jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shivali Amit Wagle | R Harikrishnan | Ketan Kotecha
Source: Ingénierie des systèmes d information (article, 2024-04-25)
Field: Chemistry — Analytical Chemistry

Bilinear LSTM with Bayesian Gaussian Optimization for Predicting Tomato Plant Disease Using Meteorological Parameters Climate change threatens agriculture; as a result, adaptation measures are required to withstand agricultural produce, reduce susceptibility, and improve the farm system's flexibility to climate change.Meteorological parameters like temperature and relative humidity play an essential role in the condition of disease occurrence in plants.We studied ARIMA, Prophet, and Long Short-Term Memory (LSTM) with stochastic gradient descent with momentum, RMSprop, and Adam optimizers to forecast the temperature and relative humidity.The work proposes a hybrid regression prediction model of Bilinear LSTM with Gaussian Bayesian optimization (BLSTM_bayOpt) for predicting disease in tomato plants based on weather parameters.From the six prediction models in this study, the performance of BLSTM_bayOpt in prediction with RMSE of 1.1573 and 5.5509, MAPE is 0.0556 and 0.0927, R2 is 0.9324 and 0.9475 for temperature and relative humidity, respectively.The proposed hybrid BLSTM_bayOpt model improved by 40.67%, with an MSE score for relative humidity prediction. Bilinear interpolation ; Bayesian probability ; Gaussian ; Bayesian optimization ; Gaussian process ; Artificial intelligence ; Computer science ; Machine learning ; Econometrics ; Statistics ; Mathematics ; Environmental science ; Pattern recognition (psychology) ; Physics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Water Quality Monitoring and Analysis
Authors: Dao‐Guang He | Yuan Chen | Shibing Chen | Y.C. Lin | Jia-Fu Wu
Source: Materials (article, 2024-05-29)
Field: Materials Science — Materials Chemistry

High-Temperature Tensile Characteristics of an Al–Zn–Mg–Cu Alloy: Fracture Characteristics and a Physical Mechanism Constitutive Model High-temperature tensile tests were developed to explore the flow features of an Al-Zn-Mg-Cu alloy. The fracture characteristics and microstructural evolution mechanisms were thoroughly revealed. The results demonstrated that both intergranular fractures and ductile fractures occurred, which affected the hot tensile fracture mechanism. During high-temperature tensile, the second phase (Al<sub>2</sub>CuMg) at the grain boundaries (GBs) promoted the formation and accumulation of dimples. With the continual progression of high-temperature tensile, the aggregation/coarsening of dimples along GBs appear, aggravating the intergranular fracture. The coalescence and coarsen of dimples are reinforced at higher tensile temperatures or lower strain rates. Considering the impact of microstructural evolution and dimple formation/coarsening on tensile stresses, a physical mechanism constitutive (PMC) equation is herein proposed. According to the validation and analysis, the predictive results were in preferable accordance with the testing data, showing the outstanding reconfiguration capability of the PMC model for high-temperature tensile features in Al-Zn-Mg-Cu alloys. Materials science ; Alloy ; Ultimate tensile strength ; Mechanism (biology) ; Constitutive equation ; Fracture (geology) ; Composite material ; Metallurgy ; Structural engineering ; Finite element method ; Engineering Microstructure and mechanical properties ; Aluminum Alloy Microstructure Properties ; Metal Forming Simulation Techniques
Authors: Nicolas Monge | A. Deschamps | Massih-Reza Amini
Source: Acta Crystallographica Section A Foundations and Advances (article, 2024-02-29)
Field: Materials Science — Materials Chemistry

