jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Khanh Nguyen-Trong
Source: International Journal of Advanced Computer Science and Applications (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

Evaluation of Wood Species Identification Using CNN-Based Networks at Different Magnification Levels Wood species identification (WoodID) is a crucial task in many industries, including forestry, construction, and furniture manufacturing. However, this process currently requires highly trained individuals and is time-consuming. With the recent advances in machine learning and computer vision techniques, automatic WoodID using macro-images of cross-section wood has gained attention. Nevertheless, existing works have been evaluated on ad-hoc datasets with pre-fixed magnification levels. To address this issue, this paper proposes an evaluation of deep learning-based methods for WoodID on multiple datasets with varying magnification levels. Several popular Convolutional Neural Networks, including DenseNet, ResNet50, and MobileNet, were examined to identify the best network and magnification levels. The experiments were conducted on five datasets with different magnifications, including a self-collected dataset and four existing ones. The results demonstrate that the DenseNet121 network achieved superior accuracy and F1-Score on the 20X dataset. The findings of this study provide useful insights into the development of automatic WoodID systems for practical applications. Computer science ; Convolutional neural network ; Magnification ; Task (project management) ; Identification (biology) ; Artificial intelligence ; Process (computing) ; Machine learning ; Deep learning ; Artificial neural network ; Pattern recognition (psychology) ; Data mining Wood and Agarwood Research ; Industrial Vision Systems and Defect Detection ; Identification and Quantification in Food
Authors: Huizi Zeng | Xinyu Zhao | Yi Wang | Xiaoman Dong | Anmin Liu | Xuefeng Ren
Source: ChemistrySelect (article, 2023-06-14)
Field: Materials Science — Materials Chemistry

Investigation of the Inhibition Mechanism of Organic Corrosion Inhibitors on the Copper Surface by DFT study and MD simulations Abstract Supports of the National Natural Science Foundation of China (21908017 and 21902021), the Undergraduate Innovation and Entrepreneurship Training Program Dalian University of Technology (20211014110934), the Foundation of State Key Laboratory of High‐efficiency Utilization of Coal and Green Chemical Engineering (2022‐K70), the Fundamental Research Funds for the Central Universities (DUT22LK09), the Open Foundation of Key Laboratory of Industrial Ecology and Environmental Engineering, MOE (KLIEEE‐20‐01, KLIEEE‐21‐02), and the Hefei Advanced Computing Center for this work are gratefully acknowledged. Foundation (evidence) ; Engineering ; China ; Engineering management ; Management ; Geography ; Archaeology Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Metal and Thin Film Mechanics
Authors: Jérôme Sol | Hugo Prod’homme | Luc Le Magoarou | Philipp del Hougne
Source: arXiv (Cornell University) (preprint, 2023-07-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Experimentally realized physical-model-based wave control in metasurface-programmable complex media The reconfigurability of radio environments with programmable metasurfaces is considered a key feature of next-generation wireless networks. Identifying suitable metasurface configurations for desired wireless functionalities requires a precise setting-specific understanding of the intricate impact of the metasurface configuration on the wireless channels. Yet, to date, the relevant short and long-range correlations between the meta-atoms due to proximity and reverberation are largely ignored rather than precisely captured. Here, we experimentally demonstrate that a compact model derived from first physical principles can precisely predict how wireless channels in complex scattering environments depend on the programmable-metasurface configuration. The model is calibrated using a very small random subset of all possible metasurface configurations and without knowing the setup's geometry. Our approach achieves two orders of magnitude higher precision than a deep learning-based digital-twin benchmark while involving hundred times fewer parameters. Strikingly, when only phaseless calibration data is available, our model can nonetheless retrieve the precise phase relations of the scattering matrix as well as their dependencies on the metasurface configuration. Thereby, we achieve coherent wave control (focusing or enhancing absorption) and phase-shift-keying backscatter communications without ever having measured phase information. Finally, our model is also capable of retrieving the essential properties of scattering coefficients for which no calibration data was ever provided. These unique generalization capabilities of our pure-physics model significantly alleviate the measurement complexity. Our approach is also directly relevant to dynamic metasurface antennas, microwave-based signal processors as well as emerging in situ reconfigurable nanophotonic, optical and room-acoustical systems. Reconfigurability ; Computer science ; Wireless ; Electronic engineering ; Scattering ; Benchmark (surveying) ; Physics ; Optics ; Telecommunications ; Engineering Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Advanced Antenna and Metasurface Technologies
Authors: János L. Lábár | Klára Hajagos-Nagy | Partha Pratim Das | Alejandro Gómez Pérez | G. Radnóczi
Source: Nanomaterials (article, 2023-12-14)
Field: Materials Science — Materials Chemistry

