jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tony Chuquizuta | Segundo G. Chávez | Alberto Claudio Miano | Marta Castro‐Giráldez | P. Fito | Hubert Arteaga | Wilson Castro
Source: Measurement Food (article, 2024-03-30)
Field: Chemistry — Analytical Chemistry

Determination of hydration kinetic of pinto beans: A hyperspectral images application Hydration is a typical operation applied to legumes before cooking, reducing time and the associated energy cost. To monitor the process, mass balance method is the most used methodology, despite this method is destructive, repetitive, and time-consuming. For that reason. hyperspectral techniques are presented as an alternative for assessing the hydration process since it is a noninvasive method. Therefore, the objective of this work was to evaluate the technique of hyperspectral imaging for studying the hydration kinetics of pinto beans. For this purpose, a sample of pinto beans was hydrated in distilled water, determining moisture content during the process and taking hyperspectral images by reflectance mode, in the range 400 to 800 nm until constant mass. The moisture content was modelled using Peleg and a sigmoidal model. Next, the images were pre-treated and the median spectral profile for each bean was obtained. Then, a regression model was fitted, using the wavelength that maximized the coefficient of determination (R2) and minimized the root mean square error (RMSE). The results show that Peleg model fit experimental data with R2 in the range of 0.974 to 0.989 while sigmoidal model of 0.997 to 0.999. On other hand, mean spectral profiles at 632 nm and sigmoidal model give the higher metrics 0.997 and 38.3 for R2 and RMSE respectively. The results showed that hyperspectral imaging in reflectance mode is a tool capable of measuring the hydration level of beans with higher performance at 632 nm, with a determination coefficient R2 higher than 0.98. Hyperspectral imaging ; Remote sensing ; Kinetic energy ; Environmental science ; Geology ; Physics Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Food Chemistry and Fat Analysis
Authors: Jakob Assländer
Source: PubMed (preprint, 2024-09-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Magnetization transfer explains most of the $T_1$ variability in the MRI literature The results suggest that a mono-exponential model does not adequately describe longitudinal relaxation in biological tissue. Therefore, <i>T</i> <sub>1</sub> in biological tissue should be considered only a <i>semi</i>-<i>quantitative</i> metric that is inherently contingent upon the imaging methodology; and comparisons between different <i>T</i> <sub>1</sub>-mapping methods and the use of simplistic spin systems-such as doped-water phantoms-for validation should be viewed with caution. Magnetization ; Transfer (computing) ; Magnetization transfer ; Physics ; Computer science ; Medicine ; Magnetic field ; Quantum mechanics Magnetic Properties of Alloys
Authors: Smita Borkar
Source:  (article, 2024-06-29)
Field: Materials Science — Biomaterials

Fabrication of Novel Nanofiber Patch Containing BoerhaviaDiffusa Extract for Wound Healing Application Using Response Surface Methodology In this study, we successfully prepared a Boerhavia diffusa extract-loaded nanofiber wound dressing using the electrospinning method, optimizing the process through a 3x2 factorial design. The optimized nanofiber, denoted as BRNF7, exhibited excellent characteristics, including an entrapment efficiency of 85.12% and a drug loading of 9%. Furthermore, the swelling index was measured at 86.6, demonstrating its potential as an effective wound dressing material. Characterization of the BRNF7 nanofiber revealed promising results, as FTIR spectra exhibited all the expected peaks, while XRD analysis indicated a highly crystalline structure with intense peaks. DSC thermograms displayed a characteristic peak at 133.42°C, corresponding to Boerhavia diffusa extract. Thermal analysis, as indicated by TGA thermograms, showed that the nanofiber gradually decomposed at a higher temperature range, signifying its thermal stability. Wound dressing various parameter included as the film thickness ranged from 0.069 to 0.074 mm, and all films exhibited excellent folding endurance, ensuring flexibility. Moisture interaction studies demonstrated good physical stability of the film under high humidity conditions and maintained integrity under dry conditions. The films exhibited a moisture absorption range of 3.8% to 4.6% and a moisture loss range of 3.2% to 4.7%. Tensile strength values ranged from 2.14 to 2.85 kg/cm², and the percentage of drug content was uniform between 90.15% and 92.11%.Furthermore, our Boerhavia diffusa extract-loaded Nanofibrous dressing displayed notable antibacterial activity against various pathogens and exhibited promising wound-healing effects, making it a potential candidate for advanced wound care. This study provides a comprehensive assessment of the BRNF7 nanofiber, emphasizing its potential in biomedical applications. Nanofiber ; Fabrication ; Materials science ; Wound healing ; Nanotechnology ; Biomedical engineering ; Composite material ; Engineering ; Medicine ; Surgery Silk-based biomaterials and applications ; Electrospun Nanofibers in Biomedical Applications
Authors: Ziyan Li
Source: Academic Journal of Science and Technology (article, 2024-07-12)
Field: Chemistry — Analytical Chemistry

Discriminant Analysis of Soil Type by Laser Induced Breakdown Spectroscopy Combined with GA-BP Neural Network Laser induced breakdown spectroscopy (LIBS) and genetic algorithm (GA) were used to optimize the neural network for classification and identification of six kinds of crop soils. Through laser-induced breakdown of soil surface to produce plasma spectrum, 100 sets of spectral data of each soil were collected by spectrometer, and the main element characteristic spectral lines were accurately calibrated with NIST atomic spectrum database as reference. In the experiment, six characteristic spectral lines are selected for analysis. After the dimension of spectral data is reduced by principal component analysis (PCA), the recognition efficiency of BP neural network is greatly improved, and the average recognition accuracy is as high as 99.5%. The study of LIBS technology and BP neural network can provide reference for finding suitable soil for crop planting. Laser-induced breakdown spectroscopy ; Linear discriminant analysis ; Artificial neural network ; Spectroscopy ; Laser ; Type (biology) ; Pattern recognition (psychology) ; Materials science ; Artificial intelligence ; Biological system ; Analytical Chemistry (journal) ; Computer science ; Chemistry ; Biology ; Physics ; Optics ; Chromatography ; Astronomy ; Ecology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis
Authors: B. Zheng | Siyu Zhu | Xingping Zhou | Tong Liu
Source: arXiv (Cornell University) (preprint, 2024-10-19)
Field: Materials Science — Materials Chemistry

Classifying extended, localized and critical states in quasiperiodic lattices via unsupervised learning Classification of quantum phases is one of the most important areas of research in condensed matter physics. In this work, we obtain the phase diagram of one-dimensional quasiperiodic models via unsupervised learning. Firstly, we choose two advanced unsupervised learning algorithms, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Ordering Points To Identify the Clustering Structure (OPTICS), to explore the distinct phases of Aubry-André-Harper model and quasiperiodic p-wave model. The unsupervised learning results match well with traditional numerical diagonalization. Finally, we compare the similarity of different algorithms and find that the highest similarity between the results of unsupervised learning algorithms and those of traditional algorithms has exceeded 98\%. Our work sheds light on applications of unsupervised learning for phase classification. Quasiperiodic function ; Statistical physics ; Physics ; Theoretical physics ; Condensed matter physics Quasicrystal Structures and Properties
Authors: Rufo I. Marasigan | Mon Arjay F. Malbog | Enrique D. Festijo | Dranreb Earl Juanico
Source: E3S Web of Conferences (article, 2024-01-01)
Field: Chemistry — Inorganic Chemistry

CocoSense: Coconut Tree Detection and Localization using YOLOv7 Coconut farming in the Philippines often needs help with challenges in efficient tree monitoring, directly affecting its productivity and sustainability. Although prevalent, traditional methodologies, such as field surveys, exhibit labor intensiveness and potential data inaccuracy constraints. This study sought to leverage the capabilities of the YOLOv7 object detection algorithm to enhance coconut tree monitoring. Our objectives centered on (1) precise detection of coconut trees using orthophotos, (2) their enumeration, and (3) generating accurate coordinates for each tree. The DJI Phantom 4 RTK unmanned aerial vehicle (UAV) was used to capture high-resolution images of the study area in Tiaong, Quezon. Post-acquisition, these images underwent processing and annotation to generate datasets for training the YOLOv7 model. The algorithm's output shows a remarkable 98% accuracy rate in tree detection, with an average localization accuracy of 86.30%. The results demonstrate the potential of YOLOv7 in accurately detecting and localizing coconut trees under diverse environmental conditions. Tree (set theory) ; Computer science ; Mathematics ; Combinatorics Coconut Research and Applications ; Date Palm Research Studies ; Oil Palm Production and Sustainability
Authors: Maksym Grishyn | Kostiantyn Beglov
Source: Journal of Automation Mobile Robotics & Intelligent Systems (article, 2024-03-04)
Field: Materials Science — Materials Chemistry

