Modeli umjetne neuronske mreže za predviđanje gustoće i kinematičke viskoznosti različitih sustava biogoriva i njihovih mješavina s dizelskim gorivom. Usporedna analiza

In the present article, two models based on the artificial neural network methodology (ANN) have been optimised to predict the density (ρ) and kinematic viscosity (μ) of different systems of biofuels and their blends with diesel fuel. An experimental database of 1025 points, including 34 systems (15 pure systems, 14 binary systems, and 5 ternary systems) was used for the development of these models. These models use six inputs, which are temperature (T) in the range of −10 – 200 °C, volume fractions (X1, X2, X3) in the range of 0–1, and to distinguish these systems, we used kinematic viscosity at 20 °C in the range of 0.67–74.19 mm2 s–1 and density at 20 °C in the range of 0.7560–0.9188 g cm–3. The best results were obtained with the architecture of {6-26-2: 6 neurons in the input layer – 26 neurons in the hidden layer – 2 neurons in the output layer}. Results of comparison between experimental and simulated values in terms of the correlation coefficients were: R2 = 0.9965 for density, and R2 = 0.9938 for kinematic viscosity. A 238 new database experimental of 4 systems (2 pure systems, 1 binary system, and 1 ternary system) was used to check the accuracy of the two ANN models previously developed. Results of prediction performances in terms of the correlation coefficients were: R2 = 0.9980 for density, and R2 = 0.9653 for kinematic viscosity. Comparison of validation results with those of the other studies shows that the neural network models gave far better results. This work is licensed under a Creative Commons Attribution 4.0 International License.U ovom članku dva modela zasnovana na metodologiji umjetne neuronske mreže (ANN) optimizirana su za predviđanje gustoće (ρ) i kinematičke viskoznosti (μ) različitih sustava biogoriva i njihovih mješavina s dizelskim gorivom. Za razvoj tih modela upotrijebljena je eksperimentalna baza podataka od 1025 točaka, uključujući 34 sustava (15 čistih sustava, 14 binarnih sustava i 5 ternarnih sustava). Ti modeli koriste šest ulaza: temperatura (T) u rasponu od −10 do 200 °C, volumni udjeli (X1, X2, X3) u rasponu 0 – 1, a za razlikovanje tih sustava korištena je kinematička viskoznost pri 20 °C u rasponu 0,67 – 74,19 mm2 s–1 i gustoća pri 20 °C u rasponu 0,7560 – 0,9188 g cm–3. Najbolji rezultati dobiveni su arhitekturom {6-26-2: 6 neurona u ulaznom sloju – 26 neurona u skrivenom sloju – 2 neurona u izlaznom sloju}. Rezultati usporedbe eksperimentalnih i simuliranih vrijednosti u smislu korelacijskih koeficijenata bili su: R2 = 0,9965 za gustoću i R2 = 0,9938 za kinematičku viskoznost. Za provjeru točnosti dva prethodno razvijena modela ANN upotrijebljeno je 238 novih eksperimentalnih baza podataka s 4 sustava (2 čista sustava, 1 binarni sustav i 1 ternarni sustav). Rezultati performansi predviđanja s obzirom na korelacijske koeficijente bili su: R2 = 0,9980 za gustoću i R2 = 0,9653 za kinematičku viskoznost. Usporedba rezultata validacije s rezultatima drugih studija pokazuje da su modeli neuronske mreže dali znatno bolje rezultate. Ovo djelo je dano na korištenje pod licencom Creative Commons Imenovanje 4.0 međunarodna

A Quantitative Structure Activity Relationship for acute oral toxicity of pesticides on rats: Validation, Domain of Application and Prediction

International audienceQuantitative Structure Activity Relationship (QSAR) models are expected to play an important role in the risk assessment of chemicals on humans and the environment. In this study, we developed a validated QSAR model to predict acute oral toxicity of 329 pesticides to rats because a few QSAR models have been devoted to predict the Lethal Dose 50 (LD50) of pesticides on rats. This QSAR model is based on 17 molecular descriptors, and is robust, externally predictive and characterized by a good applicability domain. The best results were obtained with a 17/9/1 Artificial Neural Network model trained with the Quasi Newton back propagation (BFGS) algorithm. The prediction accuracy for the external validation set was estimated by the Q2ext and the Root Mean Square error (RMS) which are equal to 0.948 and 0.201, respectively. 98.6% of external validation set is correctly predicted and the present model proved to be superior to models previously published. Accordingly, the model developed in this study provides excellent predictions and can be used to predict the acute oral toxicity of pesticides, particularly for those that have not been tested as well as new pesticides

Gamma ray production cross sections in proton induced reactions on natural Mg, Si and Fe targets over the proton energy range 30 up to 66 MeV

Gamma-ray excitation functions have been measured for 30, 42, 54 and 66 MeV proton beams accelerated onto C + O (Mylar), Mg, Si, and Fe targets of astrophysical interest at the separate-sector cyclotron of iThemba LABS in Somerset West (Cape Town, South Africa). A large solid angle, high energy resolution detection system of the Eurogam type was used to record Gamma-ray energy spectra. Derived preliminary results of Gamma-ray line production cross sections for the Mg, Si and Fe target nuclei are reported and discussed. The current cross section data for known, intense Gamma-ray lines from these nuclei consistently extend to higher proton energies previous experimental data measured up to Ep ~ 25 MeV at the Orsay and Washington tandem accelerators. Data for new Gamma-ray lines observed for the first time in this work are also reported.Comment: 11 pages, 6 figures. IOP Institute of Physics Conference Nuclear Physics in Astrophysics VII, 28th EPF Nuclear Physics Divisional Conference, May 18-22 2015, York, U

Measurement and analysis of nuclear γ-ray production cross sections in proton interactions with Mg, Si, and Fe nuclei abundant in astrophysical sites over the incident energy range E = 30–66 MeV

The modeling of nuclear γ -ray line emission induced by highly accelerated particles in astrophysical sites (e.g., solar flares, the gas and dust in the inner galaxy) and the comparison with observed emissions from these sites needs a comprehensive database of related production cross sections. The most important reactions of protons and α particles are those with abundant target elements like C, O, N, Ne, Mg, Si, and Fe at projectile energies extending from the reaction threshold to a few hundred MeV per nucleon. In this work, we have measured γ -ray production cross section excitation functions for 30, 42, 54, and 66 MeV proton beams accelerated onto nat C , C + O (Mylar), nat Mg , nat Si , and 56 Fe targets of astrophysical interest at the Separated Sector Cyclotron (SSC) of iThemba LABS (near Cape Town, South Africa). The AFRODITE array equipped with eight Compton suppressed high-purity (HPGe) clover detectors was used to record γ -ray line energy spectra. For known, intense lines previously reported experimental data measured up to E p ≃ 25 MeV at the Washington and Orsay tandem accelerators were thus extended to higher proton energies. Our experimental data for the last three targets are reported here and discussed with respect to previous data and to the Murphy et al. compilation [Astrophys. J. Suppl. Ser. 183, 142 (2009)]

On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview

Railway switch and crossing (S&C) systems have a very complex structure that requires not only a large number of components (such as rails, check rails, switches, crossings, turnout bearers, slide chair, etc.) but also different types of components and technologies (mechanical devices to operate switches, electrical and/or electronic devices for control, etc.). This complexity of railway S&C systems makes them vulnerable to failures and malfunctions that can ultimately cause delays and even fatal accidents. Thus, it is crucial to develop suitable condition monitoring techniques to deal with fault detection and diagnosis (FDD) in railway S&C systems. The main contribution of this paper is to present a comprehensive review of the existing FDD techniques for railway S&C systems. The aim is to overview the state of the art in rail S&C and in doing so to provide a platform for researchers, railway operators, and experts to research, develop and adopt the best methods for their applications; thereby helping ensure the rapid evolution of monitoring and fault detection in the railway industry at a time of the increased interest in condition based maintenance and the use of high-speed trains on the rail network