Simple ePDF: A Pair Distribution Function Method Based on Electron Diffraction Patterns to Reveal the Local Structure of Amorphous and Nanocrystalline Materials Amorphous, glassy or disordered materials play important roles in developing structural materials from metals or ceramics, devices from semiconductors or medicines from organic compounds. Their local structure is frequently similar to crystalline ones. A computer program is presented here that runs under the Windows operating system on a PC to extract pair distribution function (PDF) from electron diffraction in a transmission electron microscope (TEM). A polynomial correction reduces small systematic deviations from the expected average Q-dependence of scattering. Neighbor distance and coordination number measurements are supplemented by either measurement or enforcement of number density. Quantification of similarity is supported by calculation of Pearson's correlation coefficient and fingerprinting. A rough estimate of fractions in a mixture is computed by multiple least-square fitting using the PDFs from components of the mixture. PDF is also simulated from crystalline structural models (in addition to measured ones) to be used in libraries for fingerprinting or fraction estimation. Crystalline structure models for simulations are obtained from CIF files or str files of ProcessDiffraction. Data from inorganic samples exemplify usage. In contrast to previous free ePDF programs, our stand-alone program does not need a special software environment, which is a novelty. The program is available from the author upon request. Pair distribution function ; Nanocrystalline material ; Amorphous solid ; Materials science ; Radial distribution function ; Similarity (geometry) ; Distribution function ; Diffraction ; Transmission electron microscopy ; Statistical physics ; Computational physics ; Computer science ; Crystallography ; Optics ; Mathematics ; Nanotechnology ; Chemistry ; Physics ; Thermodynamics ; Mathematical analysis ; Computational chemistry ; Molecular dynamics ; Artificial intelligence ; Image (mathematics) X-ray Diffraction in Crystallography ; X-ray Spectroscopy and Fluorescence Analysis ; Iron oxide chemistry and applications
Authors: Garimi Tirumala Jyothesh Kumar | B. S. A. Andrews | V D N Kumar Abbaraju
Source: International Journal for Research in Applied Science and Engineering Technology (article, 2023-10-31)
Field: Chemistry — Spectroscopy

The Significance of System Suitability in High-Performance Liquid Chromatography (HPLC) Analysis: Ensuring Accurate and Reliable Results High-Performance Liquid Chromatography (HPLC) has emerged as a fundamental technique in modern analytical chemistry, serving as a powerful tool for the separation, identification, and quantification of [1] diverse compounds. One critical aspect that ensures the validity and credibility of HPLC results is the concept of system suitability. This abstract highlights the importance of system suitability in HPLC analysis, emphasizing its role in ensuring accurate and reliable results. System suitability is a set of pre-defined criteria that evaluate the performance of the HPLCsystem, encompassing factors such as resolution, peak symmetry, tailing factor, retention time, and peak area reproducibility. Adhering to these criteria is essential for consistent and precise analyses, as they confirm that the instrument is operating optimally and capable of generating dependable data High-performance liquid chromatography ; Chromatography ; Reproducibility ; Credibility ; Resolution (logic) ; Computer science ; Identification (biology) ; Set (abstract data type) ; Chemistry ; Artificial intelligence Analytical Chemistry and Chromatography ; Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Ambuj Mehrish | Abhinav Ramesh Kashyap | Yingting Li | Navonil Majumder | Soujanya Poria
Source:  (article, 2023-08-14)
Field: Materials Science — Materials Chemistry

