jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jixiang Jing | Yau Chuen Yiu | Cong Chen | Dangyuan Lei | Lei Shao | Qi Wang | Kwai Hei Li | Ngai Wong | Zhiqin Chu
Source: Advanced Photonics Research (article, 2022-01-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Data‐Mining‐Assisted Design of Structural Colors on Diamond Metasurfaces Artificial colors generated by light–matter interactions are widely achieved using metal or dielectric nanostructures. However, those systems are susceptible to reported issues like high optical loss and insufficient robustness, limiting their practical deployment. To address these limitations, the concept of a low‐loss and high‐saturation color pixel design based on diamond metasurfaces is proposed and demonstrated. The numerical simulation and analytic evaluation are performed to demonstrate the feasibility of realizing high‐quality structural colors with diamond metasurfaces. Moreover, a tensor completion‐based algorithm is introduced to assist the design of vivid colors, shortening the required runtime of simulation by four orders of magnitude. Such a tensor‐based algorithm can precisely predict a complete set of generated colors through partially simulated conditions. Particularly, when the missing rate is lower than 0.8, the obtained prediction accuracy is no less than 85.1%, 90.6%, and 90.9% for cube‐, cylinder‐, and cone‐shaped diamond metasurfaces, respectively. The current study provides a solid theoretical foundation for achieving all‐diamond optical metasurfaces, and the present data‐mining approach could be expanded to other photonic structure designs and parametric optimization. Robustness (evolution) ; Diamond ; Computer science ; Parametric statistics ; Photonics ; Algorithm ; Materials science ; Computational science ; Optoelectronics ; Mathematics Metamaterials and Metasurfaces Applications ; Acoustic Wave Phenomena Research ; Plasmonic and Surface Plasmon Research
Authors: Bashar A. F. Esmail | Sławomir Kozieł | Stanisław Szczepański | Huda A. Majid
Source: IEEE Access (article, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Overview of Approaches for Compensating Inherent Metamaterials Losses Metamaterials are synthetic composite structures with extraordinary electromagnetic properties not readily accessible in ordinary materials. These media attracted massive attention due to their exotic characteristics. However, several issues have been encountered, such as the narrow bandwidth and inherent losses that restrict the spectrum and the variety of their applications. The losses have become the principal limiting factor when employing metamaterials in real-world applications. Consequently, overcoming them is crucially important and of practical necessity. This paper discusses the practical applications of metamaterials in constructing functional devices and the effects of the losses on such devices. In more depth, it reviews the available approaches for reducing the metamaterial losses developed over the last two decades in the light of available literature. These approaches include the utilization of the principles of electromagnetically induced transparency (EIT), geometric tailoring of the metamaterial structures, and embedding gain materials. Further, computational optimization techniques, such as particle swarm optimization (PSO) and genetic algorithm (GA), are also discussed to design low-loss metamaterials. The EIT-like metamaterial and the including of gain materials are systematic and universal approaches exhibiting low loss approaching zero. In contrast, the other two are not systematic and universal approaches. Metamaterial ; Computer science ; Bandwidth (computing) ; Limiting ; Transformation optics ; Metamaterial cloaking ; Embedding ; Electronic engineering ; Metamaterial absorber ; Materials science ; Telecommunications ; Optoelectronics ; Engineering ; Tunable metamaterials ; Mechanical engineering ; Artificial intelligence Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Plasmonic and Surface Plasmon Research
Authors: Yulu Wang | Hsiao‐Dong Chiang | Na Dong
Source: Energies (article, 2022-09-07)
Field: Materials Science — Materials Chemistry

Power-Line Partial Discharge Recognition with Hilbert–Huang Transform Features Partial discharge (PD) has caused considerable challenges to the safety and stability of high voltage equipment. Therefore, highly accurate and effective PD detection has become the focus of research. Hilbert–Huang Transform (HHT) features have been proven to have great potential in the PD analysis of transformer, gas insulated switchgear and power cable. However, due to the insufficient research available on the PD features of power lines, its application in the PD recognition of power lines has not yet been systematically studied. In the present study, an enhanced light gradient boosting machine methodology for PD recognition is proposed; the HHT features are extracted from the signal and added to the feature pool to improve the performance of the classifier. A public power-line PD recognition contest dataset is introduced to evaluate the effectiveness of the proposed feature. Numerical studies along with comparison results demonstrate that the proposed method can achieve promising performances. This method which includes the HHT features contributes to the detection of PD in power lines. Partial discharge ; Switchgear ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Boosting (machine learning) ; Classifier (UML) ; Transformer ; Engineering ; Voltage ; Electrical engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Elevator Systems and Control
Authors: Daniel Alejandro Cano Saenz | Carlos Felipe Ordóñez Urbano | Holman Raul Gaitan Mesa | Rubiel Vargas-Cañas
Source: Data (article, 2022-08-11)
Field: Chemistry — Organic Chemistry

