A conceptual model for sheet-flow drawn from rapid granular flow theories

33rd IAHR Congress: Water Engineering for a Sustainable Environment

The thickness of the transport layer in stratified geomorphic flows

River Flow 2010

Geomorphic dam-break flows. Part I: conceptual model

Proceedings of the Institution of Civil Engineers - Water Management 163 Issue WM6

Lawyers, judges, and judicial reform: A conceptual framework and a quantitative exploration

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Economics from the NOVA – School of Business and Economics

Can the complex mixture of sediment contaminants cause endocrine disruption on a benthic fish? A case study with Solea senegalensis

Pollution in coastal ecosystems is a serious threat to the biota and human populations there residing. Anthropogenic activities in these ecosystems are the main cause of contamination by endocrine disruption compounds (EDCs), which can interfere with hormonal regulation and cause adverse effects to growth, stress response and reproduction. Although the chemical nature of many EDCs is unknown, it is believed that most are organic contaminants. Under an environmental risk assessment for a contaminated estuary (the Sado, SW Portugal), the present work intended to detect endocrine disruption in a flatsfish, Solea senegalensis Kaup, 1858, and its potential relationship to organic toxicants. Animals were collected from two areas in the estuary with distinct influences (industrial and rural) and from an external reference area. To evaluate endocrine disruption, hepatic vitellogenin (VTG) concentrations in males and gonad histology were analysed. As biomarkers of exposure to organic contaminants, cytochrome P450 (CYP1A) induction and the ethoxyresorufin-O-deethylase (EROD) activity were determined. The results were contrasted to sediment contamination levels, which are overall considered low, although the area presents a complex mixture of toxicants. Either males or females were found sexually immature and showed no significant evidence of degenerative pathologies. However, hepatic VTG concentrations in males from the industrial area in estuary were superior than those from the Reference, even reaching levels comparable to those in females, which may indicate an oestrogenic effect resulting from the complex contaminant mixture. These individuals also presented higher levels of CYP1A induction and EROD activity, which is consistent with contamination by organic substances. The combination of the results suggest that the exposure of flatfish to an environment contaminated by mixed toxicants, even at low levels, may cause endocrine disruption, therefore affecting populations, which implies the need for further research in identification of potential EDCs, their sources and risks at ecosystem scale.

R&D spillovers in an endogenous growth model with physical capital, human capital and varieties

There is a family of models with Physical, Human capital and R&D for which convergence properties have been discussed (Arnold, 2000a; Gómez, 2005). However, spillovers in R&D have been ignored in this context. We introduce spillovers in this model and derive its steady-state and stability properties. This new feature implies that the model is characterized by a system of four differential equations. A unique Balanced Growth Path along with a two dimensional stable manifold are obtained under simple and reasonable conditions. Transition is oscillatory toward the steady-state for plausible values of parameters.

Transitional dynamics of an endogenous growth model with an erosion effect

The convergence features of an Endogenous Growth model with Physical capital, Human Capital and R&D have been studied. We add an erosion effect (supported by empirical evidence) to this model, and fully characterize its convergence properties. The dynamics is described by a fourth-order system of differential equations. We show that the model converges along a one-dimensional stable manifold and that its equilibrium is saddle-path stable. We also argue that one of the implications of considering this “erosion effect” is the increase in the adherence of the model to data.