jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Franco Coltraro | Jaume Amorós | Maria Alberich‐Carramiñana | Carme Torras
Source: arXiv (Cornell University) (preprint, 2023-03-25)
Field: Materials Science — Polymers and Plastics

A novel collision model for inextensible textiles and its experimental validation In this work, we introduce a collision model specifically tailored for the simulation of inextensible textiles. The model considers friction, contacts, and inextensibility constraints all at the same time without any decoupling. Self-collisions are modeled in a natural way that allows considering the thickness of cloth without introducing unwanted oscillations. The discretization of the equations of motion leads naturally to a sequence of quadratic problems with inequality and equality constraints. In order to solve these problems efficiently, we develop a novel active-set algorithm that takes into account past active constraints to accelerate the resolution of unresolved contacts. We put to a test the developed collision procedure with diverse scenarios involving static and dynamic friction, sharp objects, and complex-topology folding sequences. Finally, we perform an experimental validation of the collision model by comparing simulations with recordings of real textiles as given by a $\textit{Motion Capture System}$. The results are very accurate, having errors around 1 cm for DIN A2 textiles (42 x 59.4 cm) even in difficult scenarios involving fast and strong hits with a rigid object. Collision ; Discretization ; Collision detection ; Decoupling (probability) ; Computer science ; Set (abstract data type) ; Quadratic equation ; Collision response ; Object (grammar) ; Algorithm ; Sequence (biology) ; Simulation ; Topology (electrical circuits) ; Mathematics ; Mathematical analysis ; Artificial intelligence ; Geometry ; Engineering ; Control engineering Textile materials and evaluations ; 3D Shape Modeling and Analysis ; Interactive and Immersive Displays
Authors: Minghang He
Source: Academic Journal of Science and Technology (article, 2023-12-28)
Field: Materials Science — Materials Chemistry

Dynamic Impact Mechanical Test of Rubber Based on DIC Method In order to study the strain field of rubber material in the impact process, the impact test of rubber body was carried out by E-SHPB device. Combined with high-speed photography and DIC technology, the non-contact measurement of rubber materials was carried out, and the obtained data were compared and screened. The impact compression diagram and strain field change cloud diagram of rubber in the impact process were obtained. The test results show that during the dynamic impact process, the strain concentration phenomenon first occurs at the contact area between the rubber body and the incident rod in the early stage of impact. As the stress wave propagates inside the material, the strain concentration phenomenon gradually shifts, and finally tends to the middle area to produce a larger strain concentration. At this time, the rubber body is highly compressed and reaches a critical state of springback. Natural rubber ; Materials science ; Composite material ; Split-Hopkinson pressure bar ; Compression (physics) ; Strain rate High-Velocity Impact and Material Behavior ; Mechanical Engineering and Vibrations Research ; Tree Root and Stability Studies
Authors: Nicholas A. Nobile | Chuanyu Lian | Hongyi Sun | Yi‐Siou Huang | Brian Mills | Cosmin‐Constantin Popescu | Dennis M. Callahan | Juejun Hu | Carlos Rı́os | Nathan Youngblood
Source: arXiv (Cornell University) (preprint, 2023-06-26)
Field: Materials Science — Materials Chemistry

Nonvolatile Tuning of Bragg Structures Using Transparent Phase-Change Materials Bragg gratings offer high-performance filtering and routing of light on-chip through a periodic modulation of a waveguide's effective refractive index. Here, we model and experimentally demonstrate the use of Sb2Se3, a nonvolatile and transparent phase-change material, to tune the resonance conditions in two devices which leverage periodic Bragg gratings: a stopband filter and Fabry-Perot cavity. Through simulations, we show that similar refractive indices between silicon and amorphous Sb2Se3 can be used to induce broadband transparency, while the crystalline state can enhance the index contrast in these Bragg devices. Our experimental results show the promise and limitations of this design approach and highlight specific fabrication challenges which need to be addressed in future implementations. Stopband ; Materials science ; Refractive index ; Optoelectronics ; Fiber Bragg grating ; Optics ; Broadband ; Slow light ; Electronic engineering ; Photonic crystal ; Wavelength ; Physics Phase-change materials and chalcogenides ; Liquid Crystal Research Advancements ; Photonic and Optical Devices
Authors: Danial Kamran
Source:  (preprint, 2023-03-30)
Field: Chemistry — Analytical Chemistry

