AN ANALYTICAL NON-STATIONARY DISPERSION-DEPOSITION MODEL

In this work an atmospheric dispersion-deposition model to describe the radionuclides concentration after a hypothetical nuclear accident is presented. The developed model has two parts. The first one describes the concentration due to the material released after an arbitrary period from a given source. The second one describes the concentration change during the current period due to the dispersion of the material that is in the atmosphere. Here the solution for the first part is presented. A non-stationary analytical solution is obtained from the atmospheric diffusion equation. The model solves the transport (convection-diffusion) equation in which the contaminant settling is explicitly incorporated. Regarding the boundary conditions, it is assumed a null diffusion through the mixed layer top and an albedo at the ground level. From the general solution obtained for the contaminant concentration the Gaussian plume formula is derived as a particular case when it is assumed a stationary point source, vd =0 (total reflection) and vs,=0 (without settling). Assuming vd ≠ 0 and vs,= 0, is compared with the Source Depletion Model (SoDM) and with the Surface Depletion Model (SuDM). The agreement is excellent when comparing with the SuDM, which is the exact solution for the diffusion equation when the settling is modeled only in the boundary condition. Two additional cases are presented, the former is the steady state solution: vd ≠ 0 and vs, ≠ 0, and the last one is a non-stationary simulation with a transport time of 3600 s with vd ≠ 0 and vs, ≠ 0. For the last case, two Erf functions appear in the solution as result of time integration that model the plume front traveling in the atmosphere. This new contaminant transport model describes the concentration evolution in a more realistic way, representing the plume falling. This is an improvement respect to the known dispersion-deposition models (SoDM, SuDM)

The Financial Accelerator Under Learning and The Role of Monetary Policy

Financial frictions have been shown to play an important role amplifying business cycles fluctuations. In this paper we show that the financial accelerator mechanism, analyzed by Bernanke, Gertler and Gilrchrist (1999), combined with adaptive learning can amplify business cycle fluctuations significantly as the balance sheet channel interacts with the presence of endogenous asset price “bubbles”. These large business cycle fluctuations are amplified in a non-linear way by the size of the shocks and by the degree of financial fragility in the economy determined by its leverage. Our preliminary results indicate that even in the presence of endogenous bubbles, responding aggressively to inflation reduces output and inflation volatility. If the central bank adjusts its policy instrument in response to asset price fluctuations, it may reduce output volatility and even inflation volatility in the short run. However, that monetary policy conduct leads to a surge in inflation several periods after the shocks. A policy that aggressively responds to changes in asset prices may marginally reduce output volatility with respect to a policy that reacts aggressively to inflation, but also at the cost of generating inflationary pressures.

Controlled Swapping of Nanocomposite Surface Wettability by Multilayer Photopolymerization

Single-layered photopolymerized nanocomposite films of polystyrene and TiO2 nanorods change their wetting characteristics from hydrophobic to hydrophilic when deposited on substrates with decreasing hydrophilicity. Interestingly, the addition of a second photopolymerized layer causes a swapping in the wettability, so that the final samples result converted from hydrophobic to hydrophilic or vice versa. The wettability characteristics continue to be swapped as the number of photopolymerized layers increases. In fact, odd-layered samples show the same wetting behavior as single-layered ones, while even-layered samples have the same surface characteristics as double-layered ones. Analytical surface studies demonstrate that all samples, independently of the number of layers, have similar low roughness, and that the wettability swap is due to the different concentration of the nanocomposites constituents on the samples surface. Particularly, the different interactions between the hydrophilic TiO2 nanorods and the underlying layer lead to different amounts of nanorods exposed on the nanocomposites surface. Moreover, due to the unique property of TiO2 to reversibly increase its wettability upon UV irradiation and subsequent storage, the wetting characteristics of the multilayered nanocomposites can be tuned in a reversible manner. In this way, a combination of substrate, number of photopolymerized layers, and external UV light stimulus can be used in order to precisely control the surface wettability properties of nanocomposite films, opening the way to a vast number of potential applications in microfluidics, protein assays, and cell growth

Light-controlled directional liquid drop movement on TiO2 nanorods-based nanocomposite photopatterns.

Patterned polymeric coatings enriched with colloidal TiO(2) nanorods and prepared by photopolymerization are found to exhibit a remarkable increase in their water wettability when irradiated with UV laser light. The effect can be completely reversed using successive storage in vacuum and dark ambient environment. By exploiting the enhancement of the nanocomposites hydrophilicity upon UV irradiation, we prepare wettability gradients along the surfaces by irradiating adjacent surface areas with increasing time. The gradients are carefully designed to achieve directional movement of water drops along them, taking into account the hysteresis effect that opposes the movement as well as the change in the shape of the drop during its motion. The accomplishment of surface paths for liquid flow, along which the hydrophilicity gradually increases, opens the way to a vast number of potential applications in microfluidics

On-line Independent Support Vector Machines for Cognitive Systems

Learning from experience and adapting to changing stimuli are fundamental capabilities for artificial cognitive systems. This calls for on-line learning methods able to achieve high accuracy while at the same time using limited computer power. Research on autonomous agents has been actively investigating these issues, mostly using probabilistic frameworks and within the context of navigation and learning by imitation. Still, recent results on robot localization have clearly pointed out the potential of discriminative classifiers for cognitive systems. In this paper we follow this approach and propose an on-line version of the Support Vector Machine (SVM) algorithm. Our method, that we call On-line Independent SVM, builds a solution on-line, achieving an excellent accuracy vs.~compactness trade-off. In particular the size of the obtained solution is always bounded, implying a bounded testing time. At the same time, the algorithm converges to the optimal solution at each incremental step, as opposed to similar approaches where optimality is achieved in the limit of infinite number of training data. These statements are supported by experiments on standard benchmark databases as well as on two real-world applications, namely $(a)$ place recognition by a mobile robot in an indoor environment, and $(b)$ human grasping posture classification