jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Elisabete A. De Nadai Fernandes | Gustavo Nazato Furlan | Robson C. de Lima | Márcio Arruda Bacchi | Silvana Regina Vicino Sarriés | Gabriel Adrián Sarriés
Source: Journal of Radioanalytical and Nuclear Chemistry (article, 2022-11-15)
Field: Chemistry — Analytical Chemistry

Elemental profile of dietary supplements and agricultural byproducts evaluated by neutron activation analysis Neutron activation analysis ; Elemental analysis ; Cluster (spacecraft) ; Chemistry ; Agriculture ; Food science ; Environmental chemistry ; Radiochemistry ; Biology ; Inorganic chemistry Heavy Metals in Plants ; Nuclear Physics and Applications ; Isotope Analysis in Ecology
Authors: Markus Meuwly
Source: CHIMIA International Journal for Chemistry (article, 2022-06-29)
Field: Chemistry — Spectroscopy

Computational Vibrational Spectroscopy Vibrational spectroscopy is a powerful technique to characterize the near-equilibrium dynamics of molecules in the gas and the condensed phase. This contribution summarizes efforts from computer-based methods to gain insight into the relationship between structure and spectroscopic response. Methods for this purpose include physics-based and machine-learned energy functions, and methods that separate sampling conformational space and determining the data for spectral analysis such as map-based techniques. Spectroscopy ; Vibrational energy ; Statistical physics ; Molecule ; Sampling (signal processing) ; Phase space ; Gas phase ; Chemical physics ; Physics ; Computer science ; Computational physics ; Computational chemistry ; Chemistry ; Physical chemistry ; Optics ; Thermodynamics ; Quantum mechanics Spectroscopy and Laser Applications ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Nor Syahira Jak Jailani | Zuraida Muhammad | Mohd Hezri Fazalul Rahiman | Mohd Nasir Taib
Source: International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering (article, 2022-09-21)
Field: Chemistry — Analytical Chemistry

Intelligent grading of kaffir lime oil quality using non-linear support vector machine &lt;span lang="EN-US"&gt;This paper presents kaffir lime oil quality grading using the intelligent system classification method, a non-linear support vector machine (NSVM). This method classifies the quality kaffir lime oil into two groups: high and low quality, based on their significant chemical compounds. The 90 data of kaffir lime oil were used in this project from high to low quality. The abundance (%) of significant chemical compounds will act as the input and high or low quality as an output. The 90 data will be divided into two sets: training and testing data sets with a ratio of 8:2. The radial basis function (RBF) optimization kernel parameters in NSVM. Using the implementation of MATLAB software version R2020a, all data and analysis work was performed automatically. The results showed that the NSVM model met all performance criteria for 100% accuracy, sensitivity, specificity, and precision.&lt;/span&gt; Support vector machine ; Grading (engineering) ; Lime ; Computer science ; MATLAB ; Kernel (algebra) ; Artificial intelligence ; Pattern recognition (psychology) ; Mathematics ; Engineering ; Materials science ; Operating system Spectroscopy and Chemometric Analyses
Authors: Ewa Ropelewska
Source:  (article, 2022-02-15)
Field: Chemistry — Analytical Chemistry

Postharvest Authentication of Potato Cultivars Using Machine Learning to Provide High-Quality Products The correct identification of potato cultivars is of great importance for both processing and cultivation due to differences in properties. The objective of this study was to discriminate potato cultivars &ldquo;Irga&rdquo;, &ldquo;Riviera&rdquo; and &ldquo;Colomba&rdquo; using models developed based on selected textures of tuber images converted to color channels R, G, B, L, a, b, X, Y, Z, U, V. The highest accuracies of cultivar identification of potato tubers reached 99% for the IBk classifier and 98% for Multilayer Perceptron. The developed models can be used to avoid mixing potato cultivars. Postharvest cultivar authentication can contribute to providing consumers with high-quality products. Cultivar ; Postharvest ; Horticulture ; Mathematics ; Biology Spectroscopy and Chemometric Analyses ; Potato Plant Research ; Advanced Chemical Sensor Technologies
Authors: Jingcheng Wang | Dongxiao Li | Ralf Betzholz | Jianming Cai
Source: Physical Review Applied (article, 2022-08-15)
Field: Materials Science — Materials Chemistry

