Inhibition of atmospheric corrosion of mild steel by sodium benzoate treatment

The objective of this study was to evaluate the effectiveness of sodium benzoate as an inhibitor to slow down or prevent atmospheric corrosion/discoloration of the local mild steel during storage in the Arabian Gulf region. Test specimens were prepared from locally produced reinforcing steel products. The inhibitor solution was applied on steel specimens at a concentration of 100 mM for 1 day at room temperature. Wooden exposure racks were used to hold as-received and inhibitor-treated specimens during atmospheric exposure for different periods. Corrosion was evaluated through weight loss determination and electrochemical technique. As expected, the Arabian Gulf atmosphere was corrosive on the as-received local mild steel. On the other hand, treatment of steel with sodium benzoate lowered its corrosion rate during initial days of its exposure to atmosphere. However, atmospheric corrosion inhibition performance of sodium benzoate deteriorated with exposure time after 30 or more days of atmospheric exposure, and the corrosion rates of sodium benzoate-treated specimens reached that of the unprotected specimens at the end of 90 days of atmospheric exposure.     

Performance failure analysis of EDFA cascades in optical DWDM packet-switched networks

A simple and comprehensive technique to determine the probability that a cascade of erbium-doped fiber amplifiers (EDFAs) may be driven into unacceptable regimes of bit error rate (BER) and/or gain levels is presented. This technique allows network designers to determine the tolerances by which the signal power levels may deviate from their predesigned average values and still give acceptable gain variances and BERs at the receiver. We show that even in the signal power range well above the receiver sensitivities (-38 dBm/ch) where the gain spread is not significant, the corresponding spread in BER due to random arrival of packets might result in unacceptable performance. We show for typical levels of operation, the BER temporarily (for about 3 /spl mu/s) deviates to below 10/sup -9/ (10/sup -15/) with a probability of 10/sup -3/ (10/sup -2/), for 100 (64) channels. We show that the gain spread for a single EDFA can be negligible for a range of signal and pump powers at a given average gain.     

Transformations in machining. Part 1. enhancement of wavelet transformation neural network (WT-NN) combination with a preprocessor

Properly selected transformation methods obtain the most significant characteristics of metal cutting data efficiently and simplify the classification. Wavelet Transformation (WT) and Neural Networks (NN) combination was used to classify the experimental cutting force data of milling operations previously. Preprocessing (PreP) of the approximation coefficients of the WT is proposed just before the classification by using the Adaptive Resonance Theory (ART2) type NNs. Genetic Algorithm (GA) was used to estimate the weights of each coefficient of the PreP. The WT-PreP-NN (ART2) combination worked at lower vigilances by creating only a few meaningful categories without any errors. The WT-NN (ART2) combination could obtain the same error rate only if very high vigilances are used and many categories are allowed.     

Modelling 3-D cutting dynamics with neural networks

Identification of 3-D cutting dynamics requires an expensive experimental set-up and complicated analysis. Recently, time series methods were used to model cutting dynamics. This approach allows a simpler experimental set-uup and estimates the discrete transfer functions used for simulation and/or calculation of frequency domain characteristics of the system. In this paper, the use of neural networks is proposed to model the 3-D cutting dynamics. Neural networks can be trained using the same experimental set-up used for the time series methods. However, several time series models (for different cutting speeds) can be represented with a single neural network, and cutting forces can be studied for varying cutting speed conditions. Also, four neural networks were used to store the frequency domain characteristics of the thrust direction cutting force. In this study, the estimation errors for the neural networks were less than 7% of the defined range (the difference between the maximum and minimum of the data).     

Fuzzy multiattribute evaluation of R&D projects using a real options valuation model

R&D project selection decision is very important in two ways. First, in many organizations, R&D budget represents huge investment. Project selection decisions could be thought with the strategic objectives and plans of the firm. Second, R&D projects' organizational returns are multidimensional in nature and risky in terms of projected outcome. Real options approach helps to calculate this risky side of the selection process. This paper considers that multidimensional side of the R&D project selection process. Another consideration is the vagueness in the evaluation process. The fuzzy analytic hierarchy process, which takes monetary (fuzzy real option value) and nonmonetary (capability, success probability, trends, etc.) criteria into account, is used to make this selection among alternative R&D projects. A real case study is given to illustrate the application of the proposed approach. © 2008 Wiley Periodicals, Inc.     

Joint forecasts of Dow Jones stocks under general multivariate loss function

When forecasts are assessed by a general loss (cost-of-error) function, the optimal point forecast is, in general, not the conditional mean, and depends on the conditional volatility—which, for stock returns, is time-varying. In order to provide forecasts of daily returns of 30 DJIA stocks under a general multivariate loss function, the following issues are addressed. We discuss what conditions define a multivariate loss function, and a simple class of such functions is proposed. Based on suitable combinations of univariate losses, the suggested multivariate functions are convenient for practical applications with many variables. To keep the computational aspect tractable, a flexible multivariate GARCH model is employed in estimating the conditional forecast distributions. The model easily copes with large number of series while allowing for skewness, fat tails, non-ellipticity, and tail dependence. Based on Engle’s DCC GARCH, it uses multivariate affine generalized hyperbolic distributions as conditional probability law, and the number of parameters to be estimated simultaneously does not depend on the number of series. The model is fitted using daily data from 2002 to 2007 (keeping data from 2008 for out-of-sample forecasts), and a bootstrap procedure is used to derive point forecasts under several multivariate loss functions of the proposed type.     

Min-degree constrained minimum spanning tree problem: New formulation via Miller–Tucker–Zemlin constraints

Given an undirected network with positive edge costs and a positive integer $d>2$, the minimum-degree constrained minimum spanning tree problem is the problem of finding a spanning tree with minimum total cost such that each non-leaf node in the tree has a degree of at least $d$. This problem is new to the literature while the related problem with upper bound constraints on degrees is well studied. Mixed-integer programs proposed for either type of problem is composed, in general, of a tree-defining part and a degree-enforcing part. In our formulation of the minimum-degree constrained minimum spanning tree problem, the tree-defining part is based on the Miller–Tucker–Zemlin constraints while the only earlier paper available in the literature on this problem uses single and multi-commodity flow-based formulations that are well studied for the case of upper degree constraints. We propose a new set of constraints for the degree-enforcing part that lead to significantly better solution times than earlier approaches when used in conjunction with Miller–Tucker–Zemlin constraints.     

Integrating multi-objective genetic algorithm based clustering and data partitioning for skyline computation

Skyline computation in databases has been a hot topic in the literature because of its interesting applications. The basic idea is to find non-dominated values within a database. The task is mainly a multi-objective optimization process as described in this paper. This motivated for our approach that employs a multi-objective genetic algorithm based clustering approach to find the pareto-optimal front which allows us to locate skylines within a given data. To tackle large data, we simply split the data into manageable subsets and concentrate our analysis on the subsets instead of the whole data at once. The proposed approach produced interesting results as demonstrated by the outcome from the conducted experiments.     

Urban Sustainability Incentives for Residential Water Conservation: Adoption of Multiple High Efficiency Appliances

Effects of multiple types of water use efficiency appliances on long term water savings and water use trend shifts were analyzed. The study group included senior and low income families in the urban areas of Miami-Dade County, Florida, USA. The participants in the study group experienced continuous and significant water savings within 3 years of the implementation of the water conservation incentives. Water savings were observed at approximately 200 l per household per day, which is about 31 % reduction in household water demand in comparison to the average residential water demand within the County. The water use profile of participants showed noticeable shifts over time in water demand frequency curves toward lower water consumption rates. The cost-saving analysis showed that adoption of multiple water efficiency appliances contributed to the highest annual monetary savings (i.e., high water savings and moderate product costs). Future conservation program planning efforts should take both water savings and product cost into account in order to achieve the greatest benefits.