The reflections of the out-of-jail qualification: the resocialization of ex-prisoners in the penitentiary complex of São Pedro de Alcantara (SC-Brazil)

Neste artigo, analisam-se os reflexos da capacitação na ressocialização dos ex-detentos do Complexo Penitenciário de São Pedro de Alcântara (SC, Brasil). Relata-se a atividade de capacitação nessa unidade prisional, descrevem-se formas de ressocialização e apontam-se potencialidades e fragilidades da relação capacitação-ressocialização. Este estudo de caso de abordagem qualitativa envolveu um gerente e vinte ex-detentos em liberdade. Além de documentos secundários e observação direta, realizaram-se entrevistas semiestruturadas por telefone, cujos dados foram gravados, transcritos e analisados de forma categorial. O receio dos egressos em participar da pesquisa pode ser considerado uma limitação da mesma. Os dados revelaram que os programas de treinamento mais relevantes potencializam a independência dos ex-detentos e aumentam sua autoestima profissional; porém, não dão conta de suprir os interesses financeiros, a falta de apoio familiar devido à prática de alguns crimes e a dificuldade tanto na aceitação como na concessão de oportunidade de trabalho por parte da sociedade.In this article, the reflections of the qualification in the resocialization of ex-prisoners from the Penitentiary of São Pedro de Alcantara (SC, Brazil) are analyzed. The activity of qualification in this prison unit is reported, ways of resocialization are described and the strengths and weaknesses of the relationship qualification-resocialization are pointed out. This case study of a qualitative approach involved a manager and twenty former prisoners already released. In addition to secondary documents and direct observation, semistructured interviews were conducted by telephone, whose data were recorded; transcribed and analyzed is a categorical way. The fear of the ex-prisoners in participating in the study can be considered a limitation for it. The data revealed that the most relevant qualification programs potentialize the independence of the ex-prisoners and increase their professional self-esteem. However, cannot handle to meet financial concerns, lack of family support due to the practice of some crimes and the difficulty both in acceptance and in being provided with work opportunity by society

Dual consistency loss for contour-aware segmentation in medical images

Medical image segmentation is a paramount task for several clinical applications, namely for the diagnosis of pathologies, for treatment planning, and for aiding image-guided surgeries. With the development of deep learning, Convolutional Neural Networks (CNN) have become the state-of-the-art for medical image segmentation. However, issues are still raised concerning the precise object boundary delineation, since traditional CNNs can produce non-smooth segmentations with boundary discontinuities. In this work, a U-shaped CNN architecture is proposed to generate both pixel-wise segmentation and probabilistic contour maps of the object to segment, in order to generate reliable segmentations at the object's boundaries. Moreover, since the segmentation and contour maps must be inherently related to each other, a dual consistency loss that relates the two outputs of the network is proposed. Thus, the network is enforced to consistently learn the segmentation and contour delineation tasks during the training. The proposed method was applied and validated on a public dataset of cardiac 3D ultrasound images of the left ventricle. The results obtained showed the good performance of the method and its applicability for the cardiac dataset, showing its potential to be used in clinical practice for medical image segmentation.Clinical Relevance-The proposed network with dual consistency loss scheme can improve the performance of state-of-the-art CNNs for medical image segmentation, proving its value to be applied for computer-aided diagnosis.- (undefined

Coarse- and fine-scale patterns of distribution and habitat selection places an Amazonian floodplain curassow in double jeopardy

Patterns of habitat selection are influenced by local productivity, resource availability and predation risk. Species have taken millions of years to hone the macro- and micro-habitats they occupy, but these may now overlap with contemporary human threats within natural species ranges. Wattled Curassow (Crax globulosa), an endemic galliform species of the western Amazon, is threatened by both hunting and habitat loss, and is restricted to whitewater floodplain forests of major Amazonian rivers. In this study conducted along the Juruá River, Amazonas, Brazil, we quantified the ranging ecology and fine-scale patterns of habitat selection of the species. We estimated the home range size of C. globulosa using conventional VHF telemetry. To estimate patterns of habitat selection, we used geolocations of day ranges to examine the extent and intensity of use across the floodplain, which were then compared to a high-resolution flood-map of the study area. We captured two females and one male, which were monitored for 13 months between September 2014 and September 2015. Average home range size was 283 ha, based on the 95% aLoCoH estimator. Wattled Curassows selected areas of prolonged flood-pulses (6-8 months/year) and had a consistent tendency to be near open water, usually in close proximity to river banks and lakes, especially during the dry season. Amazonian floodplains are densely settled, and the small portions of floodplain habitat used by Wattled Curassows are both the most accessible to hunters and most vulnerable to deforestation. As a result, the geographic and ecological distribution of Wattled Curassows places them at much higher extinction risk at multiple spatial scales, highlighting the need to consider habitat preferences within their conservation strategy

The challenge of long-distance over-the-air wireless links in the ocean: a survey on water-to-water and water-to-land miot communication

