jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: S Shree Vakthi | CSS Raksha | M. Sangeetha
Source: REST Journal on Data Analytics and Artificial Intelligence (article, 2024-09-06)
Field: Chemistry — Analytical Chemistry

AI in Business AI Driven Water Content Measurement for Detecting Internal Rot in Fruits: Enhancing Quality Control in Grocery Stores The quality of fruits in grocery stores is often compromised by internal rot, which is not easily detectable through visual inspection alone. This paper proposes an advanced approach to improving fruit quality assessment by integrating Near-Infrared Spectroscopy (NIRS) with Artificial Intelligence (AI). NIRS is employed to non-invasively measure the water content and other internal characteristics of fruits, which are indicative of their freshness and quality. By analyzing the spectral data obtained from NIRS, this approach enables the detection of anomalies that suggest internal rot or spoilage.The AI component of this methodology leverages machine learning algorithms, specifically Convolutional Neural Networks (CNNs), to process and interpret the spectral data. CNNs are trained on a large dataset of fruit images with corresponding quality labels, enabling the AI system to accurately classify and predict the quality of the fruits based on their internal conditions. The AI analysis provides detailed insights into the fruit's quality, offering a more precise and reliable assessment compared to traditional inspection methods.This integrated approach not only enhances the accuracy of quality assessments but also improves operational efficiency by reducing the reliance on manual inspections. The system offers real-time feedback, allowing grocery store owners and suppliers to promptly identify and address quality issues, thereby minimizing waste and increasing consumer satisfaction.Overall, the combination of NIRS and AI represents a significant advancement in fruit quality management, providing a sophisticated tool for ensuring the freshness and quality of produce. This methodology promises to transform how fruits are assessed, leading to more effective quality control and better outcomes for both suppliers and consumers. Quality (philosophy) ; Control (management) ; Content (measure theory) ; Business ; Computer science ; Food science ; Mathematics ; Artificial intelligence ; Chemistry ; Physics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Industrial Vision Systems and Defect Detection
Authors: Samuel Verdú | Cristina Fuentes | Ana Fuentes | Alberto Pérez | José Manuel Barat Baviera | Raúl Grau
Source: Journal of Food Engineering (article, 2024-01-12)
Field: Chemistry — Analytical Chemistry

Control of cod-liver oil composition with laser scattering imaging combined with machine learning procedures: The cases of adulteration and oxidation Chemistry ; Chemical oxygen demand ; Cod liver oil ; Vegetable oil ; Multivariate statistics ; Food science ; Chromatography ; Mathematics ; Environmental science ; Environmental engineering ; Statistics Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Meat and Animal Product Quality
Authors: ABDELMOUMEN SHAD SERROUNE | Khasani | James Kakiecwski | Marshall Brenton | Abdellatif SERROUNE | Dalia M. Abdel Basset | Hicham SERROUNE | Sebastien Gescot
Source: International Journal Of Medical Science And Clinical Invention (article, 2024-11-01)
Field: Materials Science — Materials Chemistry

