jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sudheer Reddy J | Vikram Kedambadi Vasu
Source: Proceedings on Engineering Sciences (article, 2024-08-29)
Field: Materials Science — General Materials Science

INFLUENCE OF CUTTING INSERTS IN MACHINING Al-Si ALLOY-BASED METAL MATRIX COMPOSITES To better comprehend the machinability studies during MMCs, the effect of cutting tool while machining MMCs, the cutting forces that induce and importantly the influence of cutting tool were carried out. Polycrystalline diamond (PCD) and uncoated tungsten carbide (K10) cutting tools were used to machine a eutectic (Al-12wt% Si alloy) and a hypereutectic (Al-17wt%Si) matrix material with Silicon Carbide (SiCP) and Aluminium Oxide (Al2O3P) in the particulate form at 0, 5, 7.5, and 10 volume percent volume fractions as MMCs. To further characterize the machinability of the composites, SEM analysis of chips created during machining and the cutting inserts was performed. Based on the results of the MMC machining operations, the Built-up Edge (BUE) development on the K10 insert has caused it to exert significantly higher cutting forces than the PCD. Machining of SiCp and Al2O3P reinforced Al-Si alloys utilizing K10 insert showed a comparatively bigger difference in cutting forces, indicating that the cutting forces exerted by PCD did not significantly alter with varied composite composition. Materials science ; Machining ; Alloy ; Composite material ; Matrix (chemical analysis) ; Metal ; Metallurgy Material Properties and Applications ; Engineering Technology and Methodologies ; Surface Treatment and Coatings
Authors: Jesús D. Castaño | Francis Beaudry
Source: Rapid Communications in Mass Spectrometry (article, 2024-11-04)
Field: Chemistry — Spectroscopy

Optimization of protein identifications through the use of different chromatographic approaches and bioinformatic pipelines Our results highlight the impact of different experimental approaches on proteome coverage. Changes in chromatographic columns, data acquisition, or bioinformatic analysis can significantly increase the number of protein identifications (>400%). Thus, this research provides a reference upon which to build a successful proteomic workflow with different considerations at every step. Context (archaeology) ; Computer science ; Identification (biology) ; Proteome ; Workflow ; Data acquisition ; Data mining ; Chromatography ; Chemistry ; Computational biology ; Database ; Biology Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Identification and Quantification in Food
Authors: Xiaotong Su | Zhifang Zhao | Min Zeng | Fei Zhao | Ziyang Li | Yu Zheng
Source: Agronomy (article, 2024-12-31)
Field: Chemistry — Analytical Chemistry

Multispectral Inversion of Starch Content in Rice Grains from Yingjiang County Based on Feature Band Selection Algorithms The starch content in rice grains is a key factor in determining their quality. An optimal starch level not only ensures grain fullness, improving storage stability, but also enhances the stickiness and viscosity of cooked rice, thereby boosting its palatability and nutritional value. However, traditional methods for monitoring starch content are expensive and lack the capability to provide rapid spatial distribution information across large areas. To address this limitation, this study focuses on mature rice grains in the Yingjiang region, leveraging multispectral data from the Sentinel-2 satellite. First and second derivative transformations were applied to the multispectral reflectance data, followed by the use of three feature selection algorithms to identify key spectral bands. BP neural networks and ELM neural network regression models were then integrated to quantitatively estimate starch content across the study area. As a result, high-precision spatial distribution maps of starch content were generated, providing a novel and efficient method for large-scale rapid monitoring. The results demonstrate that, compared to full-band data, the use of SPA feature selection significantly improved the predictive accuracy of both BP and ELM models, despite a slight increase in the models’ MSE. Similarly, CARS feature selection also contributed substantially to enhancing the accuracy of the BP and ELM models. In contrast, UVE feature selection significantly reduced the MSE of the BP model, improving predictive precision, with the model achieving an R2 of 0.8061 and an MSE of 0.3896. This study highlights that the inversion method, which combines feature selection algorithms with machine learning models, can effectively enhance the predictive accuracy of starch content estimation. Among the tested approaches, the combination of UVE feature selection and BP neural networks delivered the best performance. These findings confirm the feasibility of utilizing Sentinel-2 satellite multispectral data for the quantitative inversion of agronomic parameters across large agricultural areas, providing robust technical support for precision agriculture. Multispectral image ; Feature selection ; Inversion (geology) ; Feature (linguistics) ; Computer science ; Algorithm ; Selection (genetic algorithm) ; Content (measure theory) ; Artificial intelligence ; Remote sensing ; Pattern recognition (psychology) ; Mathematics ; Geology ; Seismology Spectroscopy and Chemometric Analyses ; Remote Sensing and Land Use ; Water Quality Monitoring and Analysis
Authors: Lyubov Vinogradova | Kristina Komarova | Alexey Lukin | Maxim Zhuravlev | Dmitry Deniskin | Anastasia Poliakova | М. В. Чудинов | Maxim Gureev | Marine Dogonadze | Tatiana Vinogradova | Elizaveta Rogacheva | Л. А. Краева | Yuri B. Porozov | Viktor Korzhikov‐Vlakh
Source: International Journal of Molecular Sciences (article, 2024-12-29)
Field: Chemistry — Organic Chemistry

You Win Some, You Lose Some: Modifying the Molecular Periphery of Nitrofuran-Tagged Diazaspirooctane Reshapes Its Antibacterial Activity Profile The use of the concept of privileged structures significantly accelerates the search for new leads and their optimization. 6-(methylsulfonyl)-8-(4-methyl-4<i>H</i>-1,2,4-triazol-3-yl)-2-(5-nitro-2-furoyl)-2,6-diazaspiro[3.4]octane <b>1</b> has been identified as a lead, with MICs of 0.0124-0.0441 μg/mL against <i>MTb</i> multiresistant strains. Several series of structural analogues have been synthesized, including variations in the periphery and simplifications of their scaffolds. All synthesized compounds were tested against the <i>MTb</i> H37Rv strain and ESKAPE panel of pathogens using serial broth dilutions. However, an attempt to optimize structure of <b>1</b> did not lead to the development of more active compounds which can work against <i>MTb</i>, but to substances with high activity against <i>S. aureus</i>. Induced-fit docking and MM-GBSA calculations determined a change in the likely biotarget from deazaflavin-dependent nitroreductase to azoreductases. The privileged nature of the scaffold was demonstrated by the detection of a different type of activity. Chemistry ; Nitroreductase ; Docking (animal) ; Octane ; Antibacterial activity ; Combinatorial chemistry ; Stereochemistry ; Biochemistry ; Bacteria ; Biology ; Enzyme ; Organic chemistry ; Medicine Click Chemistry and Applications ; Biochemical and Molecular Research ; Synthesis and Biological Evaluation
Authors: Mong Hien Thi Nguyen | Minh Nguyen | Mau Tung Nguyen
Source: EAI Endorsed Transactions on Sustainable Manufacturing and Renewable Energy (article, 2024-10-03)
Field: Materials Science — Polymers and Plastics

An approach to determining garment sizes with fuzzy logic This paper introduces a method for determining men's trousers sizes using a fuzzy logic technique. The Sugeno model is employed in a MISO fuzzy system with three inputs and one output. The process begins by choosing primary dimensions from the size chart, specifically one horizontal and one vertical dimension, followed by defining the value ranges for the membership functions. The model results, based on a size chart that includes six different dimensions. In this study, waist girth and outseam are selected as the primary dimensions, acting as input variables for the simulation model. Fuzzy logic is utilized to determine the size based on the Min-Max rule, with the IF-THEN structure effectively implementing commands within this model. The result of this process is an optimal size selection that aligns more accurately with the individual's body measurements. Moreover, the application of fuzzy logic significantly reduces the time required for size determination compared to traditional methods. This approach offers an alternative method for size selection, one that accounts for the inherent variability in body measurements, thus providing a more tailored and accurate fit for consumers. The study underscores the potential of fuzzy logic to enhance the efficiency and effectiveness of garment sizing systems, offering a promising solution to the challenges posed by standardized sizing methods. Fuzzy logic ; Computer science ; Artificial intelligence Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Color perception and design
Authors: Anita Csordás
Source: Agris on-line Papers in Economics and Informatics (article, 2024-12-27)
Field: Chemistry — Analytical Chemistry

Image-Based Solutions for Precision Food Loss Evaluation The high amount of food loss and waste significantly challenges the sustainable development. The agriculture needs rapid and fundamental transformation to enhance its efficient and sustainabile operation. However, to measure precisely the effect of the new policies and practices is also difficult. The present study analyses the applied methods’ data sources, as one of the key factors regarding the effective estimation of food loss and waste. By conducting a systematic literature review using the PRISMA approach, a lack of scientific focus was found related to the new data collection methods. Based on the selected articles reasonably slight amount joined the application of image processing to food loss estimation related purposes. The reviewed studies principally used the image-based solutions for the prevention and reduction of on-farm food loss. This recognition lighted up the application of image processing in agriculture, but only the thematic map analysis revealed the privileged status of ”plant disease detection” within the studied area. The results suggest the possibility of applying image-based data sources to quantify food loss. Even though the limitations of agricultural image processing, the application of new data sources, and methods could considerably improve the accuracy of food loss and waste quantification in addition to the operation on farm level in short term. Image (mathematics) ; Computer vision ; Computer science ; Artificial intelligence ; Mathematics Spectroscopy and Chemometric Analyses ; Food Supply Chain Traceability
Authors: Yichen Feng | Yin Hui | Hao Zhang | Lihua Wu | Haihui Dong | Jiawen Li
Source: Journal of Marine Science and Engineering (article, 2024-12-02)
Field: Chemistry — Analytical Chemistry

Independent Tri-Spectral Integration for Intelligent Ship Monitoring in Ports: Bridging Optical, Infrared, and Satellite Insights Image-based ship monitoring technology has extensive applications, and is widely used in various aspects of port management, including illegal activity surveillance, vessel identification at entry and exit points, channel and berth management, unmanned vessel control, and incident warning and emergency response. However, most current ship identification technologies rely on a single information source, reducing detection accuracy in the complex and variable marine environment. To address this issue, this paper proposes a knowledge transfer-based ship identification system integrating three modules. The system enables synchronized monitoring of visible light coastal images, satellite cloud images, and infrared spectrum images, thereby mitigating problems such as low detection accuracy and poor adaptability of image recognition. Additionally, extensive supplementary experiments were conducted to evaluate the effectiveness of the preprocessing and data augmentation modules as well as the transfer learning module. The study also discusses the limitations of current deep learning-based ship monitoring models, particularly their poor adaptability to image recognition and inability to achieve all-weather, round-the-clock monitoring. Experimental results based on three ship monitoring datasets demonstrate that the proposed system, by integrating three distinct detection conditions, outperforms other models with an F1-score of 98.74%, approximately 10% higher than most existing ship detection systems. Bridging (networking) ; Satellite ; Remote sensing ; Infrared ; Environmental science ; Computer science ; Engineering ; Aerospace engineering ; Geology ; Physics ; Optics ; Computer security Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Remote-Sensing Image Classification
Authors: Fabián A. García Daza | Antonio M. Puertas | Alejandro Cuetos | Alessandro Patti
Source: Molecular Physics (article, 2024-08-29)
Field: Materials Science — Materials Chemistry

Superdiffusion and heterogeneous dynamics in liquid crystals by microrheology F.A.G.D. and A.P. acknowledge a María Zambrano fellowship, funded by the NextGenerationEU/PRTR program of the European Union, the Plan de Recuperación, Transformación y Resiliencia, and the Ministerio de Universidades. A.C., A.M.P. and A.P. acknowledge, respectively, grants PID2021-126121NB-I00, PID2021-127836NB-I00 and PID2022-136540NB-I00 awarded by MICIU/AEI/10.13039/501100011033 and ERDF A way of making Europe. A.P. also acknowledges grant P21_00015 funded by Junta de Andalucía - Consejería de Universidad, Investigación e Innovación. Microrheology ; Dynamics (music) ; Molecular dynamics ; Chemical physics ; Statistical physics ; Physics ; Materials science ; Chemistry ; Thermodynamics ; Rheology ; Quantum mechanics Material Dynamics and Properties ; Rheology and Fluid Dynamics Studies ; Liquid Crystal Research Advancements
Authors: Brahim Belahcene
Source: Heliyon (article, 2024-08-30)
Field: Materials Science — Materials Chemistry

Unsupervised meta-analysis on chemical elements and atomic energy prediction: A case study on the periodic table This paper presents an artificial classification and atomic energy correlation analysis of the chemical components. The choice of data mining is due to its robustness, which can explore intrinsic or hidden relationships between chemical components and their properties. The Mendeleev table is conceivably the earliest example of the data analysis technique in materials science. However, the classical periodic table represents the arrangement of chemical elements based on particular periodicities, which has the issue of property progression for a few chemical components. In this investigation, we utilized one of the unsupervised data mining methods (principal component analysis) to explore knowledge from the chemical components database based on all the prepared properties. The main objective is to make an artificial classification of chemical components depending on their accessible physical and energetic properties. The results revealed the effectiveness of the data mining method in appreciating the relationships between the variables and properties that offer a new approach to seeing a Mendeleev table. The final step of this work highlights the significance of predictive polynomials that permit the scientific community to make atomic total energy predictions for each chemical component, from helium to lawrencium. Principal component analysis ; Table (database) ; Periodic table ; Computer science ; Robustness (evolution) ; Data mining ; Component (thermodynamics) ; Component analysis ; Artificial intelligence ; Chemistry ; Physics ; Thermodynamics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: Hugo Amedei | Niels B. Paul | Brian Foo | Lisa Neuenroth | Stephan E. Lehnart | Henning Urlaub | Christof Lenz
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-04-26)
Field: Chemistry — Spectroscopy

mini-Complexome Profiling (mCP), an FDR-controlled workflow for global targeted detection of protein complexes Abstract Introduction Co-fractionation mass spectrometry couples native-like separations of protein/protein complexes with mass spectrometric proteome analysis for global characterization of protein networks. The technique allows for both de novo detection of complexes and for the detection of subtle changes in their protein composition. The typical requirement for fine-grained fractionation of &gt;80 fractions, however, translates into significant demands on sample quantity and mass spectrometric instrument time, and represents a significant barrier to experimental replication and the use of scarce sample material (ex. Patient biopsies). Methods We developed mini-Complexome Profiling (mCP), a streamlined workflow with reduced requirements for fractionation and, thus, biological material and laboratory and instrument time. Soluble and membrane-associated protein complexes are extracted from biological material under mild conditions, and fractionated by Blue Native electrophoresis using commercial equipment. Each fraction is analyzed by data independent acquisition mass-spectrometry, and known protein complexes are detected based on the coelution of known components using a novel R package with a controlled false discovery rate approach. The tool is available to the community on a GitHub repository. Results mCP was benchmarked using HEK293 cell lysate and exhibited performance similar to established workflows, but from a significantly reduced number of fractions. We then challenged mCP by performing comparative complexome analysis of cardiomyocytes isolated from different chambers from a single mouse heart, where we identified subtle chamber-specific changes in mitochondrial OxPhos complexes. Discussion The reduced sample and instrument time requirements open up new applications of co-fractionation mass spectrometry, specifically for the analysis of sparse samples such as human patient biopsies. The ability to identify subtle changes between similar tissue types (left/right ventricular and atrial cardiomyocytes) serves as a proof of principle for comparative analysis of mild/asymptomatic disease states. Workflow ; Profiling (computer programming) ; Computational biology ; Computer science ; Chemistry ; Biology ; Database ; Operating system Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Bioinformatics and Genomic Networks
Authors: Xuan-Bac Nguyen | Hoang-Quan Nguyen | Hugh Churchill | Samee U. Khan | Khoa Luu
Source: arXiv (Cornell University) (preprint, 2024-08-07)
Field: Materials Science — Materials Chemistry

Hierarchical Quantum Control Gates for Functional MRI Understanding Quantum computing has emerged as a powerful tool for solving complex problems intractable for classical computers, particularly in popular fields such as cryptography, optimization, and neurocomputing. In this paper, we present a new quantum-based approach named the Hierarchical Quantum Control Gates (HQCG) method for efficient understanding of Functional Magnetic Resonance Imaging (fMRI) data. This approach includes two novel modules: the Local Quantum Control Gate (LQCG) and the Global Quantum Control Gate (GQCG), which are designed to extract local and global features of fMRI signals, respectively. Our method operates end-to-end on a quantum machine, leveraging quantum mechanics to learn patterns within extremely high-dimensional fMRI signals, such as 30,000 samples which is a challenge for classical computers. Empirical results demonstrate that our approach significantly outperforms classical methods. Additionally, we found that the proposed quantum model is more stable and less prone to overfitting than the classical methods. Control (management) ; Quantum ; Computer science ; Physics ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science
Authors: Thomas Willerer | Thomas Brinkmann | Klaus Drechsler
Source: Polymers (article, 2024-11-17)
Field: Materials Science — Polymers and Plastics

Development and Application of a Cooling Rate Dependent PVT Model for Injection Molding Simulation of Semi Crystalline Thermoplastics This technical paper delves into the creation and application of an enhanced mathematical model for semi crystalline thermoplastics based on the Pressure-Volume-Temperature (PVT) Two Domain Tait Equation. The model is designed to incorporate the impact of the cooling rate on the specific volume of the material. This is achieved by utilizing Flash differential scanning calorimetry (fDSC) measurements, thereby ensuring a direct correlation to the actual behavior of the material in reality. The practical application of the model in the context of injection molding simulation was also considered. This was done by integrating the mathematical model into the Moldflow software via the Solver API. The paper underscores the discontinuity issue inherent in the traditional Tait equation with cooling rates and proposes a solution that guarantees a correct transition from the liquid to the solid phase, even at high cooling rates and pressures. The results demonstrated a realistic PVT curve across a wide range of cooling rates and high pressures. The model was put to the test using a 3D tetrahedron meshed calculation model in the injection molding simulation. This study marks a significant step forward in the simulation of injection molding processes, as it successfully bridges the gap between real material properties and simplified simulation, paving the way for more accurate and efficient simulations in the future. Molding (decorative) ; Materials science ; Context (archaeology) ; Mechanical engineering ; Solver ; Simulation software ; Stefan problem ; Computer science ; Mechanics ; Simulation ; Composite material ; Software ; Engineering ; Mathematics ; Physics Polymer crystallization and properties ; Injection Molding Process and Properties ; Polymer Foaming and Composites