This paper introduces an original 2-valued semantics for Normal Logic Programs (NLP), important on its own. Nevertheless, its name draws attention to that it is inspired by and generalizes Stable Model semantics (SM). The definitional distinction consists in the revision of one feature of SM, namely its treatment of odd loops over default negation. This single revised aspect, addressed by means...

RSM is a replacement sensor model that replaces with high fidelity all aspects of the original sensor model for virtually all imaging sensors. This includes the ground-to-image relationship, adjustability, and rigorous error propagation. RSM can do so for both satellite and airborne platforms as well as for different sensor modalities (optical and SAR). This paper provides a general introductio...

In this paper, the estimation capacities of the response surface methodology (RSM) and artificial neural network (ANN), in a microwave-assisted extraction method to determine the amount of zinc in fish samples were investigated. The experiments were carried out based on a 3-level, 4-variable Box-Behnken design. The amount of zinc was considered as a function of four independent variables, namel...

Response Surface Methodology (RSM) searches for the input combination that optimizes the simulation output. RSM treats the simulation model as a black box. Moreover, this paper assumes that simulation requires much computer time. In the first stages of its search, RSM locally fits first-order polynomials. Next, classic RSM uses steepest descent (SD); unfortunately, SD is scale dependent. Theref...

Rapeseed meal (RSM) was subjected to different physical or chemical pretreatments to decrease residual, hard to degrade carbohydrates and to improve fermentability of RSM polysaccharides. Next, these pretreated samples were in vitro digested and fermented, with or without the addition of commercial pectinolytic enzymes. Remaining carbohydrates were quantified, and two physical characteristics w...

Modeling and optimization were performed to enhance production of lactase through submerged fermentation by Bacillus subtilis VUVD001 using artificial neural networks (ANN) and response surface methodology (RSM). The effect of process parameters namely temperature (°C), pH, and incubation time (h) and their combinational interactions on production was studied in shake flask culture by Box-Behnk...

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...

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...