jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dandan Fu | Qiaohua Wang | Meihu Ma | Yixiao Ma | Bin Wang
Source: Journal of Food Process Engineering (article, 2019-02-11)
Field: Chemistry — Analytical Chemistry

Nondestructive prediction modeling of S‐ovalbumin content in stored eggs based on hyperspectral fusion information Abstract Hyperspectral imaging technology was used to nondestructively predict S‐ovalbumin content by obtaining transmission spectral and fused image data. Hyperspectral images of eggs with spectral wavelengths between 300 and 1,100 nm were collected. Three image features were selected to use as variables to model. After spectral pretreatment, 14 characteristic wavelengths were selected using competitive adaptive reweighted sampling algorithm (CARS). Nondestructive prediction models of S‐ovalbumin content were developed based on either image feature parameters, a combination of spectral characteristic wavelengths, or image‐spectral fusion data after dimensionality reduction. A partial least squares (PLS) model and genetic algorithm to optimize a back propagation neural network (GA‐BP) model was established based on these three kinds of data. From comparisons between PLS and GA‐BP models based on the same type of each of the three data types, we determined that hyperspectral fusion data and a GA‐BP model can improve the performance of a S‐ovalbumin nondestructive prediction model. It had a simpler structure with fewer variables. So, it can be used to nondestructively and rapidly predict protein content in egg white. Practical applications Current nondestructive methods to evaluate egg quality primarily focus on the changes of simple external and internal quality, such as egg weight and simple chemical indicators. Due to the individual differences between eggs, the above methods are under a limited application scope. Changes in S‐ovalbumin content do not depend on egg weight, and so forth, individual differences have little effect on it. Changes in S‐ovalbumin content of various varieties of eggs are alike because the biochemical processes that affect egg white protein content is fundamentally the same across egg varieties, and its traditional detection method is to perform complex biochemical tests after breaking. Therefore, if the egg's internal protein content can detected quickly without breaking it, it is more accurate and more profound from the biochemical essence and it can not only solve the shortcomings of biochemical detection, but also provide important theory and application technology for the detection and classification of egg quality. Hyperspectral imaging ; Ovalbumin ; Partial least squares regression ; Biological system ; Computer science ; Feature (linguistics) ; Artificial intelligence ; Pattern recognition (psychology) ; Content (measure theory) ; Mathematics ; Machine learning ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Mohan Srinivasarao | Germano S. Iannacchione | Atul N. Parikh
Source: MRS Bulletin (article, 2019-02-01)
Field: Materials Science — Biomaterials

Biologically inspired far-from-equilibrium materials Biochemical engineering ; Autocatalytic reaction ; Materials science ; Nanotechnology ; Polymer science ; Engineering physics ; Astrobiology ; Engineering ; Physics ; Statistical physics Supramolecular Self-Assembly in Materials ; Micro and Nano Robotics ; Calcium Carbonate Crystallization and Inhibition
Authors: Adib J. Samin | David A. Andersson | Edward F. Holby | Blas P. Uberuaga
Source: Physical review. B./Physical review. B (article, 2019-05-15)
Field: Materials Science — Materials Chemistry

