jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Iara Jeanice Souza Ferreira | Sarah L. F. de O. Almeida | Acácio Figueirêdo Neto | Daniel dos Santos Costa
Source: Engenharia Agrícola (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

DETERMINATION OF QUALITY AND RIPENING STAGES OF ‘PACOVAN’ BANANAS USING VIS-NIR SPECTROSCOPY AND MACHINE LEARNING This paper aimed to develop predictive models to determine total soluble solids, firmness, and ripening stages of ‘Pacovan’ bananas, using Vis-NIR spectroscopy and machine learning algorithms. A total of 384 bananas were divided into different days of storage (0, 3, 6, 9, 12, 15, 18, and 21 days) at two temperatures (25°C and 20°C). Bananas were subjected to spectral analysis using a spectrometer operating in spectral range of 350 – 2500 nm. Physicochemical parameters of quality, total soluble solids, and firmness were determined by reference analyses. Different machine learning algorithms were used to develop regression models and supervised classification. The best model for total soluble solids was the Random Forest with variable selection, showing an R2cv of 0.90 and RMSECV of 2.31. The best model for firmness was the Support Vector Machine with variable selection, showing an R2cv of 0.84 and RMSECV of 7.98. The best classification model for different ripening stages was the Multilayer Perceptron with variable selection, which achieved the precision of 74.22%. Therefore, Vis-NIR spectroscopy associated with machine learning algorithms is a promising tool for monitoring the quality and ripening stages of ‘Pacovan’ bananas. Ripening ; Feature selection ; Support vector machine ; Artificial intelligence ; Chemistry ; Multilayer perceptron ; Random forest ; Mathematics ; Biological system ; Machine learning ; Pattern recognition (psychology) ; Computer science ; Food science ; Artificial neural network ; Biology Spectroscopy and Chemometric Analyses ; Sensory Analysis and Statistical Methods ; Advanced Chemical Sensor Technologies
Authors: Ruijiao Ma | Yuntao Zhang | Jiakuan Chen | Haoyu Wen
Source: SHS Web of Conferences (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Patent research front mining of GaN semiconductor based on the LDA model This paper aims to identify and analyze research front topics of GaN semiconductor. The research method was to mine and identify topics based on GaN patent data by using the LDA model and topic intensity index. Finally, through experiments, we obtained and analyzed 4 research front topics. The results provided informatics support for revealing the research status and trend of GaN semiconductor. Front (military) ; Semiconductor ; Informatics ; Index (typography) ; Computer science ; Materials science ; Data mining ; Optoelectronics ; Engineering ; World Wide Web ; Mechanical engineering ; Electrical engineering Machine Learning in Materials Science ; Technology Assessment and Management ; Innovation Diffusion and Forecasting
Authors: Najat Mohammad Nazari | Masdi Muhammad | Ainul Akmar Mokhtar
Source: Tribology online (article, 2022-07-30)
Field: Chemistry — Analytical Chemistry

Prediction of Lubrication Oil Parameter Degradation to Extend the Oil Change Interval Based on Gaussian Process Regression (GPR) In this work, the degradation of selected lubrication oil parameters until the specified threshold is predicted based on Gaussian process regression (GPR) to extend the oil change interval. Kinematic viscosity (40°C) and total acid number (TAN) was selected based on Mahalanobis-Taguchi Gram-Schmidt (MTGS) analysis. Kinematic viscosity (40°C) degradation prediction model presented a better result with an R squared of 0.61835, RMSE of 0.97142 and predicted maximum oil age of 31.9 months. The predicted maximum oil age value is validated by modelling the degradation of remaining lubrication oil parameters namely water content and particle counts, these parameters are within its threshold value at 31.9 months. The study proves that the degradation of kinematic viscosity (40°C) can be predicted based on GPR modelling. The corresponding oil age at threshold of parameter was identified to be longer than the actual duration of lubrication oil in service and the oil change interval can be extended beyond 26 months. Lubrication ; Interval (graph theory) ; Taguchi methods ; Materials science ; Viscosity ; Degradation (telecommunications) ; Linear regression ; Mathematics ; Statistics ; Engineering ; Composite material Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Lubricants and Their Additives
Authors: Seyed Nasrollah Tabatabaei | Raheleh Faridi‐Majidi | Safieh Boroumand | Faezeh Norouz | Mahya Rahmani | Fatemeh Rezaie | Farzaneh Fayazbakhsh | Reza Faridi‐Majidi
Source: IEEE Transactions on NanoBioscience (review, 2022-06-20)
Field: Materials Science — Biomaterials

