jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sunee Pongpinigpinyo | Suphachai Phawiakkharakun | Pinyo Taeprasartsit
Source: Current Applied Science and Technology (article, 2021-05-18)
Field: Chemistry — Analytical Chemistry

Acoustic Sensing for Quality Edible Evaluation of Sriracha Pineapple Using Convolutional Neural Network Sriracha pineapple is a pineapple of the Smooth Cayenne variety and is one of Thailand's favorite tropical fruits. It is not only one of the most favorite agricultural products for the pineapple processing and canning industries but it is also widely consumed as a fresh fruit. Many exporters and consumers prefer to measure juiciness before processing food products and consuming, respectively. The traditional method used by pineapple sellers or farmers for fruit juiciness classification is to tap the pineapple using force impulse techniques with a rubber-tipped stick or a person's middle finger tapping. However, these traditional methods of classification require a lot of expertise and experience. Thus, the inspector's perception of accomplishing the classification process may be inclined to errors and uneven results. This paper proposes a combination of acoustic sensing and convolutional neural network (CNN). The tapping sound of 30 Sriracha pineapple samples using force impulse techniques was recorded on smartphone. The tapping sounds were processed into a juiciness classification system of three classes: (1) Juiciness 1, (2) Juiciness 2, and (3) Juiciness 3. The system involved a combination of acoustic sensing and CNN to compare the results between Mel Frequency Cepstral Coefficient (MFCC) and Mel-Spectrogram features extraction, with replication of the same CNN model, to evaluate the pineapple's edibility from its juiciness level. Experimental results showed that MFCC combined with CNN performed the best, with an accuracy of 96.67% and F1-score of 0.97. It outperformed the Mel-spectrogram combined with CNN. Convolutional neural network ; Mel-frequency cepstrum ; Tapping ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Mathematics ; Speech recognition ; Feature extraction ; Engineering Spectroscopy and Chemometric Analyses ; Date Palm Research Studies
Authors: Jorune Sakalauskaite | Meaghan Mackie | Alberto J. Taurozzi | Matthew J. Collins | Frédéric Marin | Beatrice Demarchi
Source: Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics (article, 2021-09-08)
Field: Materials Science — Biomaterials

The degradation of intracrystalline mollusc shell proteins: A proteomics study of Spondylus gaederopus Shell (structure) ; Degradation (telecommunications) ; Proteomics ; Chemistry ; Materials science ; Computer science ; Composite material ; Biochemistry Calcium Carbonate Crystallization and Inhibition ; Marine Biology and Environmental Chemistry ; Marine Bivalve and Aquaculture Studies
Authors: Thinley Jamtsho | Najitha Banu | Chimi Kinley
Source: PLANT ARCHIVES (article, 2021-03-10)
Field: Materials Science — Materials Chemistry

CRITICAL REVIEW ON PAST, PRESENT AND FUTURE SCOPE OF DIAMONDBACK MOTH MANAGEMENT Diamondback moth, Plutella xylostella (L.) (Lepidoptera: Plutellidae), has become the most destructive insect pest of Brassicaceae plants globally. Numerous control methods are available to control the moth, such as host plant resistance, physical controls, chemical controls, cultural controls, and biological controls. The continued application of insecticides has led to the development of resistance to almost 97 chemical compounds. The biological methods also became inefficient in the control of the moth. Therefore, nanotechnology would provide green and efficient alternatives for controlling the pest without harming environment. This review focuses on control methods used to manage diamondback moth and nanomaterials’ potential in insect pest management as modern nanotechnology approaches. It focuses on the past, present, and future scope of diamondback moth management Diamondback moth ; Plutellidae ; Plutella ; Integrated pest management ; Scope (computer science) ; Lepidoptera genitalia ; Biology ; Pest control ; PEST analysis ; Biotechnology ; Risk analysis (engineering) ; Agronomy ; Ecology ; Business ; Botany ; Computer science Nanoparticles: synthesis and applications ; Electrochemical Analysis and Applications
Authors: Ruidong Li | Wenyong Huang | Guanlan Shang | Xiaobing Zhang | Xin Wang | Jianguo Liu | Yong Wang | Junfeng Qiao | Xing Fan | Kai Wu | Wenhua Zi
Source: Journal of the Brazilian Chemical Society (article, 2021-10-21)
Field: Chemistry — Analytical Chemistry