Biofeed through Bioconversion process with the Engineered Methylomicrobium buryatense Strain 5GBC1-RO1 This study presents a novel approach to mitigate methane emissions while simultaneously addressing the growing demand for sustainable animal feed through the development of an engineered methanotrophic strain, Methylomicrobium buryatense 5GB1C-RO1. Utilizing advanced genetic engineering techniques, including CRISPR/Cas9 and horizontal gene transfer, we have optimized the ribulose monophosphate (RuMP) cycle and enhanced oxidase activity in this strain. The bioconversion process is facilitated by innovative bioreactor designs, including Two-Phase Partitioning Bioreactors (TPPBs) and Inverse Membrane Bioreactors (IMBRs), which significantly improve methane solubility and mass transfer. Through metabolic flux analysis and computational modeling, we have achieved high biomass yields and efficient methane utilization. The resulting biofeed demonstrates a superior nutritional profile, with optimized macronutrient content and essential components. This integrated approach not only contributes to greenhouse gas mitigation but also offers a promising solution for sustainable animal nutrition. Our findings suggest that the 5GB1C-RO1 strain and associated bioprocesses have the potential to revolutionize both environmental protection and agricultural sustainability Bioconversion ; Medicine ; Strain (injury) ; Process (computing) ; Microbiology ; Biochemistry ; Internal medicine ; Biology Magnesium Oxide Properties and Applications ; Layered Double Hydroxides Synthesis and Applications ; Aerogels and thermal insulation
Authors: Yanlin燴eng | Yao燣in | Yiting燞e | Tong燣i | Jing燣i | Baijuan燱ang | Yi燳ang
Source: International journal of agricultural and biological engineering (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Enhanced progressive fusion method for the efficient detection of multi-scale lightweight citrus fruits Human labor efficiency has become unable to keep the pace with gradually annual citrus increasing production. Highly efficient and intelligent citrus picking and accurate yield estimation is the key to solve the problem. Success heavily depends on detection accuracy, prediction speed, and easy model deployment. Traditional target detection methods often fail to achieve balanced results in all those aspects. An improved YOLOv8 network model with four significant features is proposed. First, a lightweight FasterNet network structure was introduced to the backbone network, which reduced the number of parameters and computations while maintaining high-precision detection. Second, a progressive feature pyramid network AFPN structure was added to the neck network. Third, a parallel multi-branch attention mechanism PMBA was added before the detection head to improve the sensing ability after the feature fusion network. Fourth, a Wise-IoU was introduced to replace the original CIoU loss function to make the whole training process converge faster. Based on this, this study proposes an improved version of the YOLOv8 model: the FAP-YOLOv8. This improved model achieved an average accuracy (mAP@0.5) of 97.2% on the citrus datasets, with an accuracy that was 4.7% higher than the original YOLOv8, which was 19.2%, 7.4%, 5.1%, 4.9%, and 5.2% higher than the other models: Faster R-CNN, CenterNet, YOLOv5s, YOLOx-s, and YOLOv7, respectively. The number of parameters was reduced by 55.45%, the computation was reduced by 20% compared to the YOLOv8 benchmark, and the frame rate reached 46.51 fps to meet the detection requirements of lightweight networks. The experiments showed that the FAP-YOLOv8 models all outperformed the comparison models. Consequently, the proposed FAP-YOLOv8 model can help solve the citrus detection problem in orchards, which can be better applied to edge devices and provides strong support for intelligent orchard management. Keywords: citrus fruit detection, enhanced progressive fusion model, multi-scale lightweight, attention mechanism DOI: 10.25165/j.ijabe.20241706.8802 Citation: Zeng Y L, Lin Y, He Y T, Li T, Li J, Wang B J, et al. Enhanced progressive fusion method for the efficient detection of multi-scale lightweight citrus fruits. Int J Agric & Biol Eng, 2024; 17(6): 218–229. Fusion ; Scale (ratio) ; Horticulture ; Computer science ; Environmental science ; Biology ; Geography ; Cartography Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food
Authors: Elena Khozhaenko | В. В. Ковалев | E. A. Podkorytova | G.K. Kondrateva
Source:  (article, 2024-12-11)
Field: Materials Science — Biomaterials

THE CADMIUM-BINDING PROPERTIES OF THE CARRAGEENANS Scientific research of the SCO countries: synergy and integration Cadmium ; Computer science ; Chemistry ; Materials science ; Metallurgy Calcium Carbonate Crystallization and Inhibition
Authors: Florian Le Bourdais | Nicolas Leymarie | Audrey Gardahaut
Source: SPIRE - Sciences Po Institutional REpository (article, 2024-06-30)
Field: Materials Science — Materials Chemistry

Understanding effective elastic constants and elastic symmetry in RUS: from single crystals to textured polycrystal Voir aussi hal-05522904 Symmetry (geometry) ; Deformation (meteorology) ; Resonant ultrasound spectroscopy ; Elasticity (physics) ; Constant (computer programming) ; Work (physics) Quasicrystal Structures and Properties ; Microstructure and mechanical properties ; Metallurgical and Alloy Processes
Authors: Tobias Pietz | Sukrit Gupta | Christoph N. Schlaffner | Saima Ahmed | Hanno Steen | Bernhard Y. Renard | Katharina Baum
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-03-25)
Field: Chemistry — Spectroscopy

