jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Haoyu Niu | Juan Landivar | Nick Duffield
Source: Advances in Modern Agriculture (article, 2024-02-01)
Field: Materials Science — Polymers and Plastics

Classification of cotton water stress using convolutional neural networks and UAV-based RGB imagery &lt;p&gt;Embracing smart irrigation management techniques empowers growers to irrigate with greater efficiency, thereby promoting sustainable agricultural production. In this context, growers often rely on crop evapotranspiration (ETc) as a key factor in making informed irrigation decisions, underscoring the significance of accurately determining and spatially mapping crop water status. Technological progress, exemplified by the emergence of unmanned aerial vehicles (UAVs), has brought about a revolutionary shift in agricultural monitoring. UAV platforms can capture high-resolution images with centimeter-level spatial accuracy and offer higher temporal coverage compared to satellite imagery. Considering these advancements, this study introduces a robust method for classifying water stress in cotton using a compact UAV platform and convolutional neural networks (CNN). The experiment was conducted at the USDA-ARS Cropping Systems Research Laboratory (CSRL) in Lubbock, Texas, where the cotton field was divided into 12 drip zones. The study included three replications to evaluate four irrigation treatments: “rainfed”, “full irrigation”, “percent deficit of full irrigation”, and “time delay of full irrigation”. The results demonstrated that the CNN model successfully classified the cotton water stress using the UAV-based RGB image, achieving an overall best prediction accuracy of approximately 91%. By segmenting the original cotton images into separate canopy and soil areas using morphological image processing methods, the authors also isolated and analyzed the individual contributions of these components to cotton water stress. Additionally, a random forest classifier revealed the relative importance of different image features in the classification process through feature importance analysis. These findings highlighted the state-of-the-art performance of the proposed system in cotton water stress classification and provided valuable insights into the key image features contributing to accurate classification. The authors concluded that integrating UAV-based RGB imagery and CNN models had great potential for assessing water stress in cotton.&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt; Convolutional neural network ; Artificial intelligence ; RGB color model ; Water stress ; Computer science ; Remote sensing ; Pattern recognition (psychology) ; Geology ; Biology Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Dyeing and Modifying Textile Fibers
Authors: Rui Li | Qiming Sun | Xing Zhang | Garnet Kin‐Lic Chan
Source: arXiv (Cornell University) (preprint, 2024-07-12)
Field: Chemistry — Physical and Theoretical Chemistry

Introducing GPU-acceleration into the Python-based Simulations of Chemistry Framework We introduce the first version of GPU4PySCF, a module that provides GPU acceleration of methods in PySCF. As a core functionality, this provides a GPU implementation of two-electron repulsion integrals (ERIs) for contracted basis sets comprising up to g functions using Rys quadrature. As an illustration of how this can accelerate a quantum chemistry workflow, we describe how to use the ERIs efficiently in the integral-direct Hartree-Fock Fock build and nuclear gradient construction. Benchmark calculations show a significant speedup of two orders of magnitude with respect to the multi-threaded CPU Hartree-Fock code of PySCF, and performance comparable to other GPU-accelerated quantum chemical packages including GAMESS and QUICK on a single NVIDIA A100 GPU. Python (programming language) ; Computer science ; Acceleration ; Computational science ; General-purpose computing on graphics processing units ; Computer graphics (images) ; Parallel computing ; Programming language ; Physics ; Graphics ; Classical mechanics Various Chemistry Research Topics
Authors: Batuhan Yildirim | James Doutch | Jacqueline M. Cole
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Multi-task scattering-model classification and parameter regression of nanostructures from small-angle scattering data Machine learning (ML) can be employed at the data-analysis stage of small-angle scattering (SAS) experiments. Scattering ; Small-angle scattering ; Regression ; Regression analysis ; Nanostructure ; Small-angle X-ray scattering ; Machine learning ; Artificial intelligence ; Computer science ; Materials science ; Physics ; Statistics ; Optics ; Mathematics ; Nanotechnology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced X-ray and CT Imaging
Authors: Jong-Hoon Kim | Ic‐Pyo Hong
Source: Journal of Electromagnetic Engineering and Science (article, 2024-03-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inverse Design of Electromagnetic Metasurfaces Utilizing Infinite and Separate Latent Space Yielded by a Machine Learning-Based Generative Model This study proposes an inverse design framework for metasurfaces based on a neural network capable of generating infinite and continuous latent representations to fully span the electromagnetic metasurfaces (EMMS) property space. The inverse design of EMMS inherently poses the one-to-many mapping problem, since one set of electromagnetic properties can be provided by many different shapes of scatterers. Previous studies have addressed this issue by introducing machine learning-based generative models and regularization strategies. However, most of these approaches require highly complex operating configurations or external modules for preprocessing datasets. In contrast, this study aimed to construct a more streamlined and end-to-end solver by building a network to process multimodal datasets and then incorporating a classification scheme into the network. The validity of the idea was confirmed by comparing the accuracy of the results predicted by the proposed approach and the outcomes simulated using PSSFSS. Computer science ; Preprocessor ; Regularization (linguistics) ; Inverse ; Solver ; Generative grammar ; Construct (python library) ; Artificial neural network ; Artificial intelligence ; Set (abstract data type) ; Process (computing) ; Machine learning ; Algorithm ; Mathematics Metamaterials and Metasurfaces Applications ; Animal Vocal Communication and Behavior ; Advanced Optical Imaging Technologies
Authors: Danial Fatchurrahman | Noelia Castillejo | Maulidia Hilaili | Lucia Russo | Ayoub Fathi-Najafabadi | Anisur Rahman
Source: Horticulturae (article, 2024-12-13)
Field: Chemistry — Analytical Chemistry

A Novel Damage Inspection Method Using Fluorescence Imaging Combined with Machine Learning Algorithms Applied to Green Bell Pepper Fluorescence imaging has emerged as a powerful tool for detecting surface damage in fruits, yet its application to vegetables such as green bell peppers remains underexplored. This study investigates the fluorescent characteristics of minor mechanical damage, specifically 5 × 5 mm cuts in the exocarp of green bell peppers, which conventional digital imaging techniques fail to classify accurately. Chlorophyll fluorescence imaging was combined with machine learning algorithms—including logistic regression (LR), artificial neural networks (ANN), random forests (RF), k-nearest neighbors (kNN), and the support vector machine (SVM) to classify damaged and sound fruit. The machine learning models demonstrated a high classification accuracy, with calibration and prediction accuracies exceeding 0.86 and 0.96, respectively, across all algorithms. These results underscore the potential of fluorescence imaging as a non-invasive, rapid, and cheaper method for assessing mechanical damage in green bell peppers, offering valuable applications in quality control and postharvest management. Random forest ; Support vector machine ; Postharvest ; Artificial intelligence ; Pepper ; Machine learning ; Computer science ; Chlorophyll fluorescence ; Calibration ; Algorithm ; Pattern recognition (psychology) ; Horticulture ; Mathematics ; Chlorophyll ; Biology ; Statistics Spectroscopy and Chemometric Analyses ; Date Palm Research Studies ; Postharvest Quality and Shelf Life Management
Authors: Laura Backer | Martina Kinzig | Fritz Sörgel | Oliver Scherf‐Clavel | Ulrike Holzgrabe
Source: Journal of Pharmaceutical and Biomedical Analysis (article, 2024-04-19)
Field: Chemistry — Analytical Chemistry

Targeted and untargeted screening for impurities in losartan tablets marketed in Germany by means of liquid chromatography/high resolution mass spectrometry Technical advances in the field of quality analysis allow an increasingly deeper look into the impurity profile of drugs. The ability to detect unexpected impurities in addition to known impurities ensures the supply of high-quality drugs and can prevent recalls due to the detection of harmful unexpected impurities, as has happened recently with the N-nitrosamine and azido impurities in losartan (LOS) drug products. In the present study, the LC-MS/HRMS approach described by Backer et al. was applied to an even more complex system, being the investigation of 35 LOS drug products and combination preparations purchased in 2018 and 2022 in German pharmacies. The film-coated tablets were analysed by means of four LC-MS/HRMS method variants. For the separation a Zorbax RR StableBond C18 column (3.0 ×100 mm, particle size of 3.5 µm, pore size of 80 Å), a gradient elution and for mass spectrometric detection a qTOF mass spectrometer with electrospray ionization in positive and negative mode was used. An information-dependent acquisition method was applied for the acquisition of high-resolution mass spectrometry data. The combination of an untargeted and a targeted screening approach revealed the finding of eight impurities in total. Beside the five LOS related compounds, LOS impurity F, J, K, L, M, and related compound D from amlodipine besilate, LOS azide and an unknown derivative thereof were detected. Identification and structure elucidation, respectively, were successfully performed using in silico fragmentation. Differences in the impurity profiles of drug products from 2018 and 2022 could be observed. This study shows that broad screening approaches like this are applicable to the analysis of drug products and can be an important enhancement of the quality assurance of medicinal products. Chemistry ; Chromatography ; Mass spectrometry ; Losartan ; Impurity ; High resolution ; Liquid chromatography–mass spectrometry ; Resolution (logic) ; High-performance liquid chromatography ; Angiotensin II ; Organic chemistry ; Biochemistry Analytical Methods in Pharmaceuticals ; Pesticide Residue Analysis and Safety ; Analytical Chemistry and Chromatography
Authors: Umesh C. Roy | Angelo Bongiorno
Source: The Journal of Physical Chemistry C (article, 2024-12-02)
Field: Materials Science — Materials Chemistry

