jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: M. Zucca | Nawfal Al‐Zubaidi R‐Smith | Lucie Bartova | Helko E. van den Brom | Luca Callegaro | Alessandro Cultrera | Lars Fast | Aldo Girimonte | Melika Hassanzadeh | Andrea Mariscotti | Athanasios Masouras | Juan Medved | Salvatore Musumeci | Giovanna Nicol | Mohamed Ouameur | Gert Rietveld
Source: Measurement Sensors (article, 2024-12-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

MetSuperCap: Metrology for static and dynamic characterisation of supercapacitors Unlike Li-ion batteries, supercapacitors (SCs) do not use rare or polluting metals, have a life cycle even two orders of magnitude higher than that of batteries, and do not suffer from either runaway effects or extreme temperatures. They therefore represent an eco-friendly and reliable technology, which can replace batteries or accompany them in high power density applications. To support the growing use of SCs in real and real-time applications, accurate characterization even under operating conditions is required. Furthermore, validated circuital and software models are needed to identify the SCs behaviour in dynamic applications. Along slower but highly accurate methods, novel quick, traceable, and effective measurement techniques are required to evaluate SCs’ State of Charge (SoC) and State of Health (SoH) and to promote the uptake of SC and banks in consumer, energy, transport, aerospace and in many other applications. The MetSuperCap project aims to improve the characterisation of SCs and SC banks by providing an accurate and reliable identification of their parameters in the laboratory and in operation. Metrology ; Supercapacitor ; Nanotechnology ; Materials science ; Capacitance ; Physics ; Optics ; Electrode Supercapacitor Materials and Fabrication ; Fuel Cells and Related Materials ; Electric and Hybrid Vehicle Technologies
Authors: Palash Paul | Asis Mukhopadhyay
Source: Textile & Leather Review (article, 2024-03-15)
Field: Materials Science — Polymers and Plastics

Optimization of Process Parameters Based on Machine Learning and Determination of Relative Importance of Process Variables on Jute Ply Yarn Breaking Extension The breaking extension of jute ply yarn is comparatively low and ordinarily varies from 1.5 – 3.5% depending upon the process parameters like number of plies, single yarn twist factor and ply to single yarn twist ratio. To achieve various levels of jute ply yarn breaking extension within the above range, optimization of the three process variables was done in this study using a machine learning-based decision tree. For such optimization, twenty-seven different types of jute ply yarns were produced using three levels of singles' twist factor, number of ply and ply-to-single twist ratio. A total of 216 observed breaking extension values of these yarns were used for regression-based machine learning wherein 67% of test data were used to train the mode and the remaining 33% of test data were used for validation. An 8-node decision tree thus achieved from the model was used for the optimization process. A boxplot vs. terminal node graph was also used for classified optimization of ply breaking extension for various levels. The study conducted in this work reveals that the breaking extension of jute ply yarn varies directly with the number of plies, where 4-ply jute yarn produces maximum breaking extension and 2-ply produces the minimum. It was also observed that the decision tree was useful for the judicial selection of process parameters to achieve various levels of jute ply yarn breaking extension, wherein, it was found that the critical values for ply to single yarn twist ratio and single yarn twist factor were 0.80 and 26. The study also shows that apart from the individual influence of the variables on the breaking extension of ply yarns, interactions between variables also influence the breaking extension of jute ply yarn. Yarn ; Breaking strength ; Twist ; Process (computing) ; Extension (predicate logic) ; Mathematics ; Structural engineering ; Computer science ; Composite material ; Materials science ; Engineering ; Geometry Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Plant Surface Properties and Treatments
Authors: Go Soma | Komatsu, Kento | Nakano, Yoshiaki | Tanemura, Takuo
Source: arXiv (Cornell University) (preprint, 2024-01-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Complete vectorial optical mode converter using multi-layer metasurface A vectorial optical mode converter that can transform an orthogonal set of multiple input vector beams into another orthogonal set of vector beams is attractive for a wide range of applications in optics and photonics. While multi-plane light conversion (MPLC) and metasurface (MS) technologies have been explored to individually address multiple spatial mode conversion and polarization mode manipulation, there has been no universal methodology to simultaneously convert a set of multiple vectorial modes, having non-uniform spatial distributions in both their complex amplitude and polarization, to another set of multiple vectorial modes. In this paper, we demonstrate versatile devices based on the MPLC concept incorporating multi-layer locally birefringent MSs and present a general design formalism for complete vectorial mode conversion in arbitrary cases. The effectiveness of our proposed method is confirmed experimentally by demonstrating a 6-mode (3 spatial modes $\times$ 2 polarization modes) multiplexer, fabricated on a compact chip with a ~0.65 mm$^2$ lateral size in a folded MS configuration. Additionally, we verify its applicability to more advanced functional devices through numerical demonstration of a mode-division-multiplexed dual-polarization coherent receiver and spatial-mode-multiplexed vectorial holography. The versatility of our protocol makes it suitable for designing a myriad of multi-input-multi-output devices, providing a powerful tool for realizing universal optical mode converters for a wide range of applications. Multiplexing ; Polarization (electrochemistry) ; Photonics ; Computer science ; Optics ; Holography ; Electronic engineering ; Physics ; Telecommunications ; Engineering Metamaterials and Metasurfaces Applications ; Orbital Angular Momentum in Optics ; Photonic and Optical Devices
Authors: Р. А. Платова | E.V. Zhirkova | D. A. Metlenkin | A. A. Lysenkova | Yu. T. Platov | В. А. Рассулов
Source: Food systems (article, 2024-04-15)
Field: Chemistry — Analytical Chemistry

Prediction of technological properties of wheat flour by combination of UV-VIS-NIR spectroscopy and multivariate analysis methods Over the last decades, optical spectroscopy methods that do not require complex sample preparation have been widely used to identify and control the composition of food products. In the present study, the possibility of using UV-VIS-NIR spectroscopy combined with multivariate analysis for grading wheat flour into groups differing in technological properties was analyzed. UV-VIS-NIR spectra contain information on the combination and intensity of absorption bands assigned to functional groups of the composition components and determining the technological properties of wheat flour. The database of UV-VIS-NIR spectra of wheat flour samples differing by technological properties was formed into three groups: the first group — wheat flour samples with good baking properties, the second group — with reduced baking properties, the third group — with low baking properties. The visible range of UV-VIS-NIR diffuse reflectance spectrum was used to calculate the color coordinates in the CIE colorimetric system L*a*b*. The greatest difference among the groups in the color coordinates of the samples was found for the coordinate b*, which is associated with the different content of coloring pigments. The spectra database was used to build a classification model for grading wheat flour into quality groups using a combination of principal component analysis and linear discriminant analysis (PCA-LDA) methods. The achieved results indicate that the classification model built on the training sample is able to distinguish wheat flour spectra by quality groups with an accuracy of 96.49%. The effective- ness of the model is verified using a test set of spectra of wheat flour samples. The present study confirms that the combina- tion of UV-VIS-NIR spectroscopy in conjunction with the PCA-LDA method has significant potential for determining a quality group of wheat flour based on technological properties. Spectroscopy ; Multivariate statistics ; Multivariate analysis ; Ultraviolet visible spectroscopy ; Materials science ; Analytical Chemistry (journal) ; Chemistry ; Mathematics ; Chromatography ; Statistics ; Physics ; Organic chemistry Spectroscopy and Chemometric Analyses ; Seed and Plant Biochemistry ; Water Quality Monitoring and Analysis
Authors: Blake Williams | Darryl Perry | Peter Aspesi | Jefferson D. Parker | Theodore Johnson | Wendy Su | Eduardo Tabacman | Kirk Delisle | Kayvon Avishan | Vic E. Myer | Felipa Mapa | Michael Hinterberg | Alan Williams | Lori L. Jennings | Nebojša Janjić | Joseph Loureiro
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-03-06)
Field: Chemistry — Spectroscopy

