Housing in “intramural favelas”: considerations on new forms of urban expansion in contemporary times

This paper develops a deeper look into new residential appropriations of space in marginalized areas of a large Brazilian city, while highlighting the subjective importance of housing and its meaning beyond the idea of shelter. Firstly, it presents a brief history of Rio de Janeiro’s favelas – the local version of slums – and its relationship with vacant land over the past 100 years. Then, it explains the value of self-built housing and its contribution to the consolidation of multiple and hybrid territories, highlighting their subjective character. Lastly, it presents a case study called Portelinha, located in a set of favelas known as the Maré Complex, stressing how this mixed occupation has transformed the local urban fabric, leading to the emergence of what is referred to as an “intramural favela”. This phenomenon consists of the self-construction of a smaller-scale set of houses within the walls of a former factory turned into an industrial void in the 1990s. The analysis shows how this housing appropriation is articulated with other activities, especially cultural ones, leading to a diversity of social actors, alliances and conflicts, turning it into a real disputed territory. Cases like this reflect the challenges with which architects and planners need to deal with when working in the unequal urban contexts that are so common in the Global South

Baseline pathological data of the wedge clam Donax trunculus from the Tyrrhenian Sea (Mediterranean Basin)

In recent years, a collapse in Donax trunculus fishing yields has occurred in the Tyrrhenian Sea (Mediterranean Basin). There is little information available on the impact disease may have had on D. trunculus populations. For the first time, a pathological survey was performed on the natural beds of the bivalve on the Campania and Lazio coasts, western Italy. Detected pathogens and related diseases were analysed, and their prevalence and mean intensity values were calculated. Viral particles, Chlamydia-like organisms, ciliates, coccidians, microcells and trematodes were observed. An unknown ciliate was linked to severe inflammatory and necrotic lesions in the digestive gland. Metacercariae of the trematode Postmonorchis sp. were also strongly represented in almost all samples, reaching high levels of infection; however, none of the pathogens described required the World Organisation for Animal Health to be notified. Initial results indicated that further surveys related to environmental data are necessary in order to assess the relevance of these early observations in managing the declining D. trunculus population in the Tyrrhenian Sea.postprin

Feature Selection in Big Image Datasets

[Abstract] In computer vision, current feature extraction techniques generate high dimensional data. Both convolutional neural networks and traditional approaches like keypoint detectors are used as extractors of high-level features. However, the resulting datasets have grown in the number of features, leading into long training times due to the curse of dimensionality. In this research, some feature selection methods were applied to these image features through big data technologies. Additionally, we analyzed how image resolutions may affect to extracted features and the impact of applying a selection of the most relevant features. Experimental results show that making an important reduction of the extracted features provides classification results similar to those obtained with the full set of features and, in some cases, outperforms the results achieved using broad feature vectors.This research has been financially supported in part by European Union FEDER funds, by the Spanish Ministerio de Economía y Competitividad (research project PID2019-109238GB), by the Consellería de Industria of the Xunta de Galicia (research project GRC2014/035), and by the Principado de Asturias Regional Government (research project IDI-2018-000176). CITIC as a Research Centre of the Galician University System is financed by the Consellería de Educación, Universidades e Formación Profesional (Xunta de Galicia) through the ERDF (80%), Operational Programme ERDF Galicia 2014–2020, and the remaining 20% by the Secretaria Xeral de Universidades (ref. ED431G 2019/01).Xunta de Galicia; GRC2014/035Gobierno del Principado de Asturias; IDI-2018-000176Xunta de Galicia; ED431G 2019/0

Artrodesis subastragalina artroscópica

Objetivo: evaluar la eficacia y complicaciones de la técnica artroscópica en la artrodesis subastragalina. Material y métodos: Durante los años 2007 y 2008 hemos realizado artrodesis subastragalina artroscópica por vía posterior a 12 pacientes, con una edad media de 41 años, por presentar artropatía postraumática de la articulación subastragalina, secundaria a fractura de calcáneo o astrágalo. El seguimiento medio ha sido de 9 meses. Resultados: Se ha conseguido la consolidación primaria de la artrodesis en 10 casos, evolucionando a seudoartrosis en los otros dos, que precisaron reintervención artroscópica con aporte de injerto óseo. En un caso ha existido migración proximal de un tornillo de osteosíntesis, no presentándose necrosis cutánea, infección ni afectación neurovascular en caso alguno. Conclusiones: Esta técnica se ha mostrado eficaz para la consecución de la artrodesis subastragalina, disminuyendo sensiblemente el porcentaje de complicaciones en relación con la cirugía abierta.Objective: To evaluate the effectiveness and complications of arthroscopic technique in subtalar arthrodesis. Methods: Between 2007 and 2008 we performed arthroscopic subtalar fusions with a posterior approach in 12 patients with a mean age of 41 years old, with a diagnose of posttraumatic subtalar arthropaty secondary to calcaneus or talus fractures. The mean follow-up was 9 months. Results:We obtained primary consolidation in 10 cases. Two cases developed non-union and required an arthroscopic revision with bone grafting. In one case there was proximal migration of a screw, with no skin necrosis, infection or neurovascular impairment in any case. Conclusions: This technique has been effective in order to obtain subtalar fusion, minimizing significantly the complications associated with open surgery

Modeling in TRNSYS of a single effect evaporation system powered by a Rankine cycle

The paper presents an analysis of a Single Effect Evaporation (SEE) system as a pre-study to the feasibility of concentrated solar power plants (CSP) powering desalination units for cogeneration of water and electricity. An algorithm to model a SEE system in steady-state operation was made and is described in this work. This algorithm was implemented in TRNSYS environment, and a simple analysis was conducted of a SEE system powered by a Rankine cycle used in CSP plants

A deep learning-based dirt detection computer vision system for floor-cleaning robots with improved data collection

Floor-cleaning robots are becoming increasingly more sophisticated over time and with the addition of digital cameras supported by a robust vision system they become more autonomous, both in terms of their navigation skills but also in their capabilities of analyzing the surrounding environment. This document proposes a vision system based on the YOLOv5 framework for detecting dirty spots on the floor. The purpose of such a vision system is to save energy and resources, since the cleaning system of the robot will be activated only when a dirty spot is detected and the quantity of resources will vary according to the dirty area. In this context, false positives are highly undesirable. On the other hand, false negatives will lead to a poor cleaning performance of the robot. For this reason, a synthetic data generator found in the literature was improved and adapted for this work to tackle the lack of real data in this area. This synthetic data generator allows for large datasets with numerous samples of floors and dirty spots. A novel approach in selecting floor images for the training dataset is proposed. In this approach, the floor is segmented from other objects in the image such that dirty spots are only generated on the floor and do not overlap those objects. This helps the models to distinguish between dirty spots and objects in the image, which reduces the number of false positives. Furthermore, a relevant dataset of the Automation and Control Institute (ACIN) was found to be partially labelled. Consequently, this dataset was annotated from scratch, tripling the number of labelled images and correcting some poor annotations from the original labels. Finally, this document shows the process of generating synthetic data which is used for training YOLOv5 models. These models were tested on a real dataset (ACIN) and the best model attained a mean average precision (mAP) of 0.874 for detecting solid dirt. These results further prove that our proposal is able to use synthetic data for the training step and effectively detect dirt on real data. According to our knowledge, there are no previous works reporting the use of YOLOv5 models in this application.publishe

Large-scale Nonlinear Variable Selection via Kernel Random Features

We propose a new method for input variable selection in nonlinear regression. The method is embedded into a kernel regression machine that can model general nonlinear functions, not being a priori limited to additive models. This is the first kernel-based variable selection method applicable to large datasets. It sidesteps the typical poor scaling properties of kernel methods by mapping the inputs into a relatively low-dimensional space of random features. The algorithm discovers the variables relevant for the regression task together with learning the prediction model through learning the appropriate nonlinear random feature maps. We demonstrate the outstanding performance of our method on a set of large-scale synthetic and real datasets.Comment: Final version for proceedings of ECML/PKDD 201

Estudio del proceso de cambio conceptual y la construcción del modelo científico precursor de ser vivo en niños de pre-escolar

La presente investigación estudia el cambio conceptual y la construcción de modelos científicos precursores en un contexto socioconstructivista con niños pequeños, utilizando una metodología cualitativa. Se analizan los cambios epistemológicos y ontológicos en las concepciones de los niños acerca de los seres vivos y cómo una estrategia didáctica basada en la construcción de un modelo científico precursor basado en propiedades biológicas los promueven. Los cambios en la comprensión de los niños fueron significativos en las dimensiones ontológicas y epistemológicas, presentando diferentes patones. Los segundos proporcionaron una mejor coherencia explicativa en su sistema conceptual contribuyendo, de esta forma, a los cambios ontológicos y promoviendo, a su vez, la construcción del modelo científico precursor de ser vivo