jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Guangya Zhu | Kai Zhou | Wei Gong | Min He | Jiaming Kong | Kangle Li
Source: Energies (article, 2019-06-04)
Field: Materials Science — Materials Chemistry

Inhibition of Rejuvenation Liquid on Trees in XLPE Cables under Switching Impulse Voltages In this paper, the inhibitory effect of preinjected rejuvenation liquid on trees in cross-linked polyethylene (XLPE) cables was investigated. Experimental samples were prepared by inserting needles into XLPE samples, and many equally-spaced holes existed in the outer semiconductive layer. All cable samples were divided into two groups. One sample group was treated with rejuvenation liquid, while the other group was the control group. A tree accelerated aging system was used to obtain trees in the XLPE cable samples. During the aging experiment, an impulse voltage was applied to the samples repeatedly. The micromorphologies of the two groups were observed. Based on the micromorphologies, two parameters were determined: the initiation rate of electrical trees and the average length of trees. Furthermore, the electric field distribution was simulated to analyze the initiation of electrical trees. The results indicate that an electrical tree is much harder to initiate in the pretreated XLPE cables than in the untreated cables. This phenomenon is likely attributed to the dielectrophoretic forces in the pretreated cables. Moreover, rejuvenation liquid deposited in XLPE causes a substantial reduction in the Maxwell stress of molecular chains. Rejuvenation liquid inhibits electrical tree initiation and water tree growth to a great extent. Impulse (physics) ; Rejuvenation ; Polyethylene ; Cross-linked polyethylene ; Voltage ; Composite material ; Materials science ; Electric field ; Electrical engineering ; Biology ; Engineering ; Physics High voltage insulation and dielectric phenomena ; Advanced Sensor and Energy Harvesting Materials ; Power Transformer Diagnostics and Insulation
Authors: Jaakko K. Sarin | Jari Torniainen | Mithilesh Prakash | Lassi Rieppo | Isaac O. Afara | Juha Töyräs
Source: Scientific Data (article, 2019-08-30)
Field: Chemistry — Analytical Chemistry

Dataset on equine cartilage near infrared spectra, composition, and functional properties Near infrared (NIR) spectroscopy is a well-established technique that is widely employed in agriculture, chemometrics, and pharmaceutical engineering. Recently, the technique has shown potential in clinical orthopaedic applications, for example, assisting in the diagnosis of various knee-related diseases (e.g., osteoarthritis) and their pathologies. NIR spectroscopy (NIRS) could be especially useful for determining the integrity and condition of articular cartilage, as the current arthroscopic diagnostics is subjective and unreliable. In this work, we present an extensive dataset of NIRS measurements for evaluating the condition, mechanical properties, structure, and composition of equine articular cartilage. The dataset contains NIRS measurements from 869 different locations across the articular surfaces of five equine fetlock joints. A comprehensive library of reference values for each measurement location is also provided, including results from a mechanical indentation testing, digital densitometry imaging, polarized light microscopy, and Fourier transform infrared spectroscopy. The published data can either be used as a model of human cartilage or to advance equine veterinary research. Articular cartilage ; Osteoarthritis ; Cartilage ; Chemometrics ; Biomedical engineering ; Computer science ; Materials science ; Pathology ; Medicine ; Anatomy ; Machine learning Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Osteoarthritis Treatment and Mechanisms
Authors: Richard Mayne | Thomas C. Draper | Neil Phillips | James G. H. Whiting | Roshan Weerasekera | Claire Fullarton | Ben de Lacy Costello | Andrew Adamatzky
Source: Langmuir (article, 2019-09-17)
Field: Materials Science — Materials Chemistry

Neuromorphic Liquid Marbles with Aqueous Carbon Nanotube Cores Neuromorphic computing devices attempt to emulate features of biological nervous systems through mimicking the properties of synapses toward implementing the emergent properties of their counterparts, such as learning. Inspired by recent advances in the utilization of liquid marbles (LMs, microliter quantities of fluid coated in hydrophobic powder) for the creation of unconventional computing devices, we describe the development of LMs with neuromorphic properties through the use of copper coatings and 1.0 mg mL<sup>-1</sup> carbon nanotube (CNT)-containing fluid cores. Experimentation was performed through sandwiching the LMs between two cup-style electrodes and stimulating them with repeated dc pulses at 3.0 V. Our results demonstrate that "entrainment" of CNT-filled copper LMs via periodic pulses can cause their electrical resistance to rapidly switch between high to low resistance profiles upon inverting the polarity of stimulation: the reduction in resistance between high and low profiles was approximately 88% after two rounds of entrainment. This effect was found to be reversible through reversion to the original stimulus polarity and was strengthened by repeated experimentation, as evidenced by a mean reduction in time to switching onset of 43%. These effects were not replicated in nanotube solutions not bound inside LMs. Our electrical characterization also reveals that nanotube-filled LMs exhibit pinched loop hysteresis IV profiles consistent with the description of memristors. We conclude by discussing the applications of this technology to the development of unconventional computing devices and the study of emergent characteristics in biological neural tissue. Neuromorphic engineering ; Carbon nanotube ; Nanotube ; Materials science ; Nanotechnology ; Entrainment (biomusicology) ; Computer science ; Artificial neural network ; Physics ; Artificial intelligence ; Acoustics Pickering emulsions and particle stabilization ; Advanced Materials and Mechanics ; Micro and Nano Robotics
Authors: Zhang Jianqiang | Panpan Yang | Liu Weijuan | Yang Yanmei | Tian-Jun Yuan | Ying Hou | LI Chang-yu
Source: Journal of the Brazilian Chemical Society (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Rapid and Automatic Classification of Tobacco Leaves Using a Hand-Held DLP‑Based NIR Spectroscopy Device A hand-held near infrared (NIR) spectroscopy device is much more convenient than a traditional desktop NIR instrument. Thus, it is more suitable for the practical application. An automatic and rapid tool for grading tobacco leaves on the spot using a hand-held digital light processing (DLP)-based NIR spectroscopy device is proposed in this paper. Firstly, the spectral data of the samples is scanned with a hand-held NIR device directly from the tobacco leaves without any samples preparation procedures. Then, the training model of different classes is built and the class of each test sample is predicted by using sparse representation classification (SRC) algorithm. Comparing with the traditional linear discriminant analysis (LDA) and support vector machine (SVM) algorithms, the classification accuracy of SRC method is the highest and has the least computation time. The results show that hand-held NIR spectroscopy technology could be a novel classification tool for grading tobacco leaves in the purchasing on the spot. Spectroscopy ; Materials science ; Chemistry ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Jing Shi | Ahmed Al‐Durra | Imad Matraji | Khaled Al‐Wahedi | Mohamed A. Abou‐Khousa
Source: Oil & Gas Science and Technology – Revue d’IFP Energies nouvelles (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Application of Particle Swarm Optimization (PSO) algorithm for Black Powder (BP) source identification in gas pipeline network based on 1-D model Black Powder (BP) is a worldwide challenge that spans all stages of the natural gas industry from the producing wells to the consuming points. It can endanger the pipeline operations, damage instruments and contaminate customer supplies. The formation of BP inside natural gas pipeline mainly results from the corrosion of internal walls of the pipeline, which is a complex chemical reaction. This work aims to develop a novel algorithm for BP source identification within gas pipelines network based on a 1-D model of BP transport and deposition. The optimization algorithm for BP source identification is developed based on the well-known Particle Swarm Optimization (PSO) algorithm, which can solve constrained optimization problems. By applying this optimization algorithm on the gas transmission pipeline network, the BP source at different junctions could be identified and quantified simultaneously. Extensive simulation studies are conducted to validate the effectivity of the optimization algorithm. Particle swarm optimization ; Pipeline (software) ; Natural gas ; Pipeline transport ; Identification (biology) ; Imperialist competitive algorithm ; Multi-swarm optimization ; Mathematical optimization ; Computer science ; Optimization problem ; Algorithm ; Black box ; Engineering ; Mathematics ; Mechanical engineering ; Artificial intelligence ; Waste management Petroleum Processing and Analysis ; Water Systems and Optimization ; Mineral Processing and Grinding
Authors: Anna Veverková | Jiří Kozlík | Kristína Bartha∥ | Tomáš Chráska | Cinthia Antunes Corrêa | Josef Stráský
Source: Metals (article, 2019-11-28)
Field: Materials Science — Materials Chemistry

Mechanical Properties of Ti-15Mo Alloy Prepared by Cryogenic Milling and Spark Plasma Sintering Metastable β-Ti alloy Ti-15Mo was prepared by cryogenic ball milling in a slurry of liquid argon. Material remained ductile even at low temperatures, which suppressed particle refinement, but promoted intensive plastic deformation of individual powder particles. Repetitive deformation of powder particles is similar to the multidirectional rolling and resembles bulk severe plastic deformation (SPD) methods. Initial and milled powders were compacted by spark plasma sintering. Sintered milled powder exhibited a refined microstructure with small β-grains and submicrometer sized α-phase precipitates. The microhardness and the yield tensile strength of the milled powder after sintering at 850 °C attained 350 HV and 1200 MPa, respectively. Low ductility of the material can be attributed to high oxygen content originating from the cryogenic milling. This pioneering work shows that cryogenic milling followed by spark plasma sintering is able to produce two-phase β-Ti alloys with refined microstructure and very high strength levels. Spark plasma sintering ; Materials science ; Microstructure ; Metallurgy ; Alloy ; Ball mill ; Ultimate tensile strength ; Slurry ; Indentation hardness ; Sintering ; Composite material Titanium Alloys Microstructure and Properties ; Advanced materials and composites ; Intermetallics and Advanced Alloy Properties
Authors: Bernhard Schober | Uwe Schichler
Source: Proceedings of the Nordic Insulation Symposium (article, 2019-08-05)
Field: Materials Science — Materials Chemistry

Application of Machine Learning for Partial Discharge Classification under DC Voltage Partial discharge measurement is one of the most important diagnosis methods and well investigated under AC voltage. Furthermore, machine learning is established and has been used successfully already many years for automated recognition of PD defects. For AC voltage, there are several diagnosis methods and interpretation tools. In the field of DC voltage this is not the case, so it needs significant tools to interpret the results. In this contribution typical PD defects of HVDC GIS/GIL are investigated, but the methods can be adopted to other HV equipment as well. The machine learning techniques were realized with MATLAB and WEKA. Statistical parameters, derived from the PD pulse sequences, were used as features. A hierarchical clustering of the features was performed to analyse the separability between the PD defects. Classification was done with three popular algorithms (SVM, k-NN, ANN). The parameters of these algorithms were varied and compared to each other’s. SVM clearly outperformed the other classifiers. Partial discharge ; Support vector machine ; MATLAB ; Artificial intelligence ; Cluster analysis ; Computer science ; Voltage ; Pattern recognition (psychology) ; Machine learning ; Interpretation (philosophy) ; Field (mathematics) ; High voltage ; Data mining ; Engineering ; Mathematics ; Electrical engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrostatic Discharge in Electronics
Authors: Jan Michálek | Karel Štěpka | Michal Kozubek | Jarmila Navrátilová | Barbora Pavlatovská | Markéta Machálková | Jan Preisler | Adam Pruška
Source: Microscopy and Microanalysis (article, 2019-10-01)
Field: Chemistry — Spectroscopy

Quantitative Assessment of Anti-Cancer Drug Efficacy From Coregistered Mass Spectrometry and Fluorescence Microscopy Images of Multicellular Tumor Spheroids Spheroids-three-dimensional aggregates of cells grown from a cancer cell line-represent a model of living tissue for chemotherapy investigation. Distribution of chemotherapeutics in spheroid sections was determined using the matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI). Proliferating or apoptotic cells were immunohistochemically labeled and visualized by laser scanning confocal fluorescence microscopy (LSCM). Drug efficacy was evaluated by comparing coregistered MALDI MSI and LSCM data of drug-treated spheroids with LSCM only data of untreated control spheroids. We developed a fiducial-based workflow for coregistration of low-resolution MALDI MS with high-resolution LSCM images. To allow comparison of drug and cell distribution between the drug-treated and untreated spheroids of different shapes or diameters, we introduced a common diffusion-related coordinate, the distance from the spheroid boundary. In a procedure referred to as "peeling", we correlated average drug distribution at a certain distance with the average reduction in the affected cells between the untreated and the treated spheroids. This novel approach makes it possible to differentiate between peripheral cells that died due to therapy and the innermost cells which died naturally. Two novel algorithms-for MALDI MS image denoising and for weighting of MALDI MSI and LSCM data by the presence of cell nuclei-are also presented. Spheroid ; Mass spectrometry imaging ; Confocal ; Confocal microscopy ; Fluorescence microscope ; Fiducial marker ; Mass spectrometry ; Biomedical engineering ; Chemistry ; Materials science ; Pathology ; Fluorescence ; Cell culture ; Biology ; Chromatography ; Medicine ; Optics ; Cell biology ; Physics Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Advanced Proteomics Techniques and Applications
Authors: Kumar Yugandhar | Ting‐Yi Wang | Shayne D. Wierbowski | Elnur Elyar Shayhidin | Haiyuan Yu
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2019-04-24)
Field: Chemistry — Spectroscopy

Structure-based validation can drastically under-estimate error rate in proteome-wide cross-linking mass spectrometry studies Abstract Recent, rapid advances in cross-linking mass spectrometry (XL-MS) has enabled detection of novel protein-protein interactions and their structural dynamics at the proteome scale. Given the importance and scale of the novel interactions identified in these proteome-wide XL-MS studies, thorough quality assessment is critical. Almost all current XL-MS studies validate cross-links against known 3D structures of representative protein complexes. However, current structure validation approach only includes cross-links where both peptides mapped to the 3D structures. Here we provide theoretical and experimental evidence demonstrating this approach can drastically underestimate error rates for proteome-wide XL-MS datasets. Addressing current shortcomings, we propose and demonstrate a comprehensive set of four metrics, including orthogonal experimental validation to thoroughly assess quality of proteome-wide XL-MS datasets. Proteome ; Mass spectrometry ; Computational biology ; Computer science ; Set (abstract data type) ; Scale (ratio) ; Cross-validation ; Data mining ; Chemistry ; Biological system ; Bioinformatics ; Biology ; Machine learning ; Chromatography ; Physics Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Bioinformatics and Genomic Networks
Authors: Peng Zhao | Cheng-Kun Wang
Source: Journal of Spectroscopy (article, 2019-06-27)
Field: Chemistry — Organic Chemistry

Hardwood Species Classification with Hyperspectral Microscopic Images We propose a hardwood species identification method based on wood hyperspectral microscopic images. A SOC710VP hyperspectral stereomicroscope was used to acquire microscopic images of a hardwood cross section. In these microscopic images, each part’s spectral features are discussed. We found that the spectral divisibility of wood vessels’ peripheral and central regions in the hyperspectral microscopic images can be used for hardwood species recognition. Mathematical morphological operation and K-L divergence were used to extract spectral features at the wood vessels’ peripheral regions and central regions, respectively. By comparing wood vessels’ spectral similarity across wood species samples, we found that wood vessels’ peripheral spectral divisibility is larger than its central. Finally, the spectral information from randomly selected regions of interest (i.e., ROI) and that of wood vessels’ peripheral and central regions have value as a classification basis. In our hardwood species classification experiments, three dimensionality reduction algorithms, principal component analysis (PCA), kernel principal component analysis (KPCA), and multidimensional scaling (MDS), and the three classifiers, BP neural network, support vector machine (SVM), and Mahalanobis distance (MD), are combined to perform hardwood species classification work. Experimental results indicate that the best recognition effect can be achieved at the peripheral region of wood vessels using PCA or MDS with the MD algorithm. Hyperspectral imaging ; Pattern recognition (psychology) ; Principal component analysis ; Hardwood ; Artificial intelligence ; Computer science ; Biology ; Botany Wood and Agarwood Research ; Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection
Authors: Jannik Strötgen | Trung-Kien Tran | Annemarie Friedrich | Dragan Milchevski | Federico Tomazic | Anika Marusczyk | Heike Adel | Daria Stepanova | Felix Hildebrand | Evgeny Kharlamov
Source: OPUS (Augsburg University) (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Towards the Bosch Materials Science Knowledge Base. Knowledge base ; Base (topology) ; Engineering ethics ; Computer science ; Data science ; Engineering ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Flexible and Reconfigurable Manufacturing Systems
Authors: Cristina Viéitez | Bede P. Busby | David Ochoa | André Mateus | Marco Galardini | Areeb Jawed | Danish Memon | Clément M. Potel | Sibylle C. Vonesch | Chelsea Szu‐Tu | Mohammed Shahraz | Frank Stein | Lars M. Steinmetz | Mikhail M. Savitski | Athanasios Typas | Pedro Beltrão
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2019-12-11)
Field: Chemistry — Spectroscopy

Towards a systematic map of the functional role of protein phosphorylation Abstract Phosphorylation is a critical post-translational modification involved in the regulation of almost all cellular processes. However, less than 5% of thousands of recently discovered phosphorylation sites have a known function. Here, we devised a chemical genetic approach to study the functional relevance of phosphorylation in S. cerevisiae . We generated 474 phospho-deficient mutants that, along with the gene deletion library, were screened for fitness in 102 conditions. Of these, 42% exhibited growth phenotypes, suggesting these phosphosites are likely functional. We inferred their function based on the similarity of their growth profiles with that of gene deletions, and validated a subset by thermal proteome profiling and lipidomics. While some phosphomutants showed loss-of-function phenotypes, a higher fraction exhibited phenotypes not seen in the corresponding gene deletion suggestive of a gain-of-function effect. For phosphosites conserved in humans, the severity of the yeast phenotypes is indicative of their human functional relevance. This study provides a roadmap for functionally characterizing phosphorylation in a systematic manner. Phosphorylation ; Phenotype ; Biology ; Gene ; Mutant ; Function (biology) ; Computational biology ; Genetics ; Proteome ; Functional genomics ; Protein phosphorylation ; Posttranslational modification ; Protein function ; Genomics ; Biochemistry ; Genome ; Enzyme ; Protein kinase A Advanced Proteomics Techniques and Applications ; Protein Structure and Dynamics ; Bioinformatics and Genomic Networks
Authors: Maarten Van Craenenbroeck | Marijke Mollaert | Lars De Laêt
Source: Engineering Structures (article, 2019-09-20)
Field: Materials Science — Polymers and Plastics

The influence of test conditions and mathematical assumptions on biaxial material parameters of fabrics Orthotropic material ; Field (mathematics) ; Material properties ; Structural engineering ; Process (computing) ; Computer science ; Interpretation (philosophy) ; Test (biology) ; Constant (computer programming) ; Applied mathematics ; Materials science ; Mathematics ; Engineering ; Composite material ; Finite element method ; Geology Textile materials and evaluations ; Structural Analysis of Composite Materials ; Structural Analysis and Optimization
Authors: Jixiong Zhang | Hongyuan Yan | Yanmei Xiong | Qianqian Li | Shungeng Min
Source: RSC Advances (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

An ensemble variable selection method for vibrational spectroscopic data analysis Wavelength selection is a critical factor for pattern recognition of vibrational spectroscopic data. Not only does it alleviate the effect of dimensionality on an algorithm's generalization performance, but it also enhances the understanding and interpretability of multivariate classification models. In this study, a novel partial least squares discriminant analysis (PLSDA)-based wavelength selection algorithm, termed ensemble of bootstrapping space shrinkage (EBSS), has been devised for vibrational spectroscopic data analysis. In the algorithm, a set of subsets are generated from a data set using random sampling. For an individual subset, a feature space is determined by maximizing the expected 10-fold cross-validation accuracy with a weighted bootstrap sampling strategy. Then an ensemble strategy and a sequential forward selection method are applied to the feature spaces to select characteristic variables. Experimental results obtained from analysis of real vibrational spectroscopic data sets demonstrate that the ensemble wavelength selection algorithm can reserve stable and informative variables for the final modeling and improve predictive ability for multivariate classification models. Selection (genetic algorithm) ; Variable (mathematics) ; Feature selection ; Computer science ; Statistical physics ; Chemistry ; Physics ; Mathematics ; Artificial intelligence ; Mathematical analysis Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies