The Semantic Link Network model SLN and Resource Space Model RSM are semantic models proposed separately for effectively specifying and managing versatile resources across the Internet. Collaborating the relational semantics of SLN with the classification semantics of the RSM can support richer semantic modeling in applications. This paper introduces the Resource Class Hierarchies into the SLN ...

Three feeding groups were used: the control (SOY) was fed diets without rapeseed products, and the two experimental groups were fed with either 10% rapeseed meal (RSM) or with 12% OO rape seeds (PFRS). Half of the boars from each group were slaughtered after 1 or 2 years. In RSM and PFRS boars steroid-3-beta-ol-dehydrogenase activity was high, whilst Leydig cells were not numerous after 1 year....

An experiment was conducted to determine protein quality in processed protein sources using the content of AA, -methylisourea (OMIU)-reactive Lys, Maillard reaction products (MRP), and cross-link products; the standardized ileal digestibility (SID) of CP and AA; and growth performance in growing pigs as criteria. Differences in protein quality were created by secondary toasting (at 95°C for 30 ...

The role of PKC and RhoA/ROCK pathways in the phasic activities in the rectal smooth muscles (RSM) in the basal state is not known. We examined this issue by determining the effects of PKC inhibitors (calphostin C and Gö-6850) and a ROCK inhibitor (Y-27632) on the slow-rate (~3/min) and fast-rate (~25/min) phasic activities. We also examined the corresponding signal transduction cascades and th...

Classifier combining techniques have become popular for improving weak classifiers in recent years. The random subspace method (RSM) is an efficient classifier combining technique that can improve classification performance of weak classifiers for the small sample size (SSS) problems. In RSM, the feature subsets are randomly selected and the resulting datasets are used to train classifiers. How...

Optimization plays a key role in environmental engineering parameters since the best system performance mainly is on optimum point or optimum range. The majority of wastewater treatment processes are multi-variable and optimization through the classical method is inflexible, unreliable and time-consuming. Thus, response surface methodology (RSM), as a very efficient design and widely used techn...

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...