jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hubin Liu | Na Liu | Yuhui Yuan | Cihai Zhang | Longlian Zhao | Junhui Li
Source: Journal of Spectroscopy (article, 2022-06-23)
Field: Chemistry — Analytical Chemistry

A Variable Selection Method Based on Fast Nondominated Sorting Genetic Algorithm for Qualitative Discrimination of Near Infrared Spectroscopy A reliable and effective qualitative near-infrared (NIR) spectroscopy discrimination method is critical for excellent model building, yet the performance of models built by these methods is highly dependent on valid feature extraction. The goal of feature selection is to associate the selected variables with the property of interest, which many have done successfully. However, many of selection methods focus only on strong association with the analytes or properties of interest, neglecting correlations between variables. A variable selection method based on a fast nondominated-ranking genetic algorithm (NSGA-II) was proposed in this paper for qualitative discrimination of NIR spectra. The method had two objective functions: (1) maximizing the sum of ratios of interclass variance to intraclass variance, (2) minimizing the sum of correlation coefficients between the selected variables. FT-NIR spectra of a total of 124 tobacco samples from different origins and parts in Guizhou Province, China, were used as the experimental objects, and the part-grade discrimination models of tobacco leaves were established by combining this method with partial least squares-based discriminant analysis (PLS-DA), and compared with PLS-DA model based on the full spectrum. The results showed that the performance of PLS-DA model with the NSGA-II was improved, with a comparable or better correct discrimination rate and reasonable discrimination rate, and could discriminate different parts of the tobacco leaves well. It indicates that the NSGA-II can select a few and effective feature variables to build a high-performance qualitative discrimination model and is proved to be a promising algorithm. In addition, the method is not designed exclusively for spectral data. It is a general strategy that could be used for variable selection for other types of data. Feature selection ; Sorting ; Selection (genetic algorithm) ; Genetic algorithm ; Linear discriminant analysis ; Ranking (information retrieval) ; Pattern recognition (psychology) ; Mathematics ; Feature (linguistics) ; Partial least squares regression ; Computer science ; Statistics ; Artificial intelligence ; Algorithm ; Mathematical optimization Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: S.I. Peleshenko | Viktor Kvasnytskyi | V.Yu. Khaskin | Volodymyr Korzhyk | Illyashenko Ye. | K. Lepilina | Andrii Aloshyn
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2022-11-01)
Field: Materials Science — General Materials Science

FEATURES OF PHYSICAL AND METALLURGICAL PROCESSES DURING WELDING OF THIN-WALLED ALUMINUM ALLOY STRUCTURES USING LASER RADIATION <strong>Abstract</strong> This paper aims to study features of physical and metallurgical processes during welding of thin-walled structures made of aluminum alloys using laser radiation, including the effect of concomitant heating provided by a compressed electric arc of direct action, as well as the influence of distance between a center of anode region of compressed arc of a non-consumable electrode and an axis of laser radiation, on reduction of laser energy losses. To perform the study, modes of laser and laser-plasma welding are selected and correspondent experiments are carried out to determine the effect of specified distance, as well as metallographic analysis of experimental results is made. It is determined that applying the concomitant plasma heating in laser welding makes it possible to increase the penetration depth by 15–65% compared to conventional laser welding, including by improving radiation absorption. To improve absorption of radiation during laser-plasma welding, it is recommended that distance between a center of anode region of compressed arc of a non-consumable electrode and an axis of laser radiation should not exceed 1.0 mm, and in hybrid welding it should not exceed 0.5 mm. During laser welding of 7075 alloy, it is possible to obtain joints with sufficiently narrow seams with minimal energy input and lifetime of a melt pool. However, typical defects such as microcracks 10 –20 µm long in HAZ and oxide film inclusions in the root part of the remelted metal may occur. The use of concomitant local plasma heating during laser welding of 7075 alloy may provide reduction in graininess and increase in uniformity of hardness of joints in combination with elimination of formation of microcracks and oxide inclusions, which makes laser-plasma welding method more preferred. Alloy ; Metallurgy ; Materials science ; Welding ; Aluminium ; Laser ; Laser beam welding ; Radiation ; Optics ; Physics Material Properties and Applications ; Engineering Technology and Methodologies ; Laser and Thermal Forming Techniques
Authors: Jaehwan Lee | Kyoungah Cho | Yoonbeom Park | Sungeun Park | Hee‐eun Song | Sangsig Kim
Source: International Journal of Photoenergy (article, 2022-10-06)
Field: Materials Science — Materials Chemistry