Application of Cancer Cell Line Encyclopedia for Measuring Correlation Between Transcriptomics and Proteomics as a Guide for System-level Insights Abstract Robust and reliable proteome measurements provide mechanistic insights in biomedical research. SOMAmer (Slow Off-rate Modified Aptamer) reagents are modified, DNA-based, affinity reagents that measure defined target proteins with reproducibility and accuracy similar to monoclonal antibodies. Applying SOMAmer reagent technology, we developed SomaScan, a clinical proteome profiling platform with capability to measure 7,523 proteoforms for 6,594 human proteins by UniprotID in small sample volumes (e.g., 55μl plasma or serum). We evaluated the platform by profiling the proteome of a panel of well characterized Cell Line Encyclopedia (CCLE) cancer models. Unsupervised machine learning analyses demonstrate the SomaScan assay distinguishing cell lines on the basis of their proteome signatures, and identifying both tissue-specific and oncogenic pathways. The proteome measured by SomaScan correlates with published CCLE transcriptome at a level comparable to other published transcript to proteome studies. Taken together, we demonstrate that the SomaScan platform is a technically reproducible system suitable for biomedical and clinical applications that reliably illuminates underlying biomolecular mechanisms. Proteome ; Computational biology ; Proteomics ; Human proteome project ; Cancer cell lines ; Transcriptome ; Biology ; Bioinformatics ; Cancer cell ; Cancer ; Gene expression ; Biochemistry ; Gene ; Genetics Advanced Proteomics Techniques and Applications ; Gene expression and cancer classification ; RNA and protein synthesis mechanisms
Authors: Gang Zhao | Changyu Han | Zhongxiang Yu | Hongmei Zhang | Dadong Zhao | Guoao Yu | Zhengyi Jiang
Source: Metals (article, 2024-07-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Modeling and Research on the Defects of Pressed Rigging in a Geomagnetic Field Based on Finite Element Simulation It is very important to carry out effective safety inspections on suppression rigging because of the bad service environment of suppression rigging: marine environments. In this paper, the multi-parameter simulation method in ANSYS and ANSYS Electronics Suite simulation software is used to simulate the effect of geomagnetic fields on the magnetic induction intensity of defective pressed rigging under the variable stress in marine environments. The results of the ANSYS simulation and geomagnetic flaw detection equipment are verified. The simulation results show that, according to the multi-parameter simulation results of ANSYS and ANSYS Electronics Suite simulation software, it can be found that, under the action of transverse force, the internal stress of the pressed rigging will affect the magnetic field around pressed rigging with defects. With an increase in internal stress in the range of 0~20 MPa, the magnetic induction intensity increases to 0.55 A/m, and with an increase in internal stress in the range of 20~150 MPa, the magnetic induction intensity decreases to 0.06 A/m. From the use of a force magnetic coupling analysis method, it can be obtained, under the lateral force of the defects in suppressing rigging, that magnetic flux leakage signals decrease with an increase in the rigging’s radial distance. The experiment results show that the difference between the peak and trough of the magnetic induction intensity at the pressed rigging defect calculated by the ANSYS simulation is very consistent with the results measured by the geomagnetic flaw detection equipment. Finite element method ; Earth's magnetic field ; Field (mathematics) ; Computer science ; Geology ; Engineering ; Structural engineering ; Magnetic field ; Physics ; Mathematics ; Pure mathematics Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels ; Non-Destructive Testing Techniques
Authors: Adithya Bhaskar | Alexander Wettig | Dan Friedman | Danqi Chen