Self-contained Map classification of living things The Self Organizing Map (SOM) is an unsupervised network algorithm that projects high dimensional data into lowdimensional maps. The projection preserves the topology of the data so that similar data items are mapped to nearby locations on themap. The algorithm has been so popular because of its application in Computer Science and other areas; it has been applied in speechrecognition, pattern identification, control engineering, earthquake detection et al. This research aimed to apply the SOM in thetaxonomy of living organisms using 46 attributes. 68 animals from 6 phyla were considered and 46 attributes were used detailing theirphysical features, physiological features, evolution, adaptation, habitat et al. The features extracted were converted to 0s and 1s for theSOM algorithm to process. The result shows 96.569% accuracy of the SOM’s classification but better accuracy can be obtained if theSOM had processed the data for about 1000 iterations. This research revealed that SOM is a veritable tool or algorithm that can be usedto classify living organisms. This research will help taxonomists, biologists and students who spend much time in classifying livingorganism and it will be of help to researchers who want to explore the SOM algorithm as a solution to taxonomy of living organisms.The SOM will ease taxonomy and will help to minimize the stress and time involved in classifying thousands of living organisms. Computer science ; Self-organizing map ; Taxonomy (biology) ; Process (computing) ; Identification (biology) ; Artificial intelligence ; Projection (relational algebra) ; Data mining ; Adaptation (eye) ; Ecology ; Cluster analysis ; Algorithm ; Biology Spectroscopy and Chemometric Analyses ; Digital Imaging for Blood Diseases
Authors: Chip-Hong Chang | Stefan Katzenbeisser | Debdeep Mukhopadhyay | Ulrich Rührmair
Source: Journal of Cryptographic Engineering (article, 2023-11-01)
Field: Chemistry — Inorganic Chemistry

The ASHES 2021 special issue at JCEN Abstract This brief editorial gives a short, two-page overview of the ASHES 2021 workshop. It shall serve as an introduction for this special issue at JCEN. Computer science Inorganic Fluorides and Related Compounds ; Superconducting Materials and Applications
Authors: Takuma Masuda | Masaki Kobayashi | Koji Yatani
Source: arXiv (Cornell University) (preprint, 2023-03-17)
Field: Materials Science — Materials Chemistry

$synapse$: interactive support on photoemission spectroscopy measurement and analysis for non-expert users Photoemission spectroscopy, an experimental method based on the photoelectric effect, is now an indispensable technique used in various fields such as materials science, life science, medicine, and nanotechnology. However, part of the experimental process of photoemission spectroscopy relies on experience and intuition, which is difficult to understand for novice users. In particular, photoemission spectroscopy experiments using high-brilliance synchrotron radiation as a light source are not easy for novice users because the measurements must be performed quickly and accurately as scheduled within a limited experimental period. In addition, research on the application of information science methods to quantum data measurement, such as photoemission spectroscopy, is mainly aimed at the development of analysis methods, and few attempts have been made to clarify the problems faced by users who lack experience. In this study, we identified the problems faced by novice users of photoemission spectroscopy, implemented a native application with functions to solve these problems, and evaluated it qualitatively and quantitatively. This paper describes the contents of the field study and interview survey, the functional design and implementation of the application based on our field study and interview survey, and the results and discussion of the evaluation experiment. Photoemission spectroscopy ; Inverse photoemission spectroscopy ; Photoelectric effect ; Spectroscopy ; Angle-resolved photoemission spectroscopy ; Synchrotron radiation ; Field (mathematics) ; X-ray photoelectron spectroscopy ; Process (computing) ; Computer science ; Engineering physics ; Physics ; Optics ; Electronic structure ; Condensed matter physics ; Quantum mechanics ; Nuclear magnetic resonance ; Mathematics Machine Learning in Materials Science ; Scientific Computing and Data Management ; Electron and X-Ray Spectroscopy Techniques
Authors: Kapil Ahuja | Mithun Singh | Pathak, Kuldeep | Milind B. Ratnaparkhe
Source: arXiv (Cornell University) (preprint, 2023-12-22)
Field: Chemistry — Analytical Chemistry

A New Similarity Function for Spectral Clustering with Application to Plant Phenotypic Data Clustering species of the same plant into different groups is an important step in developing new species of the concerned plant. Phenotypic (or physical) characteristics of plant species are commonly used to perform clustering. Hierarchical Clustering (HC) is popularly used for this task, and this algorithm suffers from low accuracy. In one of the recent works (Shastri et al., 2021), the authors have used the standard Spectral Clustering (SC) algorithm to improve the clustering accuracy. They have demonstrated the efficacy of their algorithm on soybean species. In the SC algorithm, one of the crucial steps is building the similarity matrix. A Gaussian similarity function is the standard choice to build this matrix. In the past, many works have proposed variants of the Gaussian similarity function to improve the performance of the SC algorithm, however, all have focused on the variance or scaling of the Gaussian. None of the past works have investigated upon the choice of base "e" (Euler's number) of the Gaussian similarity function (natural exponential function). Based upon spectral graph theory, specifically the Cheeger's inequality, in this work we propose use of a base "a" exponential function as the similarity function. We also integrate this new approach with the notion of "local scaling" from one of the first works that experimented with the scaling of the Gaussian similarity function (Zelnik-Manor et al., 2004). Using an eigenvalue analysis, we theoretically justify that our proposed algorithm should work better than the existing one. With evaluation on 2376 soybean species and 1865 rice species, we experimentally demonstrate that our new SC is 35% and 11% better than the standard SC, respectively. Spectral clustering ; Cluster analysis ; Mathematics ; Similarity (geometry) ; Scaling ; Eigenvalues and eigenvectors ; Algorithm ; Function (biology) ; Exponential function ; Data Matrix ; Matrix (chemical analysis) ; Computer science ; Statistics ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Genetic Mapping and Diversity in Plants and Animals
Authors: Gaoyue Guo | Milica Tomašević
Source: arXiv (Cornell University) (preprint, 2023-10-16)
Field: Materials Science — Materials Chemistry

Mean-field limit of particle systems with absorption In this work, we consider one-dimensional particles interacting in mean-field type through a bounded kernel. In addition, when particles hit some barrier (say zero), they are removed from the system. This absorption of particles is instantaneously felt by the others, as, contrary to the usual mean-field setting, particles interact only with other non-absorbed particles. This makes the interaction singular as it happens through hitting times of the given barrier. In addition, the diffusion coefficient of each particle is non uniformly elliptic. We show that the particle system admits a weak solution. Through Partial Girsanov transforms we are able to relate our particles with independent stopped Brownian motions, and prove tightness and convergence to a mean-fied limit stochastic differential equation when the number of particles tends to infinity. Further, we study the limit and establish the existence and uniqueness of the classical solution to the corresponding nonlinear Fokker-Planck equation under some continuity assumption on the interacting kernel. This yields the strong well-posedness of the mean-field limit SDE and confirms that our convergence result is indeed a propagation of chaos result. Limit (mathematics) ; Absorption (acoustics) ; Particle (ecology) ; Physics ; Field (mathematics) ; Statistical physics ; Materials science ; Mathematics ; Optics ; Mathematical analysis ; Geology ; Pure mathematics Material Dynamics and Properties ; Adsorption, diffusion, and thermodynamic properties of materials ; Laser-Ablation Synthesis of Nanoparticles
Authors: Chibuike Nnamani | Norhaiza Ahmad
Source: MATEMATIKA (article, 2023-12-01)
Field: Chemistry — Analytical Chemistry

