jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Giuseppe Montanaro | Angelo Petrozza | Laura Rustioni | Francesco Cellini | Vitale Nuzzo
Source: Plant Phenomics (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Phenotyping Key Fruit Quality Traits in Olive Using RGB Images and Back Propagation Neural Networks To predict oil and phenol concentrations in olive fruit, the combination of back propagation neural networks (BPNNs) and contact-less plant phenotyping techniques was employed to retrieve RGB image-based digital proxies of oil and phenol concentrations. Fruits of cultivars (×3) differing in ripening time were sampled (~10-day interval, ×2 years), pictured and analyzed for phenol and oil concentrations. Prior to this, fruit samples were pictured and images were segmented to extract the red (R), green (G), and blue (B) mean pixel values that were rearranged in 35 RGB-based colorimetric indexes. Three BPNNs were designed using as input variables (a) the original 35 RGB indexes, (b) the scores of principal components after a principal component analysis (PCA) pre-processing of those indexes, and (c) a reduced number (28) of the RGB indexes achieved after a sparse PCA. The results show that the predictions reached the highest mean <i>R</i><sup>2</sup> values ranging from 0.87 to 0.95 (oil) and from 0.81 to 0.90 (phenols) across the BPNNs. In addition to the <i>R</i><sup>2</sup>, other performance metrics were calculated (root mean squared error and mean absolute error) and combined into a general performance indicator (GPI). The resulting rank of the GPI suggests that a BPNN with a specific topology might be designed for cultivars grouped according to their ripening period. The present study documented that an RGB-based image phenotyping can effectively predict key quality traits in olive fruit supporting the developing olive sector within a digital agriculture domain. RGB color model ; Principal component analysis ; Mathematics ; Artificial intelligence ; Mean squared error ; Cultivar ; Ripening ; Pattern recognition (psychology) ; Artificial neural network ; Pixel ; Statistics ; Olive oil ; Computer science ; Horticulture ; Food science ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Date Palm Research Studies
Authors: Z. Liu | Dengjun Qiu | Tong Yang | Jingxu Su | Chengyuan Liu | Xinyue Su | Anning Li | Ping‐Ping Sun | Jianguo Li | Yan Li | Chuanbo Ding | Shuai Zhang
Source: Molecules (review, 2023-10-09)
Field: Materials Science — Biomaterials

Research Progress of Dihydroquercetin in the Treatment of Skin Diseases Skin is a barrier to maintaining the stability of the human environment and preventing the invasion of pathogens. When skin tissue is exposed to the external environment, it will inevitably develop defects due to trauma, injury, burns, ulcers, surgery, and chronic diseases. Rapid skin repair is the key to reducing infection, relieving pain, and improving quality of life. Dihydroquercetin is a kind of flavonoid that has a wide range of pharmacological activities and can improve skin repair, skin inflammation, skin cancer, and so on. In this paper, the application of dihydroquercetin in medical dressings and the research progress in the treatment of skin-related diseases are reviewed, so as to provide reference for further developing dihydroquercetin as a drug for the treatment of skin diseases. Medicine ; Skin cancer ; Dermatology ; Drug ; Human skin ; Intensive care medicine ; Pharmacology ; Cancer ; Biology ; Internal medicine Silk-based biomaterials and applications ; Bee Products Chemical Analysis ; Skin Protection and Aging
Authors: Jie Liu | Shuang Fan | Weimin Cheng | Yang Yang | Xiaohong Li | Qi Wang | Binmei Liu | Zhuopin Xu | Yuejin Wu
Source: Foods (article, 2023-01-08)
Field: Chemistry — Analytical Chemistry

Non-Destructive Discrimination of Sunflower Seeds with Different Internal Mildew Grades by Fusion of Near-Infrared Diffuse Reflectance and Transmittance Spectra Combined with 1D-CNN Internally mildewed sunflower seeds, which cannot be recognized and discarded based on their appearance, pose a serious risk to human health. Thus, there is a need for a rapid non-destructive mildew grade discrimination method. Currently, few reports are available regarding this process. In this study, a method based on the combination of the near-infrared diffuse reflectance and near-infrared diffuse transmission (NIRr-NIRt) fusion spectra and a one-dimension convolutional neural network (1D-CNN) is proposed. The NIRr-NIRt fusion spectra can provide more complementary and comprehensive information, and therefore better discrimination accuracy, than a single spectrum. The first derivative (FD) preprocessing method could further improve the discrimination effect. By comparison against three conventional machine learning algorithms (artificial neural network (ANN), support vector machine (SVM), and K-nearest neighbor (KNN)), the 1D-CNN model based on the fusion spectra was found to perform the best. The mean prediction accuracy was 2.01%, 5.97%, and 10.55% higher than that of the ANN, SVM, and KNN models, respectively. These results indicate that the CNN model was able to precisely classify the mildew grades with a prediction accuracy of 97.60% and 94.04% for the training and test set, respectively. Thus, this study provides a non-destructive and rapid method for classifying the mildew grade of sunflower seeds with the potential to be applied in the quality control of sunflower seeds. Artificial intelligence ; Pattern recognition (psychology) ; Support vector machine ; Computer science ; Convolutional neural network ; Artificial neural network ; Sunflower ; Preprocessor ; Fusion ; k-nearest neighbors algorithm ; Machine learning ; Mathematics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Plant Pathogens and Fungal Diseases
Authors: Rui Yuan Wu | Liang Wu | Shi He | Shuo Liu | Tie Jun Cui
Source: Programmable Materials (article, 2023-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Programmable metamaterials Abstract As a major approach for controlling electromagnetic (EM) waves, metamaterials have experienced an abundant and rapid development in the 21st century. They have provided flexible and powerful techniques for controlling EM waves and brought many unique applications that are difficult to realise with natural materials. With increasing demands on dynamic controls of the EM waves, many innovations have been conducted in both three-dimensional metamaterials and two-dimensional metasurfaces, in which the meta-atom has been gradually evolved from passive to active. In 2014, coding and digital mechanisms were initially introduced to the metamaterials, further advancing the appearance of digitally programmable metamaterials. The programmable metamaterials have shown great potentials in not only real-time manipulations of the EM waves, but also direct information processing on the EM wave level. In this article, we present an in-depth review of the programmable EM metamaterials and metasurfaces, focusing on the programmable features including theoretical concepts, implementing methods and applications in EM controls. We first give a short retrospect of traditional metamaterials and metasurfaces, followed by the concepts and detailed discussions of digital coding and field-programmable metamaterials. Then, we introduce space-domain, time-domain and space–time-domain programmable metamaterials and metasurfaces, mainly focusing on their theories, functionalities, experimental implementations, and system-level applications. Finally, we conclude the current advances of the programmable metamaterials and metasurfaces, and give a prospect for the future developments. Metamaterial ; Computer science ; Split-ring resonator ; Transformation optics ; Electronic engineering ; Physics ; Optics ; Engineering Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Jacob Stamm | Sung Hyun Kwon | Shawn Sandhu | Moaid Shaik | Rituparna Das | Jesse Sandhu | Bradley Curenton | Clayton Wicka | Benjamin G. Levine | Liangliang Sun | Marcos Dantus
Source: The Journal of Physical Chemistry Letters (article, 2023-11-02)
Field: Chemistry — Organic Chemistry

The Surprising Dynamics of the McLafferty Rearrangement We report femtosecond time-resolved measurements of the McLafferty rearrangement following the strong-field tunnel ionization of 2-pentanone, 4-methyl-2-pentanone, and 4,4-dimethyl-2-pentanone. The pump-probe-dependent yields of the McLafferty product ion are fit to a biexponential function with fast (∼100 fs) and slow (∼10 ps) time constants, the latter of which is faster for the latter two compounds. Following nearly instantaneous ionization, the fast time scale is associated with rotation of the molecule to a six-membered cyclic intermediate that facilitates transfer of the γ-hydrogen, while the ∼50-100 times longer time scale is associated with a π-bond rearrangement and bond cleavage between the α- and β-carbons to produce the enol cation. These experimental measurements are supported by <i>ab initio</i> molecular dynamics trajectories, which further confirm the time scale of this important stepwise reaction in mass spectrometry. Dynamics (music) ; Psychology Axial and Atropisomeric Chirality Synthesis ; Catalytic Alkyne Reactions ; Asymmetric Hydrogenation and Catalysis
Authors: Volodymyr Gunka | Volodymyr Hidei | Iurii Sidun | Yuriy Demchuk | Vitalii Stadnik | Pavlo Shapoval | Khrystyna Sobol | Nataliya Vytrykush | Michael Bratychak
Source: Coatings (article, 2023-06-30)
Field: Materials Science — Polymers and Plastics

Wastepaper Sludge Ash and Acid Tar as Activated Filler Aggregates for Stone Mastic Asphalt Nowadays, the most common ways to dispose of acid tars and paper production waste are burial or incineration, but it is ecologically and economically expedient to use such waste in building materials. A new variant of filler aggregate—wastepaper sludge ash (paper production waste) and a chemical activator for filler aggregates—acid tar (oil-refining industry waste) is proposed. Elemental and mineralogical compositions of wastepaper sludge ash for comparison with commodity limestone mineral powder are established. Chemical activation of wastepaper sludge ash and limestone mineral powder was carried out and the wetting properties of the obtained materials were investigated by means of primary and neutralized acid tar. The physical and mechanical properties of stone mastic asphalt samples with different filler aggregate variants were studied. The possible chemical transformations in stone mastic asphalt using limestone mineral powder and wastepaper sludge ash activated by acid tar are shown. The possibility of replacing traditional limestone mineral powder with industrial wastepaper sludge ash has been proven, and the effectiveness of activating filler aggregates by acid tar has been confirmed. It was established that acid tar as an activator for filler aggregates does not require neutralization because stone mastic asphalt, in this case, demonstrates better properties compared to acid tar neutralization. As a result of our research, the application of wastepaper sludge ash is possible as a filler aggregate for stone mastic asphalt. And to obtain improved stone mastic asphalt characteristics, wastepaper sludge ash is required to activate 5 wt.% acid tar. Filler (materials) ; Asphalt ; tar (computing) ; Aggregate (composite) ; Pulp and paper industry ; Waste management ; Incineration ; Oil sands ; Chemistry ; Materials science ; Composite material Polymer Synthesis and Characterization ; Mining and Gasification Technologies ; Recycling and utilization of industrial and municipal waste in materials production
Authors: Melanie Cheung See Kit | Tyler C. Cropley | Christian Bleiholder | Christopher D. Chouinard | Frank Sobott | Ian Webb
Source: The Analyst (article, 2023-11-17)
Field: Chemistry — Spectroscopy

The role of solvation on the conformational landscape of α-synuclein Native ion mobility mass spectrometry has been used extensively to characterize ensembles of intrinsically disordered protein (IDP) conformers, but the extent to which the gaseous measurements provide realistic pictures of the solution conformations for such flexible proteins remains unclear. Therefore, we systematically studied the relationship between the solution and gaseous structural ensembles by measuring electrospray charge state and collision cross section (CCS) distributions for cationic and anionic forms of α-synuclein (αSN), an anionic protein in solution, as well as directly probed gas phase residue to residue distances <i>via</i> ion/ion reactions between gaseous α-synuclein cations and disulfonic acid linkers that form strong electrostatic bonds. We also combined results from in-solution protein crosslinking identified from native tandem mass spectrometry (MS/MS) with an initial αSN ensemble generated computationally by IDPConformerGenerator to generate an experimentally restrained solution ensemble of αSN. CCS distributions were directly calculated for the solution ensembles determined by NMR and compared to predicted gaseous conformers. While charge state and collision cross section distributions are useful for qualitatively describing the relative structural dynamics of proteins and major conformational changes induced by changes to solution states, the predicted and measured gas phase conformers include subpopulations that are significantly different than those expected from completely "freezing" solution conformations and preserving them in the gas phase. However, insights were gained on the various roles of solvent in stabilizing various conformers for extremely dynamic proteins like α-synuclein. Conformational isomerism ; Chemistry ; Ion-mobility spectrometry ; Solvation ; Ion ; Chemical physics ; Gas phase ; Mass spectrometry ; Molecular dynamics ; Residue (chemistry) ; Cationic polymerization ; Tandem mass spectrometry ; Electrospray ionization ; Computational chemistry ; Molecule ; Physical chemistry ; Organic chemistry Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: Guowei Yu | Huihui Li | Yujie Li | Yating Hu | Gang Wang | Benxue Ma | Huting Wang
Source: Foods (article, 2023-04-22)
Field: Chemistry — Analytical Chemistry

Multiscale Deepspectra Network: Detection of Pyrethroid Pesticide Residues on the Hami Melon The problem of pyrethroid residues has become a topical issue, posing a potential food safety concern. Pyrethroid pesticides are widely used to prevent and combat pests in Hami melon cultivation. Due to its high sensitivity and accuracy, gas chromatography (GC) is used most frequently for detecting pyrethroid pesticide residues. However, GC has a high cost and complex operation. This study proposed a deep-learning approach based on the one-dimensional convolutional neural network (1D-CNN), named Deepspectra network, to detect pesticide residues on the Hami melon based on visible/near-infrared (380-1140 nm) spectroscopy. Three combinations of convolution kernels were compared in the single-scale Deepspectra network. The convolution group of "5 × 1" and "3 × 1" kernels obtained a better overall performance. The multiscale Deepspectra network was compared to three single-scale Deepspectra networks on the preprocessing spectral data and obtained better results. The coefficient of determination (R2) for lambda-cyhalothrin and beta-cypermethrin was 0.758 and 0.835, respectively. The residual predictive deviation (RPD) for lambda-cyhalothrin and beta-cypermethrin was 2.033 and 2.460, respectively. The Deepspectra networks were compared with two conventional regression models: partial least square regression (PLSR) and support vector regression (SVR). The results showed that the multiscale Deepspectra network outperformed the other models. It was found that the multiscale Deepspectra network could be a novel approach for the quantitative estimation of pyrethroid pesticide residues on the Hami melon. These findings can also provide an effective strategy for spectral analysis. Pyrethroid ; Cypermethrin ; Artificial intelligence ; Support vector machine ; Mathematics ; Cyhalothrin ; Pattern recognition (psychology) ; Partial least squares regression ; Biological system ; Pesticide ; Machine learning ; Computer science ; Statistics ; Biology ; Agronomy Spectroscopy and Chemometric Analyses ; Pesticide Residue Analysis and Safety ; Water Quality Monitoring and Analysis
Authors: Sumit Devarshi | Amar Pndhare | Pradnya Lokhande | Joseph P. Mooney | Sandeep Chakrabarti
Source: ES Materials & Manufacturing (article, 2023-01-01)
Field: Materials Science — Polymers and Plastics

Natural Fibre Reinforced Thermoplastic Composite Synthesis Methods and Potential Applications Recently, the climate-friendly and sustainable characteristics of natural fibers as a reinforced composite have attracted much attention in applications ranging from textiles to transportation.Furthermore, the thermoplastic polymer matrix composite of natural fibers gives them distinct advantages such as high specific strength, lightweight properties, an abundance of supply, corrosion resistance, feasibility, and flexibility in design.The characteristics of natural fibers particularly enhance both their scope and use case within the automotive sector while boosting the transportation economy.This review aims to expand on the appropriate and widely used natural fibers as a thermoplastic composite for fiber reinforcement.The review covers the various reinforcement types, manufacturing methods, mechanical properties of natural fiber-reinforced composites, and their environmental suitability.Finally, the sectors in which natural fiber-reinforced composites are becoming popular and the future challenges of natural fiber-reinforced composites are summarized. Materials science ; Natural fiber ; Composite number ; Composite material ; Fiber-reinforced composite ; Automotive industry ; Thermoplastic ; Flexibility (engineering) ; Engineering Natural Fiber Reinforced Composites ; Tribology and Wear Analysis ; Electromagnetic wave absorption materials
Authors: Sascha Diefenbacher | V. M. Mikuni | Benjamin Nachman
Source: arXiv (Cornell University) (preprint, 2023-08-23)
Field: Materials Science — Materials Chemistry

Refining Fast Calorimeter Simulations with a Schrödinger Bridge Machine learning-based simulations, especially calorimeter simulations, are promising tools for approximating the precision of classical high energy physics simulations with a fraction of the generation time. Nearly all methods proposed so far learn neural networks that map a random variable with a known probability density, like a Gaussian, to realistic-looking events. In many cases, physics events are not close to Gaussian and so these neural networks have to learn a highly complex function. We study an alternative approach: Schrödinger bridge Quality Improvement via Refinement of Existing Lightweight Simulations (SQuIRELS). SQuIRELS leverages the power of diffusion-based neural networks and Schrödinger bridges to map between samples where the probability density is not known explicitly. We apply SQuIRELS to the task of refining a classical fast simulation to approximate a full classical simulation. On simulated calorimeter events, we find that SQuIRELS is able to reproduce highly non-trivial features of the full simulation with a fraction of the generation time. Artificial neural network ; Gaussian ; Fraction (chemistry) ; Computer science ; Probability density function ; Statistical physics ; Function (biology) ; Calorimeter (particle physics) ; Bridge (graph theory) ; Refining (metallurgy) ; Algorithm ; Artificial intelligence ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Computational Physics and Python Applications
Authors: Leilei Gu | Hongzhan Liu | Zhongchao Wei | Ruihuan Wu | Jianping Guo
Source: Photonics (article, 2023-07-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optimized Design of Plasma Metamaterial Absorber Based on Machine Learning Metamaterial absorbers have become a popular research direction due to their broad application prospects, such as in radar, infrared imaging, and solar cell fields. Usually, nanostructured metamaterials are associated with a large number of geometric parameters, and traditional simulation designs are time consuming. In this paper, we propose a framework for designing plasma metamaterial absorbers in both a forward prediction and inverse design composed of a primary prediction network (PPN) and an auxiliary prediction network (APN). The framework can build the relationship between the geometric parameters of metamaterials and their optical response (reflection spectra, absorption spectra) from a large number of training samples, thus solving the problem of time-consuming and case-by-case numerical simulations in traditional metamaterial design. This framework can not only improve forward prediction more accurately and efficiently but also inverse design metamaterial absorbers from a given required optical response. It was verified that it is also applicable to absorbers of different structures and materials. Our results show that it can be used in metamaterial absorbers, chiral metamaterials, metamaterial filters, and other fields. Metamaterial ; Metamaterial absorber ; Computer science ; Reflection (computer programming) ; Inverse ; Optics ; Radar ; Metamaterial cloaking ; Materials science ; Tunable metamaterials ; Physics ; Telecommunications ; Mathematics ; Geometry Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies ; Acoustic Wave Phenomena Research
Authors: Stephanie Yerdelen | Yihui Yang | Justin L. Quon | Charles D. Papageorgiou | Christopher Mitchell | Ian Houson | Ján Šefčı́k | Joop H. ter Horst | Alastair J. Florence | Cameron J. Brown
Source: Crystal Growth & Design (article, 2023-01-18)
Field: Materials Science — Materials Chemistry

Machine Learning-Derived Correlations for Scale-Up and Technology Transfer of Primary Nucleation Kinetics Scaling up and technology transfer of crystallization processes have been and continue to be a challenge. This is often due to the stochastic nature of primary nucleation, various scale dependencies of nucleation mechanisms, and the multitude of scale-up approaches. To better understand these dependencies, a series of isothermal induction time studies were performed across a range of vessel volumes, impeller types, and impeller speeds. From these measurements, the nucleation rate and growth time were estimated as parameters of an induction time distribution model. Then using machine learning techniques, correlations between the vessel hydrodynamic features, calculated from computational flow dynamic simulations, and nucleation kinetic parameters were analyzed. Of the 18 machine learning models trained, two models for the nucleation rate were found to have the best performance (in terms of % of predictions within experimental variance): a nonlinear random Forest model and a nonlinear gradient boosting model. For growth time, a nonlinear gradient boosting model was found to outperform the other models tested. These models were then ensembled to directly predict the probability of nucleation, at a given time, solely from hydrodynamic features with an overall root mean square error of 0.16. This work shows how machine learning approaches can be used to analyze limited datasets of induction times to provide insights into what hydrodynamic parameters should be considered in the scale-up of an unseeded crystallization process. Nucleation ; Impeller ; Scaling ; Nonlinear system ; Statistical physics ; Computer science ; Machine learning ; Mathematics ; Thermodynamics ; Physics Crystallization and Solubility Studies ; Calcium Carbonate Crystallization and Inhibition ; nanoparticles nucleation surface interactions