Automated selection of nanoparticle models for small-angle X-ray scattering data analysis using machine learning Small-angle X-ray scattering (SAXS) is widely used to analyze the shape and size of nanoparticles in solution. A multitude of models, describing the SAXS intensity resulting from nanoparticles of various shapes, have been developed by the scientific community and are used for data analysis. Choosing the optimal model is a crucial step in data analysis, which can be difficult and time-consuming, especially for non-expert users. An algorithm is proposed, based on machine learning, representation learning and SAXS-specific preprocessing methods, which instantly selects the nanoparticle model best suited to describe SAXS data. The different algorithms compared are trained and evaluated on a simulated database. This database includes 75 000 scattering spectra from nine nanoparticle models, and realistically simulates two distinct device configurations. It will be made freely available to serve as a basis of comparison for future work. Deploying a universal solution for automatic nanoparticle model selection is a challenge made more difficult by the diversity of SAXS instruments and their flexible settings. The poor transferability of classification rules learned on one device configuration to another is highlighted. It is shown that training on several device configurations enables the algorithm to be generalized, without degrading performance compared with configuration-specific training. Finally, the classification algorithm is evaluated on a real data set obtained by performing SAXS experiments on nanoparticles for each of the instrumental configurations, which have been characterized by transmission electron microscopy. This data set, although very limited, allows estimation of the transferability of the classification rules learned on simulated data to real data. Small-angle X-ray scattering ; Computer science ; Scattering ; Preprocessor ; Machine learning ; Data set ; Set (abstract data type) ; Artificial intelligence ; Transferability ; Optics ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Ole‐Christian Galbo Engstrøm | Erik Schou Dreier | Birthe Møller Jespersen | Kim Steenstrup Pedersen
Source: The Journal of Open Source Software (article, 2024-07-23)
Field: Chemistry — Analytical Chemistry

IKPLS: Improved Kernel Partial Least Squares and FastCross-Validation Algorithms for Python with CPU and GPU ImplementationsUsing NumPy and JAX Engstrøm et al., (2024). IKPLS: Improved Kernel Partial Least Squares and Fast Cross-Validation Algorithms for Python with CPU and GPU Implementations Using NumPy and JAX. Journal of Open Source Software, 9(99), 6533, https://doi.org/10.21105/joss.06533 Python (programming language) ; Parallel computing ; Computer science ; Kernel (algebra) ; Implementation ; Computational science ; Algorithm ; Programming language ; Mathematics ; Discrete mathematics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Fault Detection and Control Systems
Authors: Anoopshi Johari | Sanjeev Naithani | Baljinder Kaur | Abhinav Bhatnagar | Brajesh Kumar Kaushik
Source: Journal of Nanophotonics (article, 2024-02-29)
Field: Materials Science — Materials Chemistry

Ge2Sb2Te5 patch embedded ring resonator for optical switching A Ge2Sb2Te5 (GST)-based microring resonator is designed and analyzed for optical switching-based applications. First, the optical properties of GST material having thicknesses of 130 to 230 nm on two commonly used substrates, i.e., silicon (Si) and silicon dioxide (SiO2), are investigated in detail. The phase-change property of GST plays an important role in tuning the resonant wavelength, absorption, and transmission loss in the ring resonator as its crystalline state has a higher refractive index (RI) compared with the amorphous state. Due to the phase-change property of the GST material, the effective RI of the waveguide core changes. A patch of GST is embedded on a ring resonator to optimize the response of light coupled in the ring and straight waveguide, that is tunable between its amorphous phase (a-GST) and crystalline (c-GST) phase. The difference in refractive indices between the two states of a phase-change material (PCM) renders it an optimal choice for the optical active devices used in light modulation and switching applications. The GST is employed as an active material in the proposed structure as it is significantly stable and results in reduced insertion loss (IL) compared with other PCMs. In the proposed design of the ring resonator, the finite-difference time-domain simulation demonstrates high extinction ratios of 16.05 and 20.50 dB at the THROUGH and DROP ports, respectively. Moreover, the low insertion losses (IL) of 0.33 dB at the THROUGH port in crystalline phase and 0.8 dB at the DROP port in amorphous phase, have been observed. The Q-factor of the ring resonator without a GST patch is 5.18×105, whereas with a GST patch, it is 5.13×105 and 1.28×105 in the amorphous and crystalline phases, respectively. Resonator ; Materials science ; Refractive index ; Insertion loss ; Optoelectronics ; Extinction ratio ; Optics ; Amorphous solid ; Optical switch ; Waveguide ; Absorption (acoustics) ; Port (circuit theory) ; Wavelength ; Electronic engineering ; Chemistry ; Physics Phase-change materials and chalcogenides ; Photonic and Optical Devices ; Liquid Crystal Research Advancements
Authors: Kaveh H. Babai | Fei Long | Martin Malý | Keitaro Yamashita | Garib N. Murshudov
Source: Acta Crystallographica Section D Structural Biology (article, 2024-12-01)
Field: Materials Science — Materials Chemistry

