Predicting forest attributes In southeast Alaska using artificial neural networks

Artificial neural network (ANN) methods are used to predict forest characteristics. The data source is the Southeast Alaska (SEAK) Grid Inventory, a ground survey compiled by the USDA Forest Service at several thousand sites. The main objective of this article is to predict characteristics at unsurveyed locations between grid sites. A secondary objective is to evaluate the relative performance of different ANNs. Data from the grid sites are used to train six ANNs: multilayer perceptron, fuzzy ARTMAP, probabilistic, generalized regression, radial basis function, and learning vector quantization. A classification and regression tree method is used for comparison. Topographic variables are used to construct models: latitude and longitude coordinates, elevation, slope, and aspect. The models classify three forest characteristics: crown closure, species land cover, and tree size/structure. Models are constructed using n-fold cross-validation. Predictive accuracy is calculated using a method that accounts for the influence of misclassification as well as measuring correct classifications. The probabilistic and generalized regression networks are found to be the most accurate. The predictions of the ANN models are compared with a classification of the Tongass national forest in southeast Alaska based on the interpretation of satellite imagery and are found to be of similar accuracy.

Modeling of titanium alloys by using artificial neural networks

Titanium alloy exhibits an excellent combination of bio-compatibility, corrosion resistance, strength and toughness. The microstructure of an alloy influences the properties. The microstructures depend mainly on alloying elements, method of production, mechanical, and thermal treatments. The relationships between these variables and final properties of the alloy are complex, non-linear in nature, which is the biggest hurdle in developing proper correlations between them by conventional methods. So, we developed artificial neural networks (ANN) models for solving these complex phenomena in titanium alloys.

In the present work, ANN models were used for the analysis and prediction of the correlation between the process parameters, the alloying elements, microstructural features, beta transus temperature and mechanical properties in titanium alloys. Sensitivity analysis of trained neural network models were studied which resulted a better understanding of relationships between inputs and outputs. The model predictions and the analysis are well in agreement with the experimental results. The simulation results show that the average output-prediction error by models are less than 5% of the prediction range in more than 95% of the cases, which is quite acceptable for all metallurgical purposes.

Artificial Neural Network for Compositional Ionic Liquid Viscosity Prediction

Being a new generation of green solvents and high-tech reaction media of the future, ionic liquids have increasingly attracted much attention. Of particular interest in this context are room temperature ionic liquids (in short as ILs in this paper). Due to the relatively high viscosity, ILs is expected to be used in the form of solvent diluted mixture with reduced viscosity in industrial application, where predicting the viscosity of IL mixture has been an important research issue. Different IL mixture and many modelling approaches have been investigated. The objective of this study is to provide an alternative model approach using soft computing technique, i.e., artificial neural network (ANN) model, to predict the compositional viscosity of binary mixtures of ILs [C _n-mim][NTf ₂] with n=4, 6, 8, 10 in methanol and ethanol over the entire range of molar fraction at a broad range of temperatures from T=293.0-328.0K. The results show that the proposed ANN model provides alternative way to predict compositional viscosity successfully with highly improved accuracy and also show its potential to be extensively utilized to predict compositional viscosity taking account of IL alkyl chain length, as well as temperature and compositions simultaneously, i.e., more complex intermolecular interactions between components in which it would be hard or impossible to establish the analytical model. This illustrates the potential application of ANN in the case that the physical and thermodynamic properties are highly non-linear or too complex. © 2012 Copyright the authors.

Artificial neural network model to predict compositional viscosity over a broad range of temperatures

The objective of this study is to provide an alternative model approach, i.e., artificial neural network (ANN) model, to predict the compositional viscosity of binary mixtures of room temperature ionic liquids (in short as ILs) [C _n-mim] [NTf ₂] with n=4, 6, 8, 10 in methanol and ethanol over the entire range of molar fraction at a broad range of temperatures from T=293.0328.0K. The results show that the proposed ANN model provides alternative way to predict compositional viscosity successfully with highly improved accuracy and also show its potential to be extensively utilized to predict compositional viscosity over a wide range of temperatures and more complex viscosity compositions, i.e., more complex intermolecular interactions between components in which it would be hard or impossible to establish the analytical model. © 2010 IEEE.

A further investigation of the kinetic demixing/decomposition of La0.6Sr0.4Co0.2Fe0.8O3- δ oxygen separation membranes

Kinetic demixing and decomposition were studied on three La_0.6Sr_0.4Co_0.2Fe_0.8O_{3- δ} oxygen-separation hollow fibre membrane modules, which were operated under a 0.21/0.009bar oxygen partial pressure difference at 950°C for 1128, 3672 and 5512h, respectively. The post-operation membranes were characterized by Secondary Ion Mass Spectrometry, Scanning Electronic Microscope, Energy Dispersive Spectrum and X-ray Diffraction. The occurrence of kinetic demixing and decomposition was confirmed through the microstructural evolution of the membranes. Secondary-phase grains were found on the air-side surface of the membranes after the long-term operation and Co and Fe enrichment as well as La depletion was found on the surface and in the bulk at the air side. Cation diffusivities were found to be in the order Co>Fe>Sr>La. Kinetic demixing and decomposition rates of the membranes at the air side were found to be self-accelerating with time; the role of A-site deficiency in the perovskite lattice in the bulk near the air side surface is implicated in the mechanism. The oxygen permeability was not affected by the kinetic demixing and decomposition of the material during long-term operation (up to 5512h), however, we may expect permeability to be affected by secondary phase formation on the air-side surface at even longer operational times. © 2010 Elsevier B.V.

Electrochemical promotion of a Pt catalyst supported on La 0.6Sr 0.4Co 0.2Fe 0.8O 3 - δ hollow fibre membranes

The use of wireless electrochemical promotion of catalysis (EPOC) of a Pt catalyst supported on a mixed ionic electronic conducting hollow fibre membranes is investigated. This reactor configuration offers high surface areas per unit volume and is ideally suited for scaled-up applications. The MIEC membrane used is the La _0.6Sr _0.4Co _0.2Fe _0.8O ₃ perovskite (LSCF) with a Pt catalyst film deposited on the outer surface of the LSCF membrane. Experimental results showed that after initial catalyst deactivation (in the absence of an oxygen chemical potential difference across the membrane) the catalytic rate can be enhanced by using an oxygen sweep and wireless EPOC can be used for the in situ regeneration of a deactivated catalyst. © 2012 Elsevier B.V.

Electrochemical promotion of catalysis using solid-state proton-conducting membranes

A solid-state electrochemical reactor with ceramic proton-conducting membrane has been used to study the effect of electrochemically induced hydrogen spillover on the catalytic activity of platinum during ethylene oxidation. Suitable proton-conducting electrolyte membranes (Gd-doped BaPrO ₃ (BPG) and Y-doped BaZrO₃ (BZY)) were fabricated. These materials were chosen because of their protonic conductivity in the operational temperature region of the reaction (400-700 °C). The BZY-based electrochemical cell was used to investigate the open-circuit voltage (OCV) dependence on H₂ partial pressure with comparison being made to the theoretical OCV as predicted by the Nernst equation. Furthermore, the BZY pellets were used to study the effect of proton transfer of the catalytic activity of platinum during ethylene oxidation. The reaction was found to exhibit electrochemical promotion at 400 °C and to be electrophilic in nature, i.e. proton addition to the platinum surface resulted in an increase in reaction rate. At higher temperatures, the rate was not affected, within experimental error, by proton addition or removal. Under similar conditions, AC impedance showed that there was a large overall cell resistance at 400 °C with significantly decreased resistance at higher temperatures. It is possible that there could be a relationship between large cell resistances and the onset of electrochemical promotion in this system but there is, as yet, no conclusive evidence for this. © 2003 Elsevier B.V. All rights reserved.

Non-parametric bill of quantities estimation of concrete road bridges' superstructure: an artificial neural networks approach

Bridge construction responds to the need for environmentally friendly design of motorways and facilitates the passage through sensitive natural areas and the bypassing of urban areas. However, according to numerous research studies, bridge construction presents substantial budget overruns. Therefore, it is necessary early in the planning process for the decision makers to have reliable estimates of the final cost based on previously constructed projects. At the same time, the current European financial crisis reduces the available capital for investments and financial institutions are even less willing to finance transportation infrastructure. Consequently, it is even more necessary today to estimate the budget of high-cost construction projects -such as road bridges- with reasonable accuracy, in order for the state funds to be invested with lower risk and the projects to be designed with the highest possible efficiency. In this paper, a Bill-of-Quantities (BoQ) estimation tool for road bridges is developed in order to support the decisions made at the preliminary planning and design stages of highways. Specifically, a Feed-Forward Artificial Neural Network (ANN) with a hidden layer of 10 neurons is trained to predict the superstructure material quantities (concrete, pre-stressed steel and reinforcing steel) using the width of the deck, the adjusted length of span or cantilever and the type of the bridge as input variables. The training dataset includes actual data from 68 recently constructed concrete motorway bridges in Greece. According to the relevant metrics, the developed model captures very well the complex interrelations in the dataset and demonstrates strong generalisation capability. Furthermore, it outperforms the linear regression models developed for the same dataset. Therefore, the proposed cost estimation model stands as a useful and reliable tool for the construction industry as it enables planners to reach informed decisions for technical and economic planning of concrete bridge projects from their early implementation stages.

Clathrin adaptor epsinR is required for retrograde sorting on early endosomal membranes

Retrograde transport links early/recycling endosomes to the trans-Golgi network (TGN), thereby connecting the endocytic and the biosynthetic/secretory pathways. To determine how internalized molecules are targeted to the retrograde route, we have interfered with the function of clathrin and that of two proteins that interact with it, AP1 and epsinR. We found that the glycosphingolipid binding bacterial Shiga toxin entered cells efficiently when clathrin expression was inhibited. However, retrograde transport of Shiga toxin to the TGN was strongly inhibited. This allowed us to show that for Shiga toxin, retrograde sorting on early/recycling endosomes depends on clathrin and epsinR, but not AP1. EpsinR was also involved in retrograde transport of two endogenous proteins, TGN38/46 and mannose 6-phosphate receptor. In conclusion, our work reveals the existence of clathrin-independent and -dependent transport steps in the retrograde route, and establishes a function for clathrin and epsinR at the endosome-TGN interface.

Factores que influenciam o peso e crescimento de cabritos da raça Murciana-granadina, em aleitamento artificial

A exploração caprina de leite tem evoluído no sentido de alguma intensificação, com recurso a raças de elevado potencial produtivo, de que é exemplo a raça Murciana- Granadina. O leite constitui a principal fonte de receita destas explorações. Complementarmente, vendem animais para carne e, as de melhor nível genético, animais para reprodutores. Analisaram-se os pesos de 241 cabritos da raça Murciana-Granadina, numa exploração comercial, com o objectivo de quantificar os pesos e crescimento de cabritos, e identificar os factores que os influenciam. Os cabritos foram aleitados artificialmente, em regime ad libitum, com leite de substituição comercial, dispondo ainda de concentrado comercial, feno de luzerna e palha. Os cabritos foram pesados ao nascimento e, posteriormente, semanalmente, até aos 60 dias de idade. Calcularam-se os respetivos pesos ajustados, bem como os ganhos médios diários, a diferentes idades padrão. Procedeu-se a uma análise de variância com um modelo linear que incluiu os efeitos da época de parto, tipo de parto, sexo e idade da cabra. Foram registados pesos superiores nos partos simples e duplos, relativamente aos triplos, e nos machos, relativamente às fêmeas. Os ganhos médios diários, a partir do mês de idade, registaram valores inferiores na época inverno-primavera, comparativamente com a época primavera-verão. Dairy goat farming has evolved towards intensification, with increased use of high milk-yielding breeds, including the Murciano-Granadina breed. Milk is the main source of farm income. Secondary income sources are the sale of animals for meat and, in genetically superior herds, the sale of breeding animals. The weights of 241 commercial farms artificially reared Murciano-Granadina kids were analyzed with the objective of quantifying weight and growth and identifying variation factors. Kids were artificially reared to weaning, on ad libitum commercial milk replacer, commercial concentrate, lucerne hay and straw. Kids were weighed at birth and at weekly intervals until 60 days of age. Age adjusted weights and growth-rates were calculated. A variance analysis was performed with a model including the effects of season of birth, number of kids per kidding, sex and age of dam. Single and twin-born kids had higher weights than triplets, and males had higher weights than females. Average daily gain after one month of age was lower for kids born in winter-spring than for those born in spring-summer