ADAPTERMIX: Exploring the Efficacy of Mixture of Adapters for Low-Resource TTS Adaptation There are significant challenges for speaker adaptation in textto-speech for languages that are not widely spoken or for speakers with accents or dialects that are not well-represented in the training data. To address this issue, we propose the use of the ”mixture of adapters” method. This approach involves adding multiple adapters within a backbone-model layer to learn the unique characteristics of different speakers. Our approach outperforms the baseline, with a noticeable improvement of 5% observed in speaker preference tests when using only one minute of data for each new speaker. Moreover, following the adapter paradigm, we fine-tune only the adapter parameters (11% of the total model parameters). This is a significant achievement in parameter-efficient speaker adaptation, and one of the first models of its kind. Overall, our proposed approach offers a promising solution to the speech synthesis techniques, particularly for adapting to speakers from diverse backgrounds. Adaptation (eye) ; Computer science ; Resource (disambiguation) ; Speech recognition ; Psychology ; Computer network ; Neuroscience Electronic and Structural Properties of Oxides ; Advanced Memory and Neural Computing
Authors: Raoul Heese | Jens Wetschky | Carina Rohmer | Susanne M. Bailer | Michael Bortz
Source: Beverages (article, 2023-08-18)
Field: Chemistry — Analytical Chemistry

Fast and Non-Invasive Evaluation of Yeast Viability in Fermentation Processes Using Raman Spectroscopy and Machine Learning Fermentation processes used for producing alcoholic beverages such as beer, wine, and cider have a long history, having been developed early on across different civilizations. In most instances, yeast strains are used for fermentation processes, e.g., at breweries and wineries. Monitoring of yeast viability, cell count, and growth behavior is essential to ensure a controlled fermentation process. However, classical microbiological techniques to monitor fermentation process parameters are time-consuming and require sampling, along with the risk of contamination. Nowadays, industries are moving toward automation and digitalization. This necessitates state-of-the-art process analytical technologies to ensure an efficient and controlled process to obtain high-quality product outputs. Hence, there is a strong need for a fast, non-invasive, and generally applicable method to evaluate the viability of yeast cells during fermentation to warrant the standardization and purity of produced products in industrial applications. The aim of our study is to discriminate between viable and non-viable yeast in various culture media using Raman spectroscopy (RS) followed by data analysis with machine learning (ML) tools. These techniques allow for rapid, non-invasive analysis addressing the limitations of traditional methods. The present work primarily focuses on the evaluation of RS combined with predictive ML models in a non-real-time setting. Our goal is to adapt these techniques for future application in real-time monitoring and determination of yeast viability in biotechnological processes. We demonstrate that RS, in combination with ML, is a promising tool for non-invasive inline monitoring of fermentation processes. Yeast ; Fermentation ; Biochemical engineering ; Process (computing) ; Process engineering ; Standardization ; Computer science ; Viability assay ; Biotechnology ; Pulp and paper industry ; Machine learning ; Food science ; Engineering ; Chemistry ; Biology ; Biochemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Listeria monocytogenes in Food Safety
Authors: Feiyun Wang | Chengxu Lv | Yuxuan Pan | Liming Zhou | Bo Zhao
Source: Applied Sciences (article, 2023-11-02)
Field: Chemistry — Analytical Chemistry

Efficient Non-Destructive Detection for External Defects of Kiwifruit External defects of kiwifruit seriously affect its added commercialization. To address the existing problems, kiwifruit external defects detection has a few methods for detecting multi-category defects and weak adaptability to complex images. In this study, we proposed ResNet combined with CBAM for the automatic detection of external defects in kiwifruit. The experiment first built an acquisition device to obtain high-quality images. The optimal fusion scheme of ResNet and CBAM was investigated, the network training parameters were optimized, and Adam was used to accelerate the convergence speed of the model. It was found that the average recognition accuracy of ResNet34 + CBAM for kiwifruit was 99.6%, and all evaluation metrics were greater than 99%. Meanwhile, the experiment selected AlexNet, VGG16, InceptionV3, ResNet34, and ResNet34 + CBAM for comparison. The results showed that the recognition accuracy of ResNet34 + CBAM was 7.9%, 12.7%, 11.8%, and 4.3% higher than that of AlexNet, VGG16, InceptionV3, and ResNet34, respectively. Therefore, it can be concluded that ResNet34 + CBAM has the advantages of high recognition accuracy and good stability for kiwifruit external defect sample detection. It provides a technical guarantee for online detection and sorting of kiwifruit and other fruit defects. Computer science ; Artificial intelligence ; Materials science Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Industrial Vision Systems and Defect Detection
Authors: Muhammet Ayanoglu | Rose Montgomery | Jason Harp | Yadukrishnan Sasikumar
Source: Journal of Nuclear Materials (article, 2023-11-22)
Field: Materials Science — Materials Chemistry