Tropical Wood Species Recognition: A Dataset of Macroscopic Images Forests are of incalculable value due to the ecosystem services they provide to humanity such as carbon storage, climate regulation and participation in the hydrological cycle. The threat to forests grows as the population increases and the activities that are carried out in it, such as: cattle rearing, illegal trafficking, deforestation and harvesting. Moreover, the environmental authorities do not have sufficient capacity to exercise strict control over wood production due to the vast variety of timber species within the countries, the lack of tools to verify timber species in the supply chain and the limited available and labelled digital data of the forest species. This paper presents a set of digital macroscopic images of eleven tropical forest species, which can be used as support at checkpoints, to carry out studies and research based on macroscopic analysis of cross-sectional images of tree species such as: dendrology, forestry, as well as algorithms of artificial intelligence. Images were acquired in wood warehouses with a digital magnifying glass following a protocol used by the Colombian Ministry of Environment, as well as the USA Forest Services and the International Association of Wood Anatomists. The dataset contains more than 8000 images with resolution of 640 × 480 pixels which includes 3.9 microns per pixel, and an area of (2.5 × 1.9) square millimeters where the anatomical features are exposed. The dataset presents great usability for academics and researchers in the forestry sector, wood anatomists and personnel who work with computational models, without neglecting forest surveillance institutions such as regional autonomous corporations and the Ministry of the Environment. Deforestation (computer science) ; Ecosystem services ; Population ; Christian ministry ; Pixel ; Work (physics) ; Ecosystem ; Geography ; Computer science ; Environmental resource management ; Business ; Agroforestry ; Ecology ; Environmental science ; Artificial intelligence ; Engineering ; Biology ; Political science Wood and Agarwood Research ; Remote Sensing and LiDAR Applications
Authors: Yoan R. Monneau | Cyrielle Arrault | Coraline Duroux | Marie Martin | Fabien Chirot | Luke Mac Aleese | Marion Girod | Clothilde Comby‐Zerbino | Agnès Hagège | Olivier Walker | Maggy Hologne
Source: Physical Chemistry Chemical Physics (article, 2022-12-16)
Field: Chemistry — Electrochemistry

Structural and dynamical insights into SilE silver binding from combined analytical probes Silver has been used for its antimicrobial properties to fight infection for thousands of years. Unfortunately, some Gram-negative bacteria have developed silver resistance causing the death of patients in a burn unit. The genes responsible for silver resistance have been designated as the sil operon. Among the proteins of the sil operon, SilE has been shown to play a key role in bacterial silver resistance. Based on the limited information available, it has been depicted as an intrinsically disordered protein that folds into helices upon silver ion binding. Herein, this work demonstrates that SilE is composed of 4 clearly identified helical segments in the presence of several silver ions. The combination of analytical and biophysical techniques (NMR spectroscopy, CD, SAXS, HRMS, CE-ICP-MS, and IM-MS) reveals that SilE harbors four strong silver binding sites among the eight sites available. We have also further evidenced that SilE does not adopt a globular structure but rather samples a large conformational space from elongated to more compact structures. This particular structural organization facilitates silver binding through much higher accessibility of the involved His and Met residues. These valuable results will advance our current understanding of the role of SilE in the silver efflux pump complex mechanism and will help in the future rational design of inhibitors to fight bacterial silver resistance. Chemistry ; Physics ; Nanotechnology ; Materials science Electrochemical Analysis and Applications ; Molecular Sensors and Ion Detection ; Trace Elements in Health
Authors: Ke Sun | Yujie Zhang | Si-yuan Tong | Chang-bao Wang
Source: Research Square (preprint, 2022-06-08)
Field: Chemistry — Analytical Chemistry