Performance Enhancement of Hybrid Energy Devices Using Cooling Patches In this study, we demonstrated the enhancement of the output power of a hybrid energy device (HED) using a cooling patch that does not consume any external electric power. The HED consisted of a photovoltaic cell (PVC) and a thermoelectric generator (TEG); the cooling patch was attached to the TEG. When the PVC was exposed to solar irradiance, the cooling patch lowered the temperature of the PVC and increased the thermal gradient across the TEG, thereby increasing the output power. For an HED with a cooling patch at an irradiance of 1000 W/m2, the output power increased to 24.2 mW, as compared to the output power of 19.9 mW for an HED without any cooling patch. Materials science ; Thermoelectric generator ; Water cooling ; Optoelectronics ; Irradiance ; Power (physics) ; Nuclear engineering ; Solar energy ; Solar irradiance ; Thermal ; Composite material ; Environmental science ; Thermoelectric effect ; Electrical engineering ; Optics ; Physics ; Meteorology ; Thermodynamics Advanced Thermoelectric Materials and Devices ; solar cell performance optimization ; Thermal Radiation and Cooling Technologies
Authors: Yutao Li | Liliang Wang | Hao Yu | Jiayi An | Yan Pei | Zheng Qian
Source: Applied Sciences (article, 2022-05-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Dynamic Hysteresis Model for TMR-Current Sensors Based on Probability Estimation of Hysteresis Operator and Its Switching Time Hysteresis is one of the main factors affecting the measurement accuracy of TMR sensors, especially in dynamic measurements. The commonly used Preisach hysteresis compensation model has some problems, such as complex modeling and difficulty in accurately measuring the step time, resulting in low accuracy in dynamic measurements. In this paper, considering the distribution characteristics of the conversion time of the hysteresis operator in dynamic measurements, a dynamic hysteresis model based on the probability estimation of the hysteresis operator and its conversion time is proposed. Compared with the existing methods, this method only needs to calculate the distribution of the sensor hysteresis operator to realize the calculation of hysteresis characteristics without a physical model or fitting algorithm. It has good generalization performance and a corresponding fast speed. In the test of two typical TMR sensors, compared with the transmission Preisach model, the maximum error of this method is reduced by 46.7%, the variance can be reduced by 90.2%, and the average value can be reduced by 65.1%. Hysteresis ; Operator (biology) ; Control theory (sociology) ; Compensation (psychology) ; Computer science ; Physics ; Artificial intelligence Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Electric Motor Design and Analysis
Authors: Lanaia Maciel | Rafael Oliveira Martins | Daniel Gondim | João Victor Ataíde Oliveira | Julia Pereira | Gustavo Nicolau PEREIRA | Leonardo Matos Ferreira | Andréa Rodrigues Chaves | Boniek G. Vaz
Source: Brazilian Journal of Analytical Chemistry (article, 2022-08-03)
Field: Chemistry — Spectroscopy

Desorption Electrospray Ionization Imaging for Neurotransmitters Evaluation: A Potential Approach to Parkinson’s Disease Monitoring Parkinson's disease (PD) is globally known as the most common movement disorder and the second most common neurodegenerative disease. The disease includes the symptoms of involuntary limb tremors, stiffness, or inflexibility of limbs and joints, among others. Due to this, scientific reports on analytical methodologies to evaluate the progression of neurodegenerative diseases are extremely necessary. Traditional methods include histochemical, immunohistochemical, and ligand-based approaches, however, these approaches still suffer from selectivity limitations of association, leading to a wrong evaluation. In this context, mass spectrometry imaging methods, such as desorption electrospray ionization (DESI), are potential approaches to visualize the distribution of biomarkers that can lead to the information on the progress of PD. This review aims to bring a discussion of some DESI methodologies reported in the literature for the assessment of neurotransmitters associated with PD. Parkinson's disease ; Context (archaeology) ; Disease ; Neuroscience ; Desorption electrospray ionization ; Medicine ; Computer science ; Psychology ; Pathology ; Chemistry ; Ionization ; Biology ; Chemical ionization Mass Spectrometry Techniques and Applications ; Electrochemical sensors and biosensors ; Microfluidic and Capillary Electrophoresis Applications
Authors: Y Zhang | Jie Wang | Hongyu LUO | Juan YANG | Xiuhong Wu | Quan WU | Yingfu Zhong
Source: Food Science and Technology (article, 2022-11-21)
Field: Chemistry — Analytical Chemistry

Rapid prediction of Yongchuan Xiuya tea quality by using near infrared spectroscopy coupled with chemometric methods The current developmental trend is to evaluate the quality of Yongchuan Xiuya tea rapidly. After spectrum pre-processing, near infrared spectroscopy (NIRS) coupled with synergy interval partial least squares (siPLS), principal component analysis (PCA) and back propagation-artificial neural network (BP-ANN) was applied to rapidly and non-destructively predict the quality of Yongchuan Xiuya tea. External Yongchuan Xiuya tea samples were used for the actual application of the proposed model. The best pre-processing method was multiple scattering correction coupled with second derivative, and the characteristic spectral regions selected by siPLS were 4381.5-4755.6 cm-1, 4759.5-5133.6 cm-1, 6266.6-6637.8 cm-1 and 7389.9-7760.2 cm-1. The cumulative contribution rate was 99.05% for the first three principal components of the characteristic spectra regions. The transfer function, root mean square error and determinant coefficient of the best BP-ANN prediction model were the tanh function, 0.384 and 0.977, respectively. The root mean square error and determinant coefficient of the external 10 Yongchuan Xiuya tea samples were 0.406 and 0.969, respectively. These results showed that NIRS combined with BP-ANN algorithm can be used to evaluate the quality of Yongchuan Xiuya tea rapidly and accurately. Mathematics ; Principal component analysis ; Mean squared error ; Coefficient of determination ; Second derivative ; Root mean square ; Near-infrared spectroscopy ; Analytical Chemistry (journal) ; Chemistry ; Statistics ; Mathematical analysis ; Chromatography ; Optics Spectroscopy and Chemometric Analyses ; Fermentation and Sensory Analysis ; Phytochemicals and Antioxidant Activities
Authors: Xuwen Deng | Songxiao Hui | Wenjun Ye | Rui Liu | Liang Huang
Source: Materials (article, 2022-06-27)
Field: Materials Science — Materials Chemistry

Numerical Simulation and Process Optimization on Hot Twist-Stretch Straightening of Ti-6Al-4V Alloy Profile Ti-6Al-4V profiles prepared by hot extrusion are usually accompanied by bending and twisting. The hot twist-stretch straightening is an effective strategy such that the bending deflection and twisting angle can be simultaneously decreased by a single straightening process. In addition, utilizing stress relaxation effect, the residual stress and springback can be greatly reduced by holding the straightening temperature and strain constant for a period after twist-stretch straightening. In this study, the hot deformation behaviors of the Ti-6Al-4V profile were revealed by experiments. The tensile model was obtained by uniaxial tensile tests within ranges of temperatures (500-700 °C) and strain rates (5 × 10<sup>-5</sup>-1 × 10<sup>-3</sup> s<sup>-1</sup>). The creep constitutive model was acquired with stress relaxation experiments in ranges of temperatures (500-700 °C) and pre-strain of 1.5%. Then, the coupled thermo-mechanical model of hot twist-stretch straightening was established. Based on orthogonal experiment strategy, the effects of straightening temperature, stretch strain, and holding time on the bending deflection and torsion angle of profile were investigated systematically and the process was optimized. The straightening accuracy is significantly affected by straightening temperature and holding time. By using optimized process parameters in practical straightening experiments, the deflection/length and angle/length after straightening does not exceed 2‱ and 2.5‱°/mm, respectively, which is basically consistent with the numerical simulation result. Materials science ; Ultimate tensile strength ; Deflection (physics) ; Creep ; Stress relaxation ; Composite material ; Extrusion ; Torsion (gastropod) ; Residual stress ; Twist ; Structural engineering ; Geometry ; Optics ; Mathematics ; Physics Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming ; Microstructure and mechanical properties
Authors: K. Lavanya | R. Subalakshmi | Tamizharasi Thirugnanam | Lydia Jane | Akila Victor
Source: Scalable Computing Practice and Experience (article, 2022-12-23)
Field: Chemistry — Analytical Chemistry

Unsupervised Unmixing and Segmentation of Hyper Spectral Images Accounting for Soil Fertility A crucial component of precision agriculture is the capability to assess the fertility of soil by looking at the precise distribution and composition of its different constituents. This study aims to investigate how different machine learning models may be used to assess soil fertility using hyperspectral pictures. The development of images using a random mixing of different soil components is the first phase, and the hyper spectral bands utilized to create the images are not used again during the analysis procedure. The resulting end members are then acquired by applying the NFINDR algorithm to the process of spectral unmixing this image. The comparison between these end members and the band values of the known elements is then quantified., i.e. it is represented as a graph of band values obtained through spectral unmixing. Finally we quantify the similarities between both graphs and proceed towards the classification of the hyper spectral image as fertile or infertile. In order to classify the hyper spectral image as fertile or infertile, we quantify the similarities between the two graphs. Clustering and picture segmentation algorithms have been devised to help with this process, and a comparison is then made to show which techniques are the most effective. Hyperspectral imaging ; Cluster analysis ; Pattern recognition (psychology) ; Computer science ; Segmentation ; Fertility ; Image segmentation ; Artificial intelligence ; Mathematics ; Population Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Feng Yu | Yifan Chen | Bo Liu | Tao Wang | Zhaoliang Ding | Ziwen Yi | Yiping Zhu | Jing Li
Source: Scientific Reports (article, 2022-12-30)
Field: Chemistry — Spectroscopy

DIA mass spectrometry characterizes urinary proteomics in neonatal and adult donkeys Health monitoring is critical for newborn animals due to their vulnerability to diseases. Urine can be not only a useful and non-invasive tool (free-catch samples) to reflect the physiological status of animals but also to help monitor the progression of diseases. Proteomics involves the study of the whole complement of proteins and peptides, including structure, quantities, functions, variations and interactions. In this study, urinary proteomics of neonatal donkeys were characterized and compared to the profiles of adult donkeys to provide a reference database for healthy neonatal donkeys. The urine samples were collected from male neonatal donkeys on their sixth to tenth days of life (group N) and male adult donkeys aging 4-6 years old (group A). Library-free data-independent acquisition (direct DIA) mass spectrometry-based proteomics were applied to analyze the urinary protein profiles. Total 2179 urinary proteins were identified, and 411 proteins were differentially expressed (P < 0.05) between the two groups. 104 proteins were exclusively expressed in group N including alpha fetoprotein (AFP), peptidase-mitochondrial processing data unit (PMPCB), and upper zone of growth plate and cartilage matrix associated (UCMA), which might be used to monitor the health status of neonatal donkeys. In functional analysis, some differentially expressed proteins were identified related to immune system pathways, which might provide more insight in the immature immunity of neonatal donkeys. To the best of our knowledge, this is the first time to report donkey urinary proteome and our results might provide reference for urinary biomarker discovery used to monitor and evaluate health status of neonatal donkeys. Proteomics ; Mass spectrometry ; Urinary system ; Computational biology ; Biology ; Medicine ; Computer science ; Internal medicine ; Chemistry ; Chromatography ; Genetics Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Veterinary Equine Medical Research
Authors: Abbas Babaee | Kian Shahinfar | S. Mohammad Shahrtash
Source: IET Generation Transmission & Distribution (article, 2022-06-05)
Field: Materials Science — Materials Chemistry

Analytical investigations on discrimination of partial discharge defective cable termination among healthy ones with joint sheath‐ground connection Abstract Industrial complexes suffer from crosstalk of Partial Discharge (PD) defects in cable terminations connected to the same medium voltage busbars. This phenomenon happens because the sheath‐ground connections of all cables of one switchboard are first fastened together and then connected to ground, which leads to PD pulses being induced from the source termination to others. In this paper, based on mathematical investigations on PD pulse propagation and by introducing an energy‐based index, a simple rule‐based procedure is proposed for discrimination between defective terminations and healthy ones connected to common busbars. The proposed procedure has been verified by simulations even when there exists noise pollution or multiple defects of different strengths. Partial discharge ; Joint (building) ; Connection (principal bundle) ; Structural engineering ; Ground ; Electrical engineering ; Acoustics ; Engineering ; Physics ; Voltage High voltage insulation and dielectric phenomena ; Electrical Fault Detection and Protection ; Electrostatic Discharge in Electronics
Authors: Dong Ma | Yuke Liu | Qingwen Fan | Xinsheng Li | Daichuan Ma | Daibing Luo
Source: Crystals (article, 2022-11-20)
Field: Materials Science — Materials Chemistry

Prediction of Lattice Volumes of Crystal Samples by Computer Image Recognition on the X-ray Diffraction Patterns Computer image recognition (CIR) on the diffraction patterns of X-ray single crystal diffractometer was used to assist the analysis of the unit cell parameters in terms of lattice volume. This method can improve the prediction accuracy of unit cell parameters in the pre-experiment step since erroneous prediction by the software may occur for 5% tested crystal samples, which may critically affect the complete crystal data quality and cause unnecessary testing time. In the imaging processing, the diffraction pattern images saved as .img format files were firstly batch transferred into .jpg format with high quality for further treatment. Specific features of the crystal diffraction points or zones in the images were extracted for the recognition processing by color mode. The Findcontour algorithm and the Canny algorithm based on Gaussian filtering were used for the diffraction peak points and area detection. The relationship between the distribution of crystal diffraction points (DCDP) and the lattice volumes based on reciprocal lattice theory was established for the users to judge the reasonable location range of the lattice volumes. A Density model was proposed to correlate the dependence of the DCDP and the experimental data. Special crystal samples were chosen to test the CIR performance, and its accuracy for the lattice volume prediction was evaluated. The CIR method can be used for the assistance in the analysis of the lattice volume location range and for crystal structure solving. Diffraction ; Reciprocal lattice ; Lattice (music) ; Diffractometer ; Lattice constant ; Computer science ; Algorithm ; Artificial intelligence ; Crystal structure ; X-ray crystallography ; Materials science ; Mathematics ; Optics ; Crystallography ; Physics ; Chemistry X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Cultural Heritage Materials Analysis
Authors: R.B. Rebak
Source: Koroze a ochrana materialu (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Innovative accident tolerant nuclear fuel materials will help extending the life of light water reactors Abstract Nuclear power is a significant source of clean energy that can be used to mitigate climate change. In the western world, the average life of light-water power-reactors is increasing because no new reactors are being connected to the grid. In order to extend the life of the existing reactors innovative materials are being considered for the fuel of the reactors. The newer materials would be more resistant to a loss of coolant accident and allow for longer times between refueling plus permitting overall extended burn-ups, which will make the reactors safer and more economical to operate. This never used before in reactor materials include cladding concepts such as: 1) coated zirconium alloys, 2) monolithic iron-chromium-aluminum alloys, and 3) silicon carbide composites. Cladding (metalworking) ; Light-water reactor ; Coolant ; Silicon carbide ; SAFER ; Nuclear engineering ; Nuclear power ; Nuclear fuel ; Environmental science ; Waste management ; Materials science ; Forensic engineering ; Engineering ; Metallurgy ; Mechanical engineering ; Computer science Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear and radioactivity studies