jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tao He | Yang Li | Zhong Zhang | Pengfei Shen | Yu Zhang
Source: Computational Intelligence and Neuroscience (article, 2022-06-17)
Field: Materials Science — Materials Chemistry

Leakage Current Sensor and Neural Network for MOA Monitoring Metal-oxide arrester (MOA) has been widely used in electric power systems. The leakage current monitoring of MOA can not only detect the MOA's running state continuously and intelligently but also reduce the unexpected outage of the equipment, which is also beneficial to the stability of the grid. The MOA loses its protection function due to various faults caused by excessive leakage current in actual running. This article studies the monitoring method of MOA based on leakage current sensor and back propagation (BP) neural network. At first, we design a novel leakage current sensor to acquire the leakage current of MOA. Then, the leakage current measurement of MOA based on harmonic analysis is proposed. Finally, the strong training ability of the BP neural network is used to train some key parameters that can reflect the aging of MOA so as to monitor the MOA state. The experimental results show that the leakage current acquired from the simulation is close to the actual leakage current that needs to be measured. It is also shown that the proposed method has good anti-interference and can effectively monitor the aging of MOA. Through the training of the BP neural network, the experiments prove that the training method in this article is superior to other neural network training methods obviously. Artificial neural network ; Leakage (economics) ; Computer science ; Real-time computing ; Artificial intelligence High voltage insulation and dielectric phenomena ; Electrical Fault Detection and Protection ; Elevator Systems and Control
Authors: Andrea M. Burfeid-Castellanos | Michael Kloster | Sára Beszteri | U. Postel | Marzena Spyra | Martin Zurowietz | Tim W. Nattkemper | Bánk Beszteri
Source: Preprints.org (preprint, 2022-09-14)
Field: Materials Science — Biomaterials

A Digital Light Microscopic Method for Diatom Surveys Diatom identification and counting by light microscopy is a fundamental method in ecological and water quality investigations. Here we present a new variant of this method based on &amp;ldquo;digital virtual slides&amp;rdquo;, and compare it to the traditional, non-digitized light microscopy workflow. We analysed three replicates of six samples using two methods: 1) working directly on a light microscope (the &amp;ldquo;traditional&amp;rdquo; counting method), and 2) preparing &amp;ldquo;virtual digital slides&amp;rdquo; by high-resolution slide scanning and subsequently identifying and labelling individual valves or frustules using a web browser-based image annotation platform (the digital method). Both methods led to comparable results in terms of species richness, diatom indices and diatom community composition. Although counting by digital microscopy was slightly more time consuming, our experience points out that the digital workflow can not only improve the transparency and reusability of diatom counts but it can also increase taxonomic precision. The introduced digital workflow can also be applied for taxonomic inter-expert calibration through the web, and for producing training image sets for deep-learning-based diatom identification, making it a promising and versatile alternative or extension to traditional light microscopic diatom analyses in the future. Diatom ; Workflow ; Computer science ; Microscopy ; Frustule ; Transparency (behavior) ; Virtual microscopy ; Artificial intelligence ; Computer graphics (images) ; Optics ; Ecology ; Biology ; Physics ; Database Diatoms and Algae Research ; Species Distribution and Climate Change ; Freshwater macroinvertebrate diversity and ecology
Authors: Tingli Wang | Shan Yao
Source: Metals (article, 2022-02-26)
Field: Materials Science — Materials Chemistry

