jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sang‐Mok Lee | So-Won Choi | Eul‐Bum Lee
Source: Energies (article, 2023-09-30)
Field: Chemistry — Analytical Chemistry

Prediction Modeling of Flue Gas Control for Combustion Efficiency Optimization for Steel Mill Power Plant Boilers Based on Partial Least Squares Regression (PLSR) The energy-intensive steel industry, which consumes substantial amounts of electricity, meets its power demands through external electricity purchases and self-generation through the operation of its own generators. This study aimed to optimize boiler combustion efficiency and increase power generation output by deriving optimal operational values for O2 and CO within the boiler flue gas using machine learning (ML) with the aim of achieving maximum boiler efficiency. This study focuses on the power-generation boilers at steel mill P in Korea. First, 361 types of operation data from power generation equipment were collected and preprocessed. Subsequently, a partial least squares regression (PLSR) algorithm was used to develop a prediction model for O2 and CO values, known as the Boiler Flue Gas Prediction Model (BFG-PM). The prediction accuracy for O2 was notably high (83.2%), whereas that for CO was lower (53.4%). Nonetheless, the model’s reliability was high because more than 90% of the predicted values were within a 10% error range. Finally, the correlation of the BFG-PM model was applied to the performance test code (PTC) 4.0 for the boiler efficiency calculations formula, deriving the optimal O2 and CO control points. Through a simulation, it was verified that the boiler efficiency was improved by controlling the combustion air. In addition, an average increase in boiler efficiency of 0.29% was confirmed by applying it directly to the generator operating on-site. The results of this study are expected to contribute to annual cost savings, with a reduction of USD 217,000 in electricity purchasing costs and USD 19,700 in greenhouse gas emissions trading expenses. Boiler (water heating) ; Flue gas ; Combustion ; Electricity generation ; Process engineering ; Engineering ; Partial least squares regression ; Steel mill ; Blast furnace gas ; Power station ; Electricity ; Waste management ; Mathematics ; Power (physics) ; Chemistry ; Statistics ; Metallurgy ; Electrical engineering ; Materials science Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Advanced Algorithms and Applications
Authors: Pedro Sampaio | Bruna Carbas | Carla Brites
Source: Applied Sciences (article, 2023-08-09)
Field: Chemistry — Analytical Chemistry

Development of Prediction Models for the Pasting Parameters of Rice Based on Near-Infrared and Machine Learning Tools Due to the importance of rice (Oryza sativa) in food products, developing strategies to evaluate its quality based on a fast and reliable methodology is fundamental. Herein, near-infrared (NIR) spectroscopy combined with machine learning algorithms, such as interval partial least squares (iPLS), synergy interval PLS (siPLS), and artificial neural networks (ANNs), allowed for the development of prediction models of pasting parameters, such as the breakdown (BD), final viscosity (FV), pasting viscosity (PV), setback (ST), and trough (TR), from 166 rice samples. The models developed using iPLS and siPLS were characterized, respectively, by the following regression values: BD (R = 0.84; R = 0.88); FV (R = 0.57; R = 0.64); PV (R = 0.85; R = 0.90); ST (R = 0.85; R = 0.88); and TR (R = 0.85; R = 0.84). Meanwhile, ANN was also tested and allowed for a significant improvement in the models, characterized by the following values corresponding to the calibration and testing procedures: BD (Rcal = 0.99; Rtest = 0.70), FV (Rcal = 0.99; Rtest = 0.85), PV (Rcal = 0.99; Rtest = 0.80), ST (Rcal = 0.99; Rtest = 0.76), and TR (Rcal = 0.99; Rtest = 0.72). Each model was characterized by a specific spectral region that presented significative influence in terms of the pasting parameters. The machine learning models developed for these pasting parameters represent a significant tool for rice quality evaluation and will have an important influence on the rice value chain, since breeding programs focus on the evaluation of rice quality. Partial least squares regression ; Machine learning ; Mathematics ; Artificial neural network ; Calibration ; Artificial intelligence ; Computer science ; Statistics Spectroscopy and Chemometric Analyses ; Food composition and properties ; GABA and Rice Research
Authors: Leo Krammer | Rolf Breinbauer
Source: Israel Journal of Chemistry (article, 2023-01-18)
Field: Chemistry — Organic Chemistry

Activity‐Based Protein Profiling of Oxidases and Reductases Abstract Activity‐based protein profiling (ABPP) as a proteomic tool allows measuring the activity of enzyme classes or distinct proteins in their cellular context, annotating the function of uncharacterized proteins, and investigating the target profile of small molecule inhibitors. While hydrolases and other enzyme classes, which exhibit a characteristic nucleophilic residue, can be addressed by electrophilic activity‐based probes, the large enzyme class of oxidoreductases has received much less attention in ABPP. In this review the state of the art of ABPP of oxidases is presented. The most frequent principle in ABPP for oxidases is the oxidative activation of the ABPP probe leading to an electrophilic group as exemplified for alkyne or amine based warheads. Alternatively, in rare cases ABPP probes can address characteristic electrophilic or nucleophilic residues of certain oxidases. Most recently, with NQO2 the first example of ABPP for a reductase has been reported. Chemistry ; Electrophile ; Biochemistry ; Enzyme ; Nucleophile ; Combinatorial chemistry Click Chemistry and Applications ; Biotin and Related Studies ; Chemical Synthesis and Analysis
Authors: Kinga Nagy | Gabriella Gellén | Dávid Papp | Gitta Schlosser | Ágnes Révész
Source: Journal of Mass Spectrometry (article, 2023-07-06)
Field: Chemistry — Spectroscopy

Optimum collision energies for proteomics: The impact of ion mobility separation Ion mobility spectrometry (IMS) is a widespread separation technique used in various research fields. It can be coupled to liquid chromatography-mass spectrometry (LC-MS/MS) methods providing an additional separation dimension. During IMS, ions are subjected to multiple collisions with buffer gas, which may cause significant ion heating. The present project addresses this phenomenon from the bottom-up proteomics point of view. We performed LC-MS/MS measurements on a cyclic ion mobility mass spectrometer with varied collision energy (CE) settings both with and without IMS. We investigated the CE dependence of identification score, using Byonic search engine, for more than 1000 tryptic peptides from HeLa digest standard. We determined the optimal CE values-giving the highest identification score-for both setups (i.e., with and without IMS). Results show that lower CE is advantageous when IMS separation is applied, by 6.3 V on average. This value belongs to the one-cycle separation configuration, and multiple cycles may supposedly have even larger impact. The effect of IMS is also reflected in the trends of optimal CE values versus m/z functions. The parameters suggested by the manufacturer were found to be almost optimal for the setup without IMS; on the other hand, they are obviously too high with IMS. Practical consideration on setting up a mass spectrometric platform hyphenated to IMS is also presented. Furthermore, the two CID (collision induced dissociation) fragmentation cells of the instrument-located before and after the IMS cell-were also compared, and we found that CE adjustment is needed when the trap cell is used for activation instead of the transfer cell. Data have been deposited in the MassIVE repository (MSV000090944). Ion-mobility spectrometry ; Chemistry ; Mass spectrometry ; Fragmentation (computing) ; Collision-induced dissociation ; Ion ; Dissociation (chemistry) ; Collision ; Analytical Chemistry (journal) ; Chromatography ; Proteomics ; Tandem mass spectrometry ; Physical chemistry Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Analytical Chemistry and Chromatography
Authors: Mehdi Esmaieli | Masume Khodsuz | Hassan Nouri
Source: IET Science Measurement & Technology (article, 2023-12-28)
Field: Materials Science — Materials Chemistry