The Coal Quality CICS that Increases the Wear Resistance of Heat Exchanger Tubes The paper discusses the threat of decommissioning of the thermal power plant (TPP) heat exchanger tubes because of erosion and develops a computer-integrated control system (COAL QUALITY CICS THAT INCREASES THE WEAR RESISTANCE OF HEAT EXCHANGER TUBES) for the process of distribution of steam coal flows with different indicators of abrasive materials content, which is based on fuzzy logic. The problem of rapid decommissioning of TPP heat exchanger, particularly abrasive damage to furnace screen tubes, economizer, superheater, etc. This may indicate a discrepancy between the expected fuel ash content and the actual one, as well as a high content of abrasive impurities in steam coal. The work aims to develop a CICS of the wear resistance of the heat exchange surface of a steam boiler of a coal-fired power plant by measuring and fuzzy control of the content of abrasive impurities in steam coal. The problems of damage to the equipment of the TPP boiler are investigated and a system for controlling the wear resistance of the surface by automatic fuzzy control of the quality of coal is developed. In order to investigate the effectiveness of the proposed fuzzy controller, the results were investigated in a specific example, in particular, during coal preparation and combustion in the furnace of a thermal power plant. The model results confirm the feasibility of the fuzzy control method for the system with different coal quality parameters. Coal ; Heat exchanger ; Wear resistance ; Resistance (ecology) ; Materials science ; Quality (philosophy) ; Metallurgy ; Waste management ; Mechanical engineering ; Engineering ; Physics ; Biology ; Ecology Metal Alloys Wear and Properties ; Mechanical and Thermal Properties Analysis ; Coal Combustion and Slurry Processing
Authors: Amy L. Anderson | J. Piekarewicz
Source: arXiv (Cornell University) (preprint, 2024-06-03)
Field: Chemistry — Organic Chemistry

A universal reduced basis for the calibration of covariant energy density functionals The reduced basis method is used to construct a "universal" basis of Dirac orbitals that may be applicable throughout the nuclear chart to calibrate covariant energy density functionals. Relative to our earlier work using the non-relativistic Schrödinger equation, the Dirac equation adds an extra layer of complexity due to the existence of negative energy states. However, once this problem is mitigated, the resulting reduced basis is able to accurately and efficiently reproduce the high-fidelity model at a fraction of the computational cost. We are confident that the resulting reduced basis will serve as a foundational element in developing rapid and accurate emulators. In turn, these emulators will play a critical role in the Bayesian optimization of covariant energy density functionals. Covariant transformation ; Basis (linear algebra) ; Calibration ; Energy (signal processing) ; Mathematics ; Energy density ; Applied mathematics ; Physics ; Theoretical physics ; Mathematical physics ; Statistics ; Geometry Chemical Thermodynamics and Molecular Structure
Authors: Babatunde Victor Omidiji | A.A. Daniyan | Adeniyi Taiwo | K. M. Oluwasegun
Source: Journal of Minerals and Materials Characterization and Engineering (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Detection of Partial and Extended Blockages: A Case Study of Edible Oil Pipeline System This work focuses on the development and implementation of a simulation-based approach for the detection of partial and extended blockages within an edible oil pipeline system. Blockages, whether partial or extended, pose a significant operational and safety risks. This study employs computational fluid dynamics (CFD) simulations to model the flow behaviour of edible oil through pipeline under varying conditions. It leverages advanced computational fluid dynamics (CFD) simulations to analyze pressure, velocity, and temperature variations along the pipeline. By simulating scenarios with different blockage characteristics, there is establishment of distinctive patterns indicative of partial and extended obstructions. Through extensive analysis of simulation data, sensing element, and monitoring system, processing signal input and response output, the system can accurately pinpoint the location and severity of blockages, providing crucial insights for timely intervention. The detection system represents a significant advancement in pipeline monitoring technology, offering a proactive and accurate approach to identify blockages and mitigate potential risks and ensure the uninterrupted flow of edible oil, thereby enabling timely intervention and maintenance. Pipeline (software) ; Petroleum engineering ; Edible oil ; Environmental science ; Computer science ; Food science ; Chemistry ; Geology Petroleum Processing and Analysis ; Water Systems and Optimization ; Structural Integrity and Reliability Analysis
Authors: Mardem Arantes de Castro | R. Neto | Joaquim Quinteiro Uchôa | Jesimar da Silva Arantes | Orlando Gonçalves Brito | Valter Carvalho de Andrade Júnior | Jeferson Carlos de Oliveira Silva | Renato Ramos da Silva
Source: Revista de Informática Teórica e Aplicada (article, 2024-09-04)
Field: Chemistry — Analytical Chemistry

Supervised Learning Algorithms Evaluation on Sweet Potato Data for Production Indices Considering Human Consumption The sweet potato holds significant importance as a root crop cultivated globally, serving purposes in both human and animal nutrition, ethanol fuel production, and ornamental cultivation. Several genetic experiments have been undertaken to identify superior root varieties. Within this study, a refined statistical methodology has been devised to mitigate environmental factors and promote equitable comparisons among diverse varieties. Through the integration of supplementary environmental data into the experimental framework, a deeper understanding of each variety can be attained. This research signifies an inaugural endeavor towards scrutinizing post-harvest sweet potato data, aiming to unveil novel insights. Production (economics) ; Variety (cybernetics) ; Crop production ; Agricultural engineering ; Environmental research ; Machine learning ; Consumption (sociology) ; Biotechnology ; Computer science ; Agriculture ; Engineering ; Artificial intelligence ; Biology ; Geography ; Environmental planning ; Economics ; Social science ; Ecology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Sudarsun Kannan | Ulrich Kremer
Source: ACM SIGEnergy Energy Informatics Review (article, 2024-07-01)
Field: Materials Science — Materials Chemistry

