Enhancing emancipatory systems methodologies for sustainable development

Our interest lies in applying the principles of critical systems thinking to human activity systems in developing countries in situations where issues of natural resource sustainability constrain the feasible set of long-term strategies. The concept of sustainable development provides an expanded domain for critical systems thinking. The fundamental values underpinning sustainable development are that both intragenerational and intergenerational equity are important. As a consequence, key stakeholders are often excluded from power-sharing within current social systems. Addressing these issues requires renewed focus on emancipatory commitment and methodologies. To date, Ulrich's critical systems heuristics is the only critical systems methodology that offers practicable tools for emancipation. A case study analysis in Tigray, northern Ethiopia, provides insights in relation to the application of critical system heuristics to issues of sustainable development and highlights the need to extend the use of critical systems heuristics beyond the design and monitoring of structured interventions.

Mean anisotropy of homogeneous Gaussian random fields and anisotropic norms of linear translation-invariant operators on multidimensional integer lattices

Sensitivity of output of a linear operator to its input can be quantified in various ways. In Control Theory, the input is usually interpreted as disturbance and the output is to be minimized in some sense. In stochastic worst-case design settings, the disturbance is considered random with imprecisely known probability distribution. The prior set of probability measures can be chosen so as to quantify how far the disturbance deviates from the white-noise hypothesis of Linear Quadratic Gaussian control. Such deviation can be measured by the minimal Kullback-Leibler informational divergence from the Gaussian distributions with zero mean and scalar covariance matrices. The resulting anisotropy functional is defined for finite power random vectors. Originally, anisotropy was introduced for directionally generic random vectors as the relative entropy of the normalized vector with respect to the uniform distribution on the unit sphere. The associated a-anisotropic norm of a matrix is then its maximum root mean square or average energy gain with respect to finite power or directionally generic inputs whose anisotropy is bounded above by a≥0. We give a systematic comparison of the anisotropy functionals and the associated norms. These are considered for unboundedly growing fragments of homogeneous Gaussian random fields on multidimensional integer lattice to yield mean anisotropy. Correspondingly, the anisotropic norms of finite matrices are extended to bounded linear translation invariant operators over such fields.

Amino acids 1-20 of the hepatitis C virus (HCV) core protein specifically inhibit HCV IRES- dependent translation in Hep G2 cells, and inhibit both HCV IRES- and cap-dependent translation in HuH7 and CV-1 cells.

A self-modulating mechanism by the hepatitis C virus (HCV) core protein has been suggested to influence the level of HCV replication, but current data on this subject are contradictory. We examined the effect of wild-type and mutated core protein on HCV IRES- and cap-dependent translation. The wild-type core protein was shown to inhibit both IRES- and cap-dependent translation in an in vitro system. This effect was duplicated in a dose-dependent manner with a synthetic peptide representing amino acids 1-20 of the HCV core protein. This peptide was able to bind to the HCV IRES as shown by a mobility shift assay. In contrast, a peptide derived from the hepatitis B virus (HBV) core protein that contained a similar proportion of basic residues was unable to inhibit translation or bind the HCV IRES. A recombinant vaccinia-HCV core virus was used to examine the effect of the HCV core protein on HCV IRES-dependent translation in cells and this was compared with the effects of an HBV core-recombinant vaccinia virus. In CV-1 and HuH7 cells, the HCV core protein inhibited translation directed by the IRES elements of HCV, encephalomyocarditis virus and classical swine fever virus as well as cap-dependent translation, whereas in HepG2 cells, only HCV IRES-dependent translation was affected. Thus, the ability of the HCV core protein to selectively inhibit HCV IRES-dependent translation is cell-specific. N-terminal truncated (aa 1-20) HCV core protein that was expressed from a novel recombinant vaccinia virus in cells abrogated the inhibitory phenotype of the core protein in vivo, consistent with the above in vitro data.

A comparison of the whole-body and segmental methodologies of bioimpedance analysis

Theory supports the use of a segmental methodology (SM) for bioimpedance analysis (BIA) of body water (BW). However, previous studies have generally failed to show a significant improvement when the SM is used in place of a whole-body methodology. A pilot study was conducted to compare the two methodologies in control and overweight subjects. BW of each subject was measured by D2O dilution and also estimated from BIA measurements. Bland and Altman analysis was used to compare the two values of BW. The SM resulted in a small but not significantly improved limits of agreement of measured and BIA estimated BW (psimilar to0.3). This and the results of previous studies suggest that improvements in prediction of BW obtained from application of the SM may be intrinsically small and may not justify the additional effort in application.

Methodologies for calibration and predictive analysis of a watershed model

The use of a fitted parameter watershed model to address water quantity and quality management issues requires that it be calibrated under a wide range of hydrologic conditions. However, rarely does model calibration result in a unique parameter set. Parameter nonuniqueness can lead to predictive nonuniqueness. The extent of model predictive uncertainty should be investigated if management decisions are to be based on model projections. Using models built for four neighboring watersheds in the Neuse River Basin of North Carolina, the application of the automated parameter optimization software PEST in conjunction with the Hydrologic Simulation Program Fortran (HSPF) is demonstrated. Parameter nonuniqueness is illustrated, and a method is presented for calculating many different sets of parameters, all of which acceptably calibrate a watershed model. A regularization methodology is discussed in which models for similar watersheds can be calibrated simultaneously. Using this method, parameter differences between watershed models can be minimized while maintaining fit between model outputs and field observations. In recognition of the fact that parameter nonuniqueness and predictive uncertainty are inherent to the modeling process, PEST's nonlinear predictive analysis functionality is then used to explore the extent of model predictive uncertainty.