PEPerMINT: Peptide Abundance Imputation in Mass Spectrometry-based Proteomics using Graph Neural Networks Motivation: Accurate quantitative information about the protein abundance is crucial for understanding a biological system and its dynamics. Protein abundance is commonly estimated using label-free, bottom-up mass spectrometry protocols. Here, proteins are digested into peptides before quantification via mass spectrometry. However, missing peptide abundance values, which can make up more than 50% of all abundance values, are a common issue. They result in missing protein abundance values, which then hinder accurate and reliable downstream analyses. Results: To impute missing abundance values, we propose PEPerMINT, a graph neural network model working directly on the peptide level that flexibly takes both peptide-to-protein relationships in a graph format as well as amino acid sequence information into account. We benchmark our method against eleven common imputation methods on six diverse datasets, including cell lines, tissue, and plasma samples. We observe that PEPerMINT consistently outperforms other imputation methods. Its prediction performance remains high for varying degrees of missingness, different evaluation approaches and differential expression prediction. As an additional novel feature, PEPerMINT provides meaningful uncertainty estimates and allows for tailoring imputation to the user's needs based on the reliability of imputed values. Availability and implementation: The code is available at https://github.com/DILiS-lab/pepermint. Imputation (statistics) ; Missing data ; Computer science ; Data mining ; Graph ; Quantitative proteomics ; Computational biology ; Proteomics ; Machine learning ; Biology ; Theoretical computer science Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Levi M. Olevsky | M Jacques | Katherine R. Hixon
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-09-23)
Field: Materials Science — Surfaces, Coatings and Films

PoreVision: A Program for Enhancing Efficiency and Accuracy in SEM Pore Analyses Abstract Porous bulk materials are frequently utilized as cell scaffolds in tissue engineering. Previous studies have highlighted the significance of scaffold pore size and pore orientation in influencing cell migration and differentiation. Moreover, there exists a considerable body of research focused on optimizing pore characteristics to enhance scaffold performance. However, current methods for numerical pore characterization typically involve expensive machines or manual size measurements using image manipulation software. In this project, our objective is to develop a user-friendly, versatile, and freely accessible software tool using Python scripting. This tool aims to streamline and objectify pore characterization, thereby accelerating research efforts and providing a standardized framework for researchers working with porous materials. By facilitating quantitative analysis of pore size, shape, and orientation, our software tool will contribute to a more comprehensive understanding of scaffold properties and their impact on cellular behavior. Ultimately, we aim to aid researchers in the field of tissue engineering with a user-friendly tool that enhances the reproducibility and reliability of pore characterization analyses. Computer science ; Materials science Electron and X-Ray Spectroscopy Techniques ; Anodic Oxide Films and Nanostructures ; Corrosion Behavior and Inhibition
Authors: Melek Gül | Ebru Batı Ay
Source: Eurasian Journal of Biological and Chemical Sciences (article, 2024-12-07)
Field: Chemistry — Organic Chemistry

Investigation of the effects of lycorine and galanthamine extracted from Galanthus elwesii on viral and parasitic targets: An in-silico analysis and DFT Study In this study, Density Functional Theory (DFT), ADME property analysis, and molecular docking simulations were employed to evaluate the electronic structure, antiviral potential, and antiparasitic effects of lycorine and galanthamine, two alkaloids extracted from Galanthus elwesii. We conducted a comprehensive study to assess the antiviral and antiparasitic potential of lycorine and galanthamine, two alkaloids whose biosynthetic production was significantly increased by zinc supplementation. DFT calculations revealed that lycorine has a lower Egap than galanthamine, suggesting higher reactivity and lower stability, enhancing its potential as a drug candidate. Pharmacokinetic profiling indicated that galanthamine (TPSA: 41.93 Å², logP: 0.797) has a lower total polar surface area (TPSA) and higher lipophilicity (logP) compared to lycorine (TPSA: 62.16 Å², logP: -0.268), indicating that galanthamine may possess superior absorption and permeability characteristics. ADME analysis also identified galanthamine with a lower AMES toxicity score, implying reduced mutagenic risk. A total of nine target proteins, representing viral and parasitic diseases Zika virus, malaria, leishmaniasis, and dengue, were chosen for molecular docking. Molecular docking studies demonstrated that lycorine exhibited superior binding interactions (-8.76 kcal/mol), particularly against Leishmania, and displayed stronger binding affinity across all selected target proteins. Despite galanthamine's lower toxicity profile, lycorine’s enhanced reactivity and stronger binding properties suggest its higher efficacy as a therapeutic candidate based on DFT and molecular docking results, while galanthamine shows potential based on its favorable ADME profile. Lycorine ; In silico ; Biology ; Computational biology ; Botany ; Genetics ; Alkaloid Chemical synthesis and alkaloids ; Botanical Research and Chemistry ; Alkaloids: synthesis and pharmacology
Authors: Jinpeng Wang | Lei Xu | Song Mei | Haoruo Hu | Jialiang Zhou | Qing Chen
Source: International journal of agricultural and biological engineering (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Fast and accurate detection of kiwifruits in the natural environment using improved YOLOv4 Real-time detection of kiwifruits in natural environments is essential for automated kiwifruit harvesting. In this study, a lightweight convolutional neural network called the YOLOv4-GS algorithm was proposed for kiwifruit detection. The backbone network CSPDarknet-53 of YOLOv4 was replaced with GhostNet to improve accuracy and reduce network computation. To improve the detection accuracy of small targets, the upsampling of feature map fusion was performed for network layers 151 and 154, and the spatial pyramid pooling network was removed to reduce redundant computation. A total of 2766 kiwifruit images from different environments were used as the dataset for training and testing. The experiment results showed that the F1-score, average accuracy, and Intersection over Union (IoU) of YOLOv4-GS were 98.00%, 99.22%, and 88.92%, respectively. The average time taken to detect a 416×416 kiwifruit image was 11.95 ms, and the model’s weight was 28.8 MB. The average detection time of GhostNet was 31.44 ms less than that of CSPDarknet-53. In addition, the model weight of GhostNet was 227.2 MB less than that of CSPDarknet-53. YOLOv4-GS improved the detection accuracy by 8.39% over Faster R-CNN and 8.36% over SSD-300. The detection speed of YOLOv4-GS was 11.3 times and 2.6 times higher than Faster R-CNN and SSD-300, respectively. In the indoor picking experiment and the orchard picking experiment, the average speed of the YOLOv4-GS processing video was 28.4 fps. The recognition accuracy was above 90%. The average time spent for recognition and positioning was 6.09 s, accounting for about 29.03% of the total picking time. The overall results showed that the YOLOv4-GS proposed in this study can be applied for kiwifruit detection in natural environments because it improves the detection speed without compromising detection accuracy. Keywords: kiwifruits, fruit recognition, natural environments, YOLOv4 DOI: 10.25165/j.ijabe.20241705.7658 Citation: Wang J P, Xu L, Mei S, Hu H R, Zhou J L, Chen Q. Fast and accurate detection of kiwifruits in the natural environment using improved YOLOv4. Int J Agric & Biol Eng, 2024; 17(5): 222-230. Environmental science ; Mathematics Spectroscopy and Chemometric Analyses
Authors: David N. Sibley | Benjamin D. Goddard | Fouzia F. Ouali | David Fairhurst | Andrew J. Archer
Source: Edinburgh Research Explorer (preprint, 2024-12-19)
Field: Materials Science — Materials Chemistry

Coexisting multiphase and interfacial behaviour of ouzo Multi-component liquid mixtures can be both complex and fascinating, with some systems being amenable to simple experimentation at home, giving valuable insight into fundamental aspects of bulk and interfacial phase behaviour. One particularly interesting mixture is the popular drink ouzo, which has charmed both the general public and scientists by virtue of its ability to display spontaneous emulsification when water is added. When these two clear (and potable) liquids are poured into each other, a single milky-coloured liquid is formed. In previous work [Archer et al., Soft Matter 20, 5889 (2024)], the equilibrium phase-diagram for the stable liquid phases of ouzo was captured via experiment and modelling. Here we consider the case when the two liquid phases also coexist with the vapour phase (i.e. along a line of triple points) and within our model uncover the complex bulk phase behaviour for this simple beverage. As a consequence, this leads to some interesting observations, that also apply more widely, about visualising phase diagrams in ternary systems of this type. We also examine the interfacial behaviour, connecting microscopic density functional theory results with macroscopic (Neumann) predictions for the shape of droplets at interfaces. Materials science Pickering emulsions and particle stabilization ; Liquid Crystal Research Advancements
Authors: Lina Jäckering | Konstantin G. Wirth | Lukas Conrads | Jonas B. Profe | Alexander Rothstein | Hristiyana Kyoseva | Kenji Watanabe | Takashi Taniguchi | Dante M. Kennes | Christoph Stampfer | Lutz Waldecker | Thomas Taubner
Source: arXiv (Cornell University) (preprint, 2024-07-05)
Field: Materials Science — Materials Chemistry

Super-resolution imaging of nanoscale inhomogeneities in hBN-covered and encapsulated few-layer graphene Encapsulating few-layer graphene (FLG) in hexagonal boron nitride (hBN) can cause nanoscale inhomogeneities in the FLG, including changes in stacking domains and topographic defects. Due to the diffraction limit, characterizing these inhomogeneities is challenging. Recently, the visualization of stacking domains in encapsulated four-layer graphene (4LG) has been demonstrated with phonon polariton (PhP)-assisted near-field imaging. However, the underlying coupling mechanism and ability to image subdiffractional-sized inhomogeneities remain unknown. Here, we retrieve direct replicas and magnified images of subdiffractional-sized inhomogeneities in hBN-covered trilayer graphene (TLG) and encapsulated 4LG, enabled by the hyperlensing effect. This hyperlensing effect is mediated by hBN's hyperbolic PhP that couple to the FLG's plasmon polaritons. Using near-field microscopy, we identify the coupling by determining the polariton dispersion in hBN-covered TLG to be stacking-dependent. Our work demonstrates super-resolution and magnified imaging of inhomogeneities, paving the way for the realization of homogeneous encapsulated FLG transport samples to study correlated physics. Nanoscopic scale ; Graphene ; Materials science ; Nanotechnology ; Layer (electronics) ; Resolution (logic) ; Computer science Diamond and Carbon-based Materials Research ; Integrated Circuits and Semiconductor Failure Analysis ; Advancements in Semiconductor Devices and Circuit Design
Authors: Changqing Ye | Shubin Fu | Eric T. Chung
Source: arXiv (Cornell University) (preprint, 2024-04-03)
Field: Materials Science — Materials Chemistry

A fast cosine transformation accelerated method for predicting effective thermal conductivity Predicting effective thermal conductivity by solving a Partial Differential Equation (PDE) defined on a high-resolution Representative Volume Element (RVE) is a computationally intensive task. In this paper, we tackle the task by proposing an efficient and implementation-friendly computational method that can fully leverage the computing power offered by hardware accelerators, namely, graphical processing units (GPUs). We first employ the Two-Point Flux-Approximation scheme to discretize the PDE and then utilize the preconditioned conjugate gradient method to solve the resulting algebraic linear system. The construction of the preconditioner originates from FFT-based homogenization methods, and an engineered linear programming technique is utilized to determine the homogeneous reference parameters. The fundamental observation presented in this paper is that the preconditioner system can be effectively solved using multiple Fast Cosine Transformations (FCT) and parallel tridiagonal matrix solvers. Regarding the fact that default multiple FCTs are unavailable on the CUDA platform, we detail how to derive FCTs from FFTs with nearly optimal memory usage. Numerical experiments including the stability comparison with standard preconditioners are conducted for 3D RVEs. Our performance reports indicate that the proposed method can achieve a $5$-fold acceleration on the GPU platform over the pure CPU platform and solve the problems with $512^3$ degrees of freedom and reasonable contrast ratios in less than $30$ seconds. Transformation (genetics) ; Thermal conductivity ; Trigonometric functions ; Computer science ; Materials science ; Thermal ; Conductivity ; Algorithm ; Biological system ; Mathematics ; Physics ; Composite material ; Thermodynamics ; Geometry ; Chemistry Thermal properties of materials ; Heat Transfer and Optimization