Improving macromolecular structure refinement with metal-coordination restraints Metals are essential components for the structure and function of many proteins. However, accurate modelling of their coordination environments remains a challenge due to the complexity and diversity of metal-coordination geometries. To address this, a method is presented for extracting and analysing coordination information, including bond lengths and angles, from the Crystallography Open Database. By using these data, comprehensive descriptions of metal-containing components are generated. A stereochemical information generator for a particular component within a specific macromolecule leverages an example PDB/mmCIF file containing the component to account for the actual surrounding environment. A matching process has been developed and implemented to align the derived metal structures with idealized coordinates from a coordination geometry library. Additionally, various strategies, depending on the quality of the matches, were employed to compile distance and angle statistics for the refinement of macromolecular structures. The developed methods were implemented in a new program, MetalCoord, that classifies and utilizes the metal-coordination geometry. The effectiveness of the developed algorithms was tested using metal-containing components from the PDB. As a result, metal-containing components from the CCP4 monomer library have been updated. The updated monomer dictionaries, in concert with the derived restraints, can be used in most structural biology computations, including macromolecular crystallography, single-particle cryo-EM and even molecular mechanics. Macromolecule ; Component (thermodynamics) ; Computer science ; Protein Data Bank (RCSB PDB) ; Computation ; Matching (statistics) ; Crystallography ; Protein Data Bank ; Set (abstract data type) ; Biological system ; Computational science ; Algorithm ; Chemistry ; Protein structure ; Mathematics ; Physics ; Programming language ; Stereochemistry Enzyme Structure and Function ; Protein Structure and Dynamics ; RNA and protein synthesis mechanisms
Authors: Sarah Iaquinta | Shahram Khazaie | Frédéric Jacquemin | Sylvain Fréour
Source: Journal of Theoretical Computational and Applied Mechanics (article, 2024-09-18)
Field: Materials Science — Biomaterials

Accounting for the mechanical response of the cell membrane during the uptake of random nanoparticles In order to improve the efficiency of the delivery of cancer treatments to cancer cells, the cellular uptake of nanoparticles (NPs), used as drug delivery systems, is numerically investigated through a mechanical approach. The objective is to optimize the NP's mechanical and geometrical properties to enhance their entry into cancer cells while avoiding benign ones. In previous studies, these properties are modeled as constant during the process of cellular uptake. However, recent observations of the displacement of the membrane's constituents towards the region in the cell membrane where the uptake of the NPs takes place show that the mechanical properties of the membrane vary during this process. Reason for writing The important contribution of adhesion to the wrapping process is already well documented in literature. It is therefore crucial to model this parameter properly as the conclusions made with a constant adhesion model may not be accurate compared to reality. Methodology Based on the existing knowledge on the reaction of membrane constituents to interaction with NPs, a 3-parameter sigmoidal function, accounting for the delay, amplitude, and speed of the reaction, has been used to model the evolution of adhesion. A variance-based sensitivity analysis has then been performed in order to quantify the influence of these parameters on the outputs of the model. Results It was found that the introduction of a variable adhesion tends to alter the predictions of endocytosis of NPs. The contribution of the amplitude and delay is respectively 0.32 and 0.43 times as important as that of the NP's aspect ratio, which is the prominent parameter. The influence of the slope of the transition is the least important parameter and does not appear to contribute to endocytosis. Implications Hence, models of the cellular uptake of NPs should use a variable, instead of constant, adhesion in order a representative as possible of the behavior of the cell membrane. The predictions are different from those obtained using a model with constant adhesion. Adhesion ; Endocytosis ; Membrane ; Nanoparticle ; Variance (accounting) ; Biophysics ; Biological system ; Function (biology) ; Process (computing) ; Cancer cell ; Nanotechnology ; Drug delivery ; Materials science ; Displacement (psychology) ; Constant (computer programming) ; Cell ; Chemistry ; Cancer ; Computer science ; Composite material ; Biology ; Cell biology ; Accounting ; Biochemistry Nanoparticle-Based Drug Delivery ; Polymer Surface Interaction Studies ; Force Microscopy Techniques and Applications
Authors: Joshua S. Mommoh | James L. Obetta | Samuel John | Kennedy Okokpujie | Omoruyi Osemwegie | Ayokunle Awelewa
Source: Information Dynamics and Applications (article, 2024-09-23)
Field: Chemistry — Analytical Chemistry

Detection of Fruit Ripeness and Defectiveness Using Convolutional Neural Networks Ripeness ; Convolutional neural network ; Computer science ; Artificial intelligence ; Horticulture ; Biology ; Ripening Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Leonardo M. Corrêa | Simon M. Fairclough | Kaleigh M. R. Scher | Supriya Atta | Diego P. dos Santos | Caterina Ducati | Laura Fabris | D. Ugarte
Source: ACS Nano (article, 2024-09-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Atomic Structure and 3D Shape of a Multibranched Plasmonic Nanostar from a Single Spatially Resolved Electron Diffraction Map Despite the interest in improving the sensitivity of optical sensors using plasmonic nanoparticles (NPs) (rods, wires, and stars), the full structural characterization of complex shape nanostructures is challenging. Here, we derive from a single scanning transmission electron microscope diffraction map (4D-STEM) a detailed determination of both the 3D shape and atomic arrangement of an individual 6-branched AuAg nanostar (NS) with high-aspect-ratio legs. The NS core displays an icosahedral structure, and legs are decahedral rods attached along the 5-fold axes at the core apexes. The NS legs show an anomalous anisotropic spatial distribution (all close to a plane) due to an interplay between the icosahedral symmetry and the unzipping of the surfactant layer on the core. The results significantly improve our understanding of the star growth mechanism. This low dose diffraction mapping is promising for the atomic structure study of individual multidomain, multibranched, or multiphase NPs, even when constituted of beam-sensitive materials. Plasmon ; Electron diffraction ; Diffraction ; Materials science ; Electron ; Molecular physics ; Nanotechnology ; Crystallography ; Optics ; Physics ; Optoelectronics ; Chemistry ; Quantum mechanics Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Advanced biosensing and bioanalysis techniques
Authors: Corine Faehn | Grzegorz Konert | Markku Keinänen | Katja Karppinen | Kirsten Krause
Source: Plant Methods (article, 2024-11-11)
Field: Chemistry — Analytical Chemistry

Advancing hyperspectral imaging techniques for root systems: a new pipeline for macro- and microscale image acquisition and classification Our study addressed the key challenges in HSI acquisition and data processing for root system analysis and lays the groundwork for further exploration of VNIR HSI application across various scales of root system studies. This work provides a full data analysis pipeline that can be utilized as an online Python-based tool for the semi-automated analysis of root-soil HSI data. Hyperspectral imaging ; Microscale chemistry ; Pipeline (software) ; Macro ; Remote sensing ; Artificial intelligence ; Computer science ; Environmental science ; Computer vision ; Pattern recognition (psychology) ; Geology ; Mathematics Spectroscopy and Chemometric Analyses ; Soil Geostatistics and Mapping
Authors: Waleed Elhelew | Dalia Abo-Bbakr | Sahar Zayan | Muhammad Mayhoub
Source: Misr journal of agricultural engineering (article, 2024-05-18)
Field: Chemistry — Analytical Chemistry