<i>Ab initio</i> based examination of the kinetics and thermodynamics of oxygen in Fe-Cr alloys The behavior of oxygen in Fe-Cr binary alloys is important to understand for a variety of applications including fuel cell interconnects and nuclear energy systems. In this work, we performed an ab initio investigation of the binding of oxygen in the entire compositional range of Fe-Cr at minima and saddles. This database was subsequently used to parametrize concentration-dependent local cluster expansions which were utilized to examine the kinetics of oxygen transport in the alloy and the thermodynamics of the system. The behavior of oxygen in the alloy system was investigated using our cluster expansion model and a convex hull diagram was recorded. The most favorable composition for the incorporation of oxygen was found to occur at 70% Cr. The dependence of the oxygen solution energy on the Cr occupation of the nearest neighbor shells was carefully characterized. It was determined that the third nearest neighbor Cr occupation was unfavorable and that, generally speaking, the first and second nearest neighbor shell Cr occupation was favored with a few exceptions. Kinetic Monte Carlo models at dilute oxygen levels were performed using two models [a kinetically resolved activation (kRA) model and a cluster expansion model for the saddle points]. The models qualitatively agreed with the observed trends in the literature which reported a decrease in diffusivity at dilute Cr levels, but the models predicted different behaviors beyond the dilute Cr limit. Finally, using grand canonical Monte Carlo, we further examined the thermodynamics of oxygen incorporation into the alloy. Together, our results offer new insight into the behavior of oxygen in Fe-Cr alloys that have ramifications on the early stages of the corrosive behavior of the material. Cluster expansion ; Thermodynamics ; Ab initio ; Oxygen ; Cluster (spacecraft) ; Kinetic Monte Carlo ; Materials science ; Monte Carlo method ; Ab initio quantum chemistry methods ; Alloy ; Saddle point ; Physics ; Molecule ; Metallurgy Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Fusion materials and technologies
Authors: Kasun Subasinghage | Kosala Gunawardane | Nihal Kularatna | Tek Tjing Lie
Source: IEEE Access (article, 2019-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Extending the Supercapacitor-Assisted Low-Dropout Regulator (SCALDO) Technique to a Split-Rail DC–DC Converter Application The Supercapacitor-assisted low-dropout (SCALDO) regulator is a unique new DC-DC converter design approach with high end-to-end efficiency (ETEE), where a supercapacitor is used as a lossless voltage dropper in the series path of a linear regulator. This technique, in which the energy recovery happens at a very low frequency (typically in millihertz), with an efficiency multiplication factor in the range of 1.33 to 3, can be used in many different applications. For example, in a 12- 5V linear converter where the theoretical maximum efficiency is 41.67%, the SCALDO approach improves it to 83.33%. The SCALDO technique has been extended to multiple useful applications proving its versatility and has been identified as an entirely different approach compared to conventional switched capacitor converters. In this paper, we provide an in-depth discussion on the expansion of the SCALDO technique into a dual-polarity, split-rail DC power supply with high ETEE, high current, high slew-rate capability and other desirable features. It also presents the experimental results for a 12V to ± 5V prototype with steady-state performance, transient response capability, load/line regulation, and loss estimation. Low-dropout regulator ; Linear regulator ; Converters ; Capacitor ; Slew rate ; Supercapacitor ; Computer science ; Electronic engineering ; Voltage ; Switched capacitor ; Inductor ; Voltage regulator ; Regulator ; Electrical engineering ; Control theory (sociology) ; Engineering ; Dropout voltage ; Capacitance ; Physics ; Electrode Supercapacitor Materials and Fabrication ; Advanced DC-DC Converters ; Multilevel Inverters and Converters
Authors: Frederick Backler | Feng Wang
Source: Journal of Molecular Graphics and Modelling (article, 2019-11-05)
Field: Chemistry — Spectroscopy

Impact of intramolecular hydrogen bonding of gallic acid conformers on chemical shift through NMR spectroscopy Conformational isomerism ; Intramolecular force ; Chemical shift ; Chemistry ; Hydrogen bond ; Dihedral angle ; Nuclear magnetic resonance spectroscopy ; Carbon-13 NMR ; Hydrogen ; Proton NMR ; Crystallography ; Carbon-13 NMR satellite ; Molecule ; Physical chemistry ; Fluorine-19 NMR ; Stereochemistry ; Organic chemistry Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: Yi Zhang | Zhijin Zhang | Xingliang Jiang | Tian Liang | Dongdong Zhang
Source: Applied Sciences (article, 2019-08-15)
Field: Materials Science — Materials Chemistry

