jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Joseph Oddy
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-12-04)
Field: Chemistry — Analytical Chemistry

Performance of neural networks for prediction of asparagine content in wheat grain from imaging data ABSTRACT Background The prediction of desirable traits in wheat from imaging data is an area of growing interest thanks to the increasing accessibility of remote sensing technology. However, as the amount of data generated continues to grow, it is important that the most appropriate models are used to make sense of this information. Here, the performance of neural network models in predicting grain asparagine content is assessed against the performance of other models. Results Neural networks had greater accuracies than partial least squares regression models and gaussian naïve Bayes models for prediction of grain asparagine content, yield, genotype, and fertiliser treatment. Genotype was also more accurately predicted from seed data than from canopy data. Conclusion Using wheat canopy spectral data and combinations of wheat seed morphology and spectral data, neural networks can provide improved accuracies over other models for the prediction of agronomically important traits. Canopy ; Artificial neural network ; Content (measure theory) ; Grain yield ; Agronomy ; Predictive modelling ; Gaussian ; Computer science ; Artificial intelligence ; Data mining ; Mathematics ; Machine learning ; Biology ; Botany ; Chemistry Spectroscopy and Chemometric Analyses ; Genetics and Plant Breeding ; Wheat and Barley Genetics and Pathology
Authors: Joel Lapin | Xinjian Yan | Qian Dong
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-06-14)
Field: Chemistry — Spectroscopy

UniSpec: A Deep Learning Approach for Predicting Energy-Sensitive Peptide Tandem Mass Spectra and Generating Proteomics-Wide In-Silico Spectral Libraries Abstract In this report, we present UniSpec, an attention-based deep neural network designed to predict complete collision-induced fragmentation of tryptic peptides, aimed at enhancing peptide and protein identification in shotgun proteomics studies. We preprocessed spectral data from peptide tandem mass spectral libraries, compiled by the National Institute of Standards and Technology (NIST), utilizing a data approach tailored for model development, resulting in high-quality, energy-consistent spectral datasets. By analyzing all the annotated fragment ions present in these libraries, we constructed an extensive peptide fragment dictionary containing 7919 isotopic ions from sequence ions, neutral loss, internal, iminium, and amino acid fragment ions. The streamlined dictionary-based spectral training data enables UniSpec to efficiently learn the complex intensity patterns of various product ions, resulting in reliable spectral predictions for a wide range of unmodified and modified peptides. We evaluated the model’s accuracy by comparing its performance across training and testing data, considering diverse peptide characteristics like peptide classes, charge states, and sequence lengths. Our model attained a median cosine similarity score of 0.951 and 0.923 on the training and test data respectively. Contrary to existing deep learning models that often overlook a substantial part of peptide tandem mass spectra beyond the sequence b and y ion series, UniSpec can predict up to 75% of all measured fragment intensities (including unknown signals) in the raw experimental spectra. This represents a marked advancement from the 43.5% coverage achieved solely by b and y sequence ions in the NIST library spectra. For the evaluation of our model’s practical utility in predicting proteome-wide in-silico spectral libraries, we executed a benchmark test using a dataset of HeLa cells. UniSpec displayed a significant overlap of peptide identifications with the widely used search engine MS-GF+ and the NIST experimental spectral library, demonstrating its robust performance as a standalone peptide identification tool. NIST ; Peptide ; Chemistry ; Tandem mass spectrometry ; In silico ; Ion ; Deep learning ; Peptide sequence ; Artificial intelligence ; Fragmentation (computing) ; Computer science ; Mass spectrometry ; Pattern recognition (psychology) ; Chromatography ; Biochemistry ; Speech recognition Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Machine Learning in Bioinformatics
Authors: E. Nieto-Valeiras | Eshan Ganju | Nikhilesh Chawla | Javier LLorca
Source: arXiv (Cornell University) (preprint, 2023-10-09)
Field: Materials Science — Materials Chemistry