CLASSIFICATION OF DATES QUALITY USING DEEP LEARNING TECHNOLOGY BASED ON CAPTURED IMAGES Dates are a common fruit in many Middle Eastern and African nations and have religious and cultural value. one of the key elements in judging the quality of dates is sorting according to their health state. Combining rejected dates with accepted ones causes significant economic losses in both storage and exportation. Despite being a crucial stage for obtaining high-quality dates and reducing losses, this sorting process is still conducted using traditional methods. Thus, this study aims to classify date fruit quality (accepted or rejected) with machine learning technology to reduce cost, time, and improve the quality of final product. In this study, several Convolutional Neural Network architectures (Inception-v3, Inception-ResNet-v2, VGG19) were used to classify three varieties of date fruit (Mejdool, Saiedi, El-Wadi). These varieties were classified into accepted and rejected samples to build the dataset im-ages. An Arduino Automatic mobile camera shutter controller captured the dataset images. In addition to the Kaggle dataset which was added to the accepted images. The total dataset consisted of 5,945 images, comprising 3,142 accepted images and 2,803 rejected images. By comparing the results of different architectures, Inception-ResNet-v2 demonstrated the best performance, achieving an accuracy of 98.99% and a loss of 0.0344. Therefore, it can be concluded that the Inception-ResNet-v2 model could be utilized to develop a suitable computer vision system, thereby enhancing the date sorting process and facilitating the packaging of high-quality dates. Quality (philosophy) ; Artificial intelligence ; Deep learning ; Computer science ; Computer vision Spectroscopy and Chemometric Analyses ; Currency Recognition and Detection ; Advanced Statistical Methods and Models
Authors: Pablo González-Torres | Ángeles García-Ruiz | M. Dolores La Rubia
Source: Applied Sciences (article, 2024-08-25)
Field: Chemistry — Organic Chemistry

Degradation and Migration in Olive Oil Packaged in Polyethylene Terephthalate under Thermal Treatment and Storage Conditions The research addresses challenges in food safety related to the migration of contaminants from plastics to food. It focused on the physicochemical and sensory degradation of olive oils packaged in polyethylene terephthalate (PET) and subjected to thermal exposure at 40 °C and 60 °C for several weeks and a subsequent 12 months of storage, as well as the stability and migration of compounds from the PET packaging itself. Olive oils (OO) from Spanish supermarkets (a mixture of refined and virgin, with commercial identifications of mild and intense) were selected and subjected to thermal treatments at 40 °C and 60 °C for 1, 2, and 3 weeks, followed by 12 months of storage. The treatments were conducted through the following two independent experiments: Experiment A focused on immediate analysis post-thermal treatment, while Experiment B included a 12-month storage period post-thermal treatment. The presence of antimony (Sb) was analyzed using acid digestion with nitric acid (HNO3) and high-resolution inductively coupled plasma mass spectrometry (HR-ICP-MS), while the metals cadmium (Cd), copper (Cu), lead (Pb), and iron (Fe) were analyzed using inductively coupled plasma mass spectrometry (ICP-MS). The PET characterization was assessed using Fourier transform infrared spectroscopy in the mid-infrared range (FT-IR/MIR), carbonyl index, and differential scanning calorimetry (DSC). The results showed increases in the acidity index by 0.29%, the peroxide value by 25.92%, and the K268 coefficient by 51.22% between the control sample and the most severe treatments, with more pronounced effects observed after 12 months. Sensory quality declined, with reduced intensity of the “fruity” attribute and increased presence of the “rancid” defect. PET degradation was reflected in an increase in the carbonyl index and greater structural amorphization. Fe was the predominant metal, and Sb concentration increased after thermal treatments. The lack of studies on the raw consumption of oils packaged in PET and the concerns about the migration of compounds from the packaging to the food highlight the relevance of this research. This study provides new insights into the effects of thermal exposure and storage on the migration of PET contaminants into oils, contributing to the development of strategies to ensure food safety and product quality. Polyethylene terephthalate ; Degradation (telecommunications) ; Materials science ; Polyethylene ; Pulp and paper industry ; Composite material ; Waste management ; Computer science ; Engineering Edible Oils Quality and Analysis ; Food Chemistry and Fat Analysis