jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lei Tan | Yukang Zhang | Keke Han | Pingyang Dai | Yan Zhang | Yongjian Wu | Rongrong Ji
Source: arXiv (Cornell University) (preprint, 2024-11-02)
Field: Chemistry — Analytical Chemistry

RLE: A Unified Perspective of Data Augmentation for Cross-Spectral Re-identification This paper makes a step towards modeling the modality discrepancy in the cross-spectral re-identification task. Based on the Lambertain model, we observe that the non-linear modality discrepancy mainly comes from diverse linear transformations acting on the surface of different materials. From this view, we unify all data augmentation strategies for cross-spectral re-identification by mimicking such local linear transformations and categorizing them into moderate transformation and radical transformation. By extending the observation, we propose a Random Linear Enhancement (RLE) strategy which includes Moderate Random Linear Enhancement (MRLE) and Radical Random Linear Enhancement (RRLE) to push the boundaries of both types of transformation. Moderate Random Linear Enhancement is designed to provide diverse image transformations that satisfy the original linear correlations under constrained conditions, whereas Radical Random Linear Enhancement seeks to generate local linear transformations directly without relying on external information. The experimental results not only demonstrate the superiority and effectiveness of RLE but also confirm its great potential as a general-purpose data augmentation for cross-spectral re-identification. The code is available at \textcolor{magenta}{\url{https://github.com/stone96123/RLE}}. Perspective (graphical) ; Identification (biology) ; Computer science ; Artificial intelligence ; Biology Spectroscopy and Chemometric Analyses
Authors: S.M. Gutiérrez-Martínez | Instituto Politécnico Nacional | Josué David Hernández‐Varela | José Jorge Chanona‐Pérez | Juan Vicente Méndez‐Méndez | H. González-Martínez | María de Jesús Perea‐Flores | S.A. Mendoza-Vázquez | Lizbeth González-Victoriano
Source: Revista Mexicana de Ingeniería Química (article, 2024-06-18)
Field: Chemistry — Analytical Chemistry

Study of drying, thermal, shrinkage, and color kinetics using digital and thermographic imaging of potato slices under real-time convective drying Shrinkage ; Kinetics ; Thermal ; Materials science ; Convection ; Thermography ; Infrared ; Composite material ; Meteorology ; Optics ; Physics Spectroscopy and Chemometric Analyses
Authors: Hugo Chambon | Didier Fraix‐Burnet
Source: Astronomy and Astrophysics (article, 2024-05-24)
Field: Chemistry — Analytical Chemistry

Spectral similarities in galaxies through an unsupervised classification of spaxels Aims. We present the first unsupervised classification of spaxels in hyperspectral images of individual galaxies. Classes identify regions by spectral similarity and thus take all the information into account that is contained in the data cubes (spatial and spectral). Methods. We used Gaussian mixture models in a latent discriminant subspace to find clusters of spaxels. The spectra were corrected for small-scale motions within the galaxy based on emission lines with an automatic algorithm. Our data consist of two MUSE/VLT data cubes of JKB 18 and NGC 1068 and one NIRSpec/JWST data cube of NGC 4151. Results. Our classes identify many regions that are most often easily interpreted. Most of the 11 classes that we find for JKB 18 are identified as photoionised by stars. Some of them are known H II regions, but we mapped them as extended, with gradients of ionisation intensities. One compact structure has not been reported before, and according to diagnostic diagrams, it might be a planetary nebula or a denser H II region. For NGC 1068, our 16 classes are of active galactic nucleus-type (AGN) or star-forming regions. Their spatial distribution corresponds perfectly to well-known structures such as spiral arms and a ring with giant molecular clouds. A subclassification in the nuclear region reveals several structures and gradients in the AGN spectra. Our unsupervised classification of the MUSE data of NGC 1068 helps visualise the complex interaction of the AGN and the jet with the interstellar medium in a single map. The centre of NGC 4151 is very complex, but our classes can easily be related to ionisation cones, the jet, or H 2 emission. We find a new elongated structure that is ionised by the AGN along the N-S axis perpendicular to the jet direction. It is rotated counterclockwise with respect to the axis of the H 2 emission. Conclusions. Our work shows that the unsupervised classification of spaxels takes full advantage of the richness of the information in the data cubes by presenting the spectral and spatial information in a combined and synthetic way. Physics ; Astrophysics ; Galaxy ; Nebula ; Stars ; Data cube ; Stellar classification ; Astronomy Spectroscopy and Chemometric Analyses
Authors: Mohamad Bader Eddin | Sylvain Ménard | Bertrand Laratte | Karin Le Tyrant | Caroline de Pontevès
Source: SPIRE - Sciences Po Institutional REpository (article, 2024-05-01)
Field: Materials Science — Materials Chemistry