Source: arXiv (Cornell University) (preprint, 2024-06-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Finding Transformer Circuits with Edge Pruning The path to interpreting a language model often proceeds via analysis of circuits -- sparse computational subgraphs of the model that capture specific aspects of its behavior. Recent work has automated the task of discovering circuits. Yet, these methods have practical limitations, as they rely either on inefficient search algorithms or inaccurate approximations. In this paper, we frame automated circuit discovery as an optimization problem and propose *Edge Pruning* as an effective and scalable solution. Edge Pruning leverages gradient-based pruning techniques, but instead of removing neurons or components, it prunes the \emph{edges} between components. Our method finds circuits in GPT-2 that use less than half the number of edges compared to circuits found by previous methods while being equally faithful to the full model predictions on standard circuit-finding tasks. Edge Pruning is efficient even with as many as 100K examples, outperforming previous methods in speed and producing substantially better circuits. It also perfectly recovers the ground-truth circuits in two models compiled with Tracr. Thanks to its efficiency, we scale Edge Pruning to CodeLlama-13B, a model over 100x the scale that prior methods operate on. We use this setting for a case study comparing the mechanisms behind instruction prompting and in-context learning. We find two circuits with more than 99.96% sparsity that match the performance of the full model and reveal that the mechanisms in the two settings overlap substantially. Our case study shows that Edge Pruning is a practical and scalable tool for interpretability and sheds light on behaviors that only emerge in large models. Transformer ; Enhanced Data Rates for GSM Evolution ; Electronic circuit ; Computer science ; Pruning ; Electrical engineering ; Artificial intelligence ; Engineering ; Voltage ; Biology ; Horticulture Magnetic Properties and Applications ; Advanced Electrical Measurement Techniques ; Sensor Technology and Measurement Systems
Authors: Shuting Yang | J. Li | Li Ji | Xuejian Zhang | Cong Ma | Zhengyu Liu | Mengyan Ren
Source: Sustainability (article, 2024-09-13)
Field: Chemistry — Analytical Chemistry

Estimating the Canopy Nitrogen Content in Maize by Using the Transform-Based Dynamic Spectral Indices and Random Forest The monitoring of maize health status is crucial for achieving sustainable agricultural development. Canopy nitrogen content (CNC) is essential for the synthesis of proteins and chlorophyll in maize leaves and, thus, significantly influences maize growth and yield. In this study, we developed a CNC spectral estimation model based on transform-based dynamic spectral indices (TDSI) and the random forest (RF) algorithm, enabling the rapid monitoring of CNC in maize canopy leaves. A total of 60 maize canopy leaf samples and the corresponding field canopy spectra were collected. Subsequently, the canopy spectra data were transformed using centralization transformation (CT), first derivative (D1), second derivative (D2), detrend transformation (DT), and min-max normalization (MMN) methods. Three types of band combination methods (band difference, band ratio, and normalized difference) were used to construct the TDSIs. Finally, the optimal TDSI was selected and used as the independent variable, and the measured CNC was used as the dependent variable to build a CNC spectral estimation model based on the RF algorithm. Results indicated that (1) TDSIs can more accurately characterize the CNC in maize, with a correlation coefficient approximately 102% higher than those of raw spectral bands. (2) The optimal TDSIs included TDSI1247,1249CT-RI, TDSI625,641CT-NDI, TDSI540,703D1-RI, TDSI514,540D1-RI, TDSI514,530D1-DI, TDSI540,697D1-NDI, TDSI970,1357D2-DI, TDSI523,1031D2-NDI, TDSI617,620DT-RI, and TDSI2109,2127MMN-NDI. (3) The CNC spectral estimation model based on the optimal TDSIs, and the RF algorithm achieved accuracy indices with R2 and RPIQ of 0.92 and 4.99, respectively, representing a maximum improvement of approximately 67.27% over the traditional CNC spectral estimation model (based on the R2 value). This study provides an approach for the rapid and accurate estimation of CNC in maize, contributing to the sustainable development of agriculture. Canopy ; Nitrogen ; Random forest ; Environmental science ; Spectral properties ; Mathematics ; Content (measure theory) ; Tree canopy ; Agronomy ; Soil science ; Biological system ; Agroforestry ; Computer science ; Botany ; Chemistry ; Artificial intelligence ; Biology Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Soil Geostatistics and Mapping
Authors: Xiaowei Zhang | Μάρκος Πούλος | Konstantinos Termentzidis | Yang Han | Donglei Zhao | Tengfei Zhang | Xiaochuang Liu | Shijie Jia | Guolei Zhang
Source: The Journal of Physical Chemistry C (article, 2024-03-23)
Field: Materials Science — Materials Chemistry

Buckling Hydrogenated Biphenylene Network with Tremendous Stretch Extent and Anomalous Thermal Transport Properties Hydrogen adsorption is a popular and flexible method to regulate the physical properties of two-dimensional (2D) materials, such as the recently synthesized biphenylene networks. In this study, the mechanical properties and the thermal conductivity (κ) of a fully hydrogenated biphenylene network (HBPN) under strain were investigated systematically by molecular dynamics (MD) simulation and the wave-packet (WP) propagation method. It was found that HBPN could sustain an unusual strain as large as 28.8 and 34.5% along the zigzag and armchair directions, respectively, which were much larger than the other 2D buckling structures like silicene (about 19.5 and 17%, respectively). Besides, the κ of HBPN exhibited an anomalous response to the uniaxial tensile strain. Different from its mother structure, like graphene, the κ of HBPN had an increasing trend with strain, explained here with the phononic density of states (PDOS). The physical mechanism behind this nontrivial thermomechanical behavior of this planar sp2 hybridized carbon allotrope was related to the following two factors: first, the increase of the number of phonons excited in a low-frequency region, which in general carried more energy, and second, the reduction of the number of higher frequency phonons, thus the weakening of the phonon-surface scattering, both helped increase the thermal conductivity under strain. Moreover, the strain-induced flattening of the structure was another reason to weaken the coupling between phonons with in-plane and curvature vibrational modes. The WP propagation method within MD was also employed to analyze the propagation of phonons inside the HBPN, and group velocities, phonon lifetimes, and mean free paths were obtained. Our research can provide an essential reference for the application of 2D materials in the field of electronic cooling devices and the modification of thermoelectric energy conversion efficiency of materials. Phonon ; Materials science ; Condensed matter physics ; Thermal conductivity ; Silicene ; Graphene ; Nanotechnology ; Composite material ; Physics Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies
Authors: Tajim Md Hasibur Rahman | Dirk Stöbener | Andreas Fischer
Source: Surface Topography Metrology and Properties (article, 2024-07-24)
Field: Materials Science — Surfaces, Coatings and Films

Scatterometric defect measurements – uncertainty assessment by means of a virtual instrument and a statistical analysis Abstract Defects in nanostructured surfaces have to be detected in or close to the manufacturing process in the production environment. For this purpose, scatterometry promises a non-contact approach with in-process capability. However, the achievable measurement uncertainty with a scatterometric measurement principle is difficult to assess by means of experiments. Especially for nano-surfaces with stochastic features, a large sample size is required. In addition, the influence of the natural uncertainty due to the inherent surface stochastics, which causes an ultimate uncertainty limit, remains hidden. Therefore, a virtual experimentation in combination with a statistical evaluation is proposed for the uncertainty assessment of scatterometric defect measurements of nanostructured surface. One virtual experiment calculates the scattered light distribution from a randomized modelled surface. For the uncertainty assessment, (a) multiple defective surfaces with the same defect grade are modelled, (b) the surfaces’ scattered light distributions are simulated, (c) the signal processing is applied for obtaining the virtual measurement results of the defect grade, and (d) the uncertainty is determined. The proposed virtual method is realized and demonstrated for defective ZnO nanograss surfaces, where the vacancy of nanograss is the studied defect as an example. As central results for the exemplarily studied defect grade measurement, the determined achievable mean uncertainty due to the nanostructure randomness is <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>3.2</mml:mn> <mml:mspace width="0.25em"/> <mml:mo>%</mml:mo> <mml:mo>,</mml:mo> </mml:math> and the effect of the detector shot noise is negligibly small. Furthermore, the proposed method is universally applicable for any type of nanostructure/defect, and for any scatterometric principle, to clarify the respective potential for the reliable detection of nanostructure defects. Randomness ; Measurement uncertainty ; Computer science ; Materials science ; Detector ; Noise (video) ; Probability distribution ; Algorithm ; Artificial intelligence ; Mathematics ; Statistics Optical Coatings and Gratings ; Advanced Antenna and Metasurface Technologies ; Electromagnetic Scattering and Analysis