jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Petr Kawulok | Petr Podolínský | Petr Kajzar | Ivo Schindler | Rostislav Kawulok | Vojtěch ŠEVČÁK | Petr Opěla
Source: Archives of Metallurgy and Materials (article, 2018-12-12)
Field: Materials Science — Materials Chemistry

The Influence of Deformation and Austenitization Temperature on the Kinetics of Phase Transformations During Cooling of High-Carbon Steel The aim of the performed experiments was to determine the influence of deformation and of austenitization temperature on the kinetics of phase transformations during cooling of high-carbon steel (0.728 wt. % C). The CCT and DCCT diagrams for austenitization temperature 940°C and DCCT diagram for austenitization temperature 1000°C were constructed with the use of dilatometric tests. On the basis of obtained results, a featureless effect of austenitization temperature and deformation on the kinetics of phase transformations during cooling of investigated steel was observed. Critical cooling rates for the transformation of martensite in microstructure fluctuated from 5 to 7°C · s–1 (depending on the parameters of austenitization and deformation), but only at cooling rates higher than 8°C · s–1 a dominant share of martensite was observed in the investigated steel, which resulted in the significant increase of hardness. Materials science ; Kinetics ; Metallurgy ; Phase (matter) ; Industrial chemistry ; Materials processing ; Deformation (meteorology) ; Carbon fibers ; Precipitation ; Composite material ; Process engineering Metal Alloys Wear and Properties ; Microstructure and Mechanical Properties of Steels ; Material Properties and Failure Mechanisms
Authors: Ajeet Kaushik | Madhavan Nair
Source: Journal of Nanotheranostics (article, 2018-07-31)
Field: Materials Science — Materials Chemistry

Nanotheranostic, Next Generation Prerequisite for Better Health The motivation of this editorial began with a brief introduction of Society for Personalized NanoMedicine [...] Nanomedicine ; Engineering ethics ; Computer science ; Psychology ; Engineering ; Data science Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Biosensors and Analytical Detection
Authors: John Odindi | Elhadi Adam | Elfatih M. Abdel‐Rahman | Onisimo Mutanga
Source: South African Journal of Geomatics (article, 2018-02-05)
Field: Chemistry — Analytical Chemistry

Testing the spectral resolutions of the new multispectral sensors for detecting &lt;i&gt;Phaeosphaeria&lt;/i&gt; leaf spot (PLS) infestations in maize crop Maize is one of the most important subsistence and commercial crops in the world. In Africa, it is regarded as one of the most popular food crops. Recently however, significant losses due to Phaeosphaeria leaf spot (PLS) infestation have been reported. Therefore, techniques for early detection of PLS infestation are valuable for mitigating maize yield losses. Recently, remotely sensed datasets have become valuable in crop assessment. In this study, we sought to detect early PLS infestation by comparing the performance of commonly used higher spatial resolution sensors (WorldView, Quickbird, Sentinel series 2, RapidEye and SPOT 6) based on their spectrally resampled field spectra. Canopy training spectra were collected on leaves with signs of early infestation and healthy leaves spectral characteristics used for comparison. Training data was collected in 2013 growing season while test data was collected under similar conditions in 2014. The Random Forest algorithm was used to establish the Kappa and overall, user and producer's accuracies. Results showed that the RapidEye sensor with an overall classification accuracy of 86.96% and Kappa value of 0.76 performed better than the rest of the sensors while the Red, Yellow and Red-Edge bands were most useful for detecting early PLS infestation. The value of the RapidEye sensor in detecting early PLS infestation can be attributed to the optimally centred Red Red-Edge bands sensitive to changes in chlorophyll content, a consequent of PLS infestation on maize leaves. The study provides valuable insight on the value of existing sensors, based on their sensor characteristics in detecting early PLS infestation.Keywords: Phaeosphaeria leaf spot, Remote Sensing, sensors Random Forest, Variable importance Infestation ; Multispectral image ; Leaf spot ; Canopy ; Crop ; Hyperspectral imaging ; Environmental science ; Growing season ; Agronomy ; Remote sensing ; Biology ; Geography ; Botany Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Hossein Javadikia | Sajad Sabzi | Juan Ignacio Arribas
Source: Spanish Journal of Agricultural Research (article, 2018-11-12)
Field: Chemistry — Analytical Chemistry

An automatic and non-intrusive hybrid computer vision system for the estimation of peel thickness in Thomson orange Orange peel has important flavor and nutrition properties and is often used for making jam and oil in the food industry. For previous reasons, oranges with high peel thickness are valuable. In order to properly estimate peel thickness in Thomson orange fruit, based on a number of relevant image features (area, eccentricity, perimeter, length/area, blue component, green component, red component, width, contrast, texture, width/area, width/length, roughness, and length) a novel automatic and non-intrusive approach based on computer vision with a hybrid particle swarm optimization (PSO), genetic algorithm (GA) and artificial neural network (ANN) system is proposed. Three features (width/area, width/length and length/area ratios) were selected as inputs to the system. A total of 100 oranges were used, performing cross validation with 100 repeated experiments with uniform random samples test sets. Taguchi’s robust optimization technique was applied to determine the optimal set of parameters. Prediction results for orange peel thickness (mm) based on the levels that were achieved by Taguchi’s method were evaluated in several ways, including orange peel thickness true-estimated boxplots for the 100 orange database and various error parameters: the sum square error (SSE), the mean absolute error (MAE), the coefficient of determination (R2), the root mean square error (RMSE), and the mean square error (MSE), resulting in mean error parameter values of R2=0.854±0.052, MSE=0.038±0.010, and MAE=0.159±0.023, over the test set, which to our best knowledge are remarkable numbers for an automatic and non-intrusive approach with potential application to real-time orange peel thickness estimation in the food industry. Mean squared error ; Taguchi methods ; Mathematics ; Particle swarm optimization ; Orange (colour) ; Computer science ; Root mean square ; Coefficient of determination ; Artificial intelligence ; Statistics ; Approximation error ; Algorithm ; Pattern recognition (psychology) ; Optics ; Engineering Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Postharvest Quality and Shelf Life Management
Authors: Ali Asghar Pasban | Behrouz Nonahal
Source: Analytical Methods in Environmental Chemistry Journal (article, 2018-12-01)
Field: Chemistry — Analytical Chemistry

The Investigation of precision of analytical methods for determination of salt content in Iranian crude oils the pollution of the crude oil transportation route may also be somewhat effective. The presence of salts in crude oil in addition to corrosion causes the following problems. During the production of crude oil, due to the minor evaporation of the associated water happens in the effect of pressure drop between the bottom and the top of the well, causing the chloride to settle sodium is at the entrance to the well wall and the diameter of the well decreases. Crude oil ; Titration ; Salt (chemistry) ; Chemistry ; Extraction (chemistry) ; Chromatography ; Water content ; Pulp and paper industry ; Petroleum engineering ; Geology ; Engineering Petroleum Processing and Analysis ; Electrochemical Analysis and Applications
Authors: Dong Ren | Chang Zhang | Ren Shun | Zhong Zhang | Jihua Wang | Anxiang Lu
Source: International Journal of Robotics and Automation (article, 2018-01-01)
Field: Chemistry — Analytical Chemistry

AN IMPROVED APPROACH OF CARS FOR LONGJING TEA DETECTION BASED ON NEAR INFRARED SPECTRA In this paper, the near-infrared spectroscopy is used to obtain the near-infrared spectra data of tea for the detection of West Lake Longjing tea and the general Longjing tea. Noise and other redundant information contained in the full spectrum will have a negative impact on the accuracy of the models during the data processing. Using the characteristic wavelength variables to build the models is more effective than the full spectrum. The competitive adaptive reweighted sampling (CARS) is one of the most common and effective methods for the characteristic wavelength variables selection. However, the regression coefficients of variables will change with the selected samples of the model varying randomly in CARS method. Therefore, the absolute value of the regression coefficients is not always able to fully reflect the importance of the variables. This paper introduces the variable effectiveness and proposes a wavelength selection approach called effectiveness competitive adaptive reweighted sampling (ECARS) to make up for this shortfall. This study is mainly to classify the 110 samples of West Lake Longjing tea and the general Longjing tea. The training set consists of 72 samples and the prediction set contains 38 samples. After the preprocessing of the second derivative, CARS, uninformative variable elimination, backward interval partial least squares, and ECARS algorithm proposed in this paper are used for the variables selection. Then the variable subset and the full spectrum are, respectively, used to build support vector machine (SVM) model and linear discriminant analysis model for the identification of West Lake Longjing tea and the general Longjing tea. The experiment results show that: (1) the accuracy of models that are processed by the variables selection methods is higher than those of the full spectrum models and all the other models; (2) the accuracy of the ECARS-SVM model is highest, and the accuracies of the training set and prediction set are 100% and 98.4%, respectively; (3) the ECARS algorithm Infrared ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Joemax Agu | T. Christopher
Source: Mechanika (article, 2018-11-08)
Field: Materials Science — Materials Chemistry

Fracture Toughness Evaluation of AA3003 Aluminum Alloy through Alternative Experimental Methods and Finite Element Technique The fracture parameter most commonly used to analyze the integrity of engineering components is plane strain fracture toughness. The fracture toughness testing involved conventionally involves material cost and costly machine. However, alternative methods are used for in-situ applications which give reasonably good results. There is interest among researchers to use alternative methods for evaluating fracture toughness and to assess the material failure. This work aims at determining fracture toughness by ball indentation test and three point bend test and simulating those using ANSYS. The flow property of the selected material is also determined from the stress strain data by using ASTM E646-78. Excellent agreement was obtained from the experiments and empirical relations used for determination of fracture toughness by both methodsDOI: http://dx.doi.org/10.5755/j01.mech.24.5.21173 Fracture toughness ; Materials science ; Indentation ; Composite material ; Plane stress ; Finite element method ; Structural engineering ; Fracture (geology) ; Alloy ; Toughness ; Engineering High-Velocity Impact and Material Behavior ; Metal Forming Simulation Techniques ; Non-Destructive Testing Techniques
Authors: D. I. Tokarev | A. A. Drozdov | Maxim Gulyaev | Л. Д. Сиротенко | Vladimir Islamov
Source: PNIPU Bulletin The mechanical engineering materials science (article, 2018-06-30)
Field: Materials Science — General Materials Science

FEATURES TURNING PLASTICS F-4, PA-6, F-4K20 2 , , Materials science ; Composite material Material Properties and Applications
Authors: Feijun Qu | Zhengyi Jiang | Xiaogang Wang | Cunlong Zhou
Source: Advances in Materials Science and Engineering (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Analysis of Springback Behaviour in Micro Flexible Rolling of Crystalline Materials This paper presents a constitutive modelling of the polycrystalline thin metal strip under a state of combined loading in microflexible rolling. The concept of grained inhomogeneity is incorporated into the classic Chaboche hardening model that accounts for the Bauschinger effect, in order to provide more precise description and analysis of the springback mechanism in the particular forming operation. The model is first implemented in the finite element program ABAQUS to numerically predict the stress‐strain relationship of 304 stainless steel specimens over a range of average grain sizes. After validation of the developed model by comparison of predicted curves and actual stress‐strain data points, it is further applied to predict the thickness directional springback in microflexible rolling of 304 stainless steel strips with initial thickness of 250 µ m and reduction changing from 5 to 10%. The model predictions show a reasonable agreement with the experimental measurements and have proven to be more accurate than those obtained from the conventional multilinear isotropic hardening model in combination with the Voronoi tessellation technique. In addition, the variation of thickness directional springback along with the scatter effect is compared and analysed in regard to the average grain size utilising both qualitative and quantitative approaches in respect of distinct types of data at different reductions. Materials science ; Bauschinger effect ; Multilinear map ; Hardening (computing) ; Finite element method ; STRIPS ; Isotropy ; Strain hardening exponent ; Constitutive equation ; Voronoi diagram ; Structural engineering ; Composite material ; Metallurgy ; Plasticity ; Geometry ; Mathematics Microstructure and mechanical properties ; Microstructure and Mechanical Properties of Steels ; Metallurgy and Material Forming
Authors: Siti Naimah Shamsudin | Mohd Hezri Fazalul Rahiman | Mohd Nasir Taib | Azlin Ahmad | Wan Razarinah Wan Abdul Razak
Source: Journal of Fundamental and Applied Sciences (article, 2018-01-23)
Field: Chemistry — Analytical Chemistry

Escherichia coli growth modeling using neural network The assessment of water microbial quality is normally performed by verification ofEscherichia coli where the growth is in nonlinearity. NARX is computational tools that haveextensive utilization in solving nonlinear time series problems. It is well known as one of thetechnique that has the ability to predict with efficient and good performance. Using NARX, ahighly accurate model was developed to predict the growth of Escherichia coli (E. coli) basedon pH water parameter. The multiparameter portable sensor and spectrophotometer data wereused to build and train the neural network. The selection of neural network structure for pHand optical density modelling was optimized and also the training and validation wereanalyzed. The result exhibited that NARX modelling was able to predict the growth of E. colibased on pH water parameter with overall regression is 0.99956.Keywords: neural network; NARX; prediction; Escherichia coli; pH; optical density. Nonlinear autoregressive exogenous model ; Artificial neural network ; Autoregressive model ; Escherichia coli ; Nonlinear system ; Biological system ; Computer science ; Artificial intelligence ; Mathematics ; Statistics ; Chemistry ; Biology ; Biochemistry ; Physics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis
Authors: Xiangxin Li | Ming Zhou | Yazhou Luo | Gang Wang | Lin Jia
Source: Energies (article, 2018-09-14)
Field: Materials Science — Materials Chemistry

Effect of Ice Shedding on Discharge Characteristics of an Ice-Covered Insulator String during AC Flashover Considering the ice shedding phenomena on ice-covered outdoor insulators, this paper conducted the artificial experiments by using a five-unit suspension ceramic insulator string covered with wet-grown ice to investigate the effects of ice shedding on the icing discharge characteristics. According to IEEE Standard 1783/2009, the minimum flashover voltage (VMF), propagation of discharges to flashover and related leakage current (LC) were measured. It was found that VMF after ice shedding can be increased by about 17% as compared with that before ice shedding. The initiation and formation of discharge arcs across ice-free regions caused by ice shedding become difficult, showing an unstable propagating path, indeterminate arc shape and longer arc column. Although VMF under ice shedding conditions is higher than that without ice shedding, the fundamental component, and harmonics show lower amplitudes for the latter case. The ratios of harmonics to the fundamental are well in accordance with discharge characteristics during the flashover for which the ratios show a relatively stable varying tendency in the absence of ice shedding, but show changeable and indeterminate variation under ice shedding conditions. The obtained results are helpful to understand the icing state and its influence on surface discharges for preventing the icing flashovers. Icing ; Arc flash ; Mechanics ; Materials science ; Atmospheric sciences ; Insulator (electricity) ; Meteorology ; Geology ; Composite material ; Physics High voltage insulation and dielectric phenomena ; Aerosol Filtration and Electrostatic Precipitation ; Lightning and Electromagnetic Phenomena
Authors: Uri Keich | Kaipo Tamura | William Stafford Noble
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2018-10-13)
Field: Chemistry — Spectroscopy

An averaging strategy to reduce variability in target-decoy estimates of false discovery rate Abstract Decoy database search with target-decoy competition (TDC) provides an intuitive, easy-to-implement method for estimating the false discovery rate (FDR) associated with spectrum identifications from shotgun proteomics data. However, the procedure can yield different results for a fixed dataset analyzed with different decoy databases, and this decoy-induced variability is particularly problematic for smaller FDR thresholds, datasets or databases. In such cases, the nominal FDR might be 1% but the true proportion of false discoveries might be 10%. The averaged TDC protocol combats this problem by exploiting multiple independently shuffled decoy databases to provide an FDR estimate with reduced variability. We provide a tutorial introduction to aTDC, describe an improved variant of the protocol that offers increased statistical power, and discuss how to deploy aTDC in practice using the Crux software toolkit. Decoy ; False discovery rate ; Protocol (science) ; Computer science ; False positive rate ; Shotgun ; Data mining ; Statistical power ; Statistics ; Artificial intelligence ; Mathematics ; Medicine ; Biology Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Identification and Quantification in Food
Authors: Dorota Duda
Source: Studies in Logic Grammar and Rhetoric (article, 2018-12-01)
Field: Materials Science — Biomaterials

MRI Texture-Based Recognition of Dystrophy Phase in Golden Retriever Muscular Dystrophy Dogs. Elimination of Features that Evolve along with the Individual’s Growth Abstract The study investigates the possibility of applying texture analysis (TA) for testing Duchenne Muscular Dystrophy (DMD) therapies. The work is based on the Golden Retriever Muscular Dystrophy (GRMD) canine model, in which 3 phases of canine growth and/or dystrophy development are identified: the first phase (0–4 months of age), the second phase (from over 4 to 6 months), and the third phase (from over 6 months to death). Two differentiation problems are posed: (i) the first phase vs. the second phase and (ii) the second phase vs. the third phase. Textural features are derived from T2-weighted Magnetic Resonance Imaging (MRI) images. In total, 37 features provided by 8 different TA methods (statistical, filter-based, and model-based) have been tested. The work focuses on finding such textural features that evolve along with the dog’s growth. These features are indicated by means of statistical analyses and eliminated from further investigation, as they may disturb the correct assessment of response to treatment in dystrophy. The relative importance of each remaining feature is then assessed with the use of the Monte Carlo (MC) procedure. Furthermore, feature selection based on the MC procedure is employed to find the optimal subset of age-independent features. Finally, three classifiers are used for evaluating different sets of textural features: Adaptive Boosting (AB), back-propagation Neural Network (NN), and nonlinear Support Vector Machines (SVM). The best subsets of age-independent features ensure 80.0% and 78.5% of correctly identified phases of dystrophy progression, for the first (i) and second (ii) differentiation problem respectively. Duchenne muscular dystrophy ; Dystrophy ; Artificial intelligence ; Support vector machine ; Feature selection ; Pattern recognition (psychology) ; Muscular dystrophy ; Feature (linguistics) ; Magnetic resonance imaging ; Computer science ; Texture (cosmology) ; Medicine ; Pathology ; Internal medicine ; Radiology ; Image (mathematics) Silk-based biomaterials and applications ; Muscle Physiology and Disorders ; Bone Tissue Engineering Materials
Authors: P. Sanyasi Naidu et al. P. Sanyasi Naidu et al. | TJPRC
Source: International journal of computer science engineering and information technology research (article, 2018-01-01)
Field: Materials Science — Polymers and Plastics

Particles Swarm Optimization Techniques , Principle, Comparison , Application PSO compare to see the performance of the particles in the search space with respect to time. Swarm behaviour ; Computer science ; Artificial intelligence Dendrimers and Hyperbranched Polymers ; Advanced Algorithms and Applications
Authors: Larisa Haritonova
Source: E3S Web of Conferences (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

On the of neural modeling of some dynamic parameters of earthquakes and fire safety in high-rise construction The recent change in the correlation of the number of man-made and natural catastrophes is presented in the paper. Some recommendations are proposed to increase the firefighting efficiency in the high-rise buildings. The article analyzes the methodology of modeling seismic effects. The prospectivity of applying the neural modeling and artificial neural networks to analyze a such dynamic parameters of the earthquake foci as the value of dislocation (or the average rupture slip) is shown. The following two input signals were used: the power class and the number of earthquakes. The regression analysis has been carried out for the predicted results and the target outputs. The equations of the regression for the outputs and target are presented in the work as well as the correlation coefficients in training, validation, testing, and the total (All) for the network structure 2-5-5-1for the average rupture slip. The application of the results obtained in the article for the seismic design for the newly constructed buildings and structures and the given recommendations will provide the additional protection from fire and earthquake risks, reduction of their negative economic and environmental consequences. Artificial neural network ; Slip (aerodynamics) ; Regression analysis ; Seismology ; Natural frequency ; Computer science ; Engineering ; Structural engineering ; Geology ; Vibration ; Machine learning Structural mechanics and materials ; Construction Management and Sustainability ; Environmental and Industrial Safety