Study on Rice Grain Mildewed Region Recognition Based on Microscopic Computer Vision and YOLO-v5 model Abstract This study aims to develop a high-speed and nondestructive mildewed rice grain detection method. First, a set of micro images of rice grains contaminated by Aspergillus niger , Penicillium citrinum , and Aspergillus cinerea are acquired to serve as samples, and the mildewed regions are marked. Then, three YOLO-v5 models for identifying regions with contamination of Aspergillus niger , Penicillium citrinum , and Aspergillus cinerea in micro images are established. Finally, the relationship between the proportion of mildewed regions and the total number of colonies is analyzed. The results show that the proposed YOLO-v5 models achieve accuracy levels of 89.26%, 91.15%, and 90.19% when detecting regions with contamination of Aspergillus niger , Penicillium citrinum , and Aspergillus cinerea in the micro images of the verification set. The proportion of areas with contamination of Aspergillus niger / Penicillium citrinum / Aspergillus cinerea is logarithmically correlated with the logarithm of the total number of colonies ( TVC ). The corresponding determination coefficients are 0.7466, 0.7587, and 0.8148, respectively. This study provides a reference for future research on high-speed mildewed rice detection methods based on MCV technology. Penicillium citrinum ; Aspergillus niger ; Penicillium ; Aspergillus ; Contamination ; Biology ; Food science ; Botany Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Cell Image Analysis Techniques
Authors: Thomas Unold
Source: Faraday Discussions (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Accelerating research on novel photovoltaic materials The development of new materials typically takes many years or even decades. This has been particularly true for photovoltaic (PV) technologies, which require control of defects on the parts-per-million-level and consist of relatively complex device structures comprising many elements and interfaces between materials. This means that optical and electronic properties can be difficult to pin down, and also heavily depend on the details of processing. Although processing often varies from lab to lab, complete protocols are rarely reported or accessible. It is suggested that the development of novel photovoltaic materials could be greatly stimulated if information and data is more openly shared, and FAIR data management is implemented in the research community. Massive storage of research results with rich metadata in an FAIR-compliant open-access database is envisioned as a great potential for acceleration in emerging PV materials development. Photovoltaic system ; Metadata ; Computer science ; Data science ; Systems engineering ; Nanotechnology ; World Wide Web ; Materials science ; Engineering ; Electrical engineering Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Semiconductor Detectors and Materials
Authors: Rui Liu | Feng Tan | Yaxuan Wang | Bo Ma | Ming Yuan | Lianxia Wang | Xin Zhao
Source: Agriculture (article, 2022-07-18)
Field: Chemistry — Analytical Chemistry

Machine Learning Identification of Saline-Alkali-Tolerant Japonica Rice Varieties Based on Raman Spectroscopy and Python Visual Analysis The core of saline-alkali land improvement is planting suitable plants. Planting rice in saline-alkali land can not only effectively improve saline-alkali soil, but also increase grain yield. However, traditional identification methods for saline-alkali-tolerant rice varieties require tedious and time-consuming field investigations based on growth indicators by rice breeders. In this study, the visualization method of Python data processing was used to analyze the Raman spectroscopy of japonica rice in order to study a simple and efficient identification method of saline-alkali-tolerant japonica rice varieties. Three saline-alkali-tolerant japonica varieties and three saline-alkali-sensitive japonica varieties were collected from control and saline-alkali-treated fields, respectively, and the Raman spectra of 432 samples were obtained. The data preprocessing stage used filtering-difference method to process Raman spectral data to complete interference reduction and crests extraction. In the feature selection stage, scipy.signal.find_peaks (SSFP), SelectKBest (SKB) and recursive feature elimination (RFE) were used for machine feature selection of spectral data. According to the feature dimension obtained by machine feature selection, dataset partitioning by K-fold CV, the typical linear logistic regression (LR) and typical nonlinear support vector machine (SVM) models were established for classification. Experimental results showed that the typical nonlinear SVM identification model based on both RFE machine feature selection and six-fold CV dataset partitioning had the best identification rate, which was 94%. Therefore, the SVM classification model proposed in this study could provide help in the intelligent identification of saline-alkali-tolerant japonica rice varieties. Artificial intelligence ; Support vector machine ; Feature selection ; Pattern recognition (psychology) ; Mathematics ; Computer science Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Rice Cultivation and Yield Improvement
Authors: Daniel de Oliveira Fernandes | Carla Tatiana Mota Anflor | Jhon Nero Vaz Goulart | Besim Baranoğlu
Source: Metals (article, 2022-06-29)
Field: Materials Science — Materials Chemistry

Nodular Cast Iron GGG40, 60, 70 Mechanical Characterization from Bars and Blocks Obtained from Brazilian Foundry Nodular cast iron has been commonly applied in industry and many engineering applications due to its low production cost and the similarity of its mechanical properties to carbon steel. The mechanical properties of nodular cast iron are very dependent on its microstructure and also on the characteristics of the graphite nodules. In this sense, the main objective of this paper was to evaluate and characterize the nodular cast iron grades GGG40, GGG60 and GGG70 in the absence of heat treatment. In addition, specimens were obtained from casted bars and blocks without the Y-block casting process. The microstructure was analyzed by optical microscopy with the support of computational image analysis for determination of the attributes of the graphite nodules and the quantification of each phase present in the microstructure of the nodular cast iron. The results showed that the microstructure has a strong effect on the material’s strength, especially the density of graphite nodules in the material. This difference reinforces the idea that cast iron can undergo mechanical changes due to changes in the casting process, confirming the importance of checking the characteristics of the cast batch before engineering applications of the material. Cast iron ; Foundry ; Microstructure ; Graphite ; Materials science ; Sand casting ; Casting ; Metallurgy ; Characterization (materials science) ; Ductile iron ; Composite material ; Mold ; Nanotechnology Metal Alloys Wear and Properties ; Microstructure and Mechanical Properties of Steels ; Fatigue and fracture mechanics
Authors: Nutthatida Phuangsaijai | Parichat Theanjumpol | Sila Kittiwachana
Source: Molecules (article, 2022-11-25)
Field: Chemistry — Analytical Chemistry

Performance Optimization of a Developed Near-Infrared Spectrometer Using Calibration Transfer with a Variety of Transfer Samples for Geographical Origin Identification of Coffee Beans This research aimed to improve the classification performance of a developed near-infrared (NIR) spectrometer when applied to the geographical origin identification of coffee bean samples. The modification was based on the utilization of a collection of spectral databases from several different agricultural samples, including corn, red beans, mung beans, black beans, soybeans, green and roasted coffee, adzuki beans, and paddy and white rice. These databases were established using a reference NIR instrument and the piecewise direct standardization (PDS) calibration transfer method. To evaluate the suitability of the transfer samples, the Davies-Bouldin index (DBI) was calculated. The outcomes that resulted in low DBI values were likely to produce better classification rates. The classification of coffee origins was based on the use of a supervised self-organizing map (SSOM). Without the spectral modification, SSOM classification using the developed NIR instrument resulted in predictive ability (% PA), model stability (% MS), and correctly classified instances (% CC) values of 61%, 58%, and 64%, respectively. After the transformation process was completed with the corn, red bean, mung bean, white rice, and green coffee NIR spectral data, the predictive performance of the SSOM models was found to have improved (67-79% CC). The best classification performance was observed with the use of corn, producing improved % PA, % MS, and % CC values at 71%, 67%, and 79%, respectively. Calibration ; Mathematics ; Near-infrared spectroscopy ; Food science ; Chemistry ; Statistics ; Biology Spectroscopy and Chemometric Analyses ; Coffee research and impacts ; Advanced Chemical Sensor Technologies
Authors: Wangzheng Zhou | Zhekai Xu | Wei Cui | Zhenzhen Wang | Daotong Chong | Junjie Yan
Source: Frontiers in Physics (article, 2022-10-17)
Field: Chemistry — Spectroscopy

Optimized CT-TDLAS reconstruction performance evaluation of least squares with the polynomial-fitting method Computed tomography-tunable diode laser absorption spectroscopy (CT-TDLAS) has been widely used in the diagnosis of the combustion flow field. Several optimized CT reconstruction algorithms such as iteration methods, transformation methods, and nonlinear least squares were applied. Considering the industrial application background, the performances of algebraic iteration reconstruction with the simultaneous algebra reconstruction technique (SART), Tikhonov regularization, and least squares with the polynomial fitting method were discussed in this study. For the mentioned algorithm, identical simulated reconstruction parameters that contained 32-path laser structures, assumed temperature distribution, and absorption databases were adopted to evaluate the reconstruction performance including accuracy, efficiency, and measurement of environment applicability. In this study, different CT reconstruction algorithms were also used to calculate the temperature distribution of the Bunsen burner flame. The different reconstruction results were compared with thermocouple detection data. With the theoretically simulated and experimental analysis, the least squares with the polynomial fitting technique has advantages in reconstruction accuracy, calculation efficiency, and laser path applicability for the measurement condition. It will be helpful in enhancing CT-TDLAS technique development. Tikhonov regularization ; Algebraic Reconstruction Technique ; Algorithm ; Least-squares function approximation ; Polynomial ; Combustor ; Non-linear least squares ; Mathematics ; Computer science ; Iterative reconstruction ; Mathematical optimization ; Inverse problem ; Combustion ; Estimation theory ; Artificial intelligence ; Mathematical analysis ; Statistics ; Estimator Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Laser Design and Applications