Condition assessment criteria evaluation of asymmetric aged and fully aged silicone rubber insulators based on leakage current harmonics Abstract The leakage current characteristic monitoring of transmission line insulators is considered a worthy technique for insulator state prediction. In this paper, the harmonic analysis of leakage current signals has been performed to analyze the composite insulator condition. Experimental studies of asymmetrically aged, fully aged, and virgin insulators have been conducted. The effects of different aging types, pollution, and humidity on leakage current components, such as harmonics and the phase differences with the applied voltage, have been investigated. The analysis of the results shows that the effect of asymmetric aging on leakage current components is non‐linear, and this non‐linearity increases with the degree of pollution. Also, the type of insulator aging can be determined by the ratio of the third‐ and fifth‐order leakage current harmonics. For instance, the classification of insulators into virgin, asymmetrically aged, and fully aged can be determined by examining the corresponding value ranges of 0–0.5, 0.5–0.9, and &gt; 0.9, respectively. Similarly, the phase difference values of &lt; 15% and those &gt; 15% indicate the clean and polluted operating conditions individually. Insulator (electricity) ; Harmonics ; Silicone rubber ; Leakage (economics) ; Materials science ; Electrical engineering ; Acoustics ; Voltage ; Composite material ; Engineering ; Physics High voltage insulation and dielectric phenomena ; Thermal Analysis in Power Transmission ; Power Transformer Diagnostics and Insulation
Authors: Oyeyemi O. Ajayi | Lanre Akinyemi | Sikiru Adeniyi Atanda | Jason G. Walling | Ramamurthy Mahalingam
Source: Journal of Chemometrics (article, 2023-10-01)
Field: Chemistry — Analytical Chemistry

Malt quality profile of barley predicted by near‐infrared spectroscopy using partial least squares, Bayesian regression, and artificial neural network models Abstract Due to the significant cost and time involved in identifying barley lines with superior malting quality, the malting industry is searching for accurate and rapid methods to expedite the selection of superior barley lines that meet breeder's goals. The aim of this study is to compare partial least squares regression (PLSR) with advanced statistical models (Bayesian and machine learning) and reliably assess their performance in predicting malt quality traits from near infra‐red (NIR) spectral data using barley grains. Using spectral data as predictors and the malt quality traits as references, PLSR outperformed Bayesian and PCA‐ANN models for diastatic power (DP), alpha amylase (AA), malt extract (ME), wort protein (WP), soluble to total protein (S/T) ratio, and free amino nitrogen (FAN). WP had the best prediction performance for all models, with the best‐performing model, PLSR, having (RPD) values of 0.55 (1.5). The influential wavelength regions identified based on the variable importance in projection (VIP) scores and coefficient estimates for PLSR and Bayesian models, respectively, were comparatively similar for all malt quality traits. Based on these findings, PLSR analysis and wavelength selection techniques would enhance the future design and optimization of NIR prediction models in malt quality improvement programs. Partial least squares regression ; Bayesian probability ; Feature selection ; Chemometrics ; Statistics ; Mathematics ; Artificial intelligence ; Coefficient of determination ; Computer science ; Pattern recognition (psychology) ; Machine learning Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Food composition and properties
Authors: Hongliang Zhang | Jingpin Jiao | Bin Wu | Cunfu He
Source: Measurement Science and Technology (article, 2023-08-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Theoretical model of magnetoacoustic emission considering the microstructure of ferromagnetic material Abstract Magnetoacoustic emission (MAE) holds great promise for evaluating the mechanical properties of ferromagnetic materials. To refine the problems of the current theoretical and numerical models of MAE, a theoretical MAE model that considers the microscopic dependence of the hysteresis properties is proposed in this paper. The microstructure (dislocation density and grain size) and the correlation of MAE jumps are considered and incorporated into the model. Then, the influences of magnetization parameters and microstructure parameters on the envelope of the MAE signal are analyzed by the proposed theoretical model. The proposed theoretical model is then fully evaluated by simulations and experiments. The MAE experiments are conducted on ferromagnetic specimens with different hardnesses, and the MAE signals with different hardnesses are simulated by inverting the basic parameters of the MAE model with the genetic algorithm. Further, the crucial hysteresis parameters of the specimens are calculated using the results of microscopic measurements and the calculated parameters agree well with inversion results from experimental signals. The results demonstrate that the proposed theoretical model is valid for the MAE signal simulation. The trends of different hardnesses can be predicted by the MAE simulation signals. Moreover, the model can be used for theoretical analysis of the microscopic dependence of the MAE signal. Microstructure ; Ferromagnetism ; Materials science ; Hysteresis ; SIGNAL (programming language) ; Statistical physics ; Magnetization ; Envelope (radar) ; Ferromagnetic material properties ; Condensed matter physics ; Physics ; Computer science ; Composite material Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Zhengchang Liu | Pu Peng | Xiao He | Zhibo Dang | Yuchen Dai | Yuxiang Chen | Xinyuan Shao | Yu Li | Yijing Huang | Donglin Liu | Guangyi Tao | Yunhao Zhang | Zheyu Fang
Source: Advanced Physics Research (article, 2023-02-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Polarization Multiplexing Bifunctional Metalens Designed by Deep Neural Networks Abstract As planar optical elements, metasurfaces confer an unprecedented potential to manipulate light, which benefits from the deep control of the interactions between nanostructures and light. In the past decade, considerable progress has been made in various metasurfaces for on‐demand functions, drawing great interest from the scientific community. However, it is a great challenge to integrate different functions into a single metasurface, due to the incapability of manipulating light at different dimensions and the lack of universal intelligent design strategy. Here, an intelligent design platform based on deep neural networks is proposed, which can map between structure parameters and optical response. The well‐trained network model can intelligently retrieve nanostructures to meet multidimensional optical requirements of metasurfaces. Four metalenses for chiral focusing are realized by the design platform and the simulation results are highly consistent with the design target. In addition, metalenses based on arbitrary polarization at various working wavelength are also demonstrated, showing that the method has powerful design ability. Various optical properties of nanostructures, such as phase shift and polarization, are manipulated by deep neural networks, which can greatly promote the development of multifunctional devices and further pave the way for optical display, communication, computing, sensing, and other applications. Computer science ; Multiplexing ; Polarization (electrochemistry) ; Planar ; Artificial neural network ; Electronic engineering ; Computer architecture ; Artificial intelligence ; Telecommunications ; Engineering Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Photonic Crystals and Applications
Authors: S. A. Khrapak | A. G. Khrapak
Source: Physical review. E (article, 2023-12-20)
Field: Materials Science — Materials Chemistry