Research on Lifespan Prediction of Composite Insulators in a High Altitude Area Experimental Station In this paper, composite insulators of the same batch from Factory A, aged for 1–12 years in a high altitude area experimental station of Hunan province, were sampled. In order to investigate the changing law of lifespan prediction parameters with aging time, widely accepted testing methods, such as the static contact angle (CA) and hardness, were employed for composite insulators in accordance with previous research. Based on test results, lifespan prediction parameters were concluded and some parameters significantly correlated with aging time were filtered by means of correlation calculation. On this basis, a prediction method which can be used to determine the aging time of composite insulators was proposed based on a back propagation (BP) neural network. Test results indicate that parameters, including the static contact angle (θav), the relative content of Si (XSi) and O (XO) elements, and salt-fog flashover voltage (Uf), have significant correlation with aging time, and that these parameters can be used to evaluate the aging degree of composite insulators. In addition, due to the high accuracy in experimental verification, the method proposed in this paper can be used to predict the aging time of composite insulators from Factory A in high altitude areas in future research. Accelerated aging ; Composite number ; Environmental science ; Materials science ; Altitude (triangle) ; Effects of high altitude on humans ; Composite material ; Mathematics ; Meteorology ; Geography High voltage insulation and dielectric phenomena ; Smart Materials for Construction ; Infrastructure Maintenance and Monitoring
Authors: Lewei Zhu | Boxue Du | Hongna Li | Kai Hou
Source: Energies (article, 2019-07-18)
Field: Materials Science — Materials Chemistry

Effect of Polycyclic Compounds Fillers on Electrical Treeing Characteristics in XLPE with DC-Impulse Voltage Electrical tree is an important factor in the threat of the safety of cross-linked polyethylene (XLPE) insulation, eventually leading to the electrical failure of cables. Polycyclic compounds have the potential to suppress electrical treeing growth. In this paper, three types of polycyclic compounds, 2-hydroxy-2-phenylacetophenone, 4-phenylbenzophenone, and 4,4′-difluorobenzophenone are added into XLPE, denoted by A, B, and C. Electrical treeing characteristics are researched with DC-impulse voltage at 30, 60, and 90 °C, and the trap distribution and carrier mobility are characterized. It has been found that although three types of polycyclic compounds can all suppress the electrical tree propagation at different voltages and temperatures, the suppression effect of these polycyclic compounds with the same DC-impulse polarity is worse than with the opposite polarity. As the temperature increases, the suppression effect becomes weak. The energy level and deep trap density are the largest in XLPE-A composite, leading to a decrease in the charge transportation and resulting in the suppression of electrical treeing growth. Experimental results reveal that the polycyclic compound A has great application prospects in high voltage direct current (HVDC) cables. Electrical treeing ; Impulse (physics) ; Polyethylene ; Materials science ; Composite material ; Voltage ; Polarity (international relations) ; Electrical engineering ; Chemistry ; Partial discharge ; Engineering ; Physics High voltage insulation and dielectric phenomena ; Lightning and Electromagnetic Phenomena ; Electrical Fault Detection and Protection
Authors: Lian‐Hsiung Lin | Fu‐Ming Lu | Yung‐Chiung Chang
Source: International journal of agricultural and biological engineering (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Prediction of protein content in rice using a near-infrared imaging system as diagnostic technique The aim of this research was to determine the rice protein content utilizing a NIR imaging system. The developed imaging system utilized a NIR camera which installed automatically exchanged filters with the wavelength range from 870 nm to 1014 nm. Multiple liner regression (MLR), partial least square regression (PLSR), and artificial neural network (ANN) models were employed as data analysis methods for 6.18%-9.43% rice protein detections within both the NIR imaging system and commercial NIRS. A total of 180 rice samples were used in this study, of which 120 random samples were selected as a calibration set for the MLR and PLSR models. Moreover, for establishing the back-propagation ANN model, the same 120 samples were divided into two parts, 80 samples were used for network training and the other 40 were established as the monitoring set. To compare with the results of MLR, PLSR, and ANN models, the remaining 60 of the total 180 samples were established as the validation set. Applying an MLR linear regression model composed of five wavelengths; the NIR imaging system successfully detected rice protein content. The predicting results of rval2 and SEP were 0.769 and 0.294%, respectively. In PLSR model, utilizing the imaging system obtained the results of rval2 = 0.782, and SEP = 0.274% within the wavelength range from 870 nm to 1014 nm. Five significant wavelengths selected by the MLR model were the same as the input data of the ANN model, and the prediction results were rval2 = 0.806, and SEP = 0.266%. The prediction results indicated that the developed NIR imaging system has the advantages of simple, convenient operation, and high detection accuracy as well as it presents commercial potential in non-destructive detection of rice protein content. Keywords: non-destructive detection, near-infrared imaging system, inner quality, rice, protein content, agriculture product DOI: 10.25165/j.ijabe.20191202.4709 Citation: Lin L-H, Lu F-M, Chang Y-C. Prediction of protein content in rice using a near-infrared imaging system as diagnostic technique. Int J Agric & Biol Eng, 2019; 12(2): 195–200. Partial least squares regression ; Calibration ; Linear regression ; Near-infrared spectroscopy ; Content (measure theory) ; Data set ; Artificial neural network ; Mathematics ; Artificial intelligence ; Computer science ; Statistics ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Rattanawadee Panthong | Anongnart Srivihok
Source: Information (article, 2019-05-31)
Field: Chemistry — Analytical Chemistry

Liver Cancer Classification Model Using Hybrid Feature Selection Based on Class-Dependent Technique for the Central Region of Thailand Liver cancer data always consist of a large number of multidimensional datasets. A dataset that has huge features and multiple classes may be irrelevant to the pattern classification in machine learning. Hence, feature selection improves the performance of the classification model to achieve maximum classification accuracy. The aims of the present study were to find the best feature subset and to evaluate the classification performance of the predictive model. This paper proposed a hybrid feature selection approach by combining information gain and sequential forward selection based on the class-dependent technique (IGSFS-CD) for the liver cancer classification model. Two different classifiers (decision tree and naïve Bayes) were used to evaluate feature subsets. The liver cancer datasets were obtained from the Cancer Hospital Thailand database. Three ensemble methods (ensemble classifiers, bagging, and AdaBoost) were applied to improve the performance of classification. The IGSFS-CD method provided good accuracy of 78.36% (sensitivity 0.7841 and specificity 0.9159) on LC_dataset-1. In addition, LC_dataset II delivered the best performance with an accuracy of 84.82% (sensitivity 0.8481 and specificity 0.9437). The IGSFS-CD method achieved better classification performance compared to the class-independent method. Furthermore, the best feature subset selection could help reduce the complexity of the predictive model. Feature selection ; AdaBoost ; Artificial intelligence ; Decision tree ; Naive Bayes classifier ; Computer science ; Pattern recognition (psychology) ; Feature (linguistics) ; Machine learning ; Ensemble learning ; Selection (genetic algorithm) ; Class (philosophy) ; Data mining ; Support vector machine Spectroscopy and Chemometric Analyses ; Artificial Intelligence in Healthcare ; Machine Learning in Bioinformatics
Authors: Marat I. Latypov | Amil Khan | Christian Lang | Kris Kvilekval | Andrew Polonsky | McLean P. Echlin | Irene J. Beyerlein | B.S. Manjunath | Tresa M. Pollock
Source: Integrating materials and manufacturing innovation (article, 2019-03-15)
Field: Materials Science — Materials Chemistry

BisQue for 3D Materials Science in the Cloud: Microstructure–Property Linkages Abstract Accelerating the design and development of new advanced materials is one of the priorities in modern materials science. These efforts are critically dependent on the development of comprehensive materials cyberinfrastructures which enable efficient data storage, management, sharing, and collaboration as well as integration of computational tools that help establish processing–structure–property relationships. In this contribution, we present implementation of such computational tools into a cloud-based platform called BisQue (Kvilekval et al., Bioinformatics 26(4):554, 2010). We first describe the current state of BisQue as an open-source platform for multidisciplinary research in the cloud and its potential for 3D materials science. We then demonstrate how new computational tools, primarily aimed at processing–structure–property relationships, can be implemented into the system. Specifically, in this work, we develop a module for BisQue that enables microstructure-sensitive predictions of effective yield strength of two-phase materials. Towards this end, we present an implementation of a computationally efficient data-driven model into the BisQue platform. The new module is made available online (web address: https://bisque.ece.ucsb.edu/module_service/Composite_Strength/ ) and can be used from a web browser without any special software and with minimal computational requirements on the user end. The capabilities of the module for rapid property screening are demonstrated in case studies with two different methodologies based on datasets containing 3D microstructure information from (i) synthetic generation and (ii) sampling large 3D volumes obtained in experiments. Cloud computing ; Computer science ; Property (philosophy) ; Software ; Systems engineering ; Web application ; Distributed computing ; Data science ; Software engineering ; Computational science ; World Wide Web ; Engineering ; Operating system Machine Learning in Materials Science ; Composite Material Mechanics ; Microstructure and mechanical properties
Authors: Lijie Zhao | Shi-Da Zou | Yuhong Zhang | Ming‐Zhong Huang | Yue Zuo | Jia Wang | Xing-Kui Lu | Zhihao Wu | Xiangyu Liu
Source: Sensors and Materials (article, 2019-06-27)
Field: Materials Science — Surfaces, Coatings and Films

Segmentation of Activated Sludge Phase Contrast Microscopy Images Using U-Net Deep Learning Model For the activated sludge wastewater treatment process, the image segmentation of flocs and filaments has become a crucial component in the successful implementation of a sludge volume index (SVI) sensor and the early fault detection of filamentous bulking. The segmentation of a phase contrast microscopy (PCM) image is a challenging problem because of the weak greyscale distinction between flocs and filaments, as well as the artifacts of halos and shadows. In this work, we proposed an automatic floc and filament segmentation method for PCM images using a U-Net deep learning structure with data augmentation. A loss function combining the binary cross entropy (BCE) function and Dice coefficient is proposed to improve the segmentation accuracy and sensitivity with unbalanced foreground and background samples. The performance of the segmentation algorithm is evaluated by the accuracy, precision, recall, F-measure, and intersection-over-union (IoU) metrics. Lab-scale experiments on the activated sludge process have been carried out to verify the proposed image segmentation method. Our proposed U-Net models with the combined loss function give better results than the U-Net models with BCE, fully convolutional network-VGG16 (FCN-VGG16), and a traditional segmentation method. Phase contrast microscopy ; Artificial intelligence ; Contrast (vision) ; Segmentation ; Microscopy ; Materials science ; Computer vision ; Computer science ; Optics ; Physics Electron and X-Ray Spectroscopy Techniques ; Thermography and Photoacoustic Techniques ; Industrial Vision Systems and Defect Detection
Authors: Mario L. Ferrari | Iacopo Rossi | Alessandro Sorce | Aristide F. Massardo
Source: Journal of Engineering for Gas Turbines and Power (article, 2019-07-09)
Field: Materials Science — Materials Chemistry

Advanced Control System for Grid-Connected SOFC Hybrid Plants: Experimental Verification in Cyber-Physical Mode This paper presents a model predictive controller (MPC) operating a solid oxide fuel cell (SOFC) gas turbine hybrid plant at end-of-life performance condition. Its performance was assessed with experimental tests showing a comparison with a proportional integral derivative (PID) control system. The hybrid system (HS) operates in grid-connected mode, i.e., at variable speed condition of the turbine. The control system faces a multivariable constrained problem, as it must operate the plant into safety conditions while pursuing its objectives. The goal is to test whether a linearized controller design for normal operating condition is able to govern a system which is affected by strong performance degradation. The control performance was demonstrated in a cyber-physical emulator test rig designed for experimental analyses on such HSs. This laboratory facility is based on the coupling of a 100 kW recuperated microturbine with a fuel cell emulation system based on vessels for both anodic and cathodic sides. The components not physically present in the rig were studied with a real-time model running in parallel with the plant. Model output values were used as set-point data for obtaining in the rig (in real-time mode) the effect of the fuel cell system. The result comparison of the MPC tool against a PID control system was carried out considering several plant properties and the related constraints. Both systems succeeded in managing the plant, still the MPC performed better in terms of smoothing temperature gradient and peaks. PID controller ; Emulation ; Controller (irrigation) ; Solid oxide fuel cell ; Multivariable calculus ; Model predictive control ; Turbine ; Control theory (sociology) ; Control system ; Grid ; Engineering ; Hybrid system ; Operating point ; Control engineering ; Computer science ; Automotive engineering ; Temperature control ; Control (management) ; Mechanical engineering ; Anode Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Advanced Control Systems Optimization