Environmental vulnerability assessment of the Doce River basin, southeastern Brazil.

Environmental vulnerability is an important tool to understand the natural and anthropogenic impacts associated with the susceptibility to environmental damage. This study aims to assess the environmental vulnerability of the Doce River basin in Brazil through Multicriteria Decision Analysis based on Geographic Information Systems (GIS-MCDA). Natural factors (slope, elevation, relief dissection, rainfall, pedology, and geology) and anthropogenic factors (distance from urban centers, roads, mining dams, and land use) were used to determine the environmental vulnerability index (EVI). The EVI was classified into five classes, identifying associated land uses. Vulnerability was verified in water resource management units (UGRHs) and municipalities using hot spot analysis. The study employed the water quality index (WQI) to assess the EVI and global sensitivity analysis (GSA) to evaluate the model input parameters that most influence the basin's environmental vulnerability. The results showed that the regions near the middle Doce River were considered environmentally more vulnerable, especially the UGRHs Guandu, Manhuaçu, and Caratinga; and 35.9% of the basin has high and very high vulnerabilities. Hot spot analysis identified regions with low EVI values (cold spot) in the north and northwest, while areas with high values (hot spot) were concentrated mainly in the middle Doce region. Water monitoring stations with the worst WQI values were found in the most environmentally vulnerable areas. The GSA determined that land use and slope were the primary factors influencing the model's response. The results of this study provide valuable information for supporting environmental planning in the Doce River basin.

Global sensitivity analysis in physiologically-based pharmacokinetic/pharmacodynamic models of inhaled and opioids anesthetics and its application to generate virtual populations.

The integration between physiologically-based pharmacokinetics (PBPK) models and pharmacodynamics (PD) models makes it possible to describe the absorption, distribution, metabolism and excretion processes of drugs, together with the concentration-response relationship, being a fundamental framework with wide applications in pharmacology. Nevertheless, the enormous complexity of PBPK models and the large number of parameters that define them leads to the need to study and understand how the uncertainty of the parameters affects the variability of the models output. To study this issue, this paper proposes a global sensitivity analysis (GSA) to identify the parameters that have the greatest influence on the response of the model. It has been selected as study cases the PBPK models of an inhaled anesthetic and an analgesic, along with two PD interaction models that describe two relevant clinical effects, hypnosis and analgesia during general anesthesia. The subset of the most relevant parameters found adequately with the GSA method has been optimized for the generation of a virtual population that represents the theoretical output variability of various model responses. The generated virtual population has the potential to be used for the design, development and evaluation of physiological closed-loop control systems.

Vector Preference Annihilates Backward Bifurcation and Reduces Endemicity.

We propose and analyze a mathematical model of a vector-borne disease that includes vector feeding preference for carrier hosts and intrinsic incubation in hosts. Analysis of the model reveals the following novel results. We show theoretically and numerically that vector feeding preference for carrier hosts plays an important role for the existence of both the endemic equilibria and backward bifurcation when the basic reproduction number [Formula: see text] is less than one. Moreover, by increasing the vector feeding preference value, backward bifurcation is eliminated and endemic equilibria for hosts and vectors are diminished. Therefore, the vector protects itself and this benefits the host. As an example of these phenomena, we present a case of Andean cutaneous leishmaniasis in Peru. We use parameter values from previous studies, primarily from Peru to introduce bifurcation diagrams and compute global sensitivity of [Formula: see text] in order to quantify and understand the effects of the important parameters of our model. Global sensitivity analysis via partial rank correlation coefficient shows that [Formula: see text] is highly sensitive to both sandflies feeding preference and mortality rate of sandflies.

Dynamic global sensitivity analysis in bioreactor networks for bioethanol production.

Dynamic global sensitivity analysis (GSA) was performed for three different dynamic bioreactor models of increasing complexity: a fermenter for bioethanol production, a bioreactors network, where two types of bioreactors were considered: aerobic for biomass production and anaerobic for bioethanol production and a co-fermenter bioreactor, to identify the parameters that most contribute to uncertainty in model outputs. Sobol's method was used to calculate time profiles for sensitivity indices. Numerical results have shown the time-variant influence of uncertain parameters on model variables. Most influential model parameters have been determined. For the model of the bioethanol fermenter, µmax (maximum growth rate) and Ks (half-saturation constant) are the parameters with largest contribution to model variables uncertainty; in the bioreactors network, the most influential parameter is µmax,1 (maximum growth rate in bioreactor 1); whereas λ (glucose-to-total sugars concentration ratio in the feed) is the most influential parameter over all model variables in the co-fermentation bioreactor.