Research of Feeding Effect of Ductile Cast Iron under Different Riser Conditions Ductile cast iron has typical characteristics of mushy solidification and ductile expansion. The feeding effect of ductile cast iron was studied by means of numerical simulation and pouring tests. The prototypes used for experiments were designed into three types: riser without neck; riser with narrow neck; and riser-less. The corresponding molds were made of coated sand. The pouring temperature was set to 1350 °C, 1300 °C, and 1250 °C, separately. Results showed that the feeding effect could be controlled by the riser structure and the pouring temperature comprehensively. If the pouring temperature was higher than a certain value, the casting should be fed by a riser, and the riser structure would play an important role. However, it was very hard to design a riser exactly, and the unsuitable riser would cause macro porosity or surface sink. When the pouring temperature decreased to a certain value, there was no macro porosity or surface sink. It could provide a potential method to simplify the feeding process and improve the casting quality. Porosity ; Materials science ; Casting ; Metallurgy ; Sink (geography) ; Composite material Metal Alloys Wear and Properties ; Microstructure and Mechanical Properties of Steels ; Metallurgy and Material Forming
Authors: Jake Fay | Fidelma Giulia Di Lemma | Andrei Gribok | Kyle M. Paaren | Pavel Medvedev | Luca Capriotti | David Porter | Jie Lian
Source: Journal of Nuclear Materials (article, 2022-05-25)
Field: Materials Science — Materials Chemistry

An analysis of fluff formation in metallic fuel via data analyzes from EBR-II experiments and BISON fuel code modeling Burnup ; Nuclear engineering ; Work (physics) ; Chemistry ; Environmental science ; Nuclear physics ; Physics ; Thermodynamics ; Engineering Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear Engineering Thermal-Hydraulics
Authors: Jiawei Yang | Kaiyu Cui | Yidong Huang | Wei Zhang | Xue Feng | Fang Liu
Source: arXiv (Cornell University) (preprint, 2022-12-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Angle-insensitive spectral imaging based on topology-optimized plasmonic metasurfaces On-chip spectral imaging based on engineered spectral modulation and computational spectral reconstruction provides a promising scheme for portable spectral cameras. However, the angle dependence of modulation units results in the angle sensitivity of spectral imaging, which limits its practical applications. Here, we proposed a design for angle-robust spectral recovery based on a group of topology-optimized plasmonic metasurface units under a 30° field-of-view, and demonstrate angle-insensitive on-chip spectral imaging in the wavelength range of 450 to 750 nm for average polarization. Furthermore, we experimentally verified the angle-insensitive spectral filtering effects of the fabricated metasurface units, and demonstrated angle-robust spectral reconstruction with a fidelity of 98%. Our approach expands the application scale of spectral imaging, and provides a guidance for designing other angle-robust devices. Plasmon ; Optics ; Spectral imaging ; Polarization (electrochemistry) ; Hyperspectral imaging ; Spectral shape analysis ; Viewing angle ; Materials science ; Spectral sensitivity ; Modulation (music) ; Wavelength ; Spectral width ; Optoelectronics ; Physics ; Computer science ; Spectral line ; Chemistry ; Acoustics Metamaterials and Metasurfaces Applications ; Polydiacetylene-based materials and applications ; Plasmonic and Surface Plasmon Research
Authors: Jun‐Li Xu | Puneet Mishra
Source: Postharvest Biology and Technology (article, 2022-06-16)
Field: Chemistry — Analytical Chemistry

Complementary deep learning and chemometrics: A case of pear fruit centroid detection and spectral model application for fruit spectral image processing A novel case of combining deep learning and chemometrics for spectral image processing is presented. The case involved the application of deep transfer learning for detecting and locating the fruit centroid to extract pixels for spectral model development and application. The selected fruit case involved a non-symmetrical fruit pear where the interesting area for spectral model application is not the centroid of the whole fruit unlike fruit such as apples but the centroid of the belly part of the pear fruit. Hence, the task of object detection is replaced with the task of symmetrical region (fruit belly) detection on the pear fruit such that the spectral model can be applied in the centroid pixels of the symmetrical region. For spectral modelling, the latent variables based regression technique called partial least-square (PLS) regression was used. For spectral modelling, PLS was preferred over deep learning as there was a low number of samples points to train a deep spectral model. The deep transfer learning allowed 100 % correct detection of the pear fruit belly part with the intersection over union score of 0.82. Furthermore, the RMSEP = 0.77 % was attained with the PLS modelling to predict dry matter. The presented approach can support the wide application of spectral imaging for fresh fruit analysis, particularly when imaging is performed simultaneously on multiple objects and the objects are non-symmetrical in shape. PEAR ; Centroid ; Artificial intelligence ; Chemometrics ; Pattern recognition (psychology) ; Pixel ; Mathematics ; Deep learning ; Computer science ; Machine learning Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Mario Teixeira Parente | A. Schneidewind | Georg Brandl | Christian Franz | Marcus M. Noack | Martin Boehm | Marina Ganeva
Source: Frontiers in Materials (article, 2022-02-08)
Field: Materials Science — Materials Chemistry

Benchmarking Autonomous Scattering Experiments Illustrated on TAS With the advancement of artificial intelligence and machine learning methods, autonomous approaches are recognized to have great potential for performing more efficient scattering experiments. In our view, it is crucial for such approaches to provide thorough evidence about respective performance improvements in order to increase acceptance within a scientific community. Therefore, we propose a benchmarking procedure designed as a cost-benefit analysis that is applicable to any scattering method sequentially collecting data during an experiment. For a given approach, the performance assessment is based on how much benefit, given a certain cost budget, it is able to acquire in predefined test cases. Different approaches thus get a chance for comparison and can make their advantages explicit and visible. Key components of the procedure, i.e., cost measures, benefit measures, and test cases, are made precise for the setting of three-axes spectrometry (TAS) as an illustration. Finally, we discuss neglected aspects and possible extensions for the TAS setting and comment on the procedure’s applicability to other scattering methods. A Python implementation of the procedure to simplify its utilization by interested researchers from the field is also provided. Benchmarking ; Computer science ; Python (programming language) ; Artificial intelligence ; Machine learning ; Field (mathematics) ; Key (lock) ; Mathematics Machine Learning in Materials Science ; Spectroscopy and Chemometric Analyses ; X-ray Spectroscopy and Fluorescence Analysis
Authors: László Mihály Vas | Péter Tamás | Eszter Bognár | Péter Nagy | Róbert Késmárszky | Károly Pap | Gábor Szebényi
Source: Biomechanics and Modeling in Mechanobiology (article, 2022-10-26)
Field: Materials Science — Polymers and Plastics

Nonlinear fiber-bundle-cells-based phenomenological modeling of human tissue samples Certain assemblies of fibers, called fiber bundles, play a crucial role in the statistical macroscale properties of fibrous structures like natural or artificial materials. Based on the concept of using idealized statistical fiber bundle cells (FBCs) as model elements, the software named FiberSpace was developed by us earlier for the phenomenological modeling of the tensile test process of real fibrous structures. The model fibers of these FBCs had been considered linear elastic, which was suitable for modeling certain textiles and composites. However, the biological tissues are multilevel structures with fiber-like building elements on every structural level where the fiber elements on the dominant level are statistical bundles of elementary fibers. Hence, their modeling required us to introduce model fibers of nonlinear mechanical behavior and derive the proper mathematical formulas for the calculation of the expected tensile force processes of the FBCs. Accordingly, we developed a new version of FiberSpace. The proposed nonlinear FBCs-based modeling method is essentially phenomenological that decomposes the measured and averaged stress-strain curve into the weighted sum of the responses of different idealized nonlinear FBCs. However, this decomposition can give certain information about the fibrous structure and some details of its damage and failure sub-processes. A special application of nonlinear E-bundles, where the measured stress-strain curve is expanded into a product-function series, may give another type of description for the failure process and can be applied to single measurements of structured failure process containing significant peaks and drops as well. The fitted phenomenological FBC models provide a decomposition of the measured force-strain curve, which enables to construct informative damage and failure maps. The applicability of the phenomenological modeling method and the fitting procedure is demonstrated with the tensile test data of some human and animal tissues, such as facial nerves and tendons. Nonlinear system ; Fiber bundle ; Bundle ; Phenomenological model ; Fiber ; Materials science ; Ultimate tensile strength ; Stress (linguistics) ; Process (computing) ; Biological system ; Computer science ; Mathematics ; Physics ; Composite material ; Statistics Textile materials and evaluations ; Mechanical Behavior of Composites ; Elasticity and Material Modeling
Authors: Lin Yuan | Jingcheng Zhang | Quan Deng | Yingying Dong | Haolin Wang | Xiankun Du
Source: Phyton (article, 2022-10-13)
Field: Chemistry — Analytical Chemistry

