jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: D Albert-Weiss | U Netzelmann | A Osman
Source:  (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Multitask learning approach for fruit ripeness prediction using a dual band thermal camera Multi-Task Learning allows to comprehend multiple tasks based on a single neural architecture by making the assumption that the tasks are related. We propose a multi-head architecture that allows to classify melon fruits into ripeness classes by taking images based on dual bandaid-and longwave infrared (IR) thermography. To study the ripening labels based on sugar content, firmness and aroma are studied. We are able to show that multi-task learning delivers superior results for multimodal and multi-label situation when different tasks exhibit a positive transfer Ripeness ; Computer science ; Artificial intelligence ; Task (project management) ; Transfer of learning ; Thermography ; Dual (grammatical number) ; Pattern recognition (psychology) ; Machine learning ; Computer vision ; Ripening ; Infrared ; Engineering ; Chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: Behzad Nouri | Seyed Saeid Mohtasebi | Lena Beheshti Moghadam | Mahmoud Omid | Seyed Morteza Mohtasebi
Source: Research Square (preprint, 2022-12-23)
Field: Chemistry — Analytical Chemistry

Rapid monitoring of milk fat using Image processing coupled with ANN and PSO methods Abstract Monitoring the main compositions of milk content like fat, lactose, protein and total solids, has become a major challenge in dairy cattle farming. For quantitative determination of fat content in milk based on the relation of milk color features different methods have been used, but long time, high cost, and need for experts for analysis are some disadvantages of them. In this study, for rapid monitoring of milk fat content, novel technology of image processing coupled with artificial neural network (ANN) and Particle swarm optimization (PSO) methods has been applied. The estimated milk fat content of the best proposed method was extensively compared with the reference sample (R 2 =0.99, MAE=0.22, and MSE=0.05). Moreover, effect of water on color components of milk with different percentages of fat content have been investigated. Results approved the proposed method as a reliable, rapid and low-cost method for monitoring milk fat content. Lactose ; Artificial neural network ; Particle swarm optimization ; Food science ; Cow milk ; Milk fat ; Mathematics ; Computer science ; Artificial intelligence ; Chemistry ; Algorithm Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Advanced Chemical Sensor Technologies
Authors: Michael Kloster | Andrea M. Burfeid-Castellanos | Daniel Langenkämper | Tim W. Nattkemper | Bánk Beszteri
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-07-16)
Field: Materials Science — Biomaterials

Improving deep learning-based segmentation of diatoms in gigapixel-sized virtual slides by object-based tile positioning and object integrity constraint Abstract Diatoms represent one of the morphologically and taxonomically most diverse groups of microscopic eukaryotes. Light microscopy-based taxonomic identification and enumeration of frustules, the silica shells of these microalgae, is broadly used in aquatic ecology and biomonitoring. One key step in emerging digital variants of such investigations is segmentation, a task that has been addressed before, but usually in manually captured megapixel-sized images of individual diatom cells with a mostly clean background. In this paper, we applied deep learning-based segmentation methods to gigapixel-sized, high-resolution scans of diatom slides with a realistically cluttered background. This setup requires large slide scans to be subdivided into small images (tiles) to apply a segmentation model to them. This subdivision (tiling), when done using a sliding window approach, often leads to cropping relevant objects at the boundaries of individual tiles. We hypothesized that in the case of diatom analysis, reducing the amount of such cropped objects in the training data can improve segmentation performance by allowing for a better discrimination of relevant, intact frustules or valves from small diatom fragments, which are considered irrelevant when counting diatoms. We tested this hypothesis by comparing a standard sliding window / fixed-stride tiling approach with two new approaches we term object-based tile positioning with and without object integrity constraint. With all three tiling approaches, we trained Mask-R-CNN and U-Net models with different amounts of training data and compared their performance. Object-based tiling with object integrity constraint led to an improvement in pixel-based precision by 12–17 percentage points without substantially impairing recall when compared with standard sliding window tiling. We thus propose that training segmentation models with object-based tiling schemes can improve diatom segmentation from large gigapixel-sized images but could potentially also be relevant for other image domains. Segmentation ; Computer science ; Artificial intelligence ; Object (grammar) ; Computer vision ; Diatom ; Constraint (computer-aided design) ; Pixel ; Image segmentation ; Pattern recognition (psychology) ; Computer graphics (images) ; Biology ; Ecology ; Mathematics Diatoms and Algae Research ; Genomics and Phylogenetic Studies ; Species Distribution and Climate Change
Authors: Daiki IIDA | Mito Kokawa | Yutaka Kitamura
Source: Research Square (preprint, 2022-12-05)
Field: Chemistry — Analytical Chemistry

Estimation of Apple Mealiness by means of Laser Scattering Measurement Abstract Mealiness is a phenomenon in which intercellular adhesions in apples loosen during storage, causing soft and floury texture at the time of eating, and leading to lower consumer preference. Although apples can be stored and commercially sold throughout the year, the occurrence of mealiness is not monitored during storage. Therefore, the objective of this research was to non-destructively estimate the mealiness of apple fruit by means of laser scattering measurement. This method is based on laser light backscattering imaging but can quantify a wider range of backscattered light than the conventional method. Lasers with wavelengths of 633 nm and 850 nm were used as a light source, and after acquiring backscattered images, profiles and images were obtained. Profile features such as curve fitting coefficients and profile gradients, and image features such as statistical image features and texture features were extracted from the profiles and images, respectively. PLS, SVM, and ANN models were used for the estimation of mealiness. The results of the estimation based on these features showed that the ANN model combining both wavelengths had a higher performance (R = 0.634, RMSE = 7.621) than the models constructed from features calculated from the data obtained by a single wavelength. In order to improve the performance of model, we applied various ensemble learning. As a result, the ensemble model showed the highest performance (R = 0.682, RMSE = 7.281). These results suggest that laser scattering measurement is a promising method for estimating the apple fruit mealiness. Laser ; Wavelength ; Scattering ; Optics ; Texture (cosmology) ; Light scattering ; Mean squared error ; Mathematics ; Materials science ; Computer science ; Artificial intelligence ; Environmental science ; Statistics ; Physics ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Phuangphet Hemrattrakun | Danai Boonyakiat | Kazuhiro Nakano | Shintaroh Ohashi | Phonkrit Maniwara | Sila Kittiwachana | Parichat Theanjumpol
Source: Chiang Mai Journal of Science (article, 2022-03-01)
Field: Chemistry — Analytical Chemistry

Qualitative Characterization of Astringent and De-astringed Intact ‘Xichu’ Persimmon Fruit Using VIS/SWNIR and Chemometrics Astringency removal is considered as a key process before commercialize persimmon cv. ‘Xichu’, due to its high content of soluble tannins. Application of carbon dioxide treatment effective in removing astringency and widely used as a commercial postharvest practice for astringent persimmon fruit. The aim of this research is study to the application of visible and shortwave near infrared (VIS/SWNIR) spectroscopy in the spectral data of 400-1100 nm to discriminate astringent (A) and de-astringed (DA) fruit non-destructively based on the sensorial perception as 0.10% soluble tannin of fresh weight. The highest classifi cation accuracy score was obtained from QDA model combined with 2D preprocessing technique as 98.99% in the external validation set. Therefore, the results obtained in this study can be considered as a non-destructive analytical method to monitoring the effectiveness of the astringency removal treatment in ‘Xichu’ persimmon fruit. Astringent ; Chemistry ; Chemometrics ; Tannin ; Horticulture ; Food science ; Mathematics ; Chromatography ; Taste ; Biology Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Fermentation and Sensory Analysis
Authors: Manuel Pérez‐Escribano | Joanna Jankowska | Giovanni Granucci | Daniel Escudero
Source: ChemRxiv (preprint, 2022-12-21)
Field: Chemistry — Physical and Theoretical Chemistry

The Radiative Surface Hopping (RSH) algorithm: capturing fluorescence events in molecular systems within a semi-classical Non-Adiabatic Molecular Dynamics framework In this article, we present the Radiative Surface Hopping (RSH) algorithm which enables to model fluores- cence within a semi-classical Non-Adiabatic Molecular Dynamics framework. The algorithm has been tested for the photodeactivation dynamics of trans-4-Dimethylamino-4’-cyanostilbene (DCS). By treating on equal footing radiative and non-radiative processes, our method allows to attain a complete molecular movie of the excited-state deactivation. Our dynamics rely on a semiempirical quantum mechanical/molecular mechanical (QM/MM) Hamiltonian and has been run for hundreds of picoseconds, both in the gas phase and in isopropyl ether. The proposed approach successfully captures the first fluorescence processes occurring in DCS and it suc- ceeds in reproducing the experimental fluorescence lifetime and quantum yield, especially in the polar solvent. The analysis of the geometrical features of the emissive species during the dynamics discards the hypothesis of a twisted intramolecular charge transfer (TICT) state to be responsible for the dual emission observed experimen- tally in some polar solvents. In a nutshell, our method opens the way for theoretical studies on early-fluorescence events occurring up to hundreds of picoseconds in molecular systems. Surface hopping ; Radiative transfer ; Picosecond ; Adiabatic process ; Molecular dynamics ; Quantum yield ; Excited state ; Intramolecular force ; Conical intersection ; Fluorescence ; Hamiltonian (control theory) ; Quantum ; Chemical physics ; Chemistry ; Physics ; Molecular physics ; Atomic physics ; Quantum mechanics ; Computational chemistry Photochemistry and Electron Transfer Studies ; Strong Light-Matter Interactions ; Spectroscopy and Quantum Chemical Studies
Authors: Aleksandr S. Baburin | Dmitriy O. Moskalev | Evgeniy S. Lotkov | Olga S. Sorokina | Dmitry A. Baklykov | Sergey S. Avdeev | Kirill A. Buzaverov | G. M. Yankovskiĭ | A. V. Baryshev | Ilya A. Ryzhikov | Ilya A. Rodionov
Source: Research Square (preprint, 2022-11-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Beyond single-crystalline metals: ultralow-loss silver films on lattice-mismatched substrates Abstract High-quality factor plasmonic devices are crucial components in the fields of nanophotonics, quantum computing and sensing. The majority of these devices are required to be fabricated on non-lattice matched or transparent amorphous substrates. Plasmonic devices quality factor is mainly defined by ohmic losses, scattering losses at grain boundaries, and in-plane plasmonic scattering losses of a metal – substrate system. Here, we demonstrate the deposition technique to e-beam evaporate ultralow-loss silver thin films on transparent lattice-mismatched substrates. The process is based on evolutionary selection growth. The key feature of our approach is a well-defined control of deposition on a cooled substrate, self-crystallization and subsequent annealing for precise stress relaxation that promote further grains growth. We are able to deposit 100-nm thick ultraflat polycrystalline silver films with micrometer-scale grains and ultralow optical losses. Finally, we show ultra-high-quality factor plasmonic silver nanostructures on transparent lattice-mismatched substrate comparable to epitaxial silver. This can be of the great interest for high performance or single-molecule optical sensorics applications. Materials science ; Plasmon ; Optoelectronics ; Annealing (glass) ; Scattering ; Nanophotonics ; Amorphous solid ; Ohmic contact ; Epitaxy ; Thin film ; Crystallite ; Electroforming ; Nanolithography ; Nanotechnology ; Optics ; Crystallography ; Composite material ; Metallurgy ; Layer (electronics) ; Chemistry ; Fabrication Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Quantum Dots Synthesis And Properties
Authors: Sergey V. Sokolkov
Source: Electrochem (article, 2022-10-09)
Field: Chemistry — Electrochemistry

Electrochemical Digitization of Biological Fluids Samples Digital medicine based on the integration of all medical data from a particular patient has become a reality today, thanks to information technology. Traditional medical examinations can be supplemented by assessment results of the oxidative-anti-oxidative (OAO) status of the body. Electrochemical sensors are able to not only determine the integral indicators of the OAO system of the body but also to depict details of the processes occurring in the system. The main obstacle to the widespread use of electrochemical sensors in medical diagnostics is the extremely small amount of received information in comparison to the tens of thousands of known human diseases. The problem can be eliminated only by rethinking the purpose of electrochemical measurement within the framework of thermodynamics of information processes and information theory. In the information paradigm of electrochemical analysis of biological fluids, a sample is considered an electrochemical message created by a sensor. The purpose of electrochemical measurement is to obtain information in a volume sufficient to identify the sample composition within the range of possible concentrations of its components. The fundamentals of the thermodynamics of information processes are considered and conclusions that are of practical importance for the development of electrochemical sensors and analyzers are derived. It is shown that the potentiostatic control of the sensor is physically impacted by the electromechanical instability of the electrical double layer, which is the main source of sensor signal noise. Estimates of a minimum amount of analytical signal information required for the identification of a sample of known composition, such as a biological fluid, are provided. Examples of highly informative analytical signals for flowing and stationary samples are presented. Problems related to the visualization of such signals are noted. Digitization ; Computer science ; Obstacle ; SIGNAL (programming language) ; Sample (material) ; Electrochemical noise ; Electrochemical cell ; Electrochemical gas sensor ; Biological system ; Electrochemistry ; Process engineering ; Biomedical engineering ; Biochemical engineering ; Chemistry ; Electrode ; Engineering ; Physics ; Thermodynamics Electrochemical Analysis and Applications ; Optical Imaging and Spectroscopy Techniques ; Analytical Chemistry and Sensors
Authors: Xiaozhe Hu | Junyuan Lin
Source: arXiv (Cornell University) (preprint, 2022-07-16)
Field: Chemistry — Inorganic Chemistry

Solving Graph Laplacians via Multilevel Sparsifiers We consider effective preconditioners for solving Laplacians of general weighted graphs. Theoretically, spectral sparsifiers (SSs) provide preconditioners of optimal computational complexity. However, they are not easy to use for real-world applications due to the implementation complications. Multigrid (MG) methods, on the contrary, are computationally efficient but lack of theoretical justifications. To bridge the gap between theory and practice, we adopt ideas of MG and SS methods and proposed preconditioners that can be used in practice with theoretical guarantees. We expand the original graph based on a multilevel structure to obtain an equivalent expanded graph. Although the expanded graph has a low diameter, a favorable property for constructing SSs, it has negatively weighted edges, which is an unfavorable property for the SSs. We design an algorithm to properly eliminate the negatively weighted edges and prove that the resulting expanded graph with positively weighted edges is spectrally equivalent to the expanded graph, thus, the original graph. Due to the low-diameter property of the positively-weighted expanded graph preconditioner (PEGP), existing algorithms for finding SSs can be easily applied. To demonstrate the advantage of working with the PEGP, we propose a type of SS, multilevel sparsifier preconditioner (MSP), that can be constructed in an easy and deterministic manner. We provide some preliminary numerical experiments to verify our theoretical findings and illustrate the practical effectiveness of PEGP and MSP in real-world applications. Preconditioner ; Multigrid method ; Graph ; Computer science ; Algorithm ; Theoretical computer science ; Mathematics ; Iterative method ; Mathematical analysis Metal-Organic Frameworks: Synthesis and Applications ; Graphene research and applications ; Matrix Theory and Algorithms
Authors: Brandon C. Bukowski | Florencia A. Son | Timur İslamoğlu | Omar K. Farha | Randall Q. Snurr
Source: Diffusion fundamentals. (article, 2022-11-03)
Field: Chemistry — Inorganic Chemistry

Effect of pore topology on guest diffusion in nanoporous metal-organic frameworks Nanoporous ; Metal-organic framework ; Diffusion ; Materials science ; Metal ; Topology (electrical circuits) ; Nanotechnology ; Chemical engineering ; Chemistry ; Physics ; Organic chemistry ; Engineering ; Adsorption ; Metallurgy ; Thermodynamics ; Electrical engineering Metal-Organic Frameworks: Synthesis and Applications ; Nanoporous metals and alloys ; Nanocluster Synthesis and Applications
Authors: Ivaldo Gomes | Vinícius Câmara Costa | Fábio Alan Carqueija Amorim | Uillian Mata Cerqueira | Cléber Galvão Novaes | Thiago Pereira das Chagas | Marcos Almeida Bezerra
Source: Research Square (preprint, 2022-10-17)
Field: Chemistry — Analytical Chemistry

Application of multivariate analysis techniques in the evaluation of metal content in industrialized spices Abstract Industrialized powdered spices are widely used by the population in food preparation, although they are associated with several health problems. This relationship between spices and health problems can be explained due to the presence of some substances used to enhance the flavor and preserve them. Therefore, it is important to evaluate the metal content in these products, allowing their characterization. In this work, six metals (Zn, Fe, Ca, Mg, Na, and K) were determined in samples of dry industrialized spices that were sprayed and sold in sachets. Flame atomic absorption (FAAS) and emission (FAES) spectrometry were used to quantify the metals in the digestate obtained after the decomposition of the samples in a digester block. The following concentration ranges were found for the analyzed metals (mg Kg − 1 ): Zn (&lt; LQ-15.53), Fe (10.82–205.3), Ca (27.45–1842), Mg (114.8–1374), Na (63739–268188) e K (1560-235864). These values were evaluated using principal component analysis (PCA) and Kohonen self-organizing maps (KSOMs) techniques. The multivariate analysis allowed the recognition of grouping trends according to the spice brands, which suggests the possibility of a base material, from which the flavors are differentiated. Atomic absorption spectroscopy ; Principal component analysis ; Flavor ; Metal ; Chemistry ; Multivariate statistics ; Digestate ; Environmental chemistry ; Population ; Decomposition ; Multivariate analysis ; Metallurgy ; Materials science ; Food science ; Mathematics ; Anaerobic digestion ; Statistics Heavy Metals in Plants ; Advanced Chemical Sensor Technologies ; Spectroscopy and Chemometric Analyses
Authors: Mike Martynowycz | Tamir Gonen
Source: Physics Today (article, 2022-06-01)
Field: Materials Science — Materials Chemistry

Unlocking the potential of microcrystal electron diffraction Atoms stick together in different ways to make the molecules that compose everything we touch and see. Our bodies are made of cells. Cells, in turn, are made of lipids, proteins, nucleic acids, metabolites, and water. Every one of those molecules is made from the same handful of atoms. But although the components are the same, the molecules differ in how many atoms they have and how those atoms are arranged in space. Molecule ; Nucleic acid ; Space (punctuation) ; Electron ; Chemical physics ; Atoms in molecules ; Crystallography ; Chemistry ; Nanotechnology ; Materials science ; Physics ; Computer science ; Biochemistry ; Organic chemistry ; Quantum mechanics Enzyme Structure and Function ; Advanced Electron Microscopy Techniques and Applications ; Photosynthetic Processes and Mechanisms
Authors: Chao Wang | Yan Liu | Zhenzhen Xia | Qiao Wang | Shuo Duan | Zhiyong Gong | Jiwang Chen
Source: Research Square (preprint, 2022-05-12)
Field: Chemistry — Analytical Chemistry

Convolutional neural network based portable computer vision system for freshness assessment of crayfish (Prokaryophyllus clarkii) Abstract Developing novel techniques for freshness assessment are of the utmost importance in yield and trade of aquatic products. The crayfish ( Prokaryophyllus clarkii ) is one of the most popular freshwater products in China and its food safety should be seriously concerned. In this study, a convolutional neural network (CNN) based portable computer vision system for freshness assessment of crayfish method was proposed. A portable microscope was utilized to collect the microscopic images of crayfish with different freshness levels. The convolutional neural network was constructed and then optimized to extract features from the microscopic images. For the pictures from the portable microscope, the prediction accuracies of freshness could be 86.5% and 83.3% when the optimized networks were applied. The results indicate that the convolutional neural network based portable computer vision system may provide an alternative way for the freshness assessment in crayfish industrial chain. Crayfish ; Convolutional neural network ; Computer science ; Procambarus clarkii ; Artificial intelligence ; Machine vision ; Artificial neural network ; Computer vision ; Fishery ; Biology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring Technologies ; Industrial Vision Systems and Defect Detection