Nonlinear Elasticity of Amorphous Silicon and Silica from Density Functional Theory Density functional theory calculations and a finite deformation method are used to calculate second- and, most notably, third-order elastic constants of amorphous silicon and amorphous silicon dioxide, as represented by model structures generated via melt-quench force-field molecular dynamics simulations. Linear and nonlinear elastic constants are used to deduce macroscopic elastic moduli, such as the bulk and shear moduli, their pressure derivatives, and the elastic Grüneisen parameter. Our calculations show that the elastic properties of amorphous silicon reach the isotropic elastic limit within the nanometer length scale, attaining characteristics, both linear and nonlinear, comparable to those of crystalline silicon. In contrast, the nonlinear elastic properties of silica retain an anisotropic character over the nanometer length scales, yielding nonetheless the expected pressure-induced softening of the elastic moduli. This atypical elastic behavior is correlated to the occurrence of long-wavelength acoustic modes with negative Grüneisen parameters. Materials science ; Isotropy ; Elastic modulus ; Elasticity (physics) ; Anisotropy ; Amorphous solid ; Amorphous silica ; Linear elasticity ; Nonlinear system ; Silicon ; Length scale ; Softening ; Condensed matter physics ; Composite material ; Mechanics ; Thermodynamics ; Optics ; Physics ; Crystallography ; Finite element method ; Chemistry Silicon Nanostructures and Photoluminescence ; Thin-Film Transistor Technologies ; Mechanical and Optical Resonators
Authors: Alonzo González‐González | Oscar Sánchez-Sánchez | Lilián Yépez‐Mulia | Timoteo Delgado‐Maldonado | Lenci K. Vázquez‐Jiménez | Gabriel López‐Velázquez | De Ji | Sebastian Pacheco-Gutierrez | Laura Chino-Ríos | Diego G. Arias | Adriana Moreno‐Rodríguez | Alma D. Paz-González | Eyra Ortíz-Pérez | Gildardo Rivera
Source: Journal of Enzyme Inhibition and Medicinal Chemistry (article, 2024-10-29)
Field: Chemistry — Organic Chemistry

Expanding the antiprotozoal activity and the mechanism of action of n-butyl and iso-butyl ester of quinoxaline-1,4-di- <i>N</i> -oxide derivatives against <i>Giardia lamblia</i> , <i>Trichomonas vaginalis</i> , and <i>Entamoeba histolytica.</i> An <i>in vitro</i> and <i>in silico</i> approach In this study, n-butyl and iso-butyl quinoxaline-7-carboxylate-1,4-di-<i>N</i>-oxide derivatives were evaluated <i>in vitro</i> against <i>Giardia lamblia</i> (<i>G. lamblia</i>)<i>, Trichomonas vaginalis</i> (<i>T. vaginalis</i>), and <i>Entamoeba histolytica</i> (<i>E. histolytica</i>). The potential mechanism of action determination was approached by <i>in silico</i> analysis on <i>G. lamblia</i> and <i>T. vaginalis</i> triosephosphate isomerase (<i>Gl</i>TIM and <i>Tv</i>TIM, respectively), and on <i>E. histolytica</i> thioredoxin reductase (<i>EhTrxR</i>). Enzyme inactivation assays were performed on recombinant G<i>l</i>TIM and <i>Eh</i>TrxR. Compound T-167 showed the best giardicidal activity (IC<sub>50</sub> = 25.53 nM) and the highest inactivation efficiency against G<i>l</i>TIM without significantly perturbing its human homolog. Compounds T-142 and T-143 showed the best amoebicidal (IC<sub>50</sub> = 9.20 nM) and trichomonacidal (IC<sub>50</sub> = 45.20 nM) activity, respectively. Additionally, T-143 had a high activity as giardicial (IC<sub>50</sub> = 29.13 nM) and amoebicidal (IC<sub>50</sub> = 15.14 nM), proposing it as a broad-spectrum antiparasitic agent. Compounds T-145, and T-161 were the best <i>Eh</i>TrxR inhibitors with IC<sub>50</sub> of 16 µM, and 18 µM, respectively. Antiprotozoal ; Quinoxaline ; Chemistry ; Giardia lamblia ; Stereochemistry ; Medicinal chemistry ; Giardia ; Organic chemistry ; Biochemistry ; Biology ; Microbiology ; In vitro Synthesis and Biological Evaluation ; Synthesis and Reactions of Organic Compounds
Authors: Weikang Xian | Amitesh Maiti | Andrew P. Saab | Ying Li
Source: Soft Matter (article, 2024-01-01)
Field: Chemistry — Organic Chemistry

Development of a coarse-grained molecular dynamics model for poly(dimethyl-<i>co</i>-diphenyl)siloxane Polydimethylsiloxane is an important polymeric material with a wide range of applications. However, environmental effects like low temperature can induce crystallization in this material with resulting changes in its structural and dynamic properties. The incorporation of phenyl-siloxane components, <i>e.g.</i>, as in a poly(dimethyl-<i>co</i>-diphenyl)siloxane random copolymer, is known to suppress such crystallization. Molecular dynamics (MD) simulations can be a powerful tool to understand such effects in atomistic detail. Unfortunately, all-atomistic molecular dynamics (AAMD) is limited in both spatial dimensions and simulation times it can probe. To overcome such constraints and to extend to more useful length- and time-scales, we systematically develop a coarse-grained molecular dynamics (CGMD) model for the poly(dimethyl-<i>co</i>-diphenyl)siloxane system with bonded and non-bonded interactions determined from all-atomistic simulations by the iterative Boltzmann inversion (IBI) method. Additionally, we propose a lever rule that can be useful to generate non-bonded potentials for such systems without reference to the all-atomistic ground truth. Our model captures the structural and dynamic properties of the copolymer material with quantitative accuracy and is useful to study long-time dynamics of highly-entangled systems, sequence-dependent properties, phase behaviour, <i>etc.</i> Polydimethylsiloxane ; Siloxane ; Molecular dynamics ; Copolymer ; Materials science ; Dynamics (music) ; Chemical engineering ; Polymer chemistry ; Nanotechnology ; Chemistry ; Composite material ; Computational chemistry ; Polymer ; Physics Advanced Polymer Synthesis and Characterization ; Rheology and Fluid Dynamics Studies ; Polymer crystallization and properties
Authors: Abdul Hai | K. Rambabu | Ayesha S. Al Dhaheri | Shyam S. Kurup | Fawzi Banat
Source: Heliyon (review, 2024-08-01)
Field: Chemistry — Inorganic Chemistry

Tapping into Palm Sap: Insights into extraction practices, quality profiles, fermentation chemistry, and preservation techniques The quality profile, extraction yield, and fermentation chemistry of palm sap depend on various factors such as extraction technique, weather conditions, and preservation methods. This review aims to provide a detailed overview of palm sap extraction techniques and the methods for its preservation. The compositional analysis of palm sap, including physical and chemical parameters such as sugar content, acidity, and mineral composition, is discussed thoroughly. The role of microorganisms in fermentation and the effects of various influencing factors are also critically examined. Additionally, this review evaluates different preservation methods, including thermal processes, refrigeration, and electrical techniques, highlighting their effectiveness in extending the shelf life of palm sap. The review further explores the emerging impact of nanotechnology on palm sap preservation, offering insights into the latest industry challenges, developments, and future prospects. By presenting these findings, this review aims to enhance the scientific understanding of palm sap and stimulate additional research and innovation in the field, paving the way for improved production practices and product quality. Palm ; Biotechnology ; Extraction (chemistry) ; Biochemical engineering ; Environmental science ; Fermentation ; Agricultural engineering ; Chemistry ; Engineering ; Biology ; Food science Coconut Research and Applications ; Plant-Derived Bioactive Compounds ; Essential Oils and Antimicrobial Activity
Authors: T. Morino | Machiko Ode | Shoichi Hirosawa
Source: Physical review. E (article, 2024-06-10)
Field: Materials Science — Materials Chemistry

Direct CALPHAD coupling phase-field model: Closed-form expression for interface composition satisfying equal diffusion potential condition We formulated two phase-field models to compute interfacial compositions, characterized by their high computational accuracy and efficiency. The inaugural model utilizes convergence calculations to fulfill the equal diffusion potential condition, while the subsequent model obviates the need for such calculations. Despite this, its computational outcomes strongly agree with those of the preceding model. Notably, in these models, the alteration in composition attributable to interfacial curvatures aligns with the Gibbs-Thomson effect within the equilibrium system. In this study, the solidification processes of Ni-Al-Cr and Ag-Cu-Sn alloys serve as case studies to underscore the computational precision and swiftness of the proposed models. CALPHAD ; Diffusion ; Coupling (piping) ; Composition (language) ; Phase (matter) ; Convergence (economics) ; Interface (matter) ; Field (mathematics) ; Statistical physics ; Expression (computer science) ; Phase diagram ; Thermodynamics ; Materials science ; Chemistry ; Physics ; Computer science ; Mathematics ; Metallurgy ; Surface tension ; Pure mathematics ; Quantum mechanics Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties ; High Temperature Alloys and Creep
Authors: Ki‐Hoon Son | Sehoon Oh | Junho Lee | Sobin Yun | Y. J. Shin | Shaohua Yan | Chaun Jang | Hong‐Sub Lee | Hechang Lei | Se Young Park | Hyejin Ryu
Source: Nano Convergence (article, 2024-12-12)
Field: Materials Science — Materials Chemistry

Persistent ferromagnetic ground state in pristine and Ni-doped Fe3GaTe2 flakes Room-temperature magnetism and its stability upon miniaturization are essential characteristics required for materials for spintronic devices and information storage. Among various candidates, Fe<sub>3</sub>GaTe<sub>2</sub> stands out due to its high Curie temperature and strong perpendicular magnetic anisotropy (PMA), recently gaining large attention as one of the promising candidate materials for spintronics applications. In this study, we measured the thickness-dependent ferromagnetic properties of Fe<sub>3</sub>GaTe<sub>2</sub> and (Fe<sub>1 - x</sub>Ni<sub>x</sub>)<sub>3</sub>GaTe<sub>2</sub> (with x = 0.1) flakes. We observed that both pristine and Ni-doped Fe<sub>3</sub>GaTe<sub>2</sub> exhibit persistent ferromagnetism, with only a minor decrease in T<sub>C</sub> as the thickness is reduced to a few tens of nanometers. This capacity to retain robust ferromagnetic properties at reduced dimensions is highly advantageous for thin-film applications, which is crucial for the scaling of spintronic devices. Understanding and controlling thickness-dependent magnetic properties is fundamental to harnessing the full potential of Fe<sub>3</sub>GaTe<sub>2</sub> in van der Waals magnetic heterostructures and advanced spintronic technologies. Ferromagnetism ; Ground state ; Doping ; Materials science ; Condensed matter physics ; Physics ; Atomic physics 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films ; Iron-based superconductors research
Authors: Quentin Dollé | Daniel Weisz-Patrault
Source: Computational Materials Science (article, 2024-06-04)
Field: Materials Science — Materials Chemistry

Very fast simulation of growth competition between columnar dendritic grains during melt pool solidification Equiaxed crystals ; Cellular automaton ; Dendrite (mathematics) ; Degrees of freedom (physics and chemistry) ; Voronoi diagram ; Microstructure ; Materials science ; Computation ; Work (physics) ; Grain growth ; Tessellation (computer graphics) ; Convergence (economics) ; Competition (biology) ; Mechanics ; Statistical physics ; Biological system ; Computer science ; Geometry ; Mathematics ; Algorithm ; Physics ; Composite material ; Thermodynamics Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties ; Metallurgy and Material Forming