jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yizhe Huang | Xueliang Duan | Jun Wang | Zhifu Zhang | Yuanyuan Shi | Bin Huang | Enyong Xu
Source: PLoS ONE (article, 2024-05-14)
Field: Materials Science — Materials Chemistry

Interface stress transfer model and modulus parameter equivalence method for composite materials embedded with tensile pre-strain shape memory alloy fibers The constitutive model and modulus parameter equivalence of shape memory alloy composites (SMAC) serve as the foundation for the structural dynamic modeling of composite materials, which has a direct impact on the dynamic characteristics and modeling accuracy of SMAC. This article proposes a homogenization method for SMA composites considering interfacial phases, models the interface stress transfer of three-phase cylinders physically, and derives the axial and shear stresses of SMA fiber phase, interfacial phase, and matrix phase mathematically. The homogenization method and stress expression were then used to determine the macroscopic effective modulus of SMAC as well as the stress characteristics of the fiber phase and interface phase of SMA. The findings demonstrate the significance of volume fraction and tensile pre-strain in stress transfer between the fiber phase and interface phase at high temperatures. The maximum axial stress in the fiber phase is 705.05 MPa when the SMA is fully austenitic and the pre-strain increases to 5%. At 10% volume fraction of SMA, the fiber phase's maximum axial stress can reach 1000 MPa. Ultimately, an experimental verification of the theoretical calculation method's accuracy for the effective modulus of SMAC lays the groundwork for the dynamic modeling of SMAC structures. Materials science ; Composite material ; Volume fraction ; Shape-memory alloy ; Modulus ; SMA* ; Homogenization (climate) ; Micromechanics ; Stress (linguistics) ; Composite number ; Ultimate tensile strength ; Computer science Shape Memory Alloy Transformations ; Innovations in Concrete and Construction Materials ; Innovative concrete reinforcement materials
Authors: Faris M. AL‐Oqla | Hossam Faris | Maria Habib | Pedro Á. Castillo
Source: arXiv (Cornell University) (preprint, 2024-02-20)
Field: Materials Science — Polymers and Plastics

Evolving Genetic Programming Tree Models for Predicting the Mechanical Properties of Green Fibers for Better Biocomposite Materials Advanced modern technology and industrial sustainability theme have contributed implementing composite materials for various industrial applications. Green composites are among the desired alternatives for the green products. However, to properly control the performance of the green composites, predicting their constituents properties are of paramount importance. This work presents an innovative evolving genetic programming tree models for predicting the mechanical properties of natural fibers based upon several inherent chemical and physical properties. Cellulose, hemicellulose, lignin and moisture contents as well as the Microfibrillar angle of various natural fibers were considered to establish the prediction models. A one-hold-out methodology was applied for training/testing phases. Robust models were developed to predict the tensile strength, Young's modulus, and the elongation at break properties of the natural fibers. It was revealed that Microfibrillar angle was dominant and capable of determining the ultimate tensile strength of the natural fibers by 44.7% comparable to other considered properties, while the impact of cellulose content in the model was only 35.6%. This in order would facilitate utilizing artificial intelligence in predicting the overall mechanical properties of natural fibers without experimental efforts and cost to enhance developing better green composite materials for various industrial applications. Biocomposite ; Ultimate tensile strength ; Natural fiber ; Materials science ; Composite material ; Genetic programming ; Hemicellulose ; Fiber ; Composite number ; Lignin ; Cellulose ; Computer science ; Process engineering ; Biochemical engineering ; Engineering ; Artificial intelligence Natural Fiber Reinforced Composites ; Electrospun Nanofibers in Biomedical Applications ; Textile materials and evaluations
Authors: Alok Sinha
Source: Bharati International Journal of Multidisciplinary Research and Development (BIJMRD). (article, 2024-12-10)
Field: Chemistry — Inorganic Chemistry

