The power of collective spaces as a strategy against the violation of rights

This paper articulates the theoretical-methodological contributions of social occupational therapy and the experience of “Timbó em Movimento'' and “ResisTO” projects, which are part of the Rede Metuia nucleus belonging to the Federal University of Paraíba (UFPB), Brazil. It recognizes the importance of territorial practices and the social occupational therapy field for the strengthening and support of populations with diverse conditions of vulnerability. Articulation of what was observed in the Timbó em Movimento and ResisTO projects with concepts such as the intersectionality of social markers of difference, their implications for the right to the city and their effects in the daily life of youths. The article elaborates the implications of social markers in the daily life of the participants, as well as the potential of collective reference spaces and intersectoral articulation to confront violence. It is concluded that the spaces of belonging have great relevance for addressing conflicts in the social field, promoting the participation of people and the exercise of citizenship, rejecting the tendency to seek individual solutions to collective problems. Remains the challenge of taking these dialogues to the daily contexts in which violence takes place, incorporating actors that may also be involved in its reproduction

Research on pinch plasma focus devices of hundred of kilojoules to tens of joules

At present the Plasma Physics and Plasma Technology Group of the Comisión Chilena de Energía Nuclear (CCHEN) has the experimental facilities in order to study fast dense transient discharges in a wide range of energy and current, namely: I) energy from hundred of kilojoules to tens of joules, II) current from megaamperes to tens of kiloamperes. Also several diagnostics have been implemented. An overview of the work being carried out on dense pinch plasma focus discharges at the Comisión Chilena de Energía Nuclear is presented. The plasma energy density and scaling laws for the neutron yield are discussed. Possible applications of the radiation emitted are also discussed

Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations.

Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area

Intra-operative hyperspectral acquisition system used during a neurosurgical procedure at the University Hospital Doctor Negrin of Las Palmas de Gran Canaria.

<p>Intra-operative hyperspectral acquisition system used during a neurosurgical procedure at the University Hospital Doctor Negrin of Las Palmas de Gran Canaria.</p

Quantitative results of the supervised classification performed with the SVM classifier applied to the labeled data of each patient.

<p>(A) Overall accuracy results of supervised classification per SVM kernel type and patient. (B) and (C) Specificity and sensitivity results obtained using the SVM classifier with linear kernel for each patient and class employing the One-vs-All evaluation method.</p

Results of each step of the optimized spatial-spectral supervised classification of the five different patients.

<p>(A), (B), (C), (D) and (E) Synthetic RGB images generated from the HS cubes. (F), (G), (H), (I) and (J) Golden standard maps used for the supervised classification training. (K), (L), (M), (N) and (O) Supervised classification maps generated using the SVM algorithm. (P), (Q), (R), (S) and (T) FR-t-SNE one band representation of the HS cubes. (U), (V), (X), (Y) and (Z) Spatially optimized classification maps obtained after the KNN filtering.</p

Results of each step of the proposed cancer detection algorithm applied to the five different patients.

<p>(A), (B), (C), (D) and (E) Segmentation maps generated using the HKM algorithm. (F), (G), (H), (I) and (J) MV classification maps. (K), (L), (M), (N) and (O) OMD maps that take into account only the major probability per class obtained from the MV algorithm. (P), (Q), (R), (S) and (T) TMD maps that take into account the first three major probabilities per class obtained from the MV algorithm.</p

KNN filtered maps obtained with different <i>K</i> and <i>λ</i> values.

<p>(A), (B), (C), (D) and (E) filtered maps obtained with <i>K</i> equal to 5, 10, 20, 40, and 60, while keeping <i>λ</i> value fixed to 1. (F), (G), (H), (I) and (J) filtered maps obtained with <i>λ</i> equal to 0, 1, 5, 10, and 100, while keeping <i>K</i> value fixed to 40.</p

Gold standard dataset for the supervised training process.

<p>Gold standard dataset for the supervised training process.</p

Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations - Fig 7

<p>Mean and variances of the pre-processed spectral signatures of the tumor, normal and blood vessel classes of the labeled pixels from patient 1 (A) and patient 2 (B), represented in red, black and blue color respectively.</p