jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sicheng Yang | Yang Cao | C.K. Li | Juan Manuel Castagnini | Francisco J. Barba | Changyao Shan | Jianjun Zhou
Source: Current Research in Food Science (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Enhancing grain drying methods with hyperspectral imaging technology: A visualanalysis This study proposes a recognition model for different drying methods of grain using hyperspectral imaging technology (HSI) and multivariate analysis. Fresh harvested grain samples were dried using three different methods: rotating ventilation drying, mechanical drying, and natural drying. Hyperspectral images of the samples were collected within the 388-1065 nm band range. The spectral features of the samples were extracted using principal component analysis (PCA), while the texture features were extracted using second-order probability statistical filtering. Partial least squares regression (PLSR) drying models with different characteristics were established. At the same time, a BPNN (Back-propagation neural network, BPNN) based on spectral texture fusion features was established to compare the recognition effects of different models. Texture analysis indicated that the mean-image had the clearest contour, and the texture characteristics of mechanical drying were smaller than those of rotating ventilation drying and natural drying. The BPNN model established using spectral-texture feature variables showed the best performance in distinguishing grain in different drying modes, with a prediction model obtained based on the correlation coefficients of special variables. The spectral and texture feature values were fused for pseudo-color visualization expression, and the three drying methods of grain showed different colors. This study provides a reference for non-destructive and rapid detection of grain with different drying methods. Hyperspectral imaging ; Texture (cosmology) ; Principal component analysis ; Artificial intelligence ; Pattern recognition (psychology) ; Partial least squares regression ; Biological system ; Artificial neural network ; Feature (linguistics) ; Mathematics ; Computer science ; Materials science ; Statistics ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Food Drying and Modeling
Authors: Ali Yaşar
Source: European Food Research and Technology (article, 2024-03-14)
Field: Chemistry — Analytical Chemistry

Analysis of selected deep features with CNN-SVM-based for bread wheat seed classification Abstract The main ingredient of flour is processed wheat. Wheat is an agricultural product that is harvested once a year. It may be necessary to choose the variety of wheat for growing wheat and efficient harvesting. The variety of wheat is important for its economic value, taste, and crop yield. Although there are many varieties of wheat, they are very similar in colour, size, and shape, and it requires expertise to distinguish them by eye. This is very time consuming and can lead to human error. Using computer vision and artificial intelligence, such problems can be solved more quickly and objectively. In this study, an attempt was made to classify five bread wheat varieties belonging to different cultivars using Convolutional Neural Network (CNN) models. Three approaches have been proposed for classification. First, pre-trained CNN models (ResNet18, ResNet50, and ResNet101) were trained for bread wheat cultivars. Second, the features extracted from the fc1000 layer of the pre-trained CNN models ResNet18, ResNet50, and ResNet101 were classified using a support vector machine (SVM) classifier with different kernel features from machine learning techniques for classification with different variants. Finally, SVM methods were used in the second stage to classify the features obtained from the fc1000 layer of the pre-trained CNN models with an optimal set of features that can represent all features using the minimum redundancy maximum relevance (mRMR) feature selection algorithm.The accuracies obtained in the first, second, and last phases are as follows. In the first phase, the most successful method in classifying wheat grains was the ResNet18 model with 97.57%. In the second phase, the ResNet18 + ResNet50 + ResNet101 + Quadratic SVM model was the most successful model in classification using the features obtained from the ResNet CNN models with 94.08%.The accuracy for classification with the 1000 most effective features selected by the feature selection algorithm was 94.51%. Although the classification with features is slightly lower than deep learning, the classification time is much shorter and is 93%. This result confirms the great effectiveness of CNN models for wheat grain classification. Support vector machine ; Pattern recognition (psychology) ; Artificial intelligence ; Mathematics ; Computer science Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Food Supply Chain Traceability
Authors: Natália Luptáková | Jiří Svoboda | Denisa Bártková | Adam Weiser | A. Dlouhý
Source: Materials (review, 2024-07-10)
Field: Materials Science — Materials Chemistry

The Irradiation Effects in Ferritic, Ferritic–Martensitic and Austenitic Oxide Dispersion Strengthened Alloys: A Review High-performance structural materials (HPSMs) are needed for the successful and safe design of fission and fusion reactors. Their operation is associated with unprecedented fluxes of high-energy neutrons and thermomechanical loadings. In fission reactors, HPSMs are used, e.g., for fuel claddings, core internal structural components and reactor pressure vessels. Even stronger requirements are expected for fourth-generation supercritical water fission reactors, with a particular focus on the HPSM's corrosion resistance. The first wall and blanket structural materials in fusion reactors are subjected not only to high energy neutron irradiation, but also to strong mechanical, heat and electromagnetic loadings. This paper presents a historical and state-of-the-art summary focused on the properties and application potential of irradiation-resistant alloys predominantly strengthened by an oxide dispersion. These alloys are categorized according to their matrix as ferritic, ferritic-martensitic and austenitic. Low void swelling, high-temperature He embrittlement, thermal and irradiation hardening and creep are typical phenomena most usually studied in ferritic and ferritic martensitic oxide dispersion strengthened (ODS) alloys. In contrast, austenitic ODS alloys exhibit an increased corrosion and oxidation resistance and a higher creep resistance at elevated temperatures. This is why the advantages and drawbacks of each matrix-type ODS are discussed in this paper. Materials science ; Austenite ; Oxide ; Metallurgy ; Irradiation ; Dispersion (optics) ; Martensite ; Microstructure ; Optics Fusion materials and technologies ; Nuclear Materials and Properties ; Nuclear materials and radiation effects
Authors: Maha Ibrar | Sheng‐Yuan Huang | Zachery McCurtain | Shujon Naha | David Crandall | Stephen C. Jacobson | Sara E. Skrabalak
Source: Advanced Functional Materials (article, 2024-07-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Modular Anti‐Counterfeit Tags Formed by Template‐Assisted Self‐Assembly of Plasmonic Nanocrystals and Authenticated by Machine Learning Counterfeit goods are pervasive, being found in products as diverse as textiles and optical media to pharmaceuticals and sensitive electronics. Here, an anti-counterfeit platform is reported in which plasmonic nanoparticles (NPs) are used to create unique image tags that can be authenticated quickly and reliably. Specifically, plasmonic NPs are assembled into periodic arrays of NP clusters by template-assisted self-assembly (TASA), where the light scattering responses from the arrays are analyzed by dark-field optical microscopy. Tag design proved modular as plasmonic NPs with different optical responses can be selected and paired with Templates with different features (e.g., well size, well shape, and number and arrangement of wells in an array), giving access to a variety of color responses and unique images. These images can be differentiated from one another and authenticated by image analysis. Authentication methods based on shallow and deep neural networks are compared, where deep neural networks authenticated TASA tags with higher accuracy. Given the ease of tag fabrication and rapid image analysis, these platforms are ideal for on-the-fly tagging and supply-chain authentication of critical goods. Counterfeit ; Materials science ; Modular design ; Plasmon ; Nanocrystal ; Self-assembly ; Nanotechnology ; Computer science ; Optoelectronics Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Nanocluster Synthesis and Applications
Authors: Daniele Salvato | Kyle M. Paaren | Jacob Hirschhorn | Larry K. Aagesen | Fei Xu | Fidelma Giulia Di Lemma | Luca Capriotti | Tiankai Yao
Source: Journal of Nuclear Materials (article, 2024-01-18)
Field: Materials Science — Materials Chemistry

The effect of temperature and burnup on U-10Zr metallic fuel chemical interaction with HT-9: A SEM-EDS study The fuel cladding chemical interaction (FCCI) between Uranium-Zirconium-based metallic fuel and cladding materials during in-pile service is one of the most constraining phenomena affecting the performance of this fuel system. In this study, we investigated the effect of temperature and burnup on the FCCI development in two U-10 wt.% Zr (U-10Zr) fuel samples with HT-9 cladding irradiated as part of the MFF-3 irradiation test in the Fast Flux Test Facility (FFTF). One sample achieved a burnup of 13.1 at.% and operated with an average inner cladding temperature of 530°C, while the other achieved a burnup of 8.5 at.% and was subjected to an average inner cladding temperature of 615°C. Automated scanning electron microscopy (SEM) back-scattered electron (BSE) imaging of entire fuel cross-sections and SEM energy dispersive x-ray spectroscopy (EDS) analysis on specific fuel-cladding interface regions successfully provided a comprehensive characterization of the depth and type of interaction happening under different irradiation conditions. Our analysis shows that FCCI development on both fuel and cladding side is strongly influenced by the inner cladding temperature and, to some extent, the formation and integrity of Zr-rich layers between the fuel and cladding, while the impact of burnup and power is negligible. Measured FCCI thicknesses were compared to BISON simulations using both an empirical model based upon legacy data from the Experimental Breeder Reactor II (EBR II) irradiations and a mechanistic model currently under development for the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, showing satisfactory agreement. Nonetheless, this comparison supports the need for additional microstructural characterization in intermediate ranges of temperature, power, and burnups in prototypic-length pins. Burnup ; Cladding (metalworking) ; Materials science ; Nuclear engineering ; Uranium ; Irradiation ; Scanning electron microscope ; Nuclear fuel ; Composite material ; Metallurgy ; Nuclear physics ; Physics ; Engineering Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Fusion materials and technologies
Authors: Attila Nagy | Andrea Szabó | Ahmed Elbeltagi | Gift Siphiwe Nxumalo | Erika Buday-Bódi | János Tamás
Source: Frontiers in Plant Science (article, 2024-10-16)
Field: Chemistry — Analytical Chemistry

Hyperspectral indices data fusion-based machine learning enhanced by MRMR algorithm for estimating maize chlorophyll content Accurate estimation of chlorophyll is essential for monitoring maize health and growth, for which hyperspectral imaging provides rich data. In this context, this paper presents an innovative method to estimate maize chlorophyll by combining hyperspectral indices and advanced machine learning models. The methodology of this study focuses on the development of machine learning models using proprietary hyperspectral indices to estimate corn chlorophyll content. Six advanced machine learning models were used, including robust linear stepwise regression, support vector machines (SVM), fine Gaussian SVM, Matern 5/2 Gaussian stepwise regression, and three-layer neural network. The MRMR algorithm was integrated into the process to improve feature selection by identifying the most informative spectral bands, thereby reducing data redundancy and improving model performance. The results showed significant differences in the performance of the six machine learning models applied to chlorophyll estimation. Among the models, the Matern 5/2 Gaussian process regression model showed the highest prediction accuracy. The model achieved R<sup>2</sup> = 0.71 for the training set, RMSE = 338.46 µg/g and MAE = 264.30 µg/g. In the case of the validation set, the Matern 5/2 Gaussian process regression model further improved its performance, reaching R<sup>2</sup> =0.79, RMSE=296.37 µg/g, MAE=237.12 µg/g. These metrics show that Matern's 5/2 Gaussian process regression model combined with the MRMR algorithm to select optimal traits is highly effective in predicting corn chlorophyll content. This research has important implications for precision agriculture, particularly for real-time monitoring and management of crop health. Accurate estimation of chlorophyll allows farmers to take timely and targeted action. Hyperspectral imaging ; Support vector machine ; Artificial intelligence ; Machine learning ; Mean squared error ; Gaussian process ; Artificial neural network ; Algorithm ; Context (archaeology) ; Computer science ; Kriging ; Mathematics ; Gaussian ; Statistics ; Biology Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Robert Schönherr | J. Boger | J. M. Rudolph | Mareike Harms | Jacqueline Kaiser | Sophie Nachtschatt | Marla Wobbe | Rainer Duden | Peter König | Gleb Bourenkov | T. Schneider | Lars Redecke
Source: Nature Communications (article, 2024-02-24)
Field: Materials Science — Materials Chemistry

A streamlined approach to structure elucidation using in cellulo crystallized recombinant proteins, InCellCryst With the advent of serial X-ray crystallography on microfocus beamlines at free-electron laser and synchrotron facilities, the demand for protein microcrystals has significantly risen in recent years. However, by in vitro crystallization extensive efforts are usually required to purify proteins and produce sufficiently homogeneous microcrystals. Here, we present InCellCryst, an advanced pipeline for producing homogeneous microcrystals directly within living insect cells. Our baculovirus-based cloning system enables the production of crystals from completely native proteins as well as the screening of different cellular compartments to maximize chances for protein crystallization. By optimizing cloning procedures, recombinant virus production, crystallization and crystal detection, X-ray diffraction data can be collected 24 days after the start of target gene cloning. Furthermore, improved strategies for serial synchrotron diffraction data collection directly from crystals within living cells abolish the need to purify the recombinant protein or the associated microcrystals. Recombinant DNA ; Computational biology ; Protein structure ; Biology ; Genetics ; Biochemistry ; Gene Enzyme Structure and Function ; Glycosylation and Glycoproteins Research ; Enzyme Production and Characterization
Authors: Jie Ma | Minjie Li | Wanpeng Fan | Jizhan Liu
Source: Agronomy (article, 2024-11-23)
Field: Chemistry — Analytical Chemistry

State-of-the-Art Techniques for Fruit Maturity Detection For decades, fruit maturity assessment in the field was challenging for producers, researchers, and food supply agencies. Knowing the maturity stage of the fruit is significant for precision production, harvest, and postharvest management. A prerequisite is to detect and classify fruit of different maturities from the background environment. Recently, deep learning technology has become a widely used method for intelligent fruit detection, due to it having higher accuracy, reliability, and a faster processing speed compared with traditional image-processing methods. At the same time, spectral imaging approaches can predict the maturity stage by acquiring and analyzing the spectral data of fruit samples. These maturity detection methods pay more attention to the species, such as apple, cherry, strawberry, and mango, achieving the mean average precision value of 98.7% in apple fruit. This review provides an overview of the most recent methodologies developed for in-field fruit maturity estimation. The basic principle and representative research output associated with the advantages and disadvantages of these techniques were systematically investigated and analyzed. Challenges, such as environmental factors (illumination condition, occlusion, overlap, etc.), shortage of fruit datasets, calculation, and hardware costs, were discussed. The future research directions in terms of applications and techniques are summarized and demonstrated. Postharvest ; Maturity (psychological) ; Economic shortage ; Reliability (semiconductor) ; Field (mathematics) ; Computer science ; Agricultural engineering ; Artificial intelligence ; Horticulture ; Mathematics ; Engineering ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Date Palm Research Studies
Authors: Amir Khurshid | Ajaya Kumar Pani
Source: Measurement Science and Technology (article, 2024-03-22)
Field: Chemistry — Analytical Chemistry

An integrated approach combining randomized kernel PCA, Gaussian mixture modeling and ICA for fault detection in non-linear processes Abstract Principal component analysis (PCA) and independent component analysis (ICA), as well as their kernel extensions, have been widely applied in the past for industrial fault detection with Gaussian or non-Gaussian process data with linear or non-linear characteristics. Kernel-based techniques lead to computational complexity due to the high dimensionality of the dataset in the feature space. In this work, a randomization approach is used to obtain a low-rank approximation of the high-dimensional kernel matrix. A hybrid machine learning technique is proposed that integrates randomized kernel PCA (RKPCA) with ICA and Gaussian mixture modeling (GMM). The proposed approach, ICA-RKPCA-GMM, addresses the Gaussian and non-Gaussian characteristics of non-linear process data. Another hybrid algorithm combining three basic techniques of ICA, PCA and GMM is also developed (ICA-PCA-GMM). The fault detection performances of the proposed techniques (ICA-RKPCA-GMM and ICA-PCA-GMM) are compared with PCA, ICA, KPCA and combined ICA-PCA techniques by applying the techniques to two benchmark systems. Monitoring performances were evaluated by determining the false alarm rate and fault detection rate for different types of process and sensor faults. The simulation results show that the proposed ICA-RKPCA-GMM approach yields better results than individual ICA, PCA and KPCA techniques, the combined ICA-PCA and the proposed ICA-PCA-GMM technique. Independent component analysis ; Fault detection and isolation ; Kernel (algebra) ; Gaussian ; Pattern recognition (psychology) ; Computer science ; Mathematics ; Artificial intelligence ; Applied mathematics ; Physics ; Discrete mathematics Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Advanced Algorithms and Applications
Authors: Amy L. George | María Emilia Dueñas | José Luis Marín‐Rubio | Matthias Trost
Source: Expert Reviews in Molecular Medicine (review, 2024-01-01)
Field: Chemistry — Spectroscopy

Stability-based approaches in chemoproteomics Target deconvolution can help understand how compounds exert therapeutic effects and can accelerate drug discovery by helping optimise safety and efficacy, revealing mechanisms of action, anticipate off-target effects and identifying opportunities for therapeutic expansion. Chemoproteomics, a combination of chemical biology with mass spectrometry has transformed target deconvolution. This review discusses modification-free chemoproteomic approaches that leverage the change in protein thermodynamics induced by small molecule ligand binding. Unlike modification-based methods relying on enriching specific protein targets, these approaches offer proteome-wide evaluations, driven by advancements in mass spectrometry sensitivity, increasing proteome coverage and quantitation methods. Advances in methods based on denaturation/precipitation by thermal or chemical denaturation, or by protease degradation are evaluated, emphasising the evolving landscape of chemoproteomics and its potential impact on future drug-development strategies. Proteome ; Computational biology ; Leverage (statistics) ; Deconvolution ; Drug discovery ; Chemistry ; Biochemical engineering ; Computer science ; Denaturation (fissile materials) ; Proteomics ; Mass spectrometry ; Biological system ; Biology ; Machine learning ; Chromatography ; Biochemistry ; Engineering Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Monoclonal and Polyclonal Antibodies Research
Authors: Azam Nesabi | Jas Kalayan | Sara AlRawashdeh | Mohammad A. Ghattas | Richard A. Bryce
Source: Journal of Computer-Aided Molecular Design (article, 2024-03-12)
Field: Chemistry — Spectroscopy

Molecular dynamics simulations as a guide for modulating small molecule aggregation Small colloidally aggregating molecules (SCAMs) can be problematic for biological assays in drug discovery campaigns. However, the self-associating properties of SCAMs have potential applications in drug delivery and analytical biochemistry. Consequently, the ability to predict the aggregation propensity of a small organic molecule is of considerable interest. Chemoinformatics-based filters such as ChemAGG and Aggregator Advisor offer rapid assessment but are limited by the assay quality and structural diversity of their training set data. Complementary to these tools, we explore here the ability of molecular dynamics (MD) simulations as a physics-based method capable of predicting the aggregation propensity of diverse chemical structures. For a set of 32 molecules, using simulations of 100 ns in explicit solvent, we find a success rate of 97% (one molecule misclassified) as opposed to 75% by Aggregator Advisor and 72% by ChemAGG. These short timescale MD simulations are representative of longer microsecond trajectories and yield an informative spectrum of aggregation propensities across the set of solutes, capturing the dynamic behaviour of weakly aggregating compounds. Implicit solvent simulations using the generalized Born model were less successful in predicting aggregation propensity. MD simulations were also performed to explore structure-aggregation relationships for selected molecules, identifying chemical modifications that reversed the predicted behaviour of a given aggregator/non-aggregator compound. While lower throughput than rapid cheminformatics-based SCAM filters, MD-based prediction of aggregation has potential to be deployed on the scale of focused subsets of moderate size, and, depending on the target application, provide guidance on removing or optimizing a compound's aggregation propensity. News aggregator ; Cheminformatics ; Molecular dynamics ; Biological system ; Computer science ; Small molecule ; Chemistry ; Chemical space ; Drug discovery ; Computational chemistry ; Biology Analytical Chemistry and Chromatography ; Protein Structure and Dynamics ; Computational Drug Discovery Methods