Metallographic examinations and hydrogen measurements of high-burnup spent nuclear fuel cladding Burnup ; Cladding (metalworking) ; Spent nuclear fuel ; Oak Ridge National Laboratory ; Nuclear engineering ; Materials science ; Rod ; Pellet ; Nuclear fuel ; Hydride ; Plutonium ; Waste management ; Metallurgy ; Radiochemistry ; Composite material ; Chemistry ; Nuclear physics ; Engineering ; Physics Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear and radioactivity studies
Authors: Nesar Mohammadi Baneh | Hossein Navid | Jalal Kafashan | Hatef Fouladi | Úrsula Gonzales-Barrón
Source: AgriEngineering (article, 2023-02-24)
Field: Chemistry — Analytical Chemistry

Development and Evaluation of a Small-Scale Apple Sorting Machine Equipped with a Smart Vision System One of the most important matters in international trades for many local apple industries and auctions is accurate fruit quality classification. Defect recognition is a key in online computer-assisted apple sorting machines. Because of the cavity structure of the stem and calyx regions, the system tends to mistakenly treat them as true defects. Furthermore, there is no small-scale sorting machine with a smart vision system for apple quality classification where it is needed. Thus, the current study focuses on a highly accurate and feasible methodology for stem and calyx recognition based on Niblack thresholding and a machine learning technique using k-nearest neighbor (k-NN) classifiers associated with a locally designed small-scale apple sorting machine. To find an appropriate mode, the effects of different numbers of k and metric distances on stem and calyx region detection were evaluated. Results showed the effectiveness of the value of k and Euclidean distances in recognition accuracy. It is found that the 5-nearest neighbor classifier and the Euclidean distance using 80 training samples produced the best accuracy rates, at 100% for stem and 97.5% for calyx. The significance of the result is very promising in fabricating an advanced small-scale and low-cost sorting machine with a high accuracy for the horticultural industry. Artificial intelligence ; Calyx ; Sorting ; Computer science ; Machine vision ; Machine learning ; Support vector machine ; Scale (ratio) ; Classifier (UML) ; Pattern recognition (psychology) ; Computer vision ; Algorithm ; Geography ; Cartography Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Industrial Vision Systems and Defect Detection
Authors: LIU Jiaming | QUAN Dong | ZHAO Guoqun
Source: Journal of Mechanical Engineering (article, 2023-01-01)
Field: Materials Science — General Materials Science

Progress in Joint Forming Technology of Aviation Composites 摘要： 纤维增强复合材料具有高比强度、高比模量、结构性能优异以及可设计性强等优势，能够实现构件轻量化和结构-功能设计一体化，在航空航天、交通运输、能源动力以及国防科技等领域具有重要的战略地位。随着纤维增强复合材料在航空航天等领域的大量应用，复合材料之间及其与轻质合金间的高性能-高效率连接成形技术成为航空结构强度和稳定性的重要保障之一。综述纤维增强复合材料连接成形技术的最新研究进展，着重讨论复合材料间及其与轻质合金间的机械连接、胶粘剂连接以及焊接工艺。较全面地论述和分析各类复合材料连接成形的技术特征，对比总结各类连接成形技术的优缺点以及连接结构性能的提升能力，提出各类复合材料连接成形技术的未来发展和研究趋势。 Joint (building) ; Aviation ; Aeronautics ; Engineering ; Composite material ; Aerospace engineering ; Materials science ; Structural engineering Material Properties and Applications
Authors: V. E. Kaydashev | Boris N. Khlebtsov | M. E. Kutepov | Anatoliy Nikolskiy | A. T. Kozakov | Alexey Konstantinov | A.S. Mikheykin | G. Ya. Karapetyan | Е. М. Кайдашев
Source: Materials (article, 2023-03-24)
Field: Materials Science — Polymers and Plastics

Photothermal Effect and Phase Transition in VO2 Enhanced by Plasmonic Particles Phase change metasurfaces based on VO<sub>2</sub>, which are pre-heated with electric current and optically addressed by projected structured light hologram, are considered to become a new paradigm in programmed THz/middle IR flat optics. Macroscopic quasi-homogeneous arrays of Au nanoparticles show large near IR absorption and a significant photothermal effect capable of boosting a light-triggered switching of VO<sub>2</sub> and are to be carefully examined. We propose a new approach to simultaneously probe the altered temperature and electric conductivity of a hybrid Au particle-VO<sub>2</sub> film composite by monitoring a phase shift and attenuating a surface acoustic wave in a YX128° cut LiNbO<sub>3</sub> substrate. The method shows a temperature resolution of 0.1 °C comparable with the best existing techniques for studying nanoobjects and surfaces. The laser-induced photothermal effects were characterized in a macroscopic array of Au nanostars (AuNSts) with different surface coverage. In a monolayer of 10 nm Au, coupled plasmonic nanoparticles were deposited on the LiNbO<sub>3</sub> substrate. An optically triggered insulator-metal transition assisted by photothermal effect in AuNSts/VO<sub>2</sub>/TiO<sub>2</sub>/LiNbO<sub>3</sub> composites was studied at varied light power. We believe that the proposed SAW-based method is of significant importance for the characterization and optimization of radiation absorbing or/and electrically heated elements of metasurfaces and other devices for lab-on-chip and optical communication/processor technology. Materials science ; Photothermal therapy ; Photothermal effect ; Optoelectronics ; Nanoparticle ; Plasmon ; Monolayer ; Surface plasmon ; Phase transition ; Nanotechnology ; Optics Transition Metal Oxide Nanomaterials ; Metamaterials and Metasurfaces Applications ; Gas Sensing Nanomaterials and Sensors
Authors: Saeid Hamzeh | Mohammad Hajeb | Seyed Kazem Alavipanah | J. Verrelst
Source: ISPRS annals of the photogrammetry, remote sensing and spatial information sciences (article, 2023-01-13)
Field: Chemistry — Analytical Chemistry

RETRIEVAL OF SUGARCANE LEAF AREA INDEX FROM PRISMA HYPERSPECTRAL DATA Abstract. The PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite of the Italian Space Agency, lunched in 2019, has provided a new generation source of hyperspectral data showing to have high potential in vegetation variable retrieval. In this study, the newly available PRISMA spectra were exploited to retrieve Leaf Area Index (LAI) of sugarcane using a new kind of Artificial Neural Networks (ANN) so-called Bayesian Regularized Artificial Neural Network (BRANN). The suggested BRANN retrieval model was implemented over a dataset collected during a field campaign in Amir Kabir Sugarcane Agro-Industrial zone, Khuzestan, Iran, in 2020. Principle Component Analysis (PCA) was utilized to reduce the dimensionality of PRISMA data cube. An accuracy assessment based on the bootstrapping procedure indicated RMSE of 0.67 m2/m2 for the LAI retrieval by applying the BRANN model. This study is a confirmation of the high performance of the BRANN method and high potential of PRISMA images to retrieve sugarcane LAI. Hyperspectral imaging ; Leaf area index ; Bootstrapping (finance) ; Artificial neural network ; Remote sensing ; Curse of dimensionality ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Data mining ; Mathematics ; Geography ; Agronomy Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Remote Sensing and Land Use
Authors: 王婷婷 Wang Tingting | 蔡红星 Cai Hongxing | 李霜 Li Shuang | 任玉 Ren Yu | 石晶 Shi Jing | 周建伟 Zhou Jianwei | 李栋梁 Li Dongliang | 丁双双 Ding Shuangshuang | 花扬扬 Hua Yangyang | 曲冠男 Qu Guannan
Source: Laser & Optoelectronics Progress (article, 2023-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

新型超构表面成像光谱芯片研究进展 光谱成像具有良好的多维信息获取能力，广泛应用在食品安全、医学诊断、环境监测、伪装识别及军事遥感等领域。传统光谱成像系统受到分光器件的限制，其存在体积大、成本高和集成度低等问题。基于新型超构表面的成像光谱芯片可为传感器小型化、低成本提供有效解决方案。随着光谱分析需求的持续攀升，加速了超构表面成像光谱芯片的快速发展。本文综述了近年来超构表面成像光谱芯片研究进展。在此基础上，介绍了本团队最新研究成果，通过创新设计成像光谱芯片体系架构，可同时实现高能量利用率、高空间分辨率、高光谱分辨率，为芯片级光谱成像系统的应用打下良好的基础。最后论述了成像光谱芯片的发展趋势及应用前景，为实现光谱成像系统小型化提供参考。 Computer science Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies