprint, 2022-02-07)
Field: Chemistry — Spectroscopy

Liquid-liquid equilibria for aromatic extraction systems using artificial neural networks Artificial neural network ; Extraction (chemistry) ; Liquid liquid ; Computer science ; Artificial intelligence ; Biological system ; Chemistry ; Chromatography ; Biology Analytical Chemistry and Chromatography ; Spectroscopy and Chemometric Analyses ; Crystallization and Solubility Studies
Authors: Mohsen Soori
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2022-01-01)
Field: Materials Science — Materials Chemistry

Manufacturing Processes of Car Alloy Wheels Passenger cars are using the alloy wheels due to lighter wheels in order to reduce fuel consumption. The alloy wheels are usually made from light and strong alloys such as aluminum and magnesium which can improve the performances of braking system to increase safety of the driving. They are produced by casting, machining and forging operations. The production process can be analyzed and modified to increase efficiency of part production. Finite element analysis can be used to find the static and dynamic stress of the wheel in actual working conditions. Process of machining operations using turning machine tools can also be analyzed to be modified. The optimization process can be used to increase rigidity of the produced alloy wheels. New alloys properties can be tested using virtual simulation to increase performances of the alloy wheels. To increase quality of produced parts, thermal model of the low-pressure die-cast in alloy wheel production process is analyzed. So, more added value in manufacturing process of alloy wheels can be achieved. Alloy wheels are made of an alloy of light metals, namely aluminum, nickel, magnesium, or a combination of these metals. They offer performance advantages over steel wheels, as they are often several pounds lighter per wheel-less weight means quicker acceleration and faster stopping. Less weight also means less strain on suspension components. In extreme driving conditions, alloy wheels are better able to dissipate heat away from brake components than their steel counterparts. To produce the allow wheels, the casting operations are used. Alloy ; Manufacturing engineering ; Automotive engineering ; Metallurgy ; Business ; Engineering ; Materials science Metal Alloys Wear and Properties ; Metallurgy and Material Forming ; Metallurgy and Material Science
Authors: A. F. Altzoumailis | V. N. Kytopoulos
Source: International Journal of Materials (article, 2022-01-18)
Field: Materials Science — Biomaterials

Qualitative and Quantitative Determination of Montmorillonite Using Infrared Spectrometry The present study investigates the various possibilities of qualitative and quantitative analysis of montmorillonite (1/2Ca, Na) 0.7 (Al, Mg, Fe) 4 (Si, Al) 8O20 (OH) 4. n H2O (MT), major constituent of Bendonite coming the island Milos-Greece, using infrared (IR) spectroscopy and preparing the samples with KBr. X-Ray diffraction diagrams were obtained for the determination of their mineralogical composition. Observation of IR spectra of sample with different MT content revealed that the Si-O-Si vibration band at 1038cm-1 sharpens with increasing MT content. The combination of the above techniques led to a convenient determination of the minerals, contained in the sample and to a discrimination of identical oscillations, observed in different minerals at the same wave number (1038cm-1)[1]. As far as the quantitative analysis concerns, in the case of MT, thirteen comparative samples of known composition were analysed. The obtained spectra were digitized and smoothed, within the area from 800 to 1200 cm-1, using the Savitzky – Golay method, followed by Lorentz function transformation. Due to overlap from other substances, the plot of a calibration curve for MT, and consequently the development of a model, describing the absorption / concentration correlation for a specific sample, were not satisfactory. So the calibration curve according to Lambert- Beer law [2, 3], required use of samples with high concentration of MT. To overcome this problem the calibration curve for MT was constructed using dilutions of standard samples containing more than 85% of the mineral, with SiO2, at the wave number of 1038cm-1. This curve was used for the determination of MT in unknown samples giving very good results compared with the mineralogical composition obtained by XRD. Analytical Chemistry (journal) ; Calibration curve ; Montmorillonite ; Infrared spectroscopy ; Calibration ; Absorbance ; Infrared ; Chemistry ; Clay minerals ; Mineralogy ; Materials science ; Physics ; Optics ; Detection limit ; Chromatography Clay minerals and soil interactions ; Spectroscopy and Chemometric Analyses ; Adsorption, diffusion, and thermodynamic properties of materials
Authors: Kaneko Hiromasa
Source: Institutional Repositories DataBase (IRDB) (article, 2022-01-24)
Field: Materials Science — Materials Chemistry

True Gaussian mixture regression and genetic algorithm-based optimization with constraints for direct inverse analysis In molecular, material, and process design, Gaussian mixture regression (GMR) can directly predict values of explanatory variables x such as experimental and process conditions from target values of objective variables y such as properties and activities of materials. In GMR, a prediction result of a Gaussian mixture distributions is provided as a probability density distribution. The weighted average of the means of Gaussians has been used as the representative value of Gaussian mixture. In this study, true GMR has been proposed, where the predicted value of the GMR model is calculated by solving a continuous optimization problem to obtain the maximum value of the probability density function (PDF). As the continuous optimization problem cannot be solved when variables contain discrete values, a genetic algorithm is used to search for a solution that satisfies the constraints, while maximizing the value of the PDF. Numerical simulations using nonlinear functions between y and x, and the case studies using real material datasets confirmed that the proposed methods produced solutions with drastically higher values of the PDF than the traditional methods, especially when x is constrained in the inverse analysis. The Python codes of the proposed methods are freely available. Direct inverse analysis with GMR using the proposed methods is expected to improve the efficiency of molecular, material, and process design. Gaussian ; Probability density function ; Inverse ; Gaussian process ; Nonlinear regression ; Inverse problem ; Inverse Gaussian distribution ; Optimization problem ; Nonlinear system Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Optimal Experimental Design Methods
Authors: Piotr Baranowski | Anna Siedliska | Jaromir Krzyszczak | Artur Banach | Marcin Siłuch | Piotr Bartmiński | Agnieszka Wolińska | Przemysław Tkaczyk
Source: Research Square (preprint, 2022-11-09)
Field: Chemistry — Analytical Chemistry

Feasibility of applying hyperspectral imaging as a rapid method for estimating potassium content across different plant species datasets Abstract Purpose The accurate and frequent estimation of the leaf plant potassium concentration enabled by hyperspectral imaging techniques has allowed growers to optimize fertilizer applications and reduce the negative impact on the environment. In this study, we examined the feasibility of using leaf spectral data to accurately estimate the potassium content in sugar beet and celery plants. Methods Leaf images in the visible and near infrared region (VNIR: 400–1000 nm) and short-wavelength infrared region (SWIR: 1000-2500 nm) were captured by a hyperspectral camera. The potassium content was measured by ordinary destructive laboratory methods. The correlation-based feature selection (CFS) algorithm was implemented to select important wavelengths that carried the most useful information for predicting the potassium content in plant leaves. Four multivariate regression methods were tested to find a model with strong predictive performance. Results The experimental results showed that the Random Forest (RF) model using 12 bands (425, 443, 479, 599, 631, 662, 798, 863, 897, 921, 1978 and 2053 nm) had the highest accuracy for predicting potassium content in sugar beet, celery and both plant datasets (Rp2 = 0.85, Rp2 = 0.79, and Rp2 = 0.81, respectively). Conclusion The results confirm the universality of the described method. Although further validation studies involving other plant species are needed, it appears that the spectral reflectance technique could be a promising tool for the rapid, noninvasive and cost-effective estimation of K content in plant leaves, contributing to a significant step forward in precision fertilization management. Hyperspectral imaging ; Content (measure theory) ; Potassium ; Remote sensing ; Environmental science ; Computer science ; Soil science ; Artificial intelligence ; Mathematics ; Chemistry ; Geography Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Leaf Properties and Growth Measurement
Authors: Damien B. Ready | Gregory K. Potts
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-01-27)
Field: Chemistry — Spectroscopy

Needler: An Algorithm to Develop a Comprehensive Targeted MS Method Capable of Monitoring the Human Proteome Abstract Targeted proteomics for addressing biological hypotheses through quantitative mass spectrometry (MS) is increasingly popular because of its ability to deliver sensitive and reproducible results to inform protein concentration and dynamics. While the technique is powerful, the associated tooling to develop unique targeted data acquisition methods per experiment is limited, often focused on producing a minimally viable analytical approach for a limited set of analytes, rather than one which can maximally quantitate all targets of interest. We developed the Needler algorithm to produce comprehensive targeted MS methods capable of quantitating the maximum number of proteins given available instrument constraints. Using this algorithm, we demonstrate that the minimum instrument parameters required to produced targeted MS quantitation of all tryptic peptides from the human proteome are well in excess of current capabilities. Given these constraints, we provide a visualized scope of the boundaries for practical MS monitoring to maximize proteome and subproteome coverage dependent upon instrument duty cycle capabilities. Proteome ; Computer science ; Analyte ; Scope (computer science) ; Proteomics ; Set (abstract data type) ; Quantitative proteomics ; Mass spectrometry ; Computational biology ; Data mining ; Bioinformatics ; Chemistry ; Chromatography ; Biology ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Ebru EFEOĞLU | Gürkan Tuna
Source: Balkan Journal of Electrical and Computer Engineering (article, 2022-10-19)
Field: Chemistry — Analytical Chemistry

Effect of Different Kernel Functions on Hazardous Liquid Detection Using a Patch Antenna and Support Vector Machines Microwave spectroscopy method has become widespread in many applications including liquid classification. In this study, a microwave spectroscopy system that can classify liquids without opening the lid of their containers is proposed. Thus, the operators are prevented from being exposed to harmful substances and wasting time. Everyday liquids such as carbonated drinks, fruit juices, shampoo, cream and alcoholic beverages and hazardous liquids were characterized remotely by the microwave spectroscopy method in which spectroscopic signatures of a total of 52 liquids were used. In order to be able to classify liquids with the highest accuracy, it is also important to determine the most suitable measurement system as well as the correct selection of the classification algorithm and algorithm parameters that show the best performance. In this study, Support Vector Machines algorithm, which is a very successful algorithm in separating binary classes, is used. In addition, the effects of the algorithm on the classification performance have been examined using different kernel functions and cross-validation technique has been used for the performance analysis. As a result of the performance analysis, it is seen that up to 100% success can be achieved when linear or polynomial kernel functions have been preferred. Support vector machine ; Kernel (algebra) ; Microwave ; Computer science ; Algorithm ; Process engineering ; Artificial intelligence ; Mathematics ; Engineering ; Telecommunications Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: I Manukhin | V Makhlaev | D Bortsov | M Vissurkhanova | В. И. Иванов | Y Wu | S Nakagawa | T Kneafsey | B Dafflon | S Hubbard | P Rowe | / | P Carter | A Zaytsev | Ju Gorbulya | I Kondeyko | - | Christen Kowalski | J Bartelt | P | A Voellmy | B Robson | C Nuth | S Dahl | D Hlbling | S Paul | A Winsvold | T Kb | Nagler | L Huang | Z Li | B Tian | J Zhou | Delimitation Of Glacier Boundaries Using Remote Sensing Data And Machine Learning
Source:  (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Materials of the 4th All-Russian scientific and practical Conference of young specialists «ENGINEERING SURVEY IN CONSTRUCTION» - Engineering ; Computer science ; Construction engineering ; Engineering ethics ; Engineering management Structural mechanics and materials ; Industrial Engineering and Technologies ; Geodetic Measurements and Engineering Structures
Authors: Raghuraj Parihar | Mukesh Meena | Dinesh Kumar Meena | Brij Sharma
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2022-12-30)
Field: Chemistry — Organic Chemistry

CP-MLR/PLS directed quantitative structure-activity relationship study on the CDK8 inhibitory activity: The derivatives of naphthyridine and isoquinoline The CDK8 and 7dF3 inhibition activity of naphthyridine and isoquinoline derivatives have been quantitatively analyzed in terms of Dragon descriptors. The statistically validated quantitative structure-activity relationship (QSAR) models provided rationales to explain the inhibition activities of these congeners. The descriptors identified through CP-MLR analysis for the CDK8 inhibitory activity have highlighted the role of sum of the topological distances between N..N (T(N..N)), distance/detour ring index of order 6 (D/Dr06), aromatic ratio (ARR), number of double bonds (nDB), number of 6-membered rings (nR06), number of sulphur atoms (nS), number of unsubstituted sp2 hybridized aromatic carbon atoms (nCaH), number of aliphatic N hydrazines (nN-N), number of aromatic primary amines (nNH2Ph) and certain atom centered fragments such as R--CH--R (C-024), X--CR..X (C-034), H attached to C1(sp3)/C0(sp2) (H-047) and RCO-N&lt;/&gt;N-X=X (N-072) to quantify the inhibitory actions. The highest eigen value n.2 of Burden matrix/weighted by atomic polarizabilites (BEHp2) and atomic Sanderson electronegativities weighted Geary autocorrelation of lag 8 (descriptor GATS8e) have also shown prevalence to model the CDK8 inhibitory activity. PLS analysis has also corroborated the dominance of CP-MLR identified descriptors. Applicability domain analysis revealed that the suggested model matches the high quality parameters with good fitting power and the capability of assessing external data and all of the compounds was within the applicability domain of the proposed model and were evaluated correctly. The models obtained from the descriptor pool that was chosen for the CDK8 inhibitory activity is able to explain nearly 74% variance in the observed 7dF3 activities of titled compounds. Isoquinoline ; Inhibitory postsynaptic potential ; Quantitative structure–activity relationship ; Chemistry ; Stereochemistry ; Biology ; Neuroscience Multicomponent Synthesis of Heterocycles ; Nanoparticle-Based Drug Delivery ; Synthesis and Reactivity of Heterocycles
Authors: Г. В. Алексеев | V. A. Shanin | A. A. Birchenko | E V Kravtcova | М. В. Гончаров
Source: Proceedings of the Voronezh State University of Engineering Technologies (article, 2022-06-15)
Field: Chemistry — Analytical Chemistry

Improving the performance of equipment for countercurrent extraction on the basis of process simulation The article discusses the possibility and efficiency of using discrete diffusion in the extraction of materials with an internal anisotropic structure in comparison with a continuous diffusion process. For this purpose, theoretical calculations were made and experimental studies of countercurrent extraction of pectin from Jerusalem artichoke were carried out. To carry out the simulation, an equation for calculating the coefficient of internal diffusion and an equation for the adsorption isotherm during countercurrent extraction were determined. Evaluation of the efficiency of using countercurrent extraction to obtain pectin from Jerusalem artichoke was carried out by considering the influence of the effect of discrete diffusion on the rate of the process. The resulting equations made it possible to analytically evaluate the efficiency of discrete diffusion from two-zone spherical particles in comparison with continuous diffusion in multistage extraction of plant material. Having analytical data on the distribution of extractable substance concentrations in the inner and outer zones of the particle at different stages of extraction, it is possible to calculate the diffusion coefficients of materials in different zones and theoretically determine the degree of destruction of cell walls and structures at different stages of extraction. The developed technique, thanks to the analytical description of the process, allows you to select the optimal conditions for the extraction of various materials from plant and animal raw materials. Countercurrent exchange ; Diffusion ; Extraction (chemistry) ; Process (computing) ; Raw material ; Pectin ; Diffusion process ; Materials science ; Chromatography ; Biological system ; Process engineering ; Chemistry ; Mechanics ; Thermodynamics ; Computer science ; Engineering ; Physics Chromatography in Natural Products ; Microbial Metabolites in Food Biotechnology ; Polysaccharides Composition and Applications