988 resultados para Droppin Knowledge Series
Resumo:
We present a new technical simulator for the eLISA mission, based on state space modeling techniques and developed in MATLAB. This simulator computes the coordinate and velocity over time of each body involved in the constellation, i.e. the spacecraft and its test masses, taking into account the different disturbances and actuations. This allows studying the contribution of instrumental noises and system imperfections on the residual acceleration applied on the TMs, the latter reflecting the performance of the achieved free-fall along the sensitive axis. A preliminary version of the results is presented.
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This paper examines the relationship between the state and the individual in relation to an aspect of mundane family life – the feeding of babies and young children. The nutritional status of children has long been a matter of national concern and infant feeding is an aspect of family life that has been subjected to substantial state intervention. It exemplifies the imposition upon women the ‘biologico-moral responsibility’ for the welfare of children (Foucault 1991b). The state’s attempts to influence mothers’ feeding practices operate largely through education and persuasion. Through an elaborate state-sponsored apparatus, a strongly medicalised expert discourse is disseminated to mothers. This discourse warns mothers of the risks of certain feeding practices and the benefits of others. It constrains mothers through a series of ‘quiet coercions’ (