Rapid Recognizing the Producing Area of a Tobacco Leaf Using Near-Infrared Technology and a Multi-Layer Extreme Learning Machine Algorithm A novel recognition method was put forward to identify the producing areas of the flue-cured tobacco leaves rapidly and non-destructively by using a near-infrared (NIR) spectrometer and a multi-layer-extreme learning machine (ML-ELM) algorithm. In contrast to traditional linear discriminant analysis (LDA) and extreme learning machine (ELM) algorithms, the accuracy, sensitivity and specificity were the highest for the proposed ML-ELM algorithm. The ML-ELM models for different producing areas of Yunnan tobacco leaves had the best generalization ability and prediction results. Besides, the above three algorithms were also identified by using the chemical index data. The experimental results indicated that the NIR spectroscopy technology together with ML-ELM algorithm achieved the best prediction performance both using the NIR spectral data and chemical index data. It indicates that the combination of NIR and ML-ELM can recognize different producing areas of Yunnan tobacco leaves rapidly, accurately, and non-destructively. Extreme learning machine ; Tobacco leaf ; Algorithm ; Linear discriminant analysis ; Generalization ; Near-infrared spectroscopy ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Mathematics ; Engineering ; Artificial neural network ; Physics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Tao He | Shibiao Mu | Houkui Zhou | Junguo Hu
Source: Wood Research (article, 2021-03-02)
Field: Chemistry — Organic Chemistry

Wood species identification based on an ensemble of deep convolution neural networks. Our paper proposed an ensemble framework of combining three deep convolution neural networks (CNN). This method was inspired by network in network. Transfer learning used to accelerate training and deeper layers of network. Nine different CNN architectures were trained and evaluated in two wood macroscopic images datasets. After two times of 30 epochs training, our proposed network obtained 100% test rate in our dataset, which including 8 kinds of wood species and 918 images. The proposed method achieved 98.81% test recognition rate after three times training with 30 epochs in other dataset, which including 41 kinds of wood species and 11,984 images. Results showed that magnification macroscopic images can be instead of microscopic images in wood species identification, and our proposed ensemble of deep CNN can be used for wood species identification. Convolution (computer science) ; Identification (biology) ; Artificial neural network ; Convolutional neural network ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Biological system ; Mathematics ; Botany ; Biology Wood and Agarwood Research ; Image Processing and 3D Reconstruction ; Remote Sensing and LiDAR Applications
Authors: Klas Rydhmer | Raghavendra Selvan
Source: Ecological Informatics (article, 2021-10-13)
Field: Chemistry — Analytical Chemistry

Dynamic β-VAEs for quantifying biodiversity by clustering optically recorded insect signals While insects are the largest and most diverse group of terrestrial animals, constituting ca. 80% of all known species, they are difficult to study due to their small size and similarity between species. Conventional monitoring techniques depend on time consuming trapping methods and tedious microscope-based work by skilled experts in order to identify the caught insect specimen at species, or even family level. Researchers and policy makers are in urgent need of a scalable monitoring tool in order to conserve biodiversity and secure human food production due to the rapid decline in insect numbers. Novel automated optical monitoring equipment can record tens of thousands of insect observations in a single day and the ability to identify key targets at species level can be a vital tool for entomologists, biologists and agronomists. Recent work has aimed for a broader analysis using unsupervised clustering as a proxy for conventional biodiversity measures, such as species richness and species evenness, without actually identifying the species of the detected target. In order to improve upon existing insect clustering methods, we propose an adaptive variant of the variational autoencoder (VAE) which is capable of clustering data by phylogenetic groups. The proposed dynamic β-VAE dynamically adapts the scaling of the reconstruction and regularization loss terms (β value) yielding useful latent representations of the input data. We demonstrate the usefulness of the dynamic β-VAE on optically recorded insect signals from regions of southern Scandinavia to cluster unlabelled targets into possible species. We also demonstrate improved clustering performance in a semi-supervised setting using a small subset of labelled data. These experimental results, in both unsupervised- and semi-supervised settings, with the dynamic β-VAE are promising and, in the near future, can be deployed to monitor insects and conserve the rapidly declining insect biodiversity. Biodiversity ; Cluster analysis ; Insect ; Computer science ; Ecology ; Artificial intelligence ; Biology Spectroscopy and Chemometric Analyses ; Plant and animal studies ; Advanced Chemical Sensor Technologies
Authors: Jillian Rix | Boshan Mo | A. Yu. Nikiforov | Uday B. Pal | Srikanth Gopalan | Soumendra N. Basu
Source: Journal of The Electrochemical Society (article, 2021-11-01)
Field: Materials Science — Materials Chemistry