Biclustering Models Under Collinearity in Simulated Biological Experiments Biclustering models allow simultaneous detection of group observations that are related to variables in a data matrix. Such methods have been applied in biological data for classification. Collinearity is a common feature in biological data as there exist interactions between genes and proteins in their respective pathways. Such relationships could seriously reduce the efficiency of biclustering models. In this study, synthetic data are generated to investigate the effect of collinearity on the performance of biclustering models. Specifically, the data are generated and induced with varying degrees of collinearity using Cholesky decomposition, and are implanted with biclusters to produce different sets of synthetic data. The effectiveness of three models namely Biclustering by Cheng and Church (BCCC), Spectral Bicluster (BCSpectral) and Plaid Model in correctly detecting three types of biclusters in the generated data matrix were compared. The results show that all the models investigated are sensitive to changes in the level of collinearity. At low collinearity, all biclustering models were able to detect the implanted biclusters in the data correctly. As the level of collinearity in the data rise, the proportion of detected biclusters captured by the models reduces. In particular, BCC outperformed the other two models for moderate to high collinearity with a Jaccard coefficient of 0.499 to 0.875 and 0.746 to 0.936 for one and two implanted biclusters respectively. Collinearity ; Biclustering ; Computer science ; Data Matrix ; Pattern recognition (psychology) ; Cholesky decomposition ; Feature (linguistics) ; Data mining ; Statistics ; Cluster analysis ; Mathematics ; Artificial intelligence ; Chemistry Spectroscopy and Chemometric Analyses ; Gene expression and cancer classification ; Computational Drug Discovery Methods
Authors: Min Lei | Liujun Xu | Jiping Huang
Source: arXiv (Cornell University) (preprint, 2023-01-26)
Field: Chemistry — Organic Chemistry

Spatiotemporal multiphysics metamaterials with continuously adjustable functions Emerging multiphysics metamaterials offer a distinct possibility for regulating complex physical processes. However, two severe constraints still lower their functionality and tunability. First, multiphysics functionality is fixed once structures and materials are prepared, i.e., one functionality for one physical field. Second, continuous tunability is unavailable in multiphysics fields because parameters are hard to change on demand. Here, we propose the concept of spatiotemporal multiphysics metamaterials by delicately considering the temporal dimension. The spatiotemporal feature leads to multiple functions for each physical field and their continuous switching. We achieve flexible thermal and electric function switching between cloaking, sensing, and concentrating based on rotatable checkerboard structures with different rotation times, material composition, and geometric shapes. Real-time thermal and electric functions are theoretically predicted and confirmed by simulations. These results provide a promising spatiotemporal platform for realizing adaptive and intelligent multiphysics field manipulation. Multiphysics ; Metamaterial ; Cloaking ; Field (mathematics) ; Computer science ; Thermal ; Electric field ; Physics ; Finite element method ; Mathematics ; Optoelectronics Polydiacetylene-based materials and applications ; Metamaterials and Metasurfaces Applications
Authors: Manoj Varma | Achal Lama | Kehar Singh | Bishal Gurung
Source: Current Science (article, 2023-09-25)
Field: Chemistry — Analytical Chemistry

Evaluating the Performance of Crop Yield Forecasting Models Coupled with Feature Selection in Regression Framework Feature selection ; Yield (engineering) ; Regression ; Selection (genetic algorithm) ; Feature (linguistics) ; Regression analysis ; Crop yield ; Econometrics ; Computer science ; Statistics ; Machine learning ; Mathematics ; Agronomy ; Biology ; Materials science Spectroscopy and Chemometric Analyses
Authors: Xuetao Lu a | Robert E. McCulloch
Source: arXiv (Cornell University) (preprint, 2023-11-30)
Field: Chemistry — Analytical Chemistry

Correlated Bayesian Additive Regression Trees with Gaussian Process for Regression Analysis of Dependent Data Bayesian Additive Regression Trees (BART) has gained widespread popularity, inspiring numerous extensions across diverse applications. However, relatively little attention has been given to modeling dependent data. To fill this gap, we introduce Correlated BART (CBART), which extends BART to account for correlated errors. With a dummy representation, efficient matrix computation was developed for the estimation of CBART. Building on CBART, we propose CBART$\unicode{0x2010}$GP, a nonparametric regression model that integrates CBART with a Gaussian process (GP) in an additive framework. In CBART$\unicode{0x2010}$GP, CBART retrieves the true signal of covariates$\unicode{0x2010}$response relationship, while the GP extracts the dependency structure of residuals. To enable scalable inference of CBART$\unicode{0x2010}$GP, we develop a two$\unicode{0x2010}$stage analysis of variance with weighted residuals approach to substantially reduce the computational complexity. Simulation studies demonstrate that CBART-GP not only accurately recovers the true covariate$\unicode{0x2010}$response relationship but also achieves strong predictive performance. A real world application further illustrates its practical utility. Computer science ; Bayesian probability ; Gaussian process ; Regression ; Kriging ; Covariate ; Gaussian ; Flexibility (engineering) ; Machine learning ; Identification (biology) ; Artificial intelligence ; Data mining ; Algorithm ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses ; Air Quality Monitoring and Forecasting ; Gaussian Processes and Bayesian Inference
Authors: 眞由 村松
Source: Institutional Repositories DataBase (IRDB) (article, 2023-11-01)
Field: Materials Science — Materials Chemistry

My hobby is YouTube (2023) Computational solid mechanics and information technology from Keio's Faculty of Science and Technology : what is the future of computer aided engineering? Mayu Muramatsu : associate professor, Department of Mechanical Engineering Hobby ; Computer software ; Information technology ; Key (lock) ; The Internet Machine Learning in Materials Science ; Educational Robotics and Engineering ; Scientific Computing and Data Management
Authors: Marziye Jafariyazani | Daniel Masters | Andreas L. Faisst | Harry I. Teplitz | O. Ilbert
Source: arXiv (Cornell University) (preprint, 2023-11-29)
Field: Chemistry — Analytical Chemistry

Predicting the Spectroscopic Features of Galaxies by Applying Manifold Learning on Their Broad-Band Colors: Proof of Concept and Potential Applications for Euclid, Roman, and Rubin LSST Entering the era of large-scale galaxy surveys which will deliver unprecedented amounts of photometric and spectroscopic data, there is a growing need for more efficient, data driven, and less model-dependent techniques to analyze spectral energy distribution of galaxies. In this work, we demonstrate that by taking advantage of manifold learning approaches, we can estimate spectroscopic features of large samples of galaxies from their broadband photometry when spectroscopy is available only for a fraction of the sample. This will be done by applying the Self Organizing Map (SOM) algorithm on broadband colors of galaxies and mapping partially available spectroscopic information into the trained maps. In this pilot study, we focus on estimating 4000A break in a magnitude-limited sample of galaxies in the COSMOS field. We use observed galaxy colors (ugrizYJH) as well as spectroscopic measurements for a fraction of the sample from LEGA-C and zCOSMOS spectroscopic surveys to estimate this feature for our parent photometric sample. We recover the D4000 feature for galaxies which only have broadband colors with uncertainties about twice of the uncertainty of the employed spectroscopic surveys. Using these measurements we observe a positive correlation between D4000 and stellar mass of the galaxies in our sample with weaker D4000 features for higher redshift galaxies at fixed stellar masses. These can be explained with downsizing scenario for the formation of galaxies and the decrease in their specific star formation rate as well as the aging of their stellar populations over this time period. Galaxy ; Physics ; Photometric redshift ; Photometry (optics) ; Astrophysics ; Redshift ; Astronomy ; Galaxy formation and evolution ; Star formation ; Stars Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Hamed Hamzehbahmani | Ruth V. Sabariego | Benjamin Ducharne
Source:  (article, 2023-05-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

An Experimental-Analytical Approach for Condition Monitoring of Magnetic Cores with Predominant Focus on Axial off-set between the Short Circuit Points This paper presents experimental and numerical studies on laminated magnetic cores with GO electrical steels subjected to inter-laminar short circuit faults, with predominant focus on axial off-set between the short circuit points. Experimental works were performed on stacks of four Epstein size laminations under controlled sinusoidal induction at power frequency of 50 Hz and peak flux densities of 1.0 T to 1.7 T. To support the experimental results, accurate time-domain finite element (FE) models were also performed to visualise inter-laminar eddy currents caused by inter-laminar faults. Impact of each fault scenario on dynamic hysteresis loop, additional eddy current loss, and additional magnetic field was studied. Eddy current ; Laminar flow ; Mechanics ; Magnetic circuit ; Focus (optics) ; Finite element method ; Hysteresis ; Magnetic hysteresis ; Magnetic flux ; Magnetic field ; Materials science ; Short circuit ; Power (physics) ; Electronic engineering ; Physics ; Nuclear magnetic resonance ; Topology (electrical circuits) ; Engineering ; Electrical engineering ; Electromagnetic coil ; Structural engineering ; Condensed matter physics ; Voltage ; Optics ; Magnetization Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Microstructure and Mechanical Properties of Steels