Development of Sound Insulation Prediction Tool by Integration of Life Cycle Assessment International audience Sound (geography) ; Computer science ; Life-cycle assessment ; Soundproofing ; Acoustics ; Production (economics) ; Physics High voltage insulation and dielectric phenomena
Authors: Hugo Chambon | Didier Fraix‐Burnet
Source: arXiv (Cornell University) (preprint, 2024-05-27)
Field: Chemistry — Analytical Chemistry

Spectral similarities in galaxies through an unsupervised classification of spaxels We present the first unsupervised classification of spaxels in hyperspectral images of individual galaxies. Classes identify regions by spectral similarity and thus take all the information into account that is contained in the data cubes (spatial and spectral).We used Gaussian mixture models in a latent discriminant subspace to find clusters of spaxels. The spectra were corrected for small-scale motions within the galaxy based on emission lines with an automatic algorithm. Our data consist of two MUSE/VLT data cubes of JKB 18 and NGC 1068 and one NIRSpec/JWST data cube of NGC 4151.Our classes identify many regions that are most often easily interpreted. Most of the 11 classes that we find for JKB 18 are identified as photoionised by stars. Some of them are known HII regions, but we mapped them as extended, with gradients of ionisation intensities. One compact structure has not been reported before, and according to diagnostic diagrams, it might be a planetary nebula or a denser HII region. For NGC 1068, our 16 classes are of active galactic nucleus-type (AGN) or star-forming regions. Their spatial distribution corresponds perfectly to well-known structures such as spiral arms and a ring with giant molecular clouds. A subclassification in the nuclear region reveals several structures and gradients in the AGN spectra. Our unsupervised classification of the MUSE data of NGC 1068 helps visualise the complex interaction of the AGN and the jet with the interstellar medium in a single map. The centre of NGC 4151 is very complex, but our classes can easily be related to ionisation cones, the jet, or H2 emission. We find a new elongated structure that is ionised by the AGN along the N-S axis perpendicular to the jet direction. It is rotated counterclockwise with respect to the axis of the H2 emission. Our work shows that the unsupervised classification of spaxels takes full advantage of the richness of the information in the data cubes by presenting the spectral and spatial information in a combined and synthetic way. Physics ; Astrophysics ; Galaxy ; Nebula ; Stars ; Stellar classification ; Data cube ; Spectral line ; Astronomy Spectroscopy and Chemometric Analyses
Authors: Karl Kristian Krull | Syed Azmal Ali | Jeroen Krijgsveld
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-01-10)
Field: Chemistry — Spectroscopy