Real-Time Adaptive Sensing of Nuclear Spins by a Single-Spin Quantum Sensor Quantum sensing is considered to be one of the most promising subfields of quantum information to deliver practical quantum advantages in real-world applications. However, its impressive capabilities, including high sensitivity, are often hindered by the limited quantum resources available. Here, we incorporate the expected information gain (EIG) and techniques such as accelerated computation into Bayesian experimental design (BED) in order to use quantum resources more efficiently. A simulated nitrogen-vacancy center in diamond is used to demonstrate real-time operation of the BED. Instead of heuristics, the EIG is used to choose optimal control parameters in real time. Moreover, combining the BED with accelerated computation and asynchronous operations, we find that up to a tenfold speedup in absolute time cost can be achieved in sensing multiple surrounding ${}^{13}\mathrm{C}$ nuclear spins. Our work explores the possibilities of applying the EIG to BED-based quantum-sensing tasks and provides techniques useful to integrate BED into more generalized quantum sensing systems. Computer science ; Quantum computer ; Quantum sensor ; Spins ; Quantum ; Computation ; Quantum information ; Heuristics ; Speedup ; Spin (aerodynamics) ; Quantum network ; Physics ; Algorithm ; Quantum mechanics ; Parallel computing Diamond and Carbon-based Materials Research ; Atomic and Subatomic Physics Research ; Electronic and Structural Properties of Oxides
Authors: Tran Thuc Binh | Tuyen Ngoc Tran | Tran Thi Ai My | Giang Chau Dang Nguyen | Quynh Trang Thi Nguyen
Source: Preprints.org (preprint, 2022-03-22)
Field: Chemistry — Analytical Chemistry

Simultaneous Determination of Paracetamol, Ibuprofen, and Caffeine in Tablets by Molecular Absorption Spectroscopy Combined with Classical Least Square Method In this paper, the classical least square (CLS) method with molecular absorption spectrophotometric measurement was used to determine simultaneously of paracetamol (PAR), ibuprofen (IBU), and caffeine (CAF) in tablets. The absorbance spectra of the standard solutions and samples were measured over a wavelength from 220 to 300 nm with a 0.5 nm step. The concentration of PAR, IBU and CAF in the sample solutions were calculated by using a program called CLS-Excel written in Microsoft Excel 2016 and Visual Basic for Applications (VBA). The method and CLS-Excel program were tested on mixed standard laboratory samples with different PAR, IBU, and CAF concentration ratios, and they showed small errors and satisfying repeatability. An analytical procedure for tablets containing PAR, IBU, and CAF was developed. The reliability of the procedure was proved via the recovery and repeatability of the analysis results with an actual tablet sample and comparing the mean contents of active substances in the tablets obtained from the analytical procedure with the from the HPLC method. The procedure is simple with a reduced cost compared with the HPLC standard method. Repeatability ; Chromatography ; Absorbance ; Analytical Chemistry (journal) ; Chemistry ; CLs upper limits ; High-performance liquid chromatography ; Ibuprofen ; Medicine Analytical Methods in Pharmaceuticals ; Analytical Chemistry and Sensors ; Analytical Chemistry and Chromatography
Authors: Abdullahi Abubakar Mas’ud
Source: Yanbu Journal of Engineering and Science (article, 2022-06-05)
Field: Materials Science — Materials Chemistry

Experimental investigation of different cavity arrangements in the high voltage solid insulation systems The partial discharge (PD) test is a widely used technique for evaluating the performance of the insulations in the high voltage (HV) plant. Electrical sparks, or PD, can deteriorate the insulation of HV equipment. Once present, however, they become the primary mechanism of deterioration and can lead to system failure, resulting in capital costs and economic consequences. The effects of PD defects in a cavity of various sizes will be investigated in this paper. Experiments were carried out using polyethylene terephthalate (PET) samples, with various cavity arrangements, and PD data was captured over a long stressing period. Cavities of interest include single, serial, parallel and electrode bounded cavities. The result shows that the discharge patterns of serial and single cavities are similar but distinct from those of parallel cavities. Further studies using single cavities with diameters of 0.6mm and 1mm show that there is no discernible difference in their PD patterns, though smaller discharges with higher amplitude are observed in the 1mm cavity. There is a high asymmetry between the negative and positive half power cycle discharges for the electrode bounded cavity, which can be explained better by the PD mechanism. Electrode ; Materials science ; Partial discharge ; Voltage ; Polyethylene ; Amplitude ; Composite material ; Asymmetry ; Optoelectronics ; Mechanics ; Electrical engineering ; Optics ; Chemistry ; Physics ; Engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Lightning and Electromagnetic Phenomena
Authors: Lorenzo S. Corrêa | Bernardo Vieira | Jaime A. Lozano | Jader R. Barbosa | Andrew Rowe | Michaela Kuepferling | Vittorio Basso | Paulo V. Trevizoli
Source: Journal of Magnetism and Magnetic Materials (article, 2022-12-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Correlating the properties of near-room-temperature first- and second-order magnetocaloric materials Magnetic refrigeration ; Thermomagnetic convection ; Materials science ; Refrigerant ; Magnetization ; Heat capacity ; Adiabatic process ; Isothermal process ; Thermodynamics ; Magnetic field ; Condensed matter physics ; Physics ; Gas compressor Magnetic and transport properties of perovskites and related materials ; Shape Memory Alloy Transformations ; Magnetic Properties of Alloys
Authors: Unnati Parmar | Ankit Kumar Mishra Mishra
Source:  (article, 2022-12-01)
Field: Materials Science — Materials Chemistry

Mechanical Properties of NiTi Based Shape Memory Alloy used in Manufacturing of Mars Rover Wheels: Short Review Shape memory alloys (SMA) are innovatively designed for engineering applications including, but not limited to, medical devices, actuators, micromechanical systems, deployable structures, and elastocaloric devices. Prior to the manufacture of any component for the above mentioned applications, an extensive simulation study is recommended as the basic properties are required to be filled in the software. The unavailability of to-the-point literature makes this task difficult for researches. In this paper, an overview of binary NiTi based shape memory alloy is given, where its characteristic physical properties i.e. shape memory effect and super elastic effect, and mechanical properties like fatigue failure, stress-strain characteristics and yield and ultimate strength is stated for several different specimens prepared using thermomechanical methods. Shape-memory alloy ; Nickel titanium ; Actuator ; Materials science ; Pseudoelasticity ; SMA* ; Mechanical engineering ; Task (project management) ; Smart material ; Computer science ; Unavailability ; Structural engineering ; Composite material ; Engineering ; Martensite ; Artificial intelligence ; Reliability engineering Shape Memory Alloy Transformations
Authors: Parvaneh Karami | Mohsen Zandi | Ali Ganjloo
Source: Food Science and Technology (article, 2022-05-01)
Field: Chemistry — Analytical Chemistry

Mathematical, fuzzy logic and artificial neural network modeling of extraction kinetics of essential oil from aerial parts of yarrow (Achillea millefolium L.) using ohmic-assisted hydrodistillation Mathematical, fuzzy logic and artificial neural network modeling of extraction kinetics of essential oil from aerial parts of yarrow (Achillea millefolium L.) using ohmic-assisted hydrodistillation Achillea millefolium ; Essential oil ; Fuzzy logic ; Artificial neural network ; Extraction (chemistry) ; Biological system ; Computer science ; Artificial intelligence ; Botany ; Biology ; Chemistry ; Chromatography Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity ; Advanced Chemical Sensor Technologies
Authors: Max Lennart Feuerstein | Maykel Hernández‐Mesa | Andrea Kiehne | Bruno Le Bizec | Stephan Hann | Gaud Dervilly | Tim Causon
Source: ChemRxiv (preprint, 2022-07-11)
Field: Chemistry — Spectroscopy

Comparability of Steroid Collision Cross Sections Using Three Different IM-HRMS Technologies: An Interplatform Study Steroids play key roles in various biological processes and are characterized by many isomeric variants which makes their unambiguous identification challenging. Ion mobility-mass spectrometry (IM-MS) has been proposed as a suitable platform for this application, particularly using collision cross section (CCS) databases obtained from differ-ent commercial IM-MS instruments. CCS is foreseen as an ideal additional identification parameter for steroids as long-term repeatability and interlaboratory reproducibility of this measurand are excellent and matrix effects are negligible. While excellent results were demonstrated for individual IM-MS technologies, a systematic comparison of CCS derived from all major commercial IM-MS technologies has not been performed. To address this gap, a comprehensive interlabor-atory comparison of 142 CCS values derived from drift tube (DTIM-MS), traveling wave (TWIM-MS) and trapped ion mo-bility (TIM-MS) platforms using a set of 87 steroids was undertaken. Besides delivering three instrument-specific CCS databases, systematic comparisons revealed excellent interlaboratory performance for 95% of the ions with CCS biases within ±1% for TIM-MS and within ±2% for TWIM-MS with respect to DTIM-MS values. However, a small fraction of ions (<1.5 %) showed larger biases of up to 7% indicating that differences in the ion conformation sampled on different in-strument types need to be further investigated. Systematic differences between CCS derived from different IM-MS analyz-ers and implications on the applicability for non-targeted analysis are critically discussed. To the best of our knowledge this is the most comprehensive interlaboratory study comparing CCS from three different IM-MS technologies for analysis of steroids and small molecules in general. Comparability ; Repeatability ; Ion-mobility spectrometry ; Reproducibility ; Collision ; Identification (biology) ; Systematic error ; Mass spectrometry ; Analytical Chemistry (journal) ; Ion ; Computer science ; Chemistry ; Data mining ; Mathematics ; Chromatography ; Statistics ; Biology Analytical Chemistry and Chromatography ; Mass Spectrometry Techniques and Applications ; Hormonal and reproductive studies
Authors: Cecilia Bender | Sarah Straßmann | Christian Golz
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-11-18)
Field: Chemistry — Organic Chemistry

Oral bioavailability and metabolism of hydroxytyrosol from food supplements Abstract Table olives and olive oils are the main dietary sources of hydroxytyrosol (HT), a natural antioxidant compound that has emerged as a potential aid in protection against cardiovascular risk. Bioavailability studies with olive oils showed that HT is bioavailable from its free form and from conjugated forms like oleuropein and its aglycone. Still, its low dietary intake, poor bioavailability, and high inter-individual variability after absorption through the gastrointestinal tract hamper its full benefits. In a randomized, controlled, blind, cross-over study, we investigated the impact of HT metabolism and bioavailability by comparing two olive-derived watery supplements containing different doses of HT (30.58 and 61.48 mg of HT/dosage, respectively); additionally, HT-fortified olive oil was used in the control group. To this aim, plasma and urine samples were evaluated in 12 healthy volunteers following the intake of a single dose of the supplements or fortified olive oil. Blood and urine samples were collected at baseline and at 0.5, 1, 1.5, 2, 4, and 12 hours after intake. HT and its metabolites were analyzed by UHPLC-DAD-MS/MS. Pharmacokinetic results showed that dietary HT administered through the food supplements is bioavailable and biovailability increases with the administered dose. After intake, homovanillic acid, HT-3- O -sulfate, and 3,4-dihydroxyphenylacetic acid are the main metabolites found both in plasma and urine. The maximum concentrations in plasma peaked 30 minutes after intake. Being the bioavailability of a compound a fundamental prerequisite for its effect, these results promise a good potential of both food supplements for the protection against oxidative stress and the consequent cardiovascular risk. Bioavailability ; Hydroxytyrosol ; Urine ; Chemistry ; Food science ; Pharmacokinetics ; Pharmacology ; Antioxidant ; Medicine ; Biochemistry ; Polyphenol Edible Oils Quality and Analysis ; Antioxidant Activity and Oxidative Stress ; Cholesterol and Lipid Metabolism
Authors: Rafael Massahiro Yassue | Giovanni Galli | Roberto Fritsche‐Neto | Gota Morota
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-03-07)
Field: Chemistry — Analytical Chemistry