Robust wireless communication networks are a cornerstone of the modern world, allowing data to be transferred quickly and reliably. Establishing such a network at sea, a Maritime Internet of Things (MIoT), would enhance services related to safety and security at sea, environmental protection, and research. However, given the remote and harsh nature of the sea, installing robust wireless communication networks with adequate data rates and low cost is a difficult endeavor. This paper reviews recent MIoT systems developed and deployed by researchers and engineers over the past few years. It contains an analysis of short-range and long-range over-the-air radio-frequency wireless communication protocols and the synergy between these two in the pursuit of an MIoT. The goal of this paper is to serve as a go-to guide for engineers and researchers that need to implement a wireless sensor network at sea. The selection criterion for the papers included in this review was that the implemented wireless communication networks were tested in a real-world scenario.cofunded by the project K2D: Knowledge and Data from the Deep to Space with reference POCI-01-0247-FEDER-045941, cofinanced by the European Regional Development Fund (ERDF), through the Operational Program for Competitiveness and Internationalization (COMPETE2020), and by the Portuguese Foundation for Science and Technology (FCT) under the MIT Portugal Program. This work is also cofinanced by national funds through FCT–Fundação para a Ciência e Tecnologia, I.P., under project SONDA (PTDC/EME-SIS/1960/2020). T.M. thanks FCT for grant SFRH/BD/145070/201

Fast left ventricle tracking in CMR images using localized anatomical affine optical flow

"Progress in Biomedical Optics and Imaging, vol. 16, nr. 41"In daily cardiology practice, assessment of left ventricular (LV) global function using non-invasive imaging remains central for the diagnosis and follow-up of patients with cardiovascular diseases. Despite the different methodologies currently accessible for LV segmentation in cardiac magnetic resonance (CMR) images, a fast and complete LV delineation is still limitedly available for routine use. In this study, a localized anatomically constrained affine optical flow method is proposed for fast and automatic LV tracking throughout the full cardiac cycle in short-axis CMR images. Starting from an automatically delineated LV in the end-diastolic frame, the endocardial and epicardial boundaries are propagated by estimating the motion between adjacent cardiac phases using optical flow. In order to reduce the computational burden, the motion is only estimated in an anatomical region of interest around the tracked boundaries and subsequently integrated into a local affine motion model. Such localized estimation enables to capture complex motion patterns, while still being spatially consistent. The method was validated on 45 CMR datasets taken from the 2009 MICCAI LV segmentation challenge. The proposed approach proved to be robust and efficient, with an average distance error of 2.1 mm and a correlation with reference ejection fraction of 0.98 (1.9 ± 4.5%). Moreover, it showed to be fast, taking 5 seconds for the tracking of a full 4D dataset (30 ms per image). Overall, a novel fast, robust and accurate LV tracking methodology was proposed, enabling accurate assessment of relevant global function cardiac indices, such as volumes and ejection fraction.The authors acknowledge funding support from FCT - Fundação para a Ciência e Tecnologia, Portugal, in the scope of the PhD grant SFRH/BD/93443/2013 and the project EXPL/BBB-BMD/2473/2013. D. Barbosa would also like to acknowledge the kind support of the Fundação Luso-Americana para o Desenvolvimento (FLAD), which has funded the travel costs for participation at SPIE Medical Imaging 2015.info:eu-repo/semantics/publishedVersio

Automatic 3D aortic annulus sizing by computed tomography in the planning of transcatheter aortic valve implantation

Background: Accurate imaging assessment of aortic annulus (AoA) dimension is paramount to decide on the correct transcatheter heart valve (THV) size for patients undergoing transcatheter aortic valve implantation (TAVI). We evaluated the feasibility and accuracy of a novel automatic framework for multi detector row computed tomography (MDCT)-based TAVI planning. Methods: Among 122 consecutive patients undergoing TAVI and retrospectively reviewed for this study, 104 patients with preoperative MDCT of sufficient quality were enrolled and analyzed with the proposed software. Fully automatic (FA) and semi-automatic (SA) AoA measurements were compared to manual measurements, with both automated and manual-based interobserver variability (IOV) being assessed. Finally, the effect of these measures on hypothetically selected THV size was evaluated against the implanted size, as well as with respect to manually-derived sizes. Results: FA analysis was feasible in 92.3% of the cases, increasing to 100% if using the SA approach. Automatically-extracted measurements showed excellent agreement with manually-derived ones, with small biases and narrow limits of agreement, and comparable to the interobserver agreement. The SA approach presented a statistically lower IOV than manual analysis, showing the potential to reduce interobserver sizing disagreements. Moreover, the automated approaches displayed close agreement with the implanted sizes, similar to the ones obtained by the experts. Conclusion: The proposed automatic framework provides an accurate and robust tool for AoA measurements and THV sizing in patients undergoing TAVI.FCT - Fundação para a Ciência e a Tecnologia, Portugal, and the European Social Found, European Union, through the Programa Operacional Capital Humano (POCH) in the scope of the PhD grants SFRH/BD/93443/2013 (S. Queirós) and SFRH/BD/95438/2013 (P. Morais), and the project ‘PersonalizedNOS (01-0145-FEDER-000013)’ co-funded by Programa Operacional Regional do Norte (QREN), through Fundo Europeu de Desenvolvimento Regional (FEDER)info:eu-repo/semantics/publishedVersio

A review of image processing methods for fetal head and brain analysis in ultrasound images

Background and objective: Examination of head shape and brain during the fetal period is paramount to evaluate head growth, predict neurodevelopment, and to diagnose fetal abnormalities. Prenatal ultrasound is the most used imaging modality to perform this evaluation. However, manual interpretation of these images is challenging and thus, image processing methods to aid this task have been proposed in the literature. This article aims to present a review of these state-of-the-art methods. Methods: In this work, it is intended to analyze and categorize the different image processing methods to evaluate fetal head and brain in ultrasound imaging. For that, a total of 109 articles published since 2010 were analyzed. Different applications are covered in this review, namely analysis of head shape and inner structures of the brain, standard clinical planes identification, fetal development analysis, and methods for image processing enhancement. Results: For each application, the reviewed techniques are categorized according to their theoretical approach, and the more suitable image processing methods to accurately analyze the head and brain are identified. Furthermore, future research needs are discussed. Finally, topics whose research is lacking in the literature are outlined, along with new fields of applications. Conclusions: A multitude of image processing methods has been proposed for fetal head and brain analysis. Summarily, techniques from different categories showed their potential to improve clinical practice. Nevertheless, further research must be conducted to potentiate the current methods, especially for 3D imaging analysis and acquisition and for abnormality detection. (c) 2022 Elsevier B.V. All rights reserved.FCT - Fundação para a Ciência e a Tecnologia(UIDB/00319/2020)This work was funded by projects “NORTE-01–0145-FEDER- 0 0 0 059 , NORTE-01-0145-FEDER-024300 and “NORTE-01–0145- FEDER-0 0 0 045 , supported by Northern Portugal Regional Opera- tional Programme (Norte2020), under the Portugal 2020 Partner- ship Agreement, through the European Regional Development Fund (FEDER). It was also funded by national funds, through the FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020 and by FCT and FCT/MCTES in the scope of the projects UIDB/05549/2020 and UIDP/05549/2020 . The authors also acknowledge support from FCT and the Euro- pean Social Found, through Programa Operacional Capital Humano (POCH), in the scope of the PhD grant SFRH/BD/136670/2018 and SFRH/BD/136721/2018

Fetal head circumference delineation using convolutional neural networks with registration-based ellipse fitting

Examination of head shape during the fetal period is an important task to evaluate head growth and to diagnose fetal abnormalities. Traditional clinical practice frequently relies on the estimation of head circumference (HC) from 2D ultrasound (US) images by manually fitting an ellipse to the fetal skull. However, this process tends to be prone to observer variability, and therefore, automatic approaches for HC delineation can bring added value for clinical practice. In this paper, an automatic method to accurately delineate the fetal head in US images is proposed. The proposed method is divided into two stages: (i) head delineation through a regression convolutional neural network (CNN) that estimates a gaussian-like map of the head contour; and (ii) robust ellipse fitting using a registration-based approach that combines the random sample consensus (RANSAC) and iterative closest point (ICP) algorithms. The proposed method was applied to the HC18 Challenge dataset, which contains 999 training and 335 testing images. Experiments showed that the proposed strategy achieved a mean average difference of -0.11 ± 2.67 mm and a Dice coefficient of 97.95 ± 1.12% against manual annotation, outperforming other approaches in the literature. The obtained results showed the effectiveness of the proposed method for HC delineation, suggesting its potential to be used in clinical practice for head shape assessment.FCT - Fundação para a Ciência e a Tecnologia(UIDB/00319/2020

Tiller Population Density and Sward Stability of Brachiaria brizantha Continuously Stocked by Cattle

Tiller population density is one the most important parameters of sward structure and its evaluation is normally included in studies of sward dynamics. Moreover, a greater level of understanding is achieved when the survival of successive tiller generations is monitored. (Matthew et al., 2000). This would help to explain seasonal variation in tiller populations based on tiller appearance and death rates. While Brachiaria brizantha c.v. Marandu occupies up to 70 million hectares of cultivated grassland in Brazil, little is known of its ecophysiology. The objective of this work was to calculate survival probability of B. brizantha tillers and identify seasonal variation on sward stability

Bi2Te3 and Sb2Te3 Thin Films with Enhanced Thermoelectric Properties for Flexible Thermal Sensors

The influence of substrate type in boosting thermoelectric properties of co-evaporated Bi2Te3 and Sb2Te3 films (with 400 nm-thick) is here reported. Optimized power factor values are 2.7 × 10−3 W K−2 m−1 and 1.4 × 10−3 W K−2 m−1 for flexible Bi2Te3 and Sb2Te3 films, respectively. This is an important result as it is at least 2 times higher than the power factor found in the literature for flexible Bi2Te3 and Sb2Te3 films. A flexible infrared thermopile sensor was developed with high detectivity (2.50 × 107 cm √HzW−1).This work is supported by FCT with the reference project UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020—Programa Operacional Competitividade e Internacionalização (POCI) with the reference project POCI-01-0145-FEDER-006941. EMFV and JF is grateful for financial support through the FCT grants SFRH/BPD/95905/2013 and SFRH/BD/121679/2016