Sensitivity Analysis of Transportation Production Costs in Indonesia

The transportation production cost (TPC) considerably has strong influence to the national economic condition. This paper focused on the analysis of the transportation production cost sensitivity in relation to the variation of the external affecting factor, which are fuel price, rupiah exchange rate and Bank of Indonesia interest rate. Based on the R2 values, the TPC components in general have significant correlation, with the fuel prices. However, they do not have high correlation to the fluctuation of interest rate and rupiah exchange rate. The sensitivity analysis shows that a 10% rise on fuel price would cause 6%, 2%, 7%, 2.4%, and 4.9% rise on the TPC of intercity bus, ferry ship, interisland ship, train, and airline, respectively

A sensitivity analysis of the PAWN sensitivity index

The PAWN index is gaining traction among the modelling community as a sensitivity measure. However, the robustness to its design parameters has not yet been scrutinized: the size (N) and sampling (ε) of the model output, the number of conditioning intervals (n) or the summary statistic (θ). Here we fill this gap by running a sensitivity analysis of a PAWN-based sensitivity analysis. We compare the results with the design uncertainties of the Sobol’ total-order index (S*Ti). Unlike in S*Ti, the design uncertainties in PAWN create non-negligible chances of producing biased results when ranking or screening inputs. The dependence of PAWN upon (N, n, ε, θ) is difficult to tame, as these parameters interact with one another. Even in an ideal setting in which the optimum choice for (N, n, ε, θ) is known in advance, PAWN might not allow to distinguish an influential, non-additive model input from a truly non-influential model input

Evaluation Of Serum High-sensitivity C-reactive Protein Levels During Various Periods Of Pregnancy In Woman, Infected With Parvovirus - B19 Infection

The aim of the research was analyze of C-reactive protein levels in serum blood samples during various periods of pregnancy in women, infected with parvovirus B19 and in case of presence of clinical complications.129 pregnant women, infected with parvovirus B19 infection and 16 women with physiological pregnancy during first, second and third trimesters of pregnancy were examined. Depending on the presence or absence of clinical complications each group of pregnant women (I, II, III) was divided into two subgroups. The concentration of C-reactive protein in blood serum was determined by the method of immunoassay analysis using diagnostic sets of reagents (ELISA kits, USA). Statistical processing of data was carried out using the package of applied programs Microsoft Office Excel 2010 and StatSoft Statistica 6.1.The mean age of pregnant woman in our study was 26±6 years. In the Iand II groups of infected B19 parvovirus pregnant women were identified a significant increasing of C-reactive protein levels compared to controls by 62.5 % and 50.0 % (p<0.05). The largest increasing of C-reactive protein level relative to control values was observed in women with clinical complications in different pregnancy periods (p<0.05).An increase levels of the marker of systemic inflammation the C-reactive protein in the blood testifies to its active participation in the launch of a complex mechanism for the development of labor activity and the occurrence of fetal disorders, which was confirmed in groups of pregnant women with clinical complications in different periods of pregnancy

Collective Phase Sensitivity

The collective phase response to a macroscopic external perturbation of a population of interacting nonlinear elements exhibiting collective oscillations is formulated for the case of globally-coupled oscillators. The macroscopic phase sensitivity is derived from the microscopic phase sensitivity of the constituent oscillators by a two-step phase reduction. We apply this result to quantify the stability of the macroscopic common-noise induced synchronization of two uncoupled populations of oscillators undergoing coherent collective oscillations.Comment: 6 pages, 3 figure

Sensitivity bond graphs

A sensitivity bond graph, of the same structure as the system bond graph, is shown to provide a simple and effective method of generating sensitivity functions of use in optimisation. The approach is illustrated in the context of partially known system parameter and state estimation