Towards Application Centric Carbon Emission Management Carbon emissions are due to application execution on a target system (operational emissions) and the production, transportation, and disposal of the system itself (embodied emissions). This paper investigates the impacts of different resource configurations in terms of available DRAM memory on the overall carbon emission of individual application executions. We first propose an application-centric carbon footprint model that considers DRAM and CPU. We then study the model using a widely-used key-value store (RocksDB) and Graph500 applications. The results for RocksDB indicate that the minimal emission configuration is application dependent and can lead to significant emission reductions compared to application-oblivious configurations that use higher DRAM capacity without improving performance. For some applications, small performance degradation may lead to substantial additional emission reductions. Carbon fibers ; Business ; Materials science Catalytic Processes in Materials Science ; Vehicle emissions and performance ; Green IT and Sustainability
Authors: Keisuke Tajiri | Reiichi Murakami | Shunsuke KOBAYASHI | Ryuichi Tarumi | Tomohiko G. Sano
Source: arXiv (Cornell University) (preprint, 2024-10-17)
Field: Materials Science — Polymers and Plastics

Curling morphology of knitted fabrics: Structure and Mechanics Knitted fabrics are two-dimensional-like structures formed by stitching one-dimensional yarn into three-dimensional curves. Plain stitch or stockinette stitch, one of the most fundamental knitting stitches, consists of periodic lattices of bent yarns, where three-dimensional (3D) curling behavior naturally emerges at the edges. The elasticity and geometry of knitted fabrics have been studied in previous studies, primarily based on 2D modeling. Still, the relation between 3D geometry and the mechanics of knitted fabrics has not been clarified so far. The curling behavior of knits is intricately related to the forces and moments acting on the yarns, geometry of the unit knitted loops, mechanical properties, and contacts, hence requiring a 3D analysis. Here, we show that the curling of plain knits emerges through the elasticity and geometry of the knitted loops, combining desktop-scale experiments and reduced elasticity-based simulations. We find that by changing the horizontal and vertical knitting numbers, three types of curl shapes emerge: side curl and top/bottom curl shapes, which are curled only horizontally and vertically, and double curl shape, in which both curl shapes appear together. The fundamental mechanism of intricate shape deformation is clarified through the force and moment balance along yarn whose centerline shape is discretized through the B-spline curves where elastic stretching, bending, and contact mechanics are taken into account. We reveal that the 3D structure of the single-knitted loop plays a critical role in the curling behavior. Our results imply that the change in shape per a single knitted loop has the potential to control the 3D natural overall shape of knitted fabrics, and could be applied in predicting or designing more complex 3D shapes made of knitted fabrics. Curling ; Morphology (biology) ; Materials science ; Polymer science ; Mechanics ; Composite material ; Physics ; Geology Textile materials and evaluations
Authors: Masahiro Tanaka
Source:  (preprint, 2024-03-22)
Field: Materials Science — Materials Chemistry

Parameterizations for Gradient-based Markov Chain Monte Carlo on the Stiefel Manifold: A Comparative Study Orthogonal matrices play an important role in probability and statistics, particularly in high-dimensional statistical models. Parameterizing these models using orthogonal matrices facilitates dimension reduction and parameter identification. However, establishing the theoretical validity of statistical inference in these models from a frequentist perspective is challenging, leading to a preference for Bayesian approaches because of their ability to offer consistent uncertainty quantification. Markov chain Monte Carlo methods are commonly used for numerical approximation of posterior distributions, and sampling on the Stiefel manifold, which comprises orthogonal matrices, poses significant difficulties. While various strategies have been proposed for this purpose, gradient-based Markov chain Monte Carlo with parameterizations is the most efficient. However, a comprehensive comparison of these parameterizations is lacking in the existing literature. This study aims to address this gap by evaluating numerical efficiency of the four alternative parameterizations of orthogonal matrices under equivalent conditions. The evaluation was conducted for four problems. The results suggest that polar expansion parameterization is the most efficient, particularly for the high-dimensional and complex problems. However, all parameterizations exhibit limitations in significantly high-dimensional or difficult tasks, emphasizing the need for further advancements in sampling methods for orthogonal matrices. Stiefel manifold ; Markov chain Monte Carlo ; Frequentist inference ; Monte Carlo method ; Hybrid Monte Carlo ; Applied mathematics ; Mathematics ; Markov chain ; Statistical inference ; Bayesian inference ; Computer science ; Importance sampling ; Mathematical optimization ; Bayesian probability ; Statistics Machine Learning in Materials Science ; Statistical Methods and Inference ; Functional Brain Connectivity Studies