The National Fire Danger Rating System (NFDRS) indices deduced from RSM 50-km 1-day simulations from January 1998 through December 2003 are used in conjunction with a probability model to determine the expected number of fire occurrences and large fire events. The RSM simulation and forecasts are ongoing experimental products from the Experimental Climate Prediction Center (ECPC) at Scripps Ins...

Optimum conditions for extracting total phenolic compounds (TPC) and antioxidant activity from fresh dark fig (Ficus carica L.) have been investigated using response surface methodology (RSM). The Box-Behnken design was used to investigate the effects of three independent variables, acetone concentration (40-80%), temperature (25-65 °C), and time (60-120 min), on the response. Regression analys...

Kojic acid is widely used to inhibit the browning effect of tyrosinase in cosmetic and food industries. In this work, synthesis of kojic monooleate ester (KMO) was carried out using lipase-catalysed esterification of kojic acid and oleic acid in a solvent-free system. Response Surface Methodology (RSM) based on central composite rotatable design (CCRD) was used to optimise the main important re...

Artificial neural networks (ANN) and response surface methodology (RSM) were used to build a model to describe the effects of four independent variables (moisture content, concentrations of glucose, ammonium nitrate and methionine) on the yield of cephalosporin C (CPC) from Acremonium chrysogenum under solid state fermentation. The respective uses of RSM and ANN were found to be effective in lo...

Resource Space Model (RSM) is a semantic model to manage and share heterogeneous resources on the Internet. This paper focuses on the general architecture, physical implementation and application of RSM. The RSM system adopts Client/Server mode for users to utilize resource spaces through the Internet. The resource spaces are designed based on domain knowledge and stored as XML files in the ser...

Device robust-design is inherently a multiple-objective optimization problem. Using design of experiments (DoE) combined with response surface methodology (RSM) can satisfy the great incentive to reduce the number of TCAD simulations that need to be performed. However, the errors of RSM models may large enough to diminish the validity of the results for some nonlinear problems. To find the feas...

In this paper, we present a new mutation operator, Hybrid Mutation (HPRM), for a genetic algorithm that generates high quality solutions to the Traveling Salesman Problem (TSP). The Hybrid Mutation operator constructs an offspring from a pair of parents by hybridizing two mutation operators, PSM and RSM. The efficiency of the HPRM is compared as against some existing mutation operators; namely,...

R surface methodology (RSM) is a widely used method for simulation optimization. Its strategy is to explore small subregions of the decision space in succession instead of attempting to explore the entire decision space in a single attempt. This method is especially suitable for complex stochastic systems where little knowledge is available. Although RSM is popular in practice, its current appl...

Biofiltration is emerging as a promising cost effective technique for the Volatile Organic Compounds (VOCs) removal from industrial waste gases. In the present investigation a comparative modeling study has been carried out using Radial Basis Function Neural Network (RBFN) and Response Surface Methodology (RSM) to predict and optimize the performance of a biofilter system treating toluene (a mo...

Response Surface Methodology (RSM) is a popular class of methods used to build empirical models through statistical means. Rarely in the literature is the RSM applied to model characteristics of Evolutionary Algorithm (EA) based techniques used to solve dynamic optimization problems. This paper presents RSM as a modelling tool of some characteristics of a memory and EA-based approach referred t...