Assessment of slip transfer criteria for prismatic-to-prismatic slip in pure Ti from 3D grain boundary data Slip transfer and blocking across grain boundaries was studied in a Ti foil with a strong rolling texture deformed in tension. Prior to deformation, the shape of the grains and the orientation of the grain boundaries were quantified through laboratory scale diffraction contrast tomography (LabDCT). Mechanical deformation led to the activation of prismatic slip, and slip transfer/blocking was assessed in &gt; 300 grain boundaries by means of slip trace analysis and electron backscatter diffraction. A categorical model was employed to accurately assess slip transfer, and the "F1 score" of various slip transfer criteria proposed in the literature was evaluated for the first time from 3D grain boundary information. Remarkably, for the prismatic-dominated slip transfer in the current Ti sample, the results show that the best predictions of slip transfer/blocking are provided by the angle \k{appa}, which is directly related to the residual Burgers vector, and by the Luster-Morris parameter m'. In contrast, metrics based on the twist angle θ and on the LRB criterion were not able to predict accurately slip transfer/blocking. Thus, the extensive analysis of the 3D grain boundary data and the novel application of LabDCT was able to help clarify the role of grain boundary orientation on the mechanisms of plastic deformation in polycrystals with strong prismatic-dominated slip. Slip (aerodynamics) ; Materials science ; Grain boundary ; Electron backscatter diffraction ; Slip line field ; Geometry ; Composite material ; Crystallography ; Mechanics ; Mathematics ; Physics ; Thermodynamics ; Chemistry ; Shear (geology) Microstructure and mechanical properties ; Microstructure and Mechanical Properties of Steels ; Metallurgy and Material Forming
Authors: Serhad Sarica | Jianxi Luo
Source: arXiv (Cornell University) (preprint, 2023-03-23)
Field: Materials Science — Materials Chemistry

The Innovation Paradox: Concept Space Expansion with Diminishing Originality and the Promise of Creative AI Innovation, typically spurred by reusing, recombining, and synthesizing existing concepts, is expected to result in an exponential growth of the concept space over time. However, our statistical analysis of TechNet, which is a comprehensive technology semantic network encompassing over four million concepts derived from patent texts, reveals a linear rather than exponential expansion of the overall technological concept space. Moreover, there is a notable decline in the originality of newly created concepts. These trends can be attributed to the constraints of human cognitive abilities to innovate beyond an ever-growing space of prior art, among other factors. Integrating creative artificial intelligence (CAI) into the innovation process holds the potential to overcome these limitations and alter the observed trends in the future. Originality ; Space (punctuation) ; Creativity ; Process (computing) ; Computer science ; Epistemology ; Artificial intelligence ; Psychology ; Social psychology ; Philosophy Machine Learning in Materials Science
Authors: Daniel Maxa
Source: Paliva (article, 2023-03-31)
Field: Chemistry — Analytical Chemistry

Determination of rheological properties of crude oil blends The article presents an overview of widely applicable methods of calculation of viscosity of liquid mixtures, as well as rheology characterisation of crude oil. It is focused on the evaluation of rheological properties, particularly in order to assess the flow conditions in pipelines at different temperatures and flow rates, especially in the case of waxy crude oils. A specific calculation procedure was proposed and described, suitable for practical purposes, i.e. field implementation in the form of a spreadsheet calculator. Using this method, it is possible to calculate the rheological properties of a mixture of known components at a given temperature over a range of shear gradients corresponding to a specified range of flow rates in a circular pipeline of specified diameter. The proposed calculation method was verified on the properties of specific types of oils and their mixtures. Rheology ; Viscosity ; Pipeline transport ; Flow properties ; Materials science ; Range (aeronautics) ; Pipeline (software) ; Shear rate ; Flow (mathematics) ; Crude oil ; Process engineering ; Petroleum engineering ; Computer science ; Mechanics ; Composite material ; Mechanical engineering ; Geology ; Engineering Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Coal Combustion and Slurry Processing
Authors: Celal Fadıl Kumru | Celal KOCATEPE
Source: Mühendislik Bilimleri ve Tasarım Dergisi (article, 2023-12-30)
Field: Materials Science — Materials Chemistry

An EXPERIMENTAL STUDY ON DIELECTRIC PARAMETERS OF XLPE INSULATED HIGH VOLTAGE CABLES UNDER DIFFERENT OPERATING CONDITIONS High voltage underground cables used in distribution systems provide benefits including aesthetics, reliability, low maintenance costs, and resistance to environmental factors. However, their initial installation expenses are considerably higher than overhead lines, and fault detection is quite challenging. Thus, a deep analysis of dielectric parameters is crucial. Typically, electrical specifications of high voltage underground cables provided by the manufacturer are used for nominal operating conditions. However, it is essential to acknowledge that variations in operational conditions have a discernible impact on the dielectric characteristics of cable insulation. This phenomenon results in an accelerated aging process of the insulation material and contributes to unexpected failures. Hence, it is imperative to determine dielectric parameters of cable insulation under varying operational conditions and elucidate the impact of these operational states on insulation properties. This study involved experimental determination of the dielectric parameters, including capacitance, relative dielectric constant, dielectric loss, and tanδ, for a XLPE insulated high voltage underground cable, designed for a nominal voltage of 12/20 kV. These assessments were performed under varying operating conditions, specifically for distinct voltage, temperature, and frequency. Based on the acquired findings, it is evident that the dielectric characteristics of the cable insulation exhibit substantial variations in response to alterations in voltage, temperature, and frequency, even when operating within the defined nominal limits. Dielectric ; Voltage ; Reliability (semiconductor) ; Capacitance ; High voltage ; Materials science ; Electrical engineering ; Engineering ; Optoelectronics ; Physics ; Power (physics) ; Electrode High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Lightning and Electromagnetic Phenomena
Authors: Michael Widom | M. Mihalkovič
Source: arXiv (Cornell University) (review, 2023-08-17)
Field: Materials Science — Materials Chemistry

Quasicrystal structure prediction: A review Predicting quasicrystal structures is a multifaceted problem that can involve predicting a previously unknown phase, predicting the structure of an experimentally observed phase, or predicting the thermodynamic stability of a given structure. We survey the history and current state of these prediction efforts with a focus on methods that have improved our understanding of the structure and stability of known metallic quasicrystal phases. Advances in the structural modeling of quasicrystals, along with first principles total energy calculation and statistical mechanical methods that enable the calculation of quasicrystal thermodynamic stability, are illustrated by means of cited examples of recent work. Quasicrystal ; Stability (learning theory) ; Focus (optics) ; Statistical physics ; Work (physics) ; Phase (matter) ; Materials science ; Physics ; Thermodynamics ; Computer science ; Condensed matter physics ; Machine learning ; Quantum mechanics ; Optics Quasicrystal Structures and Properties ; Nanocluster Synthesis and Applications ; Crystal Structures and Properties
Authors: Amruthesh Thirumalaiswamy | Rodríguez-Cruz, Clary | Robert A. Riggleman | John C. Crocker
Source: arXiv (Cornell University) (preprint, 2023-01-31)
Field: Materials Science — Materials Chemistry