Vibrational model for thermal conductivity of Lennard-Jones fluids: Applicability domain and accuracy level Exact mechanisms of thermal conductivity in liquids are not well understood, despite a rich research history. A vibrational model of energy transfer in dense simple liquids with soft pairwise interactions seems adequate to partially fill this gap. The purpose of the present paper is to define its applicability domain and to demonstrate how well it works within the identified applicability domain in the important case of the Lennard-Jones model system. The existing results from molecular dynamics simulations are used for this purpose. Additionally, we show that a freezing density scaling approach represents a very powerful tool to estimate the thermal conductivity coefficient across essentially the entire gas-liquid region of the phase diagram, including metastable regions. A simple practical expression serving this purpose is proposed. Thermal conductivity ; Metastability ; Lennard-Jones potential ; Statistical physics ; Simple (philosophy) ; Domain (mathematical analysis) ; Phase diagram ; Molecular dynamics ; Pairwise comparison ; Scaling ; Materials science ; Thermodynamics ; Physics ; Phase (matter) ; Computer science Material Dynamics and Properties ; Phase Equilibria and Thermodynamics ; Quantum, superfluid, helium dynamics
Authors: Jiangui Zhao | Ning Chen | Tingyu Zhu | Xuerong Zhao | Ming Yuan | Zhiqiang Wang | Guo‐Liang Wang | Zhiwei Li | Huiling Du
Source: Preprints.org (preprint, 2023-07-03)
Field: Chemistry — Analytical Chemistry

Simultaneous Quantification and Visualization of Photosynthetic Pigments in Lycopersicon Esculentum Mill under Different Levels of Nitrogen Application with Visible-near Infrared Hyperspectral Imaging Technology Leaf photosynthetic pigments play a crucial role in evaluating nutritional elements and physiological states. In facility agriculture, it is vital to obtain rapidly and accurately the pigment content and distribution of leaves to ensure precise water and fertilizer management. In our research, we utilized chlorophyll a (Chla), chlorophyll b (Chlb), chlorophyll (Chll), and carotenoid (Caro) as indicators to study the variations in leaf position of Lycopersicon esculentum Mill. Under 10 nitrogen concentration applications, a total of 2610 leaves (435 samples) were collected using visible-near infrared hyperspectral imaging (VNIR-HSI). In this study, a "coarse-fine" screening strategy was proposed by using competitive adaptive reweighted sampling (CARS) and iteratively retained informative variable (IRIV) algorithm to extract characteristic wavelengths. Finally, simultaneous and quantitative models were established using partial least squares regression (PLSR). The CARS-IRIV-PLSR was used to create models to achieve a better prediction effect. The coefficient determination (R2), root mean square error (RMSE), and ratio performance deviation (RPD) were predicted to be 0.8240, 1.43, 2.38 for Chla, 0.8391, 0.53, 2.49 for Chlb, 0.7899, 2.24, 2.18 for Chll, and 0.7577, 0.27, 2.03 for Caro, respectively. The combination of these models with the pseudo-color image allowed for a visual inversion of the content and distribution of pigment. These findings have important implications for guiding pigment distribution, nutrient diagnosis, and fertilization decisions in plant growth management. Hyperspectral imaging ; Partial least squares regression ; VNIR ; Lycopersicon ; Chlorophyll ; Chlorophyll a ; Mean squared error ; Photosynthesis ; Mathematics ; Horticulture ; Botany ; Artificial intelligence ; Computer science ; Biology ; Statistics Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Remote Sensing in Agriculture
Authors: Heidi R. Abd El-Hadi | Maya S. Eissa | Hala E. Zaazaa | Basma M. Eltanany
Source: BMC Chemistry (article, 2023-12-07)
Field: Chemistry — Analytical Chemistry

Development and validation of chemometric-assisted spectrophotometric models for efficient quantitation of a binary mixture of supportive treatments in COVID-19 in the presence of its toxic impurities: a comparative study for eco-friendly assessment The use of sustainable solvents has increased significantly in recent years due to advancements in green analytical methods. The number of impurities in the drug substance determines how safe the finished product is. Therefore, during the whole medication planning process, contaminants need to be closely watched. Using chemometric models, the concentrations of hyoscine N-butyl bromide (HYO) and paracetamol (PAR) were determined in the presence of three PAR impurities [P-nitrophenol (PNP), P-aminophenol (PAP), and P-chloroacetanilide (PCA), as well as DL-tropic acid (TRO) as a HYO impurity]. It was possible to isolate and measure these dangerous impurities. Fever and spasms associated with COVID-19 are reported to be considerably reduced when PAR and HYO are taken together. Artificial neural networks, principal component regression, multivariate curve resolution-alternating least squares, and partial least squares are the four chemometric-assisted spectrophotometric models that were created and verified. All of the proposed methods' quantitative analytical potency was assessed using recoveries%, root mean square error of prediction, and standard error of prediction. For PAR, HYO, PNP, PCA, TRO, and PAP, respectively, the indicated approaches were used in the ranges of 4.00-8.00, 16.00-24.00, 1.00-5.00, 0.40-0.80, 4.00-12.00, and 2.00-6.00 µg/mL. They are able to get around difficulties like collinearity and spectral overlaps. After statistical testing, there was no discernible difference between the recommended methods and the published one. The degree of greenness of the established models was evaluated using three different green assessment methods. In the presence of their harmful impurities, PAR and HYO could be identified using the recommended methods. Chemometrics ; Collinearity ; Partial least squares regression ; Chromatography ; Impurity ; Chemistry ; Multivariate statistics ; Mathematics ; Statistics ; Analytical Chemistry (journal) ; Organic chemistry Analytical Methods in Pharmaceuticals ; Computational Drug Discovery Methods ; Antibiotics Pharmacokinetics and Efficacy