Classification of plant growth-promoting bacteria inoculation status and prediction of growth-related traits in tropical maize using hyperspectral image and genomic data Abstract Recent technological advances in high-throughput phenotyping have created new opportunities for the prediction of complex traits. In particular, phenomic prediction using hyper-spectral reflectance could capture various signals that affect phenotypes genomic prediction might not explain. A total of 360 inbred maize lines with or without plant growth-promoting bacterial inoculation management under nitrogen stress were evaluated using 150 spectral wavelengths ranging from 386 to 1021 nm and 13,826 single-nucleotide polymorphisms. Six prediction models were explored to assess the predictive ability of hyperspectral and genomic data for inoculation status and plant growth-related traits. The best models for hyperspectral prediction were partial least squares and automated machine learning. The Bayesian ridge regression and BayesB were the best performers for genomic prediction. Overall, hyper-spectral prediction showed greater predictive ability for shoot dry mass and stalk diameter, whereas genomic prediction was better for plant height. The prediction models that simultaneously accommodated both hyperspectral and genomic data resulted in a predictive ability as high as that of phenomics or genomics alone. Our results highlight the usefulness of hyperspectral-based phenotyping for management and phenomic prediction studies. Core ideas Hyperspectral reflectance data can classify plant growth-promoting bacteria inoculation status Phenomic prediction performs better than genomic prediction depending on the target phenotype AutoML is a promising approach for automating hyperparameter tuning for classification and prediction Hyperspectral imaging ; Phenomics ; Predictive modelling ; Biology ; Genomics ; Plant breeding ; Artificial intelligence ; Biotechnology ; Computational biology ; Machine learning ; Computer science ; Agronomy ; Genome ; Genetics ; Gene Spectroscopy and Chemometric Analyses ; Genetic Mapping and Diversity in Plants and Animals ; Remote Sensing in Agriculture
Authors: Mohammad Mohsin Ul Hoque | Toufique Ahmed | Tazina Shams | Md. Ikramul Islam
Source: World Journal of Advanced Research and Reviews (article, 2022-12-13)
Field: Materials Science — Polymers and Plastics

Predicting bursting strength of single jersey 100% cotton plain knitted fabrics using different machine learning models Bursting strength is an important parameter of knit fabrics. It depends on multiple factors. This study aims to determine the best machine learning model to predict bursting strength. Besides, determining the optimum GSM and yarn count to get best bursting strength using RSM (Response Surface Methodology). We tried 9 machine learning models. Among which, the XGBoost (Extreme Gradient Boosting), Random Forest and ANN models performed best having 99%, 85% and 80% R2 value, respectively. The Pearson correlation shows the most significant factors is stitch density, whereas porosity and GSM have most meaningful correlation according to scatterplot. In the same way, RSM determined the GSM and GSM square are significant for bursting strength having p-value &gt; 0.10 (at 90% confidence level). The RSM also depicted the optimum GSM range is from 150 to 245 and yarn count is 24/1 Ne to 31/1 Ne. Again, the RSM found that the GSM, yarn count, and stitch length can influence the bursting strength significantly. Bursting ; GSM ; Response surface methodology ; Mathematics ; Materials science ; Machine learning ; Composite material ; Engineering ; Computer science ; Statistics ; Telecommunications Textile materials and evaluations