Slow Relaxation and Landscape-Driven Dynamics in Viscous Ripening Foams Foams and dense emulsions display complex mechanical behavior, including intermittent rearrangement dynamics, power-law rheology, and slow recovery after perturbation. These effects have long been considered evidence for glassy physics in these and other materials having similar mechanics, such as the cytoskeleton. Here we study such anomalous mechanics in a simulated wet foam driven by ripening and find behavior that has a different physical origin than that in glasses. Rather, the dynamics is due to a balance of forces from the system's self-similar potential energy landscape and viscous stress. At the lowest viscosities, bubbles move intermittently, with the system shifting abruptly between shallow potential energy minima. For higher viscosities, in contrast, the bubbles move continuously and the system follows a tortuous, fractal path through high-dimensional configuration space, at higher mean energy than the lower viscosity case. The long-time dynamics and power-law rheology are the direct consequence of the potential energy landscape's self-similar geometry. Lastly, we find that the slow recovery of perturbed foams is due to the foam being kinetically rather than energetically trapped in high-energy portions of the energy landscape. Overall, viscous ripening foams follow a biased energy minimization pathway that explores regions of the energy landscape that are qualitatively different (flatter and smoother) than those corresponding to well-annealed glasses. Viscoelasticity ; Rheology ; Mechanics ; Power law ; Relaxation (psychology) ; Diffusion ; Energy landscape ; Materials science ; Physics ; Classical mechanics ; Statistical physics ; Thermodynamics ; Mathematics Pickering emulsions and particle stabilization ; Material Dynamics and Properties ; Liquid Crystal Research Advancements
Authors: Rintaro Yanagi | Ren Togo | Keisuke Maeda | Takahiro Ogawa | Miki Haseyama
Source: IEEE Access (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Material Compound-Property Retrieval Using Electron Microscope Images for Rubber Material Development This paper tackles the electron microscope image processing for rubber material discovery. In rubber material science fields, electron microscope images are used to observe the properties of materials during their development process. Hence, by analyzing the electron microscope images with the recent image processing technology, it is expected that further effective material developments can be realized. In this paper, we propose a rubber material compound and physical property image retrieval method using the rubber material electron microscope images. The aim of our method is to support material technologists to grasp the relationships between material compounds and physical properties visually and comprehensively. Our method constructs an electron microscope image space through a conditional image generation model. The generated images are used to retrieve materials with similar compounds and physical properties. By effectively using the constructed electron microscope image space, it is expected that the technologists visually and comprehensively understand the relationships of similar materials, and the advances in material developments are accelerated. Electron microscope ; Natural rubber ; Microscope ; Property (philosophy) ; Materials science ; Physical property ; Image processing ; Computer science ; Image (mathematics) ; Computer vision ; Optics ; Composite material ; Physics Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection
Authors: Sérgio Tavares | Pedro Reis Costa | Ludwig Krippahl | Marta B. Lopes
Source: arXiv (Cornell University) (preprint, 2023-11-25)
Field: Chemistry — Analytical Chemistry

Satellite-based feature extraction and multivariate time-series prediction of biotoxin contamination in shellfish Shellfish production constitutes an important sector for the economy of many Portuguese coastal regions, yet the challenge of shellfish biotoxin contamination poses both public health concerns and significant economic risks. Thus, predicting shellfish contamination levels holds great potential for enhancing production management and safeguarding public health. In our study, we utilize a dataset with years of Sentinel-3 satellite imagery for marine surveillance, along with shellfish biotoxin contamination data from various production areas along Portugal's western coastline, collected by Portuguese official control. Our goal is to evaluate the integration of satellite data in forecasting models for predicting toxin concentrations in shellfish given forecasting horizons up to four weeks, which implies extracting a small set of useful features and assessing their impact on the predictive models. We framed this challenge as a time-series forecasting problem, leveraging historical contamination levels and satellite images for designated areas. While contamination measurements occurred weekly, satellite images were accessible multiple times per week. Unsupervised feature extraction was performed using autoencoders able to handle non-valid pixels caused by factors like cloud cover, land, or anomalies. Finally, several Artificial Neural Networks models were applied to compare univariate (contamination only) and multivariate (contamination and satellite data) time-series forecasting. Our findings show that incorporating these features enhances predictions, especially beyond one week in lagoon production areas (RIAV) and for the 1-week and 2-week horizons in the L5B area (oceanic). The methodology shows the feasibility of integrating information from a high-dimensional data source like remote sensing without compromising the model's predictive ability. Contamination ; Satellite ; Multivariate statistics ; Warning system ; Time series ; Environmental science ; Computer science ; Early warning system ; Remote sensing ; Geography ; Machine learning ; Engineering Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food
Authors: Sophie Hermann | Matthias Schmidt
Source: arXiv (Cornell University) (preprint, 2023-08-21)
Field: Materials Science — Materials Chemistry