Nanofibers in Respiratory Masks: An Alternative to Prevent Pathogen Transmission Recent global outbreak of COVID-19 has raised serious awareness about our abilities to protect ourselves from hazardous pathogens and volatile organic compounds. Evidence suggests that personal protection equipment such as respiratory masks can radically decrease rates of transmission and infections due to contagious pathogens. To increase filtration efficiency without compromising breathability, application of nanofibers in production of respiratory masks have been proposed. The emergence of nanofibers in the industry has since introduced a next generation of respiratory masks that promises improved filtration efficiency and breathability via nanometric pores and thin fiber thickness. In addition, the surface of nanofibers can be functionalized and enhanced to capture specific particles. In addition to conventional techniques such as melt-blown, respiratory masks by nanofibers have provided an opportunity to prevent pathogen transmission. As the surge in global demand for respiratory masks increases, herein, we reviewed recent advancement of nanofibers as an alternative technique to be used in respiratory mask production. Nanofiber ; Materials science ; Nanotechnology ; Respiratory system ; Transmission (telecommunications) ; Filtration (mathematics) ; Coronavirus disease 2019 (COVID-19) ; Computer science ; Medicine ; Pathology Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Aerosol Filtration and Electrostatic Precipitation
Authors: Cagri Aydinkarahaliloglu | S. Jatar | Xiaojun Wang | Mary Fong | Vijay Gupta | Mariano Troccoli | Anthony J. Hoffman
Source: Scientific Reports (article, 2022-06-02)
Field: Chemistry — Spectroscopy

Predicting early failure of quantum cascade lasers during accelerated burn-in testing using machine learning Device life time is a significant consideration in the cost of ownership of quantum cascade lasers (QCLs). The life time of QCLs beyond an initial burn-in period has been studied previously; however, little attention has been given to predicting premature device failure where the device fails within several hundred hours of operation. Here, we demonstrate how standard electrical and optical device measurements obtained during an accelerated burn-in process can be used in a simple support vector machine to predict premature failure with high confidence. For every QCL that fails, at least one of the measurements is classified as belonging to a device that will fail prematurely-as much as 200 h before the actual failure of the device. Furthermore, for devices that are operational at the end of the burn-in process, the algorithm correctly classifies all the measurements. This work will influence future device analysis and could lead to insights on the physical mechanisms of premature failure in QCLs. Cascade ; Burn-in ; Computer science ; Process (computing) ; Reliability engineering ; Quantum ; Laser ; Quantum cascade laser ; Work (physics) ; Physics ; Mechanical engineering ; Optics ; Engineering Spectroscopy and Laser Applications ; Laser Design and Applications ; Analytical Chemistry and Sensors
Authors: ÉRIKA BEATRIZ DE LIMA CASTRO | Raylson de Sá Melo | Emanuel Magalhães da Costa | Ângela Maria dos Santos Pessoa | RAMONY KELLY BEZERRA OLIVEIRA | Cândida Hermínia Campos de Magalhães Bertini
Source: Revista Caatinga (article, 2022-10-01)
Field: Chemistry — Analytical Chemistry

CLASSIFICATION OF Phaseolus lunatus L. USING IMAGE ANALYSIS AND MACHINE LEARNING MODELS ABSTRACT Image analysis combined with machine learning models can be an excellent tool for classification of fava (Phaseolus lunatus L.) genotypes and is a low-cost system. Fava is grown by family farmers, mainly, in the Northeast and South regions of Brazil, presenting economic and social importance. Evaluations to gather information on qualitative and quantitative characters of seeds enable the description and distinction of genotypes, allowing the evaluation of variability of plant species, which is essential in breeding programs. The use of image analysis is a fast and economic tool for obtaining large quantity of information. Machine learning techniques have been developed and implemented in the agricultural sector due to technological advances and increasing use of artificial intelligence, which enables the automatization of several processes. In this context, the objective of this work was to evaluate different machine learning models to classify fava genotypes, using data obtained through image analysis. Images of fava seeds were captured using a table scanner (HP Scanjet 2004), set to true color mode, arranged upside down inside of an aluminum box fully closed during the capture of the images for an adequate illumination and prevention of environmental noises. The K-Nearest Neighbor, Naive Bayes, Linear Discriminant Analysis, Support Vector Machine, Gradient Boosting, Bootstrap Aggregating, Classification and Regression Trees, Random Forest, and C50 models were used for the study. Linear Discriminant Analysis was the model that presented the highest efficiency for classifying the genotypes, with an accuracy of 90%. Artificial intelligence ; Linear discriminant analysis ; Machine learning ; Support vector machine ; Random forest ; Computer science ; Phaseolus ; Naive Bayes classifier ; Pattern recognition (psychology) ; Mathematics ; Biology ; Agronomy Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Banana Cultivation and Research
Authors: Marco Clemente | Heloísa da Silva | Júlia Furtado Campos | Ana Carolina Gomes Mantovani | Dionísio Borsato
Source: Química Nova (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

ADDITION OF NATURAL EXTRACTS WITH ANTIOXIDANT PROPERTIES IN BIODIESEL: ANALYSIS BY NEURAL NETWORKS OF THE MULTILAYER PERCEPTRON TYPE Biodiesel is capable of replacing diesel because it has similar physicochemical properties, but this biofuel is susceptible to oxidation, which makes the application of antioxidant substances necessary. For this study, alcoholic extracts of senna leaves, hibiscus flowers, and blackberry were used. Biodiesel samples were submitted to physicochemical analysis to evaluate interference in the volume of these alcoholic extracts with antioxidant properties. The data obtained were processed using the neural network of the multilayer perceptron type (MLP). For the network’s training, 200 epochs were used. The samples were randomly divided into three groups, with 70% used for training, 15% for testing, and 15% for validation. The type of extract was considered as a categorical variable, the extract volume as a target variable, and the other ones as input variables. Among the 200 networks trained, with 5 to 20 hidden layers, the 5 with the best performance were highlighted. The Tukey test applied to the means showed no significant difference at the 5% level, between the value of the added volume and the means value predicted by the networks. The sensitive analysis showed that the most important input variable for the construction of the model was the type of extract. Biodiesel ; Artificial neural network ; Antioxidant ; Diesel fuel ; Multilayer perceptron ; Mathematics ; Volume (thermodynamics) ; Biological system ; Food science ; Artificial intelligence ; Computer science ; Pulp and paper industry ; Machine learning ; Chemistry ; Biochemical engineering ; Engineering ; Biology ; Organic chemistry ; Physics Spectroscopy and Chemometric Analyses
Authors: Raffaele Vitale | Cyril Ruckebusch | I. Burud | Harald Martens
Source: Frontiers in Chemistry (article, 2022-03-15)
Field: Chemistry — Analytical Chemistry

Hyperspectral Video Analysis by Motion and Intensity Preprocessing and Subspace Autoencoding Hyperspectral imaging has recently gained increasing attention from academic and industrial world due to its capability of providing both spatial and physico-chemical information about the investigated objects. While this analytical approach is experiencing a substantial success and diffusion in very disparate scenarios, far less exploited is the possibility of collecting sequences of hyperspectral images over time for monitoring dynamic scenes. This trend is mainly justified by the fact that these so-called hyperspectral <i>videos</i> usually result in BIG DATA sets, requiring TBs of computer memory to be both stored and processed. Clearly, standard chemometric techniques do need to be somehow adapted or expanded to be capable of dealing with such massive amounts of information. In addition, hyperspectral video data are often affected by many different sources of variations in sample chemistry (for example, light absorption effects) and sample physics (light scattering effects) as well as by systematic errors (associated, <i>e</i>.<i>g</i>., to fluctuations in the behaviour of the light source and/or of the camera). Therefore, identifying, disentangling and interpreting all these distinct sources of information represents undoubtedly a challenging task. In view of all these aspects, the present work describes a multivariate hybrid modelling framework for the analysis of hyperspectral videos, which involves spatial, spectral and temporal parametrisations of both known and unknown chemical and physical phenomena underlying complex real-world systems. Such a framework encompasses three different computational steps: 1) motions ongoing within the inspected scene are estimated by optical flow analysis and compensated through IDLE modelling; 2) chemical variations are quantified and separated from physical variations by means of Extended Multiplicative Signal Correction (EMSC); 3) the resulting light scattering and light absorption data are subjected to the On-The-Fly Processing and summarised spectrally, spatially and over time. The developed methodology was here tested on a near-infrared hyperspectral video of a piece of wood undergoing drying. It led to a significant reduction of the size of the original measurements recorded and, at the same time, provided valuable information about systematic variations generated by the phenomena behind the monitored process. Hyperspectral imaging ; Computer science ; Preprocessor ; Sample (material) ; Artificial intelligence ; Chemical imaging ; Pattern recognition (psychology) ; Remote sensing ; Computer vision ; Data mining ; Chemistry ; Geography Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Soon Yuan Jie | Leow Zhi Hoe | Suvash Chandra Paul | Vivi Anggraini
Source: Applied Sciences (article, 2022-11-11)
Field: Materials Science — Polymers and Plastics

Characterizing the Tensile Behaviour of Woven and Composite Fabrics under UV Exposure Geotextiles exposed to sunlight undergo a certain degree of UV degradation. In this study, woven and composite geotextiles were subjected to outdoor weathering tests at various exposure levels (i.e., 1, 2, 3, 4, and 5 months) to characterize their tensile behaviour. Each month, geotextiles were subjected to tensile tests according to modified ISO 13934 standards. Later, micro-scale approaches were undertaken to analyze the microstructure characteristics of the geotextiles and confirm the experimental results using scanning electron microscopy (SEM) and Fourier-transform infrared (FTIR) spectroscopy. Results indicated that woven geotextiles underwent tensile strength degradation and became more brittle after 5 months of UV exposure, which showed the highest degradation of 69.55% for strength retained, followed by composite geotextiles that only indicated a minimal degradation of 94.19% for strength retained. The corresponding strain indicated a more pronounced degradation, with retained strain percentages of 80.74% and 82.63% after 5 months of UV exposure for woven and composite geotextiles, respectively. Surface micrographs of composite geotextiles indicated that the remarkable microcracks began to develop on the polymer surface after the second month and continued to develop after that. Surface degradation of woven geotextiles under SEM was characterized by the loosening of fibre alignment, and this phenomenon was found to become significant after the first month of UV exposure. FTIR analysis of geotextiles revealed a notable chemical structure alteration in the polymer (i.e., polypropylene) that was observed through the formation of peak absorbance in the 1713 cm−1 wavenumber after UV exposure. Overall, the results from all laboratory tests agreed well with each other and this study thus provides clear evidence of the UV degradation of geotextiles in response to outdoor weathering. Ultimate tensile strength ; Materials science ; Composite number ; Composite material ; Polypropylene ; Fourier transform infrared spectroscopy ; Scanning electron microscope ; Degradation (telecommunications) ; Brittleness ; Chemical engineering Natural Fiber Reinforced Composites ; Geotechnical Engineering and Soil Stabilization ; Mechanical Behavior of Composites
Authors: Z. Nowak | Zbigniew L. Kowalewski | Tadeusz Szymczak
Source: Archives of Civil and Mechanical Engineering (article, 2022-10-13)
Field: Materials Science — Materials Chemistry

Low velocity perforation of thick magnesium alloy AM60 plates impacted by rigid conical-nose impactor Abstract The impact resistance behaviour of the plate made of the AM60 magnesium alloy at the low velocity impact perforation mode is here investigated at room temperature using a numerical approach based on experimental results. Dynamic tests were performed using an impact digital tower on the 10.0 mm thick AM60 magnesium alloy plates using cylindrical impactors with conical-nose shapes of a nominal diameter of 12.0 mm, and a nominal mass of 5.77 kg. The plates were impacted with velocities ranging from 7 to 16 m/s. During the experiments, the failure of the target plates was evaluated. Finite element (FE) model was validated using experimental results. FE simulations of the conducted experiments were performed with ABAQUS software. In simulations the strain rate dependent Johnson–Cook yield criterion with a strain hardening law was accompanied with either the ductile fracture criterion or stress triaxiality-dependent JC fracture criterion to describe the target material properties. The stress and strain distributions for different impactor velocity considered were calculated using initial impact velocity data obtained from the experiments. A range of parameters, like element size, the fracture initiation strain, friction coefficient, etc. which play an important role in the simulation, were studied. The results of numerical simulation were compared with those from the experiment obtained. A good agreement between them was achieved. The failure process of AM60 target-plate revealed that in the case of the conical-nose impactor, the ductile hole enlargement occurs during the initial stage of the impact, and subsequently, a through-thickness fracture develops causing its shear plugging failure. Materials science ; Conical surface ; Perforation ; Magnesium alloy ; Composite material ; Fracture (geology) ; Finite element method ; Strain hardening exponent ; Strain rate ; Stress (linguistics) ; Structural engineering ; Alloy ; Mechanics ; Punching ; Engineering High-Velocity Impact and Material Behavior ; Metal Forming Simulation Techniques ; Cellular and Composite Structures
Authors: Haowei Xu | So Yeon Kim | Di Chen | Jean‐Phillippe Monchoux | Thomas Voisin | Cheng Sun | Ju Li
Source: Advanced Science (article, 2022-09-30)
Field: Materials Science — Materials Chemistry

Materials Genomics Search for Possible Helium‐Absorbing Nano‐Phases in Fusion Structural Materials Civilian fusion demands structural materials that can withstand the harsh environments imposed inside fusion plasma reactors. The structural materials often transmute under 14.1 MeV fast neutrons, producing helium (He), which embrittles the grain boundary (GB) network. Here, it is shown that neutron-friendly and mechanically strong nano-phases with atomic-scale free volume can have low He-embedding energy <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:msub><mml:mi>E</mml:mi> <mml:mi>emb</mml:mi></mml:msub> <mml:annotation>${\mathcal{E}}_{\mathrm{emb}}$</mml:annotation></mml:semantics> </mml:math> and >10 at.% He-absorbing capacity, and can be especially advantageous for soaking up He on top of resisting radiation damage and creep, provided they have thermodynamic compatibility with the matrix phase, satisfactory equilibrium wetting angle, as well as a high enough melting point. The preliminary experimental demonstration proves that <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:msub><mml:mi>E</mml:mi> <mml:mi>emb</mml:mi></mml:msub> <mml:annotation>${\mathcal{E}}_{\mathrm{emb}}$</mml:annotation></mml:semantics> </mml:math> is a good ab initio predictor of He shielding potency in nano-heterophase materials, and thus, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:msub><mml:mi>E</mml:mi> <mml:mi>emb</mml:mi></mml:msub> <mml:annotation>${\mathcal{E}}_{\mathrm{emb}}$</mml:annotation></mml:semantics> </mml:math> is used as a key feature for computational screening. In this context, a list of viable compounds expected to be good He-absorbing nano-phases is presented, taking into account <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:msub><mml:mi>E</mml:mi> <mml:mi>emb</mml:mi></mml:msub> <mml:annotation>${\mathcal{E}}_{\mathrm{emb}}$</mml:annotation></mml:semantics> </mml:math> , the neutron absorption and activation cross-sections, the elastic moduli, melting temperature, the thermodynamic compatibility, and the equilbrium wetting angle of the nano-phases with the Fe matrix as an example. Materials science ; Fusion ; Wetting ; Electromagnetic shielding ; Melting point ; Fusion power ; Neutron ; Helium ; Nuclear fusion ; Context (archaeology) ; Composite material ; Thermodynamics ; Nuclear physics ; Atomic physics ; Physics ; Plasma Fusion materials and technologies ; Nuclear Materials and Properties ; Nuclear Physics and Applications
Authors: Sezen Yucel | Robert J. Moon | Linda J. Johnston | Douglas M. Fox | Byong Chon Park | E. Johan Foster | Surya R. Kalidindi
Source: Cellulose (article, 2022-09-12)
Field: Materials Science — Biomaterials

Transmission electron microscopy image analysis effects on cellulose nanocrystal particle size measurements Materials science ; Computer science ; Particle size ; Artificial intelligence ; Computer vision ; Chemical engineering ; Engineering Advanced Cellulose Research Studies ; Dyeing and Modifying Textile Fibers ; Material Properties and Processing