Energy efficient resource allocation in two-tier OFDMA networks with QoS guarantees

Wireless Networks - In this paper, we introduce the joint power and subchannel allocations techniques for OFDMA based femtocell networks with focus on uplink direction. These techniques minimize...     

Effects of process variables for biodiesel production by transesterification

In this study, biodiesel production from various vegetable oils by transesterification was studied, to determine the optimum conditions. Experiments were carried out by using different kinds of catalysts (sodium hydroxide, potassium hydroxide, barium hydroxide, pyrolitic coke and wood ash) and feedstocks (corn oil, sunflower oil, soybean oil, olive pomace oil and cottonseed oil) at 65 °C and an agitation speed of 1000 rpm. The neutralization step with controlled pH was performed by treatment with phosphoric acid. An experimental design was used to evaluate the effects of the parameters such as types of vegetable oils, kinds of catalysts, reaction time, alcohol/oil volumetric ratio and amount of catalyst, on the methyl ester conversion. Using response surface methodology, a quadratic polynomial equation was obtained by multiple regression analysis. It was found that catalyst concentration was the most effective parameter. Sodium hydroxide and potassium hydroxide exhibited a superior catalytic behavior, whereas pyrolitic coke and wood ash had to be used in excess amount or for prolonged reaction times. Moreover, the properties such as viscosity, density, calorific value, acid value, and refractive index of the biodiesel were measured. The tri‐, di‐, monoacylglycerols and glycerol residuals in the methyl esters produced were also quantified by GC analysis.     

A robust asset–liability management framework for investment products with guarantees

This paper suggests a robust asset–liability management framework for investment products with guarantees, such as guaranteed investment contracts and equity-linked notes. Stochastic programming and robust optimization approaches are introduced to deal with data uncertainty in asset returns and interest rates. The statistical properties of the probability distributions of uncertain parameters are incorporated in the model through appropriately selected symmetric and asymmetric uncertainty sets. Practical data-driven approaches for implementation of the robust models are also discussed. Numerical results using generated and real market data are presented to illustrate the performance of the robust asset–liability management strategies. The robust investment strategies show better performance in unfavorable market regimes than traditional stochastic programming approaches. The effectiveness of robust investment strategies can be improved by calibrating carefully the shape and the size of the uncertainty sets for asset returns.