Active crystallization from power functional theory We address the gas, liquid, and crystal phase behaviour of active Brownian particles in three dimensions. The nonequilibrium force balance at coexistence leads to equality of state functions for which we use power functional approximations. Motility-induced phase separation starts at a critical point and quickly becomes metastable against active freezing for Péclet numbers above a nonequilibrium triple point. The mean swim speed acts as a state variable, similar to the density of depletion agents in colloidal demixing. We obtain agreement with recent simulation results and correctly predict the strength of particle number fluctuations in active fluids. Crystallization ; Power (physics) ; Business ; Engineering ; Physics ; Chemical engineering ; Thermodynamics Crystallization and Solubility Studies
Authors: Emre Avuçlu | Bilal Şenol
Source:  (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Classification of Lemon Quality Using the Residual Convolutional Neural Network Deep Learning Model Determining lemon quality is an important post-harvest process. Lemon producers want the lemon quality classification process to be done correctly. In this way, the monetary return they will receive will be right for them. In this study, lemons with good quality and bad quality images were classified. 951 images for bad lemons and 1125 good quality images were used in the dataset. Convolutional Neural Networks (ResCNN) deep learning model and 5-fold cross validation process were performed in lemon classification process. As a result of the statistical measurements, the highest accuracy value was obtained as 0.8848 for both lemon varieties as the average value. While the highest sensitivity value was obtained from good quality lemon as 0.9013, the highest specificity value was obtained as 0.9013 from bad quality lemon variety. Convolutional neural network ; Residual ; Quality (philosophy) ; Artificial intelligence ; Computer science ; Value (mathematics) ; Mathematics ; Process (computing) ; Pattern recognition (psychology) ; Statistics ; Machine learning ; Algorithm Spectroscopy and Chemometric Analyses ; Date Palm Research Studies
Authors: Victória Hellena Matusevicius e de Castro | Taya Cristo Parreiras | Édson Luís Bolfe
Source: Revista Engenharia na Agricultura - REVENG (review, 2023-08-24)
Field: Chemistry — Analytical Chemistry

Disease detection in citrus crops using optical and thermal remote sensing: a literature review Brazil stands out in the international citrus trade, especially due to its oranges, having produced around 16 million tons in 2021. However, productivity could be increased with greater control of diseases such as greening, which has spread around the world and leads to the total loss of affected trees. Given this scenario, it is necessary to perform fast and accurate detections in order to better manage actions and inputs. Since remote sensing is a pillar of digital agriculture, a literature review was carried out to analyze the use of optical and thermal sensors for the detection of diseases that affect citrus groves. For this purpose, the international databases Scopus and Web of Science were used to select references published between 2012 and 2022, resulting in twelve studies — most from China or the United States of America. The results showed a prevalence of methodologies that combine bands and spectral indices obtained through the use of multispectral and hyperspectral sensors, predominantly on board unmanned aircrafts (UAVs). Machine learning (ML) and deep learning (DL) classification algorithms produced good results in the detection of citrus groves affected by diseases, mainly greening. These results are affected by the stage of the infection, the presence or absence of symptoms, and the spectral and spatial resolutions of the sensors: the Red-Edge band and data with higher spatial detail result in more accurate classification models. However, the analyzed literature is still inconclusive regarding the early detection of infected plants. Multispectral image ; Hyperspectral imaging ; Greening ; Remote sensing ; Computer science ; Environmental science ; Agricultural engineering ; Geography ; Artificial intelligence ; Engineering ; Biology Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI