Unsupervised neural models for country and political risk analysis

This interdisciplinary research project focuses on relevant applications of Knowledge Discovery and Artificial Neural Networks in order to identify and analyze levels of country, business and political risk. Its main goal is to help business decision-makers understand the dynamics within the emerging market countries in which they operate. Most of the neural models applied in this study are defined within the framework of unsupervised learning. They are based on Exploratory Projection Pursuit, Topology Preserving Maps and Curvilinear Component Analysis. Two interesting real data sets are analyzed to empirically probe the robustness of these models. The first case study describes information from a significant sample of Spanish multinational enterprises (MNEs). It analyses data pertaining to such aspects as decisions over the location of subsidiary enterprises in various regions across the world, the importance accorded to such decisions and the driving forces behind them. Through a projection-based analysis, this study reveals a range of different reasons underlying the internationalization strategies of Spanish MNEs and the different goals they pursue. It may be concluded that projection connectionist techniques are of immense assistance in the process of identifying the internationalization strategies of Spanish MNEs, their underlying motives and the goals they pursue. The second case study covers several risk categories that include task policy, security, and political stability among others, and it tracks the scores of different countries all over the world. Interesting conclusions are drawn from the application of several business intelligence solutions based on neural projection models, which support data analysis in the context of country and political risk analysis.     

Emission inventory: An urban public policy instrument and benchmark

Global concern with climate change has led to the development of a variety of solutions to monitor and reduce emissions on both local and global scales. Under the United Nations Framework Convention on Climate Change (UNFCCC), both developed and emerging countries have assumed responsibility for developing and updating national inventories of greenhouse gas emissions from anthropic sources. This creates opportunities and incentives for cities to carry out their own local inventories and, thereby, develop air quality management plans including both essential key players and stakeholders at the local level. The aim of this paper is to discuss the role of local inventories as an urban public policy instrument and how this type of local instrument may bring advantages countrywide in enhancing the global position of a country. Local inventories have been carried out in many cities of the world and the main advantage of this is that it allows an overview of emissions produced by different municipal activities, thereby, helps decision makers in the elaboration of efficient air quality management plans. In that way, measures aimed at the reduction of fossil fuel consumption to lower local atmospheric pollution levels can also, in some ways, reduce GHG emissions.     

High-Frequency Induction Hardening with Controlled Capacitors-Discharge Pulse-Operation Processes

Induction hardening processes with power pulses obtained by means of capacitors-discharge high-frequency generators are discussed. The process is characterized by high-power densities and heating times on the order of some tens or hundreds of milliseconds together with cooling rates obtained by self-quenching without the use of auxiliary means. The possibility of obtaining the most favorable transient temperature distribution in the workpieces by controlling the shape of the pulses and therefore the variable time rate energy supply is examined. The influence of shape of pulses and discharge circuit's electrical parameters on the heating and cooling transients has been analyzed by a thermal model which allows prediction of the depths of hardening. Measurements from hardening tests carried out on steel samples varying the pulse shapes are given in support of the calculation results.     

Integrate-and-fire neurons driven by correlated stochastic input

Neurons are sensitive to correlations among synaptic inputs. However, analytical models that explicitly include correlations are hard to solve analytically, so their influence on a neuron's response has been difficult to ascertain. To gain some intuition on this problem, we studied the firing times of two simple integrate-and-fire model neurons driven by a correlated binary variable that represents the total input current. Analytic expressions were obtained for the average firing rate and coefficient of variation (a measure of spike-train variability) as functions of the mean, variance, and correlation time of the stochastic input. The results of computer simulations were in excellent agreement with these expressions. In these models, an increase in correlation time in general produces an increase in both the average firing rate and the variability of the output spike trains. However, the magnitude of the changes depends differentially on the relative values of the input mean and variance: the increase in firing rate is higher when the variance is large relative to the mean, whereas the increase in variability is higher when the variance is relatively small. In addition, the firing rate always tends to a finite limit value as the correlation time increases toward infinity, whereas the coefficient of variation typically diverges. These results suggest that temporal correlations may play a major role in determining the variability as well as the intensity of neuronal spike trains.     

Interaction of isobutene and methacrolein with silica-supported uranium and molybdenum oxide catalysts

The adsorption of isobutene (in the temperature range 309–341K) and methacrolein (in the temperature range 296–336 K) on a Mo/(Mo+U)=0.89 catalyst were studied. The equilibrium data were analysed according to Freundlich's model. The high coverages observed at lower temperatures suggest some participation of physical adsorption in the reversible adsorption. Entropy calculations showed that these adsorbed species are best described by a mobile model which assumes bidimensional translation and two rotational degrees of freedom. Isobutene is weakly chemisorbed, producing reversible species which were identified as a π-complex formed by interaction with surface OH groups through π-electrons (infrared bands at 1655, 1615, 1470 and 1375 cm^?1), and only a small fraction of isobutene interacts strongly via lattice oxygen forming intermediate species in the oxidation reaction. However, in the presence of oxygen at 523 K a majority of the surface species are strongly held by the surface (infrared bands at 1775, 1723, 1610, 1460 and 1350 cm^?1 associated to a methacrylic complex and bands at 1590 and 1538 cm^?1 of carbonate-carboxylate species).     

A CAVE-like environment as a tool for full-size train design

Simulations of models, in all different areas, is an expanding, attractive line of work. More and more applications are taking advantage of the improvements in technology and knowledge in this field, thus achieving results that would have been impossible to achieve with a real model, or foreseeing facts that otherwise would have been encountered too late in the production process. The rail industry is one possible beneficiary of this approach. Usually, before commencing the fabrication process of a new train, the construction of a full-size model is mandatory. Instead of building this full-size real model, which leaves little room for later, last-minute modifications, a virtual model can be built in the digital realm, thus offering a new platform for easier interaction with it. In this article, a simulation of a train is presented in order to tackle visual, aesthetic and ergonomic issues. The simulation runs on a PC-based CAVE-like architecture, offering a certain degree of interaction to the user, and combines static and dynamic computer-generated imagery, both with and without stereoscopy for 3D visualisation, as well as augmented virtuality techniques for the integration of the train with its environment.     

Background synaptic activity as a switch between dynamical states in a network

A bright red light may trigger a sudden motor action in a driver crossing an intersection: stepping at once on the brakes. The same red light, however, may be entirely inconsequential if it appears, say, inside a movie theater. Clearly, context determines whether a particular stimulus will trigger a motor response, but what is the neural correlate of this? How does the nervous system enable or disable whole networks so that they are responsive or not to a given sensory signal? Using theoretical models and computer simulations, I show that networks of neurons have a built-in capacity to switch between two types of dynamic state: one in which activity is low and approximately equal for all units, and another in which different activity distributions are possible and may even change dynamically. This property allows whole circuits to be turned on or off by weak, unstructured inputs. These results are illustrated using networks of integrate-and-fire neurons with diverse architectures. In agreement with the analytic calculations, a uniform background input may determine whether a random network has one or two stable firing levels; it may give rise to randomly alternating firing episodes in a circuit with reciprocal inhibition; and it may regulate the capacity of a center-surround circuit to produce either self-sustained activity or traveling waves. Thus, the functional properties of a network may be drastically modified by a simple, weak signal. This mechanism works as long as the network is able to exhibit stable firing states, or attractors.     

Polychlorinated dibenzo-p-dioxins and dibenzofurans in the air of Seveso,Italy, 26 years after the explosion

This study reports the current levels of polychlorinated dibenzo-p-dioxins (PCDDs) and furans (PCDFs) in air at Seveso, where an explosion in a 2,4,5,-trichlorophenol production reactor occurred 26 years ago. The aims were to assess if residues of the 2,3,7,8-tetrachlorodibenzo-p-dioxin (2,3,7,8-TCDD) released during the accident and still present in soil could contaminate the above air and to investigate other potential sources in the area. Long-term air collection was carried out in zones A and B in Seveso and in a reference location in Milan, and samples were analyzed for PCDD and PCDF concentrations by gas chromatography-mass spectrometry (GC-MS). Experimental results showed that no important contribution to the air concentrations is due to the soil contamination and that contemporary sources essentially control the atmospheric burden of PCDDs and PCDFs in the Seveso area. The theoretical release of 2,3,7,8-TCDD from the soils of zones A and B of Seveso was calculated using the SoilFug model. In the worst case, the model simulated an enrichment in atmospheric 2,3,7,8-TCDD concentrations of 4 and 22% for zones A and B, respectively. The investigation of the potential emission sources in the area indicated that combustion of wood residues from furniture factories may be an additional local source of PCDDs and PCDFs.