Quantifying Percolated Triple Phase Boundary Density and Its Effects on Anodic Polarization in Ni-Infiltrated Ni/YSZ SOFC Anodes Increasing the density of percolated triple phase boundaries (TPBs) by infiltrating nanoscale electrocatalysts can improve the performance of solid oxide fuel cell (SOFC) anodes. However, the complex microstructure of these infiltrated nanocatalysts creates challenges in quantifying their role in anode performance improvements. In this research, scanning electron microscopy of fractured cross-sections of a Ni-nanocatalyst infiltrated anodic symmetric cell along with three-dimensional (3-D) reconstruction of the same anode have been used to quantify the changes in percolated TPB densities due to infiltration. This change in percolated TPB density has been compared to the improvement in anode activation polarization resistance measured by electrochemical impedance spectroscopy (EIS). It was found that increased TPB densities only partially accounted for the measured performance improvement. Distribution of relaxation times (DRT) analyses showed that a reduction in the time constants of the catalytic processes in the anode also play a role, suggesting that the added nanoscale percolated TPB boundaries are more electrochemically active as compared to the cermet TPB boundaries. Triple phase boundary ; Anode ; Materials science ; Dielectric spectroscopy ; Yttria-stabilized zirconia ; Polarization (electrochemistry) ; Solid oxide fuel cell ; Microstructure ; Oxide ; Electrochemistry ; Nanoscopic scale ; Nanomaterial-based catalyst ; Chemical engineering ; Scanning electron microscope ; Cermet ; Analytical Chemistry (journal) ; Composite material ; Ceramic ; Nanoparticle ; Electrode ; Nanotechnology ; Metallurgy ; Cubic zirconia ; Chemistry Advancements in Solid Oxide Fuel Cells ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Muhammad Awais | Laiq Khan | Saghir Ahmad | Mohsin Jamil
Source: Energies (article, 2021-03-26)
Field: Materials Science — Materials Chemistry

Feedback-Linearization-Based Fuel-Cell Adaptive-Control Paradigm in a Microgrid Using a Wavelet-Entrenched NeuroFuzzy Framework The article portrays an adaptive control paradigm for the swift response of a solid-oxide fuel cell (SOFC) in a grid-connected microgrid. The control scheme is based on an adaptive feedback-linearization-embedded fully recurrent NeuroFuzzy Laguerre wavelet control (FBL-FRNF-Lag-WC) framework. The nonlinear functions of feedback linearization (FBL) are estimated using a fully recurrent NeuroFuzzy Laguerre wavelet control (FRNF-Lag-WC) architecture with a recurrent Gaussian membership function in the antecedent part and a recurrent Laguerre wavelet in the consequent part, respectively. The performance of the proposed control scheme is validated for various stability, quality, and reliability factors obtained through a simulation testbed implemented in MATLAB/Simulink. The proposed scheme is compared against adaptive NeuroFuzzy, PID, and adaptive PID (aPID) control schemes using different performance parameters for a grid-connected load over 24 h. Control theory (sociology) ; Microgrid ; Computer science ; Wavelet ; PID controller ; Adaptive control ; Control engineering ; Engineering ; Control (management) ; Artificial intelligence Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Juan Antonio Tamayo‐Ramos | Sonia Martel-Martín | Rocío Barros | Alfredo Bol | Mert Atilhan | Santiago Aparício
Source: Journal of Molecular Liquids (article, 2021-10-06)
Field: Chemistry — Organic Chemistry

On the behavior of quercetin + organic solvent solutions and their role for C60 fullerene solubilization The nature of flavonoids in polar organic solvents solutions is studied using classical molecular dynamics simulations considering quercetin as an archetypical flavonoid and acetone, dimethylformamide and dimethyl sulfoxide as representatives of solvents with different polarity. The solvation, intermolecular forces (hydrogen bonding) and interactions of the flavonoid with the solvents are analyzed. Likewise, the role of quercetin on changing the solvent properties and the possibility of acting as a solubility enhancer for fullerenes (C60) are studied by considering the properties of C60 fullerene in quercetin plus polar solvents solutions. The reported results provide information on the nature of the considered complex liquid mixtures and analyze the possibility of using flavonoids as natural, non-toxic, modifiers of traditional polar organic solvents and to improve the solubility of complex solutes such as fullerene nanoparticles. Fullerene ; Chemistry ; Solubility ; Solvation ; Quercetin ; Acetone ; Solvent ; Intermolecular force ; Dimethylformamide ; Organic chemistry ; Hydrogen bond ; Flavonoid ; Polar ; Dimethyl sulfoxide ; Molecule Fullerene Chemistry and Applications ; Carbon Nanotubes in Composites ; Advanced Thermodynamics and Statistical Mechanics
Authors: Wojciech Bocian | Elżbieta Bednarek | Katarzyna Michalska
Source: International Journal of Molecular Sciences (article, 2021-07-01)
Field: Chemistry — Spectroscopy

Explanation of the Formation of Complexes between Representatives of Oxazolidinones and HDAS-β-CD Using Molecular Modeling as a Complementary Technique to cEKC and NMR Molecular modeling (MM) results for tedizolid and radezolid with heptakis-(2,3-diacetyl-6-sulfo)-β-cyclodextrin (HDAS-β-CD) are presented and compared with the results previously obtained for linezolid and sutezolid. The mechanism of interaction of chiral oxazolidinone ligands belonging to a new class of antibacterial agents, such as linezolid, tedizolid, radezolid, and sutezolid, with HDAS-β-CD based on capillary electrokinetic chromatography (cEKC), nuclear magnetic resonance (NMR) spectroscopy, and MM methods was described. Principles of chiral separation of oxazolidinone analogues using charged single isomer derivatives of cyclodextrin by the cEKC method were presented, including the selection of the optimal chiral selector and separation conditions, complex stoichiometry, and binding constants, which provided a comprehensive basis for MM studies. In turn, NMR provided, where possible, direct information on the geometry of the inclusion complexes and also provided the necessary structural information to validate the MM calculations. Consequently, MM contributed to the understanding of the structure of diastereomeric complexes, the thermodynamics of complexation, and the visualization of their structures. The most probable mean geometries of the studied supramolecular complexes and their dynamics (geometry changes over time) were determined by molecular dynamics methods. Oxazolidinone ligands have been shown to complex mainly the inner part of cyclodextrin, while the external binding is less privileged, which is consistent with the conclusions of the NMR studies. Enthalpy values of binding of complexes were calculated using long-term molecular dynamics in explicit water as well as using molecular mechanics, the Poisson-Boltzmann or generalized Born, and surface area continuum solvation (MM/PBSA and MM/GBSA) methods. Computational methods predicted the effect of changes in pH and composition of the solution on the strength and complexation process, and it adapted the conditions selected as optimal during the cEKC study. By changing the dielectric constant in the MM/PBSA and MM/GBSA calculations, the effect of changing the solution to methanol/acetonitrile was investigated. A fairly successful attempt was made to predict the chiral separation of the oxazolidinones using the modified cyclodextrin by computational methods. Chemistry ; Stereochemistry ; Molecular model ; Computational chemistry ; Combinatorial chemistry Analytical Chemistry and Chromatography ; Microfluidic and Capillary Electrophoresis Applications ; Chromatography in Natural Products
Authors: Filiz Ersöz | Yasemin ÇINAR
Source: European Journal of Science and Technology (article, 2021-12-10)
Field: Chemistry — Analytical Chemistry

Veri Madenciliği ve Makine Öğrenimi Yaklaşımlarının Karşılaştırılması: Tekstil Sektöründe bir Uygulama Her gün gelişmekte ve büyümekte olan teknoloji, modern dünyanın vazgeçilmez bir unsuru olmuştur. Teknolojinin hızla gelişmesiyle bilgisayar kullanımı artan dünyamızda daha fazla veri depolanmaya başlanmıştır. Oluşan bu büyük veriler tek başlarına bir anlam ifade etmemektedir. Ancak veri ve analitik alanda yetkinliklerin artırılması ile belirli örüntülere dayalı çıkarımlardan anlamlılık boyutu kazanırlar. Örüntülerin belirlenebilmesini sağlayan, yapılacak araştırmaya ve veri tipine uygun veri madenciliği ve makine öğrenimi teknikleri bulunmaktadır. Bu teknikleri ile veriler arasındaki kural, kalıp ve ilişkiler bulunur. Veri madenciliği ve makine öğrenimi teknikleri birçok farklı sektörde farklı amaçlarla kullanılabilmektedir. Bu çalışmada veri madenciliği ve makine öğrenimi arasındaki benzerlik ve farklılıklar ortaya konmaya çalışılmış ve bu disiplinlerin; veri bilimi, istatistik ve diğer disiplinler ile ortak ve ayrıştığı noktalar tespit edilmeye çalışılmıştır. Ayrıca çalışmada pantolon üreten bir tekstil firmasının verileri kullanılarak, R Studio, Python ve Knime makine öğrenimi programları yardımıyla, çoklu doğrusal regresyon, yapay sinir ağları ve karar ağaçları teknikleri uygulanmış, tahmini model sonuçlar bulunmuş ve model performansları karşılaştırılmıştır. Çalışmanın sonucunda tahminleme başarısında en iyi algoritmanın yapay sinir ağları ve en iyi makine öğrenimi programının RStudio programı olduğu sonucuna varılmıştır. Mathematics ; Humanities ; Art Spectroscopy and Chemometric Analyses ; Advanced Statistical Methods and Models ; Advanced Statistical Process Monitoring
Authors: Hao Ding | Malin C. Dixon Wilkins | Lucy M. Mottram | Lewis R. Blackburn | Daniel Grolimund | Ryan Tappero | Sarah Nicholas | Shi‐Kuan Sun | Claire L. Corkhill | Neil C. Hyatt
Source: Journal of Synchrotron Radiation (article, 2021-09-13)
Field: Chemistry — Inorganic Chemistry

Chemical state mapping of simulant Chernobyl lava-like fuel containing material using micro-focused synchrotron X-ray spectroscopy Uranium speciation and redox behaviour is of critical importance in the nuclear fuel cycle. X-ray absorption near-edge spectroscopy (XANES) is commonly used to probe the oxidation state and speciation of uranium, and other elements, at the macroscopic and microscopic scale, within nuclear materials. Two-dimensional (2D) speciation maps, derived from microfocus X-ray fluorescence and XANES data, provide essential information on the spatial variation and gradients of the oxidation state of redox active elements such as uranium. In the present work, we elaborate and evaluate approaches to the construction of 2D speciation maps, in an effort to maximize sensitivity to the U oxidation state at the U L<sub>3</sub>-edge, applied to a suite of synthetic Chernobyl lava specimens. Our analysis shows that calibration of speciation maps can be improved by determination of the normalized X-ray absorption at excitation energies selected to maximize oxidation state contrast. The maps are calibrated to the normalized absorption of U L<sub>3</sub> XANES spectra of relevant reference compounds, modelled using a combination of arctangent and pseudo-Voigt functions (to represent the photoelectric absorption and multiple-scattering contributions). We validate this approach by microfocus X-ray diffraction and XANES analysis of points of interest, which afford average U oxidation states in excellent agreement with those estimated from the chemical state maps. This simple and easy-to-implement approach is general and transferrable, and will assist in the future analysis of real lava-like fuel-containing materials to understand their environmental degradation, which is a source of radioactive dust production within the Chernobyl shelter. Synchrotron ; Lava ; Materials science ; X-ray spectroscopy ; Synchrotron radiation ; Spectroscopy ; X-ray photoelectron spectroscopy ; X-ray ; Chemical state ; Radiochemistry ; Mineralogy ; Analytical Chemistry (journal) ; Chemical engineering ; Chemistry ; Environmental chemistry ; Optics ; Physics ; Geology ; Geochemistry ; Astronomy ; Engineering ; Volcano Radioactive element chemistry and processing ; Nuclear Materials and Properties ; Hydrocarbon exploration and reservoir analysis
Authors: John Kabuba | Trésor Lukusa
Source: Research Square (preprint, 2021-05-28)
Field: Materials Science — Biomaterials

Synthesis of Gelatin-Cellulose Nanocrystals Hydrogel Membrane For Removal of Cu (II) And Co (II) From Mining Processes Wastewater Gelatin ; Membrane ; Adsorption ; Wastewater ; Cellulose ; Freundlich equation ; Chemical engineering ; Metal ions in aqueous solution ; Metal ; Nanocrystal ; Materials science ; Chemistry ; Inorganic chemistry ; Nuclear chemistry ; Waste management ; Metallurgy ; Organic chemistry Advanced Cellulose Research Studies ; Hydrogels: synthesis, properties, applications ; Adsorption and biosorption for pollutant removal