Role of Nuclear Chemistry in Environmental Applications Nuclear chemistry has emerged as a vital field in addressing contemporary environmental challenges.Its applications range from radioactive tracers for monitoring pollution and groundwater movement to the treatment of hazardous waste through radiation-induced processes.This review highlights the role of nuclear chemistry in environmental remediation, focusing on techniques such as radiolysis for degrading pollutants, isotopic methods for tracking environmental changes, and the safe disposal of nuclear waste.Advances in green nuclear technologies, including the use of radiation to reduce greenhouse gas emissions and clean contaminated ecosystems, are also discussed.By leveraging the principles of nuclear chemistry, innovative solutions for sustainable environmental management and mitigation of human impact can be achieved. Environmental chemistry ; Chemistry ; Environmental science Radioactive element chemistry and processing
Authors: Caleb Cranney | Jesse G. Meyer
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-07-11)
Field: Chemistry — Spectroscopy

Multi-dataset Integration and Residual Connections Improve Proteome Prediction from Transcriptomes using Deep Learning Proteomes are well known to poorly correlate with transcriptomes measured from the same sample. While connected, the complex processes that impact the relationships between transcript and protein quantities remains an open research topic. Many studies have attempted to predict proteomes from transcriptomes with limited success. Here we use publicly available data from the Clinical Proteomics Tumor Analysis Consortium to show that deep learning models designed by neural architecture search (NAS) achieve improved prediction accuracy of proteome quantities from transcriptomics. We find that this benefit is largely due to including a residual connection in the architecture that allows input information to be remembered near the end of the network. Finally, we explore which groups of transcripts are functionally important for protein prediction using model interpretation with SHAP. Proteome ; Transcriptome ; Residual ; Computer science ; Proteomics ; Artificial intelligence ; Machine learning ; Computational biology ; Data science ; Biology ; Bioinformatics ; Gene ; Gene expression ; Genetics Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; Bioinformatics and Genomic Networks
Authors: Zhiqiu Yao | Wenna Zou | Xinxin Zhang | Pei Nie | Haimiao Lv | Weimin Wang | Xuhong Zhao | Ying Yang | Liguo Yang
Source: Journal of the Science of Food and Agriculture (article, 2024-03-19)
Field: Chemistry — Analytical Chemistry

Integrating mid‐infrared spectroscopy, machine learning, and graphical bias correction for fatty acid prediction in water buffalo milk This study successfully developed a predictive model for fatty acids in Mediterranean buffalo milk based on FT-MIR spectroscopy. Additionally, a correction was applied to the existing measurement device, FT6000, enabling more accurate measurements of fatty acids in buffalo milk. The findings have practical implications for the food industry, offering a faster and more reliable approach to assess and monitor fatty acid composition in buffalo milk, potentially influencing product development and quality control processes. © 2024 Society of Chemical Industry. Fatty acid ; Polyunsaturated fatty acid ; Food science ; Bovine milk ; Chemistry ; Biochemistry Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Carola Gregor | Florian Grimm | Jasmin Rehman | Christian A. Wurm | Alexander Egner
Source: Cells (article, 2024-04-15)
Field: Chemistry — Organic Chemistry

Click Chemistry with Cell-Permeable Fluorophores Expands the Choice of Bioorthogonal Markers for Two-Color Live-Cell STED Nanoscopy STED nanoscopy allows for the direct observation of dynamic processes in living cells and tissues with diffraction-unlimited resolution. Although fluorescent proteins can be used for STED imaging, these labels are often outperformed in photostability by organic fluorescent dyes. This feature is especially crucial for time-lapse imaging. Unlike fluorescent proteins, organic fluorophores cannot be genetically fused to a target protein but require different labeling strategies. To achieve simultaneous imaging of more than one protein in the interior of the cell with organic fluorophores, bioorthogonal labeling techniques and cell-permeable dyes are needed. In addition, the fluorophores should preferentially emit in the red spectral range to reduce the potential phototoxic effects that can be induced by the STED light, which further restricts the choice of suitable markers. In this work, we selected five different cell-permeable organic dyes that fulfill all of the above requirements and applied them for SPIEDAC click labeling inside living cells. By combining click-chemistry-based protein labeling with other orthogonal and highly specific labeling methods, we demonstrate two-color STED imaging of different target structures in living specimens using different dye pairs. The excellent photostability of the dyes enables STED imaging for up to 60 frames, allowing the observation of dynamic processes in living cells over extended time periods at super-resolution. STED microscopy ; Bioorthogonal chemistry ; Fluorescence ; Live cell imaging ; Chemistry ; Nile red ; Biophysics ; Fluorescent protein ; Superresolution ; Click chemistry ; Nanotechnology ; Green fluorescent protein ; Cell ; Materials science ; Computer science ; Artificial intelligence ; Optics ; Biochemistry ; Biology ; Combinatorial chemistry Click Chemistry and Applications ; Advanced biosensing and bioanalysis techniques ; Cancer Research and Treatments
Authors: Janak Tiwari | Som Shrestha | Tianli Feng
Source: Journal of Building Engineering (article, 2024-05-23)
Field: Chemistry — Spectroscopy

Computational design of isotropic and anisotropic ultralow thermal conductivity polymer foams Thermal conductivity ; Isotropy ; Anisotropy ; Materials science ; Composite material ; Polymer ; Conductivity ; Thermal ; Optics ; Thermodynamics ; Physics ; Chemistry ; Physical chemistry Aerogels and thermal insulation ; Polymer Foaming and Composites ; Radiative Heat Transfer Studies
Authors: Marek Florkowski
Source: Energies (article, 2024-10-15)
Field: Materials Science — Materials Chemistry

Comparison of Effects of Partial Discharge Echo in Various High-Voltage Insulation Systems In this article, an extension of a conventional partial discharge (PD) approach called partial discharge echo (PDE), which is applied to different classes of electrical insulation systems of power devices, is presented. Currently, high-voltage (HV) electrical insulation is attributed not only to transmission and distribution grids but also to the industrial environment and emerging segments such as transportation electrification, i.e., electric vehicles, more-electric aircraft, and propulsion in maritime vehicles. This novel PDE methodology extends the conventional and established PD-based assessment, which is perceived to be one of the crucial indicators of HV electrical insulation integrity. PD echo may provide additional insight into the surface conditions and charge transport phenomena in a non-invasive way. It offers new diagnostic attributes that expand the evaluation of insulation conditions that are not possible by conventional PD measurements. The effects of partial discharge echo in various segments of insulation systems (such as cross-linked polyethylene [XLPE] power cable sections that contain defects and a twisted-pair helical coil that represents motor-winding insulation) are shown in this paper. The aim is to demonstrate the echo response on representative electrical insulating materials; for example, polyethylene, insulating paper, and Nomex. Comparisons of the PD echo decay times among various insulation systems are depicted, reflecting dielectric surface phenomena. The presented approach offers extended quantitative assessments of the conditions of HV electrical insulation, including its detection, measurement methodology, and interpretation. Partial discharge ; Echo (communications protocol) ; Electrical engineering ; Voltage ; Acoustics ; Materials science ; Environmental science ; Nuclear engineering ; Engineering ; Computer science ; Physics ; Computer security High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Angeline W. Maina | Erich-Christian Oerke
Source: Plant Methods (article, 2024-06-08)
Field: Chemistry — Analytical Chemistry

Hyperspectral imaging for quantifying Magnaporthe oryzae sporulation on rice genotypes HSI provided a precise and objective method of assessing M. oryzae conidia production on infected rice plants, revealing differences that could not be detected visually. Hyperspectral imaging ; Magnaporthe ; Biology ; Genotype ; Spore ; Magnaporthe grisea ; Agronomy ; Botany ; Oryza sativa ; Remote sensing ; Gene ; Genetics ; Geology Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Identification and Quantification in Food
Authors: Nemanja Koljančić | Larissa Oliveira Onça | Liudmyla Khvalbota | Olga Vyviurska | A. A. Gomes | Ivan Špánik
Source: Current Research in Food Science (article, 2024-01-01)
Field: Chemistry — Spectroscopy

Region of interest selection in heterogeneous digital image: Wine age prediction by comprehensive two-dimensional gas chromatography This study integrates genetic algorithm (GA) with partial least squares regression (PLSR) and various variable selection methods to identify impactful regions of interest (ROI) in heterogeneous 2D chromatogram images for predicting wine age. As wine quality and aroma evolve over time, transitioning from youthful fruitiness to mature, complex flavors, which leads to alterations in the composition of essential aroma-contributing compounds. Chromatograms are segmented into subimages, and the GA-PLSR algorithm optimizes combinations based on grayscale, red-green-blue (RGB), and hue-saturation-value (HSV) histograms. The selected subimage histograms are further refined through interval selection, highlighting the compounds with the most significant influence on wine aging. Experimental validation involving 38 wine samples demonstrates the effectiveness of this approach. Cross-validation reduces the PLS model error from 2.8 to 2.4 years within a 10 × 10 subset, and during prediction, the error decreases from 2.5 to 2.3 years. The study presents a novel approach utilizing the selection of ROI for efficient processing of 2D chromatograms focusing on predicting wine age. Wine ; Hue ; Partial least squares regression ; Artificial intelligence ; Histogram ; Grayscale ; RGB color model ; Feature selection ; Aroma ; Computer science ; Pattern recognition (psychology) ; Selection (genetic algorithm) ; Mathematics ; Machine learning ; Pixel ; Image (mathematics) ; Food science ; Chemistry Analytical Chemistry and Chromatography ; Fermentation and Sensory Analysis ; Spectroscopy and Chemometric Analyses
Authors: Nilutpal Phukon | Ksh. Robert Singh | Takhellambam Sonamani Singh | Subir Datta | Subhasish Deb | Usham Robinchandra Singh | Ghaneshwori Thingbaijam
Source: Current Agriculture Research Journal (article, 2024-09-10)
Field: Chemistry — Analytical Chemistry

Quality Assessment of Rice Using Convolution Neural Networks and Other Machine Learning Techniques- A Comparative Study Rice holds its position as the most widely cultivated crop worldwide, its demand steadily rising alongside population growth. The precise identification of grains, especially wheat and rice, holds significant importance as their ultimate utilization depends on the quality of grains prior to processing. Traditionally, grain identification tasks have been predominantly manual, relying on experienced grain inspectors and consuming considerable time. However, this manual classification process is susceptible to variations influenced by individual perception, given the subjective nature of human image interpretation. Consequently, there is an urgent need for an automated recognition system capable of accurately identifying grains under diverse environmental conditions, necessitating the application of digital image processing techniques. In this study, we focus on grading five distinct varieties of rice based on their quality, employing a range of convolution neural networks (CNN) namely, Efficientnetb0, Googlenet, MobileNetV2, Resnet50, Resnet101, and ShuffleNet. The performance of CNN towards identification and grading of rice grain is also compared to that of other parametric and Non-parametric classifiers namely, Linear Discriminant Analysis (LDA), K-Nearest Neighbor (K-NN), Naive Bayes (NB), and Back Propagation Neural Network (BPNN) using Gray Level Co-occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM) based texture features. The image dataset comprises five grades of rice, each containing 100 images, resulting in a comprehensive collection of 500 samples for analysis. It is observed that, Convolution neural networks can grade five different qualities of rice with highest accuracy of 64.4% in case of GoogleNet. Results show that, rice grading using texture features performs better with highest accuracy of 99.2% (using GLCM) and 93.4% (using GLRLM). Plant science ; Quality (philosophy) ; Artificial neural network ; Convolution (computer science) ; Computer science ; Machine learning ; Agricultural engineering ; Artificial intelligence ; Botany ; Biology ; Engineering ; Physics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Hong Liu | Honggao Liu | Jieqing Li | Yuanzhong Wang
Source: Phytochemical Analysis (article, 2024-06-27)
Field: Chemistry — Analytical Chemistry

Identification of geographical origins of <i>Gastrodia elata</i> Blume based on multisource data fusion Sequential and orthogonalized data fusion approaches and ResNet models are powerful solutions for identifying the geographic origin of G. elata BI. Gastrodia elata ; Chemistry ; Identification (biology) ; Traditional medicine ; Botany Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Agriculture, Soil, Plant Science
Authors: Mansour Rasekh | Sina Ardabili | Amir Mosavi
Source: Smart Agricultural Technology (article, 2024-08-27)
Field: Chemistry — Analytical Chemistry

Environmental assessment of soluble solids contents and pH of orange using hyperspectral method and machine learning Progress in non-destructive methods to detect the characteristics of fruits is a new and attractive process for researchers and specialists in this field. On the other hand, these researchers move toward identifying their impacts on their surroundings in line with diagnostic efficiency. One of these essential impacts is the environmental impact of the non-destructive detection process of fruits. Navel oranges are one of the most popular and widely consumed fruits, whose maturity indices such as soluble solids contents (SSC) values and acidity are considered as parameters in determining the quality of this product. This study used the hyperspectral method in the vis-NIR range to evaluate and measure navel oranges' SSC and acidity values. In the following, by applying the life cycle assessment method, the environmental impacts of measuring and evaluating these two parameters of the characteristics of navel oranges were investigated. The Impact2002+ method was used to evaluate the impact of the life cycle list. Based on the findings, the environmental impacts of SSC measurement are about 40, 42, 20, and 18 % higher than those of the environmental impacts of pH measurement from the point of view of endpoint impacts for Human Health, Ecosystem quality, climate change, and resources, respectively. The random forest modeling results showed a suitable and acceptable correlation and relationship (over 90 %) between the wavelengths selected from the feature selection stage and environmental impacts. Hyperspectral imaging ; Life-cycle assessment ; Environmental science ; Environmental impact assessment ; Agricultural engineering ; Random forest ; Environmental quality ; Computer science ; Remote sensing ; Machine learning ; Artificial intelligence ; Engineering ; Ecology ; Geography ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Luke Dicks | David Graff | Kirk E. Jordan | Connor W. Coley | Edward O. Pyzer‐Knapp
Source: Molecular Systems Design & Engineering (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

A physics-inspired approach to the understanding of molecular representations and models By drawing on similarities between energy landscapes and model response surfaces we gain new insight into model performance, even in the absence of data. Computer science ; Cognitive science ; Data science ; Physics ; Theoretical physics ; Psychology ; Epistemology ; Philosophy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Jade K. Macdonald | Anand S. Mehta | Richard R. Drake | Peggi M. Angel
Source: FEBS Letters (review, 2024-03-01)
Field: Chemistry — Spectroscopy

Molecular analysis of the extracellular microenvironment: from form to function The extracellular matrix (ECM) proteome represents an important component of the tissue microenvironment that controls chemical flux and induces cell signaling through encoded structure. The analysis of the ECM represents an analytical challenge through high levels of post-translational modifications, protease-resistant structures, and crosslinked, insoluble proteins. This review provides a comprehensive overview of the analytical challenges involved in addressing the complexities of spatially profiling the extracellular matrix proteome. A synopsis of the process of synthesizing the ECM structure, detailing inherent chemical complexity, is included to present the scope of the analytical challenge. Current chromatographic and spatial techniques addressing these challenges are detailed. Capabilities for multimodal multiplexing with cellular populations are discussed with a perspective on developing a holistic view of disease processes that includes both the cellular and extracellular microenvironment. Extracellular matrix ; Proteome ; Computational biology ; Extracellular ; Function (biology) ; Cell biology ; Chemistry ; Biology ; Nanotechnology ; Computer science ; Biochemistry ; Materials science Advanced Proteomics Techniques and Applications ; Cell Adhesion Molecules Research ; Glycosylation and Glycoproteins Research