Validation procedures in radiological diagnostic models. Neural network and logistic regression

The objective of this paper is to compare the performance of twopredictive radiological models, logistic regression (LR) and neural network (NN), with five different resampling methods. One hundred and sixty-seven patients with proven calvarial lesions as the only known disease were enrolled. Clinical and CT data were used for LR and NN models. Both models were developed with cross validation, leave-one-out and three different bootstrap algorithms. The final results of each model were compared with error rate and the area under receiver operating characteristic curves (Az). The neural network obtained statistically higher Az than LR with cross validation. The remaining resampling validation methods did not reveal statistically significant differences between LR and NN rules. The neural network classifier performs better than the one based on logistic regression. This advantage is well detected by three-fold cross-validation, but remains unnoticed when leave-one-out or bootstrap algorithms are used.

Reprisals remembered: German-Greek conflict and car sales during the Euro crisis

During the Greek debt crisis after 2010, the German government insisted on harshausterity measures. This led to a rapid cooling of relations between the Greekand German governments. We compile a new index of public acrimony betweenGermany and Greece based on newspaper reports and internet search terms. Thisinformation is combined with historical maps on German war crimes during theoccupation between 1941 and 1944. During months of open conflict between Germanand Greek politicians, German car sales fell markedly more than those of cars fromother countries. This was especially true in areas affected by German reprisals duringWorldWar II: areas where German troops committed massacres and destroyed entirevillages curtailed their purchases of German cars to a greater extent during conflictmonths than other parts of Greece. We conclude that cultural aversion was a keydeterminant of purchasing behavior, and that memories of past conflict can affecteconomic choices in a time-varying fashion. These findings are compatible withbehavioral models emphasizing the importance of salience for individual decision-making.

Analysis of the present (and impact) of women in business organizations in contemporany Spanish democracy. The impact of Spanish law of equality reviewed

Since its approval, in 2007, the Spanish Law of Equality (LO 3/2007) has been the target of many scholars on gender issues. Those analyses (and those previous to the first observable results of the Spanish Law of Equality), have largely prioritized political representative institutions and political parties as the main arenas to assess the impact of the new regulation. Nevertheless, to make a comprehensive analysis of the increase and impact of the presence of women in contemporary democracies one cannot exclude the existence of many other crucial actors in our pluralist systems, such as business organizations.In this line, in order to widen the knowledge on the presence of women in Spanish contemporary democracy, as well as to further assess the impact of Spanish Law of Equality on the presence of women in economic and political life, our paper will look at the gender bias of the executive committees in the Spanish Chambers of Commerce and business associations during the period 20010-2012. By placing those actors at the front sight, we aim to contribute with new empirical insights to the current debate on this topic.

Spectrum Sharing Models in Cognitive Radio

Spectrum scarcity demands thinking new ways tomanage the distribution of radio frequency bands so that its use is more effective. The emerging technology that can enable this paradigm shift is the cognitive radio. Different models fororganizing and managing cognitive radios have emerged, all with specific strategic purposes. In this article we review the allocation spectrum patterns of cognitive radio networks andanalyse which are the common basis of each model.We expose the vulnerabilities and open challenges that still threaten the adoptionand exploitation of cognitive radios for open civil networks.

A priori parameterisation of the CERES soil-crop models and tests against several European data sets

Mechanistic soil-crop models have become indispensable tools to investigate the effect of management practices on the productivity or environmental impacts of arable crops. Ideally these models may claim to be universally applicable because they simulate the major processes governing the fate of inputs such as fertiliser nitrogen or pesticides. However, because they deal with complex systems and uncertain phenomena, site-specific calibration is usually a prerequisite to ensure their predictions are realistic. This statement implies that some experimental knowledge on the system to be simulated should be available prior to any modelling attempt, and raises a tremendous limitation to practical applications of models. Because the demand for more general simulation results is high, modellers have nevertheless taken the bold step of extrapolating a model tested within a limited sample of real conditions to a much larger domain. While methodological questions are often disregarded in this extrapolation process, they are specifically addressed in this paper, and in particular the issue of models a priori parameterisation. We thus implemented and tested a standard procedure to parameterize the soil components of a modified version of the CERES models. The procedure converts routinely-available soil properties into functional characteristics by means of pedo-transfer functions. The resulting predictions of soil water and nitrogen dynamics, as well as crop biomass, nitrogen content and leaf area index were compared to observations from trials conducted in five locations across Europe (southern Italy, northern Spain, northern France and northern Germany). In three cases, the model’s performance was judged acceptable when compared to experimental errors on the measurements, based on a test of the model’s root mean squared error (RMSE). Significant deviations between observations and model outputs were however noted in all sites, and could be ascribed to various model routines. In decreasing importance, these were: water balance, the turnover of soil organic matter, and crop N uptake. A better match to field observations could therefore be achieved by visually adjusting related parameters, such as field-capacity water content or the size of soil microbial biomass. As a result, model predictions fell within the measurement errors in all sites for most variables, and the model’s RMSE was within the range of published values for similar tests. We conclude that the proposed a priori method yields acceptable simulations with only a 50% probability, a figure which may be greatly increased through a posteriori calibration. Modellers should thus exercise caution when extrapolating their models to a large sample of pedo-climatic conditions for which they have only limited information.

Innovation and Regulation in ICT. Keys for Development

The expansion of broadband speed and coverage over IP technology, which extend over transport and terminal access networks, has increased the demand for applications and content which by being provided over it, uniformly give rise to convergence. These shifts in technologies and enterprise business models are giving rise to the necessity for changing the perspective and scope of the Universal Service and of the regulation frameworks, with this last one based in the same principles as always but varying its application. Several aspects require special and renewed attention, such as the definition of relevant markets and dominant operators, the role of packages, interconnection of IP networks, network neutrality, the use of the spectrum with a vision of value for the citizenship, the application of the competition framework, new forms of licensing, treatment of the risk in the networks, changes in the regulatory authorities, amongst others. These matters are treated from the perspective of the actual trends in the world and its conceptual justification.

Classification models for neurocognitive impairment in HIV infection based on demographic and clinical variables

Objective: We used demographic and clinical data to design practical classification models for prediction of neurocognitive impairment (NCI) in people with HIV infection. Methods: The study population comprised 331 HIV-infected patients with available demographic, clinical, and neurocognitive data collected using a comprehensive battery of neuropsychological tests. Classification and regression trees (CART) were developed to btain detailed and reliable models to predict NCI. Following a practical clinical approach, NCI was considered the main variable for study outcomes, and analyses were performed separately in treatment-naïve and treatment-experienced patients. Results: The study sample comprised 52 treatment-naïve and 279 experienced patients. In the first group, the variables identified as better predictors of NCI were CD4 cell count and age (correct classification [CC]: 79.6%, 3 final nodes). In treatment-experienced patients, the variables most closely related to NCI were years of education, nadir CD4 cell count, central nervous system penetration-effectiveness score, age, employment status, and confounding comorbidities (CC: 82.1%, 7 final nodes). In patients with an undetectable viral load and no comorbidities, we obtained a fairly accurate model in which the main variables were nadir CD4 cell count, current CD4 cell count, time on current treatment, and past highest viral load (CC: 88%, 6 final nodes). Conclusion: Practical classification models to predict NCI in HIV infection can be obtained using demographic and clinical variables. An approach based on CART analyses may facilitate screening for HIV-associated neurocognitive disorders and complement clinical information about risk and protective factors for NCI in HIV-infected patients.

Internet Banking in Europe: a comparative analysis

A key strategic issue for banks is the implementation of internet banking. The ‘click and mortar’ model that complements classical branch banking with online facilities is competing with pure internet banks. The objective of this paper is to compare the performance of these two models across countries, so as to examine the role of differences in the banking system and technological progress. A fuzzy cluster analysis on the performance of banks in Finland, Spain, Italy and the UK shows that internet banks are hard to distinguish from banks that follow a click and mortar strategy; country borders are more important. We therefore explain bank performance by a group of selected bank features, country-specific economic and IT indicators over the period 1995-2004. We find that the strategy of banking groups to incorporate internet banks reflects some competitive edge that these banks have in their business models. Extensive technological innovation boosts internet banking.