Enhanced feature matching in single-cell proteomics characterizes response to IFN-γ and reveals co-existence of different cell states Abstract Proteome analysis by data-independent acquisition (DIA) has become a powerful approach to obtain deep proteome coverage, and has gained recent traction for label-free analysis of single cells. However, optimal experimental design for DIA-based single-cell proteomics has not been fully explored, and performance metrics of subsequent data analysis tools remain to be evaluated. Therefore, we here present DIA-ME, a data analysis strategy that exploits the co-analysis of low-input samples with a so-called matching enhancer (ME) of higher input, to increase sensitivity, proteome coverage, and data completeness. We evaluate the matching specificity of DIA-ME by a two-proteome model, and demonstrate that false discovery and false transfer are maintained at low levels when using DIA-NN software, while preserving quantification accuracy. We apply DIA-ME to investigate the proteome response of U-2 OS cells to interferon gamma (IFN-γ) in single cells, and recapitulate the time-resolved induction of IFN-γ response proteins as observed in bulk material. Moreover, we observe co- and anti-correlating patterns of protein expression within the same cell, indicating mutually exclusive protein modules and the co-existence of different cell states. Collectively our data show that DIA-ME is a powerful, scalable, and easy-to- implement strategy for single-cell proteomics. Proteome ; Proteomics ; Computational biology ; Computer science ; Single-cell analysis ; Scalability ; Matching (statistics) ; Biology ; Cell ; Cell biology ; Bioinformatics ; Mathematics ; Genetics ; Gene Advanced Proteomics Techniques and Applications ; Single-cell and spatial transcriptomics ; Advanced Biosensing Techniques and Applications
Authors: Md Tawkat Islam Khondaker | Muhammad Abdul-Mageed | Laks V. S. Lakshmanan
Source: arXiv (Cornell University) (preprint, 2024-02-25)
Field: Materials Science — Materials Chemistry

DetoxLLM: A Framework for Detoxification with Explanations Prior works on detoxification are scattered in the sense that they do not cover all aspects of detoxification needed in a real-world scenario. Notably, prior works restrict the task of developing detoxification models to only a seen subset of platforms, leaving the question of how the models would perform on unseen platforms unexplored. Additionally, these works do not address non-detoxifiability, a phenomenon whereby the toxic text cannot be detoxified without altering the meaning. We propose DetoxLLM, the first comprehensive end-to-end detoxification framework, which attempts to alleviate the aforementioned limitations. We first introduce a cross-platform pseudo-parallel corpus applying multi-step data processing and generation strategies leveraging ChatGPT. We then train a suite of detoxification models with our cross-platform corpus. We show that our detoxification models outperform the SoTA model trained with human-annotated parallel corpus. We further introduce explanation to promote transparency and trustworthiness. DetoxLLM additionally offers a unique paraphrase detector especially dedicated for the detoxification task to tackle the non-detoxifiable cases. Through experimental analysis, we demonstrate the effectiveness of our cross-platform corpus and the robustness of DetoxLLM against adversarial toxicity. Detoxification (alternative medicine) ; Chemistry ; Biochemical engineering ; Computational biology ; Computer science ; Biology ; Engineering ; Medicine Machine Learning in Materials Science
Authors: Vasyl Kliuiev | Yevhenii Bakulin
Source: Building constructions Theory and Practice (article, 2024-12-23)
Field: Materials Science — Materials Chemistry

Modern methods of identifying the hazards of destruction of buildings, structures and constructions in real operating conditions The problem of identifying the hazards of buildings, constructions and structural elements destruction in the conditions of actual operation arises from the need to ensure the safety of people and property. This problem is especially relevantin connection with the constant increase in the volume of construction and operation of building objects of various functional purposes.The importance of identifying the hazards of the destruction of operated objects is to identify potential threats of destruction in advance and to take appropriate measures to localize them This challenge requires an integrated approach, encompassing continuous monitoring of the technical condition, assessment of actual operating conditions, analysis of design resistance of structural elements, and the development of suitable strategies for maintenance, strengthening or replacement of structures, carrying out repair and restoration work or reconstruction.Destruction hazards can arise for various reasons and can induce a serious threat to life, human health, and property integrity.Some of the main factors that can lead to failure include: aging and physical wear; natural rheological processes (material degradation) and changes in design and operating conditions. In addition, the danger may increase due to natural disasters, such as earthquakes, floods, hurricanes, etc. Potential threats of destruction are hidden defects and damages caused by design errors, non-compliance of structural elements with technical conditions, poor-quality construction and installation (repair) work, which ultimately increases the level of danger of destruction during operation.It should be noted that during the building object operation, deficiencies in technical operation, changes in the design conditions of normal operation, violations of regulatory conditions and the system of planned preventive repairs can lead to the accumulation of problems that can cause destruction over time.Studying methods to identify hazards, conducting a detailed analysis of the causes and mechanisms of emergencies, and implementing appropriate measures based on these findings are crucial for the continuous improvement of destruction prevention strategies.Research methods for identifying hazards of operated building objects destruction, detailed analysis of the causes and mechanisms of emergency situations, and implementation of appropriate measures based on the data obtained allow for continuous improvement of the destruction prevention strategy. Damages ; Risk analysis (engineering) ; Work (physics) ; Property (philosophy) ; Natural (archaeology) ; Lead (geology) ; Quality (philosophy) ; Forensic engineering ; Engineering ; Construction engineering ; Computer science ; Architectural engineering ; Business ; Mechanical engineering Structural mechanics and materials
Authors: A.E. Pashkov | Aleksandr A. Pashkov | Aleksei S. Isachenko | Andrei A. Duk | Anton M. Rybak
Source: Vestnik of Nosov Magnitogorsk State Technical University (article, 2024-06-27)
Field: Materials Science — Polymers and Plastics

Study on a Fitting Process of Large-Sized Monolithic Panels Process (computing) ; Manufacturing process ; Work (physics) ; Process control ; Deformation (meteorology) Textile materials and evaluations ; Theoretical and Computational Physics ; Fluid Dynamics and Thin Films
Authors: Raquel Dantas | Catarina Ribeiro | Manuel Souto
Source: Boletim da Sociedade Portuguesa de Química (article, 2024-06-30)
Field: Chemistry — Electrochemistry

Elétrodos Orgânicos Baseados em Estruturas Orgânicas Covalentes Redox-Ativas para Baterias de Lítio Organic Electrodes Based on Redox-ActiveCovalent Organic Frameworks for Lithium Batteries.Organic electroactive materials (OEM) have garnered significant interest as alternative electrodes for metal-ion batteries owing to their abundant resource availability, high theoretical capacity, and environmental friendliness.In particular, redox-active covalent organic frameworks (COFs) have emerged as promising electrodes due to their tunable electrochemical properties, insolubility in electrolytes, and structural versatility.In this article, we review some recent strategies aimed at enhancing the energy density and power density of COF electrodes for lithium batteries from the molecular design and electrode optimization approaches.Other important aspects such as stability and scalability are also addressed.Finally, the main challenges to improve their performance and prospects for COF-based organic batteries are highlighted. Os materiais orgnicos eletroativos Redox ; Chemistry Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion
Authors: Elizaveta Tiukalova | Olugbenga Olunloyo | Kai Xiao | Andrew R. Lupini | Miaofang Chi
Source: Microscopy and Microanalysis (article, 2024-07-01)
Field: Materials Science — Materials Chemistry

Probing Atomic Structure and Excitons in 2D Heterostructures Through Cryogenic STEM-EELS The exploration of two-dimensional (2D) materials and the ability to engineer atomically thin heterostructures have opened a fascinating platform for tailoring novel material properties and uncovering exotic quantum phenomena.Vertical stacking of atomically thin 2D materials, such as transition metal dichalcogenides (TMDs), with a lattice difference/rotational alignment result in the formation of a moir lattice, often inducing new properties not present in individual layers [1].For example, the emergence of tunable Mott insulators [2], unconventional superconductivity [3], moir trapped valley excitons [4,5] and recent observation of the fractional quantum anomalous Hall effect in twisted MoTe 2 [6] have been revealed in such heterostructures.Control of the relative twist angle allows manipulation of interlayer interactions and the emergence of excitons, strongly bound electron-hole pairs localized by periodic moir potentials.Characterizing the excitonic properties of such materials has traditionally relied on optical techniques with very high spectral resolution but constrained in spatial resolution by the large diffraction-limit of visible light.A combination of scanning transmission electron microscopy (STEM) and ultrahigh energy resolution monochromated electron energy loss spectroscopy (EELS) is suitable to study localized nature of excitons in 2D moir heterostructures owing to its simultaneous high spatial and spectral resolution [7,8].As materials are scaled down to 2D and approach atomic limits, the role of defects and interfaces becomes crucial in affecting and modifying properties.Many of the challenges in moir heterostructures and their devices, particularly in achieving reproducibility, stem from the presence of interfaces and heterogeneities [9].Electron microscopy can help to understand the effect of defects and of imperfections in the devices with high spatial resolution.Combining imaging with high energy resolution EELS at cryogenic temperatures could help to shed light on an array of atomically significant properties, such as lattice reconstruction, induced strain, and local excitonic response in heterostructures.In this study, we will use advanced electron microscopy capabilities, combining atomic resolution STEM and monochromated EELS at cryogenic temperatures (liquid N 2 , 100 K), to investigate twisted moir heterostructures at the atomic level and explore localized excitonic properties with twisted MoTe 2 heterostructure. Heterojunction ; Exciton ; Materials science ; Condensed matter physics ; Atomic physics ; Chemical physics ; Optoelectronics ; Molecular physics ; Physics Electronic and Structural Properties of Oxides ; Physics of Superconductivity and Magnetism ; Quantum and electron transport phenomena
Authors: Muhammed Irshad M | Kader Ali Sarkar | Digvijay Singh Dhakre | Debasis Bhattacharya
Source: Journal of agricultural development and policy. (article, 2024-12-01)
Field: Chemistry — Analytical Chemistry

Forecasting of Coconut Price Using Time Series Modelling Technique Coconut cultivation is widespread in India, particularly in the coastal regions, with Kerala playing a pivotal role in the country's coconut production.Accurate forecasting of coconut prices is crucial for strategic planning and decision-making among farmers, agribusinesses, and policymakers.In this study, time series forecasting is employed to predict future coconut price of Kerala by analysing historical data trends.Auto Regressive Integrated Moving Average (ARIMA) models are utilized in this research for effectively capturing discernible patterns found in historical data.The Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) play a crucial role in understanding the correlation structure within the time series.They assist in identifying the order of Autoregressive (AR) and Moving Average (MA) processes incorporated into the forecasting model.Multiple combinations of AR(p) and MA (q) orders were tested, and the best model is selected based on the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), resulting in the ARIMA (1, 1, 0) with drift model.Forecasted prices are compared with actual prices using metrics such as Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) to evaluate the model's accuracy.This assessment process provides insights into the effectiveness of the ARIMA (1, 1, 0) with drift model in capturing and predicting fluctuations in coconut prices.The study aims to empower stakeholders in the coconut industry with valuable information, enabling them to make informed decisions amidst dynamic agricultural conditions and market dynamics. Series (stratigraphy) ; Time series ; Econometrics ; Computer science ; Mathematics ; Economics ; Statistics ; Geology Spectroscopy and Chemometric Analyses ; Natural Products and Biological Research ; Agricultural Economics and Practices
Authors: M. I. Bulatov | N. S. Grigoryev | A. V. Fofanov | A. F. Kosolapov | S. L. Semyonov
Source: Доклады Российской академии наук Физика технические науки (article, 2024-10-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Study of Degradation of Optical Fiber in Copper Coating It has been shown that the strength of copper-coated optical fiber in air at elevated temperatures decreases over time, primarily due to degradation of the copper coating due to oxidation, which leads to the appearance and growth of point defects on the surface of silica glass. In this case, the optical fibers remain operational at 600°C for ~1.5 hours, and at 500°C for ~16 hours. The activation energy of the process is in the range of 120–123 kJ/mol. The results obtained make it possible to predict the stability of the optical fibers in air at 300°C for ~1.5 years, and at 250°C for ~17 years. Degradation (telecommunications) ; Copper ; Coating ; Materials science ; Fiber ; Optical fiber ; Composite material ; Metallurgy ; Computer science ; Telecommunications Copper Interconnects and Reliability ; Surface Roughness and Optical Measurements ; Material Properties and Applications
Authors: Parker E. Ludwig | Shinya Mine | Valérie André
Source: Journal of Investigative Dermatology (article, 2024-07-19)
Field: Materials Science — Polymers and Plastics

705 High-performing characterization of hair fiber structure and permeation by infrared imaging spectroscopy Characterization (materials science) ; Permeation ; Infrared ; Materials science ; Fiber ; Infrared spectroscopy ; Spectroscopy ; Chemistry ; Nanotechnology ; Optics ; Composite material ; Physics ; Organic chemistry Textile materials and evaluations ; Dyeing and Modifying Textile Fibers