jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Muhammad Haziq Imran Md Azmi | Fazida Hanim Hashim | Aqilah Baseri Huddin | Mohd Shaiful Sajab
Source: Sensors (article, 2022-09-19)
Field: Chemistry — Analytical Chemistry

Correlation Study between the Organic Compounds and Ripening Stages of Oil Palm Fruitlets Based on the Raman Spectra The degree of maturity of oil palm fresh fruit bunches (FFB) at the time of harvest heavily affects oil production, which is expressed in the oil extraction rate (OER). Oil palm harvests must be harvested at their optimum maturity to maximize oil yield if a rapid, non-intrusive, and accurate method is available to determine their level of maturity. This study demonstrates the potential of implementing Raman spectroscopy for determining the maturity of oil palm fruitlets. A ripeness classification algorithm has been developed utilizing machine learning by classifying the components of organic compounds such as β-carotene, amino acid, etc. as parameters to distinguish the ripeness of fruits. In this study, 47 oil palm fruitlets spectra from three different ripeness levels-under ripe, ripe, and over ripe-were examined. To classify the oil palm fruitlets into three maturity categories, the extracted features were put to the test using 31 machine learning models. It was discovered that the Medium, Weighted KNN, and Trilayered Neural Network classifier has a maximum overall accuracy of 90.9% by using four significant features extracted from the peaks as the predictors. To conclude, the Raman spectroscopy method may offer a precise and efficient means to evaluate the maturity level of oil palm fruitlets. Ripeness ; Ripening ; Palm oil ; Palm ; Maturity (psychological) ; Chemistry ; Horticulture ; Raman spectroscopy ; Mathematics ; Food science ; Biology ; Psychology Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Coconut Research and Applications
Authors: Mani Valleti | Rama K. Vasudevan | Maxim Ziatdinov | Sergei V. Kalinin
Source: Digital Discovery (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Bayesian optimization in continuous spaces <i>via</i> virtual process embeddings Process optimization in the latent space of functions via variational autoencoder (VAE) and Bayesian Optimization (BO). We demonstrate this to optimize the curl of a kinetic ferroelectric model. Bayesian optimization ; Autoencoder ; Process optimization ; Function optimization ; Bayesian probability ; Process (computing) ; Space (punctuation) ; Computer science ; Optimization problem ; Artificial intelligence ; Mathematical optimization ; Mathematics ; Algorithm ; Machine learning ; Engineering ; Artificial neural network Machine Learning in Materials Science ; Neural Networks and Applications ; Injection Molding Process and Properties
Authors: Jue Wang | Yuankai Tian | Ruisong Zhang | Zhilan Liu | Ye Tian | Silan Dai
Source: Frontiers in Plant Science (article, 2022-06-06)
Field: Chemistry — Analytical Chemistry

Multi-Information Model for Large-Flowered Chrysanthemum Cultivar Recognition and Classification The traditional Chinese large-flowered chrysanthemum is one of the cultivar groups of chrysanthemum (<i>Chrysanthemum</i> × <i>morifolium</i> Ramat.) with great morphological variation based on many cultivars. Some experts have established several large-flowered chrysanthemum classification systems by using the method of comparative morphology. However, for many cultivars, accurate recognition and classification are still a problem. Combined with the comparative morphological traits of selected samples, we proposed a multi-information model based on deep learning to recognize and classify large-flowered chrysanthemum. In this study, we collected the images of 213 large-flowered chrysanthemum cultivars in two consecutive years, 2018 and 2019. Based on the 2018 dataset, we constructed a multi-information classification model using non-pre-trained ResNet18 as the backbone network. The model achieves 70.62% top-5 test accuracy for the 2019 dataset. We explored the ability of image features to represent the characteristics of large-flowered chrysanthemum. The affinity propagation (AP) clustering shows that the features are sufficient to discriminate flower colors. The principal component analysis (PCA) shows the petal type has a better interpretation than the flower type. The training sample processing, model training scheme, and learning rate adjustment method affected the convergence and generalization of the model. The non-pre-trained model overcomes the problem of focusing on texture by ignoring colors with the ImageNet pre-trained model. These results lay a foundation for the automated recognition and classification of large-flowered chrysanthemum cultivars based on image classification. Cultivar ; Chrysanthemum morifolium ; Biology ; Botany ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Prabu Ravindran | Adam C. Wade | Frank C. Owens | Rubin Shmulsky | Alex C. Wiedenhoeft
Source: Canadian Journal of Forest Research (article, 2022-05-25)
Field: Chemistry — Organic Chemistry

Towards sustainable North American wood product value chains, part 2: computer vision identification of ring-porous hardwoods Wood identification is vitally important for ensuring the legality of North American hardwood value chains. Computer vision wood identification (CVWID) systems can identify wood without necessitating costly and time-consuming off-site visual inspections by highly trained wood anatomists. Previous work by Ravindran and colleagues presented macroscopic CVWID models for identification of North American diffuse porous hardwoods from 22 wood anatomically informed classes using the open-source XyloTron platform. This manuscript expands on that work by training and evaluating complementary 17-class XyloTron CVWID models for the identification of North American ring porous hardwoods — woods that display spatial heterogeneity in earlywood and latewood pore size and distribution and other radial growth-rate-related features. Deep-learning models trained using 4045 images from 452 ring-porous wood specimens from four xylaria demonstrated 98% five-fold cross-validation accuracy. A field model trained on all the training data and subsequently tested on 198 specimens drawn from two additional xylaria achieved top-1 and top-2 predictions of 91.4% and 100%, respectively, and images devoid of earlywood, latewood, or broad rays did not greatly reduce the prediction accuracy. This study advocates for continued cooperation between wood anatomy and machine-learning experts for implementing and evaluating field-operational CVWID systems. Hardwood ; Artificial intelligence ; Identification (biology) ; Softwood ; Machine learning ; Porosity ; Computer science ; Pulp and paper industry ; Wood industry ; Environmental science ; Agricultural engineering ; Pattern recognition (psychology) ; Materials science ; Engineering ; Botany ; Composite material ; Forestry ; Geography ; Biology Wood and Agarwood Research ; Remote Sensing and LiDAR Applications ; Cultural Heritage Materials Analysis
Authors: Yajun Zhang | Yan Yang | Chong Ma | Liping Jiang
Source: PLoS ONE (article, 2022-07-14)
Field: Chemistry — Analytical Chemistry

Identification of multiple raisins by feature fusion combined with NIR spectroscopy Varieties of raisins are diverse, and different varieties have different nutritional properties and commercial value. In this paper, we propose a method to identify different varieties of raisins by combining near-infrared (NIR) spectroscopy and machine learning algorithms. The direct averaging of the spectra taken for each sample may reduce the experimental data and affect the extraction of spectral features, thus limiting the classification results, due to the different substances of grape skins and flesh. Therefore, this experiment proposes a method to fuse the spectral features of pulp and peel. In this experiment, principal component analysis (PCA) was used to extract baseline corrected features, and linear models of k-nearest neighbor (KNN) and linear discriminant analysis (LDA) and nonlinear models of back propagation (BP), support vector machine with genetic algorithm (GA-SVM), grid search-support vector machine (GS-SVM) and particle swarm optimization with support vector machine (PSO- SVM) coupling were used to classify. This paper compared the results of four experiments using only skin spectrum, only flesh spectrum, average spectrum of skin and flesh, and their spectral feature fusion. The experimental results showed that the accuracy and Macro-F1 score after spectral feature fusion were higher than the other three experiments, and GS-SVM had the highest accuracy and Macro-F1 score of 94.44%. The results showed that feature fusion can improve the performance of both linear and nonlinear models. This may provide a new strategy for acquiring spectral data and improving model performance in the future. The code is available at https://github.com/L-ain/Source. Support vector machine ; Artificial intelligence ; Pattern recognition (psychology) ; Linear discriminant analysis ; Principal component analysis ; Computer science ; Feature vector ; Feature extraction ; Fusion ; Mathematics Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Advanced Chemical Sensor Technologies
Authors: Can Ding | Xing Hu | Zhenjiang Gao
Source: IEEE Access (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Study on Relative Electrical Strength of SF<sub>6</sub> Substitute Gas Based on Density Functional Theory In order to achieve the goal of &#x201C;double carbon&#x201D; as soon as possible, SF<sub>6</sub> substitute gas is more environmentally friendly and applied in electrical equipment. Based on density functional theory (DFT), the ionization energy, polarizability and HOMO-LUMO energy level of the SF<sub>6</sub> substitute gas were calculated. First, some relevant electrical parameters representing the substitute gas were linearly fitted to their experimental values. Secondly, the prediction simulation equation of the relative electric strength of the substitute gas is obtained by multivariate nonlinear fitting, and then the prediction results are screened. Finally, the relationship between the electrical parameters of the surrogate gas molecule and the relationship between the relative molecular mass under the external electric field is further studied. The research results show that the ionization energy, average electron polarizability and HOMO-LUMO energy level of substitute gas molecules have a strong positive correlation with their relative electric strength, and HFOtype substitute gases can be used as SF<sub>6</sub> substitute gases. Under the external electric field, the ionization energy and HOMO-LUMO level of the surrogate gas molecules decrease with the increase of the gas molecular mass, and the average electron polarizability increases with the increase of the gas molecular mass. For the screening of alternative gases, the carbon chain should be selected to be shorter, the relative molecular mass is more significant, and the average molecular weight is higher. The fluoride of the electron polarizability functional group provides a more precise direction for screening SF<sub>6</sub> substitute gases and has a particular guiding significance. Density functional theory ; Materials science ; Chemistry ; Computational chemistry High voltage insulation and dielectric phenomena ; Inorganic Fluorides and Related Compounds ; Organic and Molecular Conductors Research
Authors: Norbert Radek | Artur Kalinowski | Łukasz J. Orman | Marcin Szczepaniak | Jacek Świderski | Dariusz Gontarski | Jozef Bronček | Jacek Pietraszek
Source: Metal ... (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Operational properties of DLC coatings and their potential application Diamond-like carbon (DLC) coatings are currently used in many areas of the economy. Due to their unique properties, such as high hardness, low friction, and high wear resistance, are also used in industry, especially on machine parts exposed to increased wear. The paper presents the results of operational tests of diamondlike carbon coatings deposited in the physical vapor deposition (PVD) process on samples made of 4H13 stainless steel. The properties of DLC coatings were assessed based on microstructure analysis, nanohardness and roughness measurements, and tribological tests. The results obtained during the tests showed that using diamond-like coatings significantly improves the tribological properties. In addition, the coatings are free of pores and microcracks. In addition, DLC layers are characterized by high nanohardness and low roughness. This type of coating can be used on surfaces of machine parts. Materials science ; Tribology ; Diamond-like carbon ; Microstructure ; Coating ; Physical vapor deposition ; Surface roughness ; Carbon fibers ; Surface finish ; Deposition (geology) ; Metallurgy ; Chemical vapor deposition ; Wear resistance ; Diamond ; Composite material ; Thin film ; Nanotechnology ; Composite number Diamond and Carbon-based Materials Research ; Metal and Thin Film Mechanics ; Tribology and Wear Analysis
Authors: Sebastian Sławski | Marek Kciuk | W. Klein
Source: Sensors (article, 2022-05-08)
Field: Materials Science — Materials Chemistry

Change in Electrical Resistance of SMA (NiTi) Wires during Cyclic Stretching In this article, the use of Nickel Titanium (NiTi) alloy as a sensor is examined. A cyclic stretching test, that has various elongations (0.5 and 1%), is administered to NiTi wires with various diameters and lengths. It is assumed that the elongation enables an observation of the change in electrical resistance of the NiTi wires, due to martensite reorientation. During the test, the stretching force, the displacement, and the electrical resistance of the NiTi wires are measured. Following the test, the resistance of all the tested samples differed compared to the starting position. Conducted research indicates that NiTi wires are very sensitive to resistance change when they are deformed. A large difference in NiTi electrical resistance was visible in all samples during the first stretching cycle. For longer wires, with a smaller diameter, resistance change was visible during almost all of the stretching cycles. However, the observed changes were very small. Based on the obtained results, it can be justifiably stated that NiTi wires could be used to build deformation sensors, which operate both online and offline. Moreover, NiTi wires with a small diameter could be used to create cyclic loading sensors. Such sensors can be used in self-sensing applications or in structural health monitoring. Nickel titanium ; Materials science ; Shape-memory alloy ; Electrical resistance and conductance ; Elongation ; Composite material ; Martensite ; Deformation (meteorology) ; Electrical resistivity and conductivity ; Metallurgy ; Microstructure ; Ultimate tensile strength ; Electrical engineering Shape Memory Alloy Transformations ; Metal and Thin Film Mechanics
Authors: M. Rodríguez-Ramos | Andreo Crnjac | D. Cosic | M. Jakšić
Source: Materials (article, 2022-01-05)
Field: Materials Science — Materials Chemistry

Ion Microprobe Study of the Polarization Quenching Techniques in Single Crystal Diamond Radiation Detectors Synthetic single crystal diamond grown using the chemical vapor deposition technique constitutes an extraordinary candidate material for monitoring radiation in extreme environments. However, under certain conditions, a progressive creation of space charge regions within the crystal can lead to the deterioration of charge collection efficiency. This phenomenon is called polarization and represents one of the major drawbacks associated with using this type of device. In this study, we explore different techniques to mitigate the degradation of signal due to polarization. For this purpose, two different diamond detectors are characterized by the ion beam-induced charge technique using a nuclear microprobe, which utilizes MeV energy ions of different penetration depths to probe charge transport in the detectors. The effect of polarization is analyzed by turning off the bias applied to the detector during continuous or discontinuous irradiation, and also by alternating bias polarity. In addition, the beneficial influence of temperature for reducing the effect of polarization is also observed. Finally, the effect of illuminating the detector with light is also measured. Our experimental results indicate that heating a detector or turning off the bias, and then applying it during continuous irradiation can be used as satisfactory methods for recovering the CCE value close to that of a prepolarized state. In damaged regions, illumination with white light can be used as a standard method to suppress the strength of polarization induced by holes. Microprobe ; Detector ; Materials science ; Diamond ; Irradiation ; Polarization (electrochemistry) ; Ion ; Chemical vapor deposition ; Optics ; Optoelectronics ; Particle detector ; Chemistry ; Physics ; Nuclear physics ; Mineralogy Diamond and Carbon-based Materials Research ; Ion-surface interactions and analysis ; High-pressure geophysics and materials
Authors: Fatih Akkoyun | Adem Özçelik | İbrahim Arpacı | Ali Erçetin | Sinan Güçlüer
Source: Sensors (article, 2022-12-23)
Field: Chemistry — Analytical Chemistry

A Multi-Flow Production Line for Sorting of Eggs Using Image Processing In egg production facilities, the classification of eggs is carried out either manually or by using sophisticated systems such as load cells. However, there is a need for the classification of eggs to be carried out with faster and cheaper methods. In the agri-food industry, the use of image processing technology is continuously increasing due to the data processing speed and cost-effective solutions. In this study, an image processing approach was used to classify chicken eggs on an industrial roller conveyor line in real-time. A color camera was used to acquire images in an illumination cabinet on a motorized roller conveyor while eggs are moving on the movement halls. The system successfully operated for the grading of eggs in the industrial multi-flow production line in real-time. There were significant correlations among measured weights of the eggs after image processing. The coefficient of linear correlation (R<sup>2</sup>) between measured and actual weights was 0.95. Image processing ; Conveyor belt ; Production line ; Digital image processing ; Food processing ; Computer science ; Computer vision ; Sorting ; Artificial intelligence ; Engineering ; Image (mathematics) ; Biology ; Mechanical engineering ; Algorithm ; Food science Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Industrial Vision Systems and Defect Detection
Authors: Yu Xi | Yu Li | Bo Chen | Guangqin Chen | Yimin Chen
Source: Security and Communication Networks (article, 2022-03-12)
Field: Materials Science — Materials Chemistry

Research on Pattern Recognition Method of Transformer Partial Discharge Based on Artificial Neural Network Power transformer is pivotal equipment in a power system, which is responsible for energy transmission and transformation, and its operating condition is related to the safe operation of the power system. In the 21st century, computer science has entered a stage of rapid development, advanced network structures and algorithms have been applied to the field of artificial intelligence, and pattern recognition theory and technology have also made great progress. In the past, the identification of partial discharge type mainly relied on the experience of operation and maintenance personnel, and manual analysis and judgment were made based on partial discharge mapping, which was not very accurate. The application of the computer pattern recognition method in the field of partial discharge type identification has changed the status quo of manual identification, and this method has substantially improved the accuracy and efficiency of identification. Pattern recognition using computer technology has been applied to the field of partial discharge analysis. Compared with manual recognition, its recognition results are accurate, recognition speed is fast, and it has great potential for development. This paper proposes an artificial neural network-based model for transformer partial discharge pattern recognition, which combines the advantages of artificial neural networks with accurate extraction of local spatial higher-order features and provides a new solution for transformer partial discharge pattern recognition. Extended experiments show that the method proposed in this paper achieves leading performance and has practical application value. Computer science ; Partial discharge ; Artificial intelligence ; Artificial neural network ; Transformer ; Pattern recognition (psychology) ; Voltage ; Electrical engineering ; Engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; High-Voltage Power Transmission Systems
Authors: Yen‐Lung Chen | Wen-Chung Chin | Chun‐Wei Tsai | Chang-Che Chiu | Ching‐Ho Tien | Zhi Ting Ye | Pin Han
Source: Micromachines (article, 2022-08-16)
Field: Materials Science — Materials Chemistry

Wide-Angle Mini-Light-Emitting Diodes without Optical Lens for an Ultrathin Flexible Light Source This report outlines a proposed method of packaging wide-angle (WA) mini-light-emitting diode (mini-LED) devices without optical lenses to create a highly efficient, ultrathin, flexible planar backlight for portable quantum dot light-emitting diode (QLED) displays. Since the luminous intensity curve for mini-LEDs generally recommends a beam angle of 120°, numerous LEDs are necessary to achieve a uniform surface light source for a QLED backlight. The light-guide layer and diffusion layer were packaged together on a chip surface to create WA mini-LEDs with a viewing angle of 180°. These chips were then combined with a quantum dot (QD) film and an optical film to create a high-efficiency, ultrathin, flexible planar light source with excellent color purity that can be used as a QLED display backlight. A 6 in (14.4 cm) light source was used as an experimental sample. When 1.44 W was supplied to the sample, the 3200-piece WA mini-LED with a flexible planar QLED display had a beam angle of 180° on the luminous intensity curve, a planar backlight thickness of 0.98 mm, a luminance of 10,322 nits, and a luminance uniformity of 92%. Backlight ; Light-emitting diode ; Luminance ; Materials science ; Planar ; Optics ; Optoelectronics ; Diode ; Viewing angle ; Luminous intensity ; Luminous efficacy ; Light guide ; Liquid-crystal display ; Layer (electronics) ; Light source ; Physics ; Computer science Quantum Dots Synthesis And Properties ; Green IT and Sustainability ; Semiconductor Lasers and Optical Devices