Differentiation of Wheat Diseases and Pests Based on Hyperspectral Imaging Technology with a Few Specific Bands Hyperspectral imaging technique is known as a promising non-destructive way for detecting plants diseases and pests. In most previous studies, the utilization of the whole spectrum or a large number of bands as well as the complexity of model structure severely hampers the application of the technique in practice. If a detection system can be established with a few bands and a relatively simple logic, it would be of great significance for application. This study established a method for identifying and discriminating three commonly occurring diseases and pests of wheat, i.e., powdery mildew, yellow rust and aphid with a few specific bands. Through a comprehensive spectral analysis, only three bands at 570, 680 and 750 nm were selected. A novel vegetation index namely Ratio Triangular Vegetation Index (RTVI) was developed for detecting anomalous areas on leaves. Then, the Support Vector Machine (SVM) method was applied to construct the discrimination model based on the spectral ratio analysis. The validating results suggested that the proposed method with only three spectral bands achieved a promising accuracy with the Overall Accuracy (OA) of 83%. With three bands from the hyperspectral imaging data, the three wheat diseases and pests were successfully detected and discriminated. A stepwise strategy including background removal, damage lesions recognition and stresses discrimination was proposed. The present work can provide a basis for the design of low cost and smart instruments for disease and pest detection. Hyperspectral imaging ; Powdery mildew ; Support vector machine ; Computer science ; Pattern recognition (psychology) ; Remote sensing ; Vegetation (pathology) ; Artificial intelligence ; Mathematics ; Agronomy ; Biology ; Geography ; Pathology Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Leaf Properties and Growth Measurement
Authors: Faris M. AL‐Oqla | Mohammed T. Hayajneh
Source: Journal of the Mechanical Behavior of Materials (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Hybrid material performance assessment for rocket propulsion Abstract Fuel based on hybrid green material is of paramount importance for various military and civilian purposes. This work presents a novel expert-based multi-criteria hierarchy and decision-making model to evaluate various chemical rocket propulsion technologies with different fuel material types under an uncertain environment. This helps select the most appropriate fuel material types according to the performance requirements considering various evaluation criteria toward achieving better performance economic benefits of the sustainable hybrid green material-based fuel for various applications. Here, the analytical hierarchy process model was utilized to enhance more informative decisions from both numerical and linguistics information regarding the performance of the fuel based on hybrid green material under an uncertain environment. The model considers various simultaneous evaluations and conflicting criteria to practically demonstrate economic, technical, and sustainable issues for the decision makers in this field. Results demonstrate that cost and size criteria are the most important in the evaluation process from experts’ points of view for the rocket propulsion technology. The fuel based on hybrid green material has the highest priority regarding the whole evaluation criteria. The sensitivity analysis illustrates the robustness of the model as well as the reliability of the drawn decisions. Robustness (evolution) ; Propulsion ; Analytic hierarchy process ; Computer science ; Process (computing) ; Risk analysis (engineering) ; Operations research ; Systems engineering ; Engineering ; Aerospace engineering Thermal and Kinetic Analysis ; Chemistry and Chemical Engineering ; Energetic Materials and Combustion
Authors: Mehmet İpekoğlu | Hasan GÜNDÜZ | Önder Albayrak
Source: European Mechanical Science (article, 2022-02-10)
Field: Materials Science — Polymers and Plastics

Production and mechanical characterization of polyethylene matrix composites with the addition of linen and cotton waste fabric filler materials In this study, linen and cotton fibers obtained from fabric waste were used as filler materials and polyethylene was used as matrix material to produce fiber reinforced polymer composites using injection molding. Use of fabric waste as filler material enables utilization of linen and cotton wastes while decreasing the amount of polyethylene used in the composites resulting in an environmentally friendly material. Prepared samples with varying content of linen and cotton wastes (5, 10 and 20 vol. %) were compared with each other and with polyethylene samples without any filler, based on their yield, tensile and fracture strengths, percent elongation and hardness values. Samples were also evaluated for their chip formation characteristics based on chip lengths at various machining speeds and examined for their visual appearance. Results indicated that linen waste fibers can be used to improve the strength of polyethylene based composites while cotton waste fibers can be utilized to enhance the elongation characteristics of the samples. Materials science ; Composite material ; Ultimate tensile strength ; Polyethylene ; Filler (materials) ; Elongation ; Molding (decorative) ; Fiber Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites ; Innovations in Concrete and Construction Materials
Authors: Robert Bücker | Carolin Seuring | Cornelia Cazey | Katharina Veith | María García-Alai | Kay Grünewald | Meytal Landau
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-01-09)
Field: Materials Science — Biomaterials

