jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Haoran Wu | Zhaoying Song | Xiaoyun Niu | Jun Liu | Jingmin Jiang | Yanjie Li
Source: Frontiers in Plant Science (article, 2022-06-28)
Field: Chemistry — Analytical Chemistry

Classification of Toona sinensis Young Leaves Using Machine Learning and UAV-Borne Hyperspectral Imagery Rapid and accurate distinction between young and old leaves of Toona sinensis in the wild is of great significance to the selection of T. sinensis varieties and the evaluation of relative yield. In this study, UAV hyperspectral imaging technology was used to obtain canopy hyperspectral data of biennial seedlings of different varieties of T. sinensis to distinguish young and old leaves. Five classification models were trained, namely Random Forest (RF), Artificial Neural Network (ANN), Decision Tree (DT), Partial Least Squares Discriminant Analysis (PLSDA), and Support Vector Machine (SVM). Raw spectra and six preprocessing methods were used to fit the best classification model. Satisfactory accuracy was obtained from all the five models using the raw spectra. The SVM model showed good performance on raw spectra and all preprocessing methods, and yielded higher accuracy, sensitivity, precision, and specificity than other models. In the end, the SVM model based on the raw spectra produced the most reliable and robust prediction results (99.62% accuracy and 99.23% sensitivity on the validation set only, and 100.00% for the rest). Three important spectral regions of 422.7~503.2, 549.2, and 646.2~687.2 nm were found to be highly correlated with the identification of young leaves of T. sinensis . In this study, a fast and effective method for identifying young leaves of T. sinensis was found, which provided a reference for the rapid identification of young leaves of T. sinensis in the wild. Hyperspectral imaging ; Linear discriminant analysis ; Artificial intelligence ; Support vector machine ; Preprocessor ; Pattern recognition (psychology) ; Random forest ; Mathematics ; Identification (biology) ; Computer science ; Botany ; Biology Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Remote Sensing in Agriculture
Authors: Christopher M. Barr | Eric Lang | Kory Burns | P. Price | Brandon Miller | Dennis D. Keiser | Assel Aitkaliyeva | Khalid Hattar
Source: Journal of Nuclear Materials (article, 2022-10-12)
Field: Materials Science — Materials Chemistry

The complex structural and chemical nature of monolithic U-10Mo fuel and Zr barrier layer Materials science ; Grain boundary ; Nanocrystalline material ; Transmission electron microscopy ; Nuclear fission product ; Characterization (materials science) ; Fission ; Chemical engineering ; Microstructure ; Metallurgy ; Nanotechnology ; Fission products ; Neutron ; Chemistry ; Radiochemistry ; Nuclear physics ; Physics Nuclear Materials and Properties ; Fusion materials and technologies ; Nuclear reactor physics and engineering
Authors: John Paul T. Yusiong
Source: Journal of ICT Research and Applications (article, 2022-04-30)
Field: Chemistry — Analytical Chemistry

A CNN-ELM Classification Model for Automated Tomato Maturity Grading Tomatoes are popular around the world due to their high nutritional value. Tomatoes are also one of the world’s most widely cultivated and profitable crops. The distribution and marketing of tomatoes depend highly on their quality. Estimating tomato ripeness is an essential step in determining shelf life and quality. With the abundant supply of tomatoes on the market, it is exceedingly difficult to estimate tomato ripeness using human graders. To address this issue and improve tomato quality inspection and sorting, automated tomato maturity classification models based on different features have been developed. However, current methods heavily rely on human-engineered or handcrafted features. Convolutional neural networks have emerged as the preferred technique for general object recognition problems because they can automatically detect and extract valuable features by directly working on input images. This paper proposes a CNN-ELM classification model for automated tomato maturity grading that combines CNNs’ automated feature learning capabilities with the efficiency of extreme learning machines to perform fast and accurate classification even with limited training data. The results showed that the proposed CNN-ELM model had a classification accuracy of 96.67% and an F1-score of 96.67% in identifying six maturity stages from the test data. Ripeness ; Artificial intelligence ; Convolutional neural network ; Computer science ; Pattern recognition (psychology) ; Machine learning ; Grading (engineering) ; Deep learning ; Engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Postharvest Quality and Shelf Life Management
Authors: David Michael Dreistadt | Julián Puszkiel | José M. Bellosta von Colbe | Giovanni Capurso | Gerd Steinebach | Stefanie Meilinger | Thi Thu Le | Myriam Covarrubias Guarneros | Thomas Klassen | Julian Jepsen
Source: Energies (article, 2022-01-24)
Field: Materials Science — Materials Chemistry

