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Diabetic macular edema is one of the leading causes of legal blindness worldwide. Early, and accessible, detection of ophthalmological diseases is especially important in developing countries, where there are major limitations to access to specialized medical diagnosis and treatment. Deep learning models, such as deep convolutional neural networks have shown great success in different computer vision tasks. In medical images they have been also applied with great success. The present paper presents a novel strategy based on convolutional neural networks to combine exudates localization and eye fundus images for automatic classification of diabetic macular edema as a support for diabetic retinopathy diagnosis.

(1) Background: This paper combines lifestyle-routine activities (L-RAT) and self-control (SCT) theories along with the literature on smartphone addiction in a joint model that addresses the multiple vulnerabilities that make the smartphone user a potential victim of cybercrime. This model, which we call the dual vulnerability model of cybercrime victimization, was subjected to empirical testing on a nationally representative sample of smartphone users. (2) Methods: Data from 2837 participants from a nationally representative sample of Spanish smartphone users were modeled using Mplus causal modeling software. (3) Results: The results of the study confirm the predictions of L-RAT and SCT in explaining cybercrime victimization (higher cybercrime victimization under conditions of high exposure, proximity, and suitability, relative absence of capable guardian, and low self-control). A significant effect of smartphone addiction on cybercrime victimization was also observed above and beyond L-RAT and SCT predictors. (4) Conclusions: The potential victim of cybercrime presents a double vulnerability, on the one hand, those identified by criminological theories such as L-RAT and SCT, and on the other hand, those derived from the deregulated-addicted use of the Internet access device (smartphone in our work).

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As cities grow in size and number of inhabitants, continuous monitoring of the environmental impact of sound sources becomes essential for the assessment of the urban acoustic environments. This requires the use of management systems that should be fed with large amounts of data captured by acoustic sensors, mostly remote nodes that belong to a wireless acoustic sensor network. These systems help city managers to conduct data-driven analysis and propose action plans in different areas of the city, for instance, to reduce citizens’ exposure to noise. In this paper, unsupervised learning techniques are applied to discover different behavior patterns, both time and space, of sound pressure levels captured by acoustic sensors and to cluster them allowing the identification of various urban acoustic environments. In this approach, the categorization of urban acoustic environments is based on a clustering algorithm using yearly acoustic indexes, such as Lday, Levening, Lnight and standard deviation of Lden. Data collected over three years by a network of acoustic sensors deployed in the city of Barcelona, Spain, are used to train several clustering methods. Comparison between methods concludes that the k-means algorithm has the best performance for these data. After an analysis of several solutions, an optimal clustering of four groups of nodes is chosen. Geographical analysis of the clusters shows insights about the relation between nodes and areas of the city, detecting clusters that are close to urban roads, residential areas and leisure areas mostly. Moreover, temporal analysis of the clusters gives information about their stability. Using one-year size of the sliding window, changes in the membership of nodes in the clusters regarding tendency of the acoustic environments are discovered. In contrast, using one-month windowing, changes due to seasonality and special events, such as COVID-19 lockdown, are recognized. Finally, the sensor clusters obtained by the algorithm are compared with the areas defined in the strategic noise map, previously created by the Barcelona city council. The developed k-means model identified most of the locations found on the overcoming map and also discovered a new area.