The Cryo-EM Structures of two Amphibian Antimicrobial Cross-β Amyloid Fibrils Abstract The amyloid-antimicrobial link hypothesis is based on antimicrobial properties found in human amyloids involved in neurodegenerative and systemic diseases, along with amyloidal structural properties found in antimicrobial peptides (AMPs) across kingdoms of life. Supporting this hypothesis, we here determined the fibril structure of two AMPs from amphibians, uperin 3.5 and aurein 3.3, by cryogenic electron microscopy (cryo-EM), revealing amyloid cross-β fibrils of mated β-sheets at atomic resolution. Uperin 3.5 displayed substantial polymorphism with a protofilament of two mated β-sheets. The determined structure was a polymorph showing a 3-blade symmetrical propeller of nine peptides per fibril layer including tight β-sheet interfaces. This cross-β cryo-EM structure complements the cross-α fibril conformation previously determined by a crystal structure, substantiating a secondary structure switch mechanism of uperin 3.5. The aurein 3.3 arrangement consisted of six peptides per fibril layer, all showing kinked β-sheets allowing a rounded compactness of the fibril. The kinked β-sheets are similar to LARKS (Low-complexity, Amyloid-like, Reversible, Kinked segments) found in human functional amyloids. The amyloidal properties of antimicrobial peptides shed light on a mechanism of regulation of animicrobial activity involving self-assembly and fibril morphological variations. Moreover, the known endurance of amyloid structures can provide a template for the design of sturdy antimicrobials. Fibril ; Cryo-electron microscopy ; Amyloid fibril ; Biophysics ; Amyloid (mycology) ; Chemistry ; Peptide ; Crystallography ; Biology ; Biochemistry ; Amyloid β Supramolecular Self-Assembly in Materials ; Antimicrobial Peptides and Activities ; Alzheimer's disease research and treatments
Authors: Yong Liu | Lingxuan Li | Cong Yu | Fuxing Zeng | Fengfeng Niu | Zhiyi Wei
Source: Biomolecules (article, 2022-07-26)
Field: Materials Science — Materials Chemistry

Cargo Recognition Mechanisms of Yeast Myo2 Revealed by AlphaFold2-Powered Protein Complex Prediction Myo2, a yeast class V myosin, transports a broad range of organelles and plays important roles in various cellular processes, including cell division in budding yeast. Despite the fact that several structures of Myo2/cargo adaptor complexes have been determined, the understanding of the versatile cargo-binding modes of Myo2 is still very limited, given the large number of cargo adaptors identified for Myo2. Here, we used ColabFold, an AlphaFold2-powered and easy-to-use tool, to predict the complex structures of Myo2-GTD and its several cargo adaptors. After benchmarking the prediction strategy with three Myo2/cargo adaptor complexes that have been determined previously, we successfully predicted the atomic structures of Myo2-GTD in complex with another three cargo adaptors, Vac17, Kar9 and Pea2, which were confirmed by our biochemical characterizations. By systematically comparing the interaction details of the six complexes of Myo2 and its cargo adaptors, we summarized the cargo-binding modes on the three conserved sites of Myo2-GTD, providing an overall picture of the versatile cargo-recognition mechanisms of Myo2. In addition, our study demonstrates an efficient and effective solution to study protein-protein interactions in the future via the AlphaFold2-powered prediction. Yeast ; Computer science ; Artificial intelligence ; Biology ; Biochemistry Enzyme Structure and Function ; Protein Structure and Dynamics ; Microtubule and mitosis dynamics