A Novel Emergency Gas-to-Power System Based on an Efficient and Long-Lasting Solid-State Hydride Storage System: Modeling and Experimental Validation In this paper, a gas-to-power (GtoP) system for power outages is digitally modeled and experimentally developed. The design includes a solid-state hydrogen storage system composed of TiFeMn as a hydride forming alloy (6.7 kg of alloy in five tanks) and an air-cooled fuel cell (maximum power: 1.6 kW). The hydrogen storage system is charged under room temperature and 40 bar of hydrogen pressure, reaching about 110 g of hydrogen capacity. In an emergency use case of the system, hydrogen is supplied to the fuel cell, and the waste heat coming from the exhaust air of the fuel cell is used for the endothermic dehydrogenation reaction of the metal hydride. This GtoP system demonstrates fast, stable, and reliable responses, providing from 149 W to 596 W under different constant as well as dynamic conditions. A comprehensive and novel simulation approach based on a network model is also applied. The developed model is validated under static and dynamic power load scenarios, demonstrating excellent agreement with the experimental results. Hydrogen storage ; Hydride ; Dehydrogenation ; Nuclear engineering ; Hydrogen ; Hydrogen fuel ; Electric power system ; Process engineering ; Compressed hydrogen ; Materials science ; Endothermic process ; Engineering ; Power (physics) ; Waste management ; Thermodynamics ; Fuel cells ; Chemical engineering ; Chemistry ; Physical chemistry Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Fuel Cells and Related Materials
Authors: Yayin Tan | Xinhao Hu | Yong Hou | Zhiqin Chu
Source: Membranes (review, 2022-09-30)
Field: Materials Science — Materials Chemistry

Emerging Diamond Quantum Sensing in Bio-Membranes Bio-membranes exhibit complex but unique mechanical properties as communicative regulators in various physiological and pathological processes. Exposed to a dynamic micro-environment, bio-membranes can be seen as an intricate and delicate system. The systematical modeling and detection of their local physical properties are often difficult to achieve, both quantitatively and precisely. The recent emerging diamonds hosting quantum defects (i.e., nitrogen-vacancy (NV) center) demonstrate intriguing optical and spin properties, together with their outstanding photostability and biocompatibility, rendering them ideal candidates for biological applications. Notably, the extraordinary spin-based sensing enable the measurements of localized nanoscale physical quantities such as magnetic fields, electrical fields, temperature, and strain. These nanoscale signals can be optically read out precisely by simple optical microscopy systems. Given these exclusive properties, NV-center-based quantum sensors can be widely applied in exploring bio-membrane-related features and the communicative chemical reaction processes. This review mainly focuses on NV-based quantum sensing in bio-membrane fields. The attempts of applying NV-based quantum sensors in bio-membranes to investigate diverse physical and chemical events such as membrane elasticity, phase change, nanoscale bio-physical signals, and free radical formation are fully overviewed. We also discuss the challenges and future directions of this novel technology to be utilized in bio-membranes. Membrane ; Nanotechnology ; Nitrogen-vacancy center ; Quantum sensor ; Nanoscopic scale ; Quantum ; Materials science ; Quantum dot ; Diamond ; Quantum technology ; Chemistry ; Physics ; Open quantum system Diamond and Carbon-based Materials Research ; Analytical Chemistry and Sensors ; Force Microscopy Techniques and Applications
Authors: Elizaveta M. Solovyeva | Julia A. Bubis | Irina A. Tarasova | Anna A. Lobas | Mark V. Ivanov | Alexey A. Nazarov | Ilya A. Shutkov | Mikhail V. Gorshkov
Source: Biochemistry (Moscow) (article, 2022-11-01)
Field: Chemistry — Spectroscopy

On the Feasibility of Using an Ultra-Fast DirectMS1 Method of Proteome-Wide Analysis for Searching Drug Targets in Chemical Proteomics Protein quantitation in tissue cells or physiological fluids based on liquid chromatography/mass spectrometry is one of the key sources of information on the mechanisms of cell functioning during chemotherapeutic treatment. Information on significant changes in protein expression upon treatment can be obtained by chemical proteomics and requires analysis of the cellular proteomes, as well as development of experimental and bioinformatic methods for identification of the drug targets. Low throughput of whole proteome analysis based on liquid chromatography and tandem mass spectrometry is one of the main factors limiting the scale of these studies. The method of direct mass spectrometric identification of proteins, DirectMS1, is one of the approaches developed in recent years allowing ultrafast proteome-wide analyses employing minute-scale gradients for separation of proteolytic mixtures. Aim of this work was evaluation of both possibilities and limitations of the method for identification of drug targets at the level of whole proteome and for revealing cellular processes activated by the treatment. Particularly, the available literature data on chemical proteomics obtained earlier for a large set of onco-pharmaceuticals using multiplex quantitative proteome profiling were analyzed. The results obtained were further compared with the proteome-wide data acquired by the DirectMS1 method using ultrashort separation gradients to evaluate efficiency of the method in identifying known drug targets. Using ovarian cancer cell line A2780 as an example, a whole-proteome comparison of two cell lysis techniques was performed, including the freeze-thaw lysis commonly employed in chemical proteomics and the one based on ultrasonication for cell disruption, which is the widely accepted as a standard in proteomic studies. Also, the proteome-wide profiling was performed using ultrafast DirectMS1 method for A2780 cell line treated with lonidamine, followed by gene ontology analyses to evaluate capabilities of the method in revealing regulation of proteins in the cellular processes associated with drug treatment. Proteome ; Proteomics ; Mass spectrometry ; Computational biology ; Tandem mass spectrometry ; Chemistry ; Quantitative proteomics ; Lysis ; Chromatography ; Biology ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Zhongbing Li | Wei Pang | Haibo Liang | Guihui Chen | Hongming Duan | Chuandong Jiang
Source: Energies (article, 2022-02-12)
Field: Chemistry — Analytical Chemistry

Fast Quantitative Modelling Method for Infrared Spectrum Gas Logging Based on Adaptive Step Sliding Partial Least Squares Infrared spectroscopy (IR) quantitative analysis technology has shown excellent development potential in the field of oil and gas logging. However, due to the high overlap of the IR absorption peaks of alkane molecules and the offset of the absorption peaks in complex environments, the quantitative analysis of IR spectroscopy applied in the field puts forward higher requirements for modelling speed and accuracy. In this paper, a new type of fast IR spectroscopy quantitative analysis method based on adaptive step-sliding partial least squares (ASS-PLS) is designed. A sliding step control function is designed to change the position of the local PLS analysis model in the full spectrum band adaptively based on the relative change of the current root mean square error and the global minimum root-mean-square error for rapid modelling. The study in this paper reveals the influence of the position and width of the local modelling window on the performance, and how to quickly determine the optimal modelling window in an uncertain sample environment. The performance of the proposed algorithm has been compared with three typical quantitative analysis methods by experiments on an IR spectrum dataset of 400 alkane samples. The results show that this method has a fast quantitative modelling speed with high analysis accuracy and stability. It has important practical value for promoting IR spectroscopy gas-logging technology. Partial least squares regression ; Mean squared error ; Sliding window protocol ; Spectroscopy ; Near-infrared spectroscopy ; Offset (computer science) ; Approximation error ; Computer science ; Algorithm ; Biological system ; Analytical Chemistry (journal) ; Mathematics ; Chemistry ; Statistics ; Optics ; Physics ; Window (computing) Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Fault Detection and Control Systems
Authors: Simona Violino | Lavinia Moscovini | Corrado Costa | Paolo Del Re | Lucia Giansante | Pietro Toscano | Francesco Tocci | Simone Vasta | Rossella Manganiello | Luciano Ortenzi | Federico Pallottino
Source: Foods (article, 2022-09-19)
Field: Chemistry — Organic Chemistry

Superior EVOO Quality Production: An RGB Sorting Machine for Olive Classification Extra virgin olive oil (EVOO) is a commercial product of high quality, thanks to its nutritional and organoleptic characteristics. The olives ripeness and the choice of harvest time according to their color and size, strongly influences the quality of the EVOO. The physical sorting of olives with machines performing rapid and objective optical selection, impossible by hand, can improve the quality of the final product. The aim of this study concerns the classification of olives into two qualitative classes, based on the maturity stage and the presence of external defects, through an industrial RGB optical sorting prototype, evaluating its performance and comparing the results with those obtained visually by trained operators. EVOOs obtained from classified olives were characterized through chemical, physical-chemical analysis and sensory profile. For the first time, the optoelectronic technologies in an industrial system was tested on olives to produce superior quality EVOO. The selection allows late harvest, obtaining oils with good characteristics from fully ripe and unripe fruits together, separating defective olives with appropriate calibration and training. Optoelectronic selection creates the opportunity to blend the obtained oils destined to different applications according to the needs of the consumer or producer, using a vanguard technology at low cost. Ripeness ; Sorting ; Organoleptic ; Quality (philosophy) ; Olive oil ; Selection (genetic algorithm) ; RGB color model ; Food science ; Product (mathematics) ; Computer science ; Artificial intelligence ; Mathematics ; Agricultural engineering ; Process engineering ; Biochemical engineering ; Ripening ; Biology ; Engineering ; Physics Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Alexander Weyman | Vlasis G. Mavrantzas | Hans Christian Öttinger
Source: The Journal of Chemical Physics (article, 2022-05-23)
Field: Materials Science — Materials Chemistry

Direct calculation of the functional inverse of realistic interatomic potentials in field-theoretic simulations We discuss the functional inverse problem in field-theoretic simulations for realistic pairwise potentials such as the Morse potential (widely used in particle simulations as an alternative to the 12-6 Lennard-Jones one), and we propose the following two solutions: (a) a numerical one based on direct inversion on a regular grid or deconvolution and (b) an analytical one by expressing attractive and repulsive contributions to the Morse potential as higher-order derivatives of the Dirac delta function; the resulting system of ordinary differential equations in the saddle-point approximation is solved numerically with appropriate model-consistent boundary conditions using a Newton-Raphson method. For the first time, exponential-like, physically realistic pair interactions are analytically treated and incorporated into a field-theoretic framework. The advantages and disadvantages of the two approaches are discussed in detail in connection with numerical findings from test simulations for the radial distribution function of a monatomic fluid at realistic densities providing direct evidence for the capability of the analytical method to resolve structural features down to the Angstrom scale. Saddle point ; Statistical physics ; Dirac delta function ; Maxima and minima ; Physics ; Pair potential ; Ordinary differential equation ; Molecular dynamics ; Morse potential ; Inverse ; Boundary value problem ; Classical mechanics ; Differential equation ; Mathematics ; Mathematical analysis ; Quantum mechanics Material Dynamics and Properties ; Protein Structure and Dynamics ; Electrostatics and Colloid Interactions
Authors: Ana Rita Alberto | Cristina Matos | Gabriel Carmona-Aparicio | Muriel Iten
Source: Advances in experimental medicine and biology (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Nanomaterials, a New Challenge in the Workplace Nanomaterials are a nanotechnological product of increasing importance given the possibilities they offer to improve quality of life and support sustainable development. Safe management of nanomaterials is needed to ensure that this emerging technology has the highest levels of acceptance among different interest groups, including workers. This chapter reviews the current state that presents the different stages of risk management applied to nanomaterials, including standardisation, regulation, risk assessment and risk control. Particularly, the chapter contextualizes the development of nanotechnologies at European level and analyses the scientific evidence available on the risks derived from nanomaterials use. Furthermore, it highlights the required conditions to encourage the responsible development of nanomaterials, as well as reflects on the lack of consensus in terms of approaches and frameworks that could facilitate standardisation adoption, regulatory enforcement and industry intervention concerning nanomaterials. Risk analysis (engineering) ; Enforcement ; Product (mathematics) ; Quality (philosophy) ; Intervention (counseling) ; Risk management ; Business ; Medicine ; Political science Nanoparticles: synthesis and applications ; Nanotechnology research and applications ; Recycling and Waste Management Techniques
Authors: V.S. Raja
Source: Frontiers in Metals and Alloys (article, 2022-05-25)
Field: Materials Science — Materials Chemistry

Grand Challenges in Metal Corrosion and Protection Research The quest to develop technologies that address growing societal needs, sustainability, environmentally-safe, and cost-effectiveness is ever growing. The suitability of these technologies to explore new resources in highly hostile conditions such as deep-sea and space has given greater impetus to corrosion research. A glance at the periodical table of elements suggests that the vast majority of the elements employed to manufacture engineering structures and devices that are integral parts of our life are thermodynamically favorable for corrosion in the chemical environments they inhabit. Additionally, miniaturization of devices and light-weighting engineering structures allow less leeway for corrosion for satisfactory performance of these components. At times, the materials made for such components are at odds with their ability to resist corrosion failures. Corrosion research, therefore, needs to involve finding ways and means to protect these materials against such failures. Although they are destined (thermodynamically) to corrode in typical chemical environments they need to serve Therefore, effective control of corrosion failures cannot involve just using the "right materials", as that may not be practically possible. Taming of environments through inhibitors and application of coatings, electrochemical means such as cathodic and anodic protection, and engineering means like structural design help to mitigate corrosion effectively. Emphasis is also placed on corrosion monitoring, inspection, life prediction, and proactive management of assets. In all these cases, understanding corrosion phenomena involving metal and environment interaction is essential. Corrosion ; Metallurgy ; Materials science Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Zhiyong Zou | T.E. Long | Qi Wang | Li Wang | Jie CHEN | B. S. Zou | Lijia Xu
Source: Food Science and Technology (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Implementation of Apple’s automatic sorting system based on machine learning In order to reduce post-harvest losses, the classification of fresh apples is crucial. Taking the hierarchical transmission control system as the object, the research was carried out on the verification of the bus network can flexibly expand the motor equipment, the stable and reliable operation of the motor, and the accuracy of Apple's classification. Combine Labview virtual instrument technology to realize the design of Apple's hierarchical transmission control system based on Controller Area Network technology. Fuzzy PID and traditional PID algorithms are used to simulate and realize the operation of brushless DC motor, and compare the advantages of brushless DC motor control based on fuzzy PID to ensure the safe and stable operation of the system. Using the machine learning algorithms model for color detection, the Support Vector Machine algorithm model finally achieved the classification of the three types of apple samples with a recognition rate of 96.7%. Sorting ; Computer science ; PID controller ; DC motor ; Support vector machine ; Controller (irrigation) ; Fuzzy logic ; Transmission (telecommunications) ; Control engineering ; CAN bus ; Transmission system ; Artificial intelligence ; Engineering ; Computer hardware ; Algorithm ; Temperature control Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI