QUADRATIC REPRESENTATION FOR ROADWAY PROFILE THAT MINIMIZES EARTHWORK COST

Roadway design usually involves choices regarding grade selection and earthwork (transportation) that can be solved using linear programming. Previous work considered the road profile as series of interconnected linear segments. In these models, constraints are included in the linear programming formulation to insure continuity of the road, which cause sharp connectivity points at the intersection of the linear segments. This sharp connectivity needs to be smoothed out after l;he linear programming solution is found and the earth in the smoothed portion of the roadway has to be moved to the landfill. In previous research, the smoothing issue is dealt with after an optimal solution is found. This increases the work required by the design engineer and consequently increases the construction cost; furthermore, the optimal solution is violated by this smoothing operation. In this paper, the issue of sharp connectivity points is resolved by representing the road profile by a quadratic function. The continuit     

A MARKOVIAN APPROACH TO DETERMINING PROCESS MEANS WITH DUAL QUALITY CHARACTERISTICS

This paper studies a production system where products are produced continuously and whose specification limits are specified for screening inspection. In this paper, we consider dual quality characteristics and different costs associated with each quality characteristic that falls below a lower specification limit or above an upper specification limit. Due to these different costs, the expected total profit will greatly depend on the process parameters, especially a process mean. This paper develops a Markovian-based model for determining the optimum process means with the consideration of dual quality characteristics in a single-stage system. The proposed model is then illustrated through a numerical example and sensitivity analysis is performed to validate the model. The results showed that the optimum process mean for both quality characteristics have a significant effect on the performance of the system. Since the literature survey shows that dealing with multi-quality characteristics is extremely limited, the proposed model, coupled with the Markovian approach, provides a unique contribution to this field.     

Multiobjective Optimal Water ResourcesManagement for a Large River Basin

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CT Brain Image: Abnormalities Recognition and Segmentation

In order to develop an automated segmentation system for Computed Tomography (CT) brain images, a new approach which consists of several unsupervised segmentation techniques was introduced. The system segments the CT brain images into three partitions, i. e., abnormalities, cerebrospinal fluid (CSF), and brain matter. Our approach consists of two phase-segmentation methods. In the first phase segmentation, k-means and fuzzy c-means (FCM) methods were implemented to segment and transform the images into the binary images. Based on the connected component in binary images, a decision tree was employed for the annotation of normal or abnormal regions, In the second phase segmentation, the modified FCM with population-diameter independent (PDI) segmentation was applied to segment the images into CSF and brain matter. The experimental results have shown that our proposed system is feasible and yield satisfactory results.     

Cu-Zn-Al2O3 nanocomposites: study of microstructure,corrosion, and wear properties

Alumina nanoparticles were added to a Cu-Zn alloy to investigate their effect on the microstructural, tribological, and corrosion properties of the prepared alloys. Alloying was performed using a mixture of copper and zinc powders with 0vol% and 5vol% of α-Al nanopowder in a satellite ball mill. The results showed that the Cu-Zn solid solution formed after 18 h of mechanical alloying. The mechan-ically alloyed powder was compacted followed by sintering of the obtained green compacts at 750℃ for 30 min. Alumina nanoparticles were uniformly distributed in the matrix of the Cu-Zn alloy. The tribological properties were evaluated by pin-on-disk wear tests, which revealed that, upon the addition of alumina nanoparticles, the coefficient of friction and the wear rate were reduced to 20% and 40%, respectively. The corrosion properties of the samples exposed to a 3.5wt% NaCl solution were studied using the immersion and potentiodynamic polarization methods, which revealed that the addition of alumina nanoparticles reduced the corrosion current of the nanocomposite by 90%.     

Forecasting stock returns an examination of stock market trading in the presence of transaction costs

The paper presents new evidence on the predictability of excess returns on common stocks for the Standard and Poor's 500 and the Dow Jones Industrial portfolios at the monthly, quarterly, and annual frequencies. It shows that recursive predictions obtained on the basis of the excess returns regressions are capable of correctly predicting a statistically significant proportion of the signs of the actual returns. The paper also shows that the switching portfolios constructed on the basis of the signs of the recursive predictions mean-variance dominate the respective market portfolios when trading takes place on a quarterly or annual basis. This result holds even under a high transaction cost scenario. However, due to the larger number of transactions at the monthly frequency the monthly switching portfolios only mean-variance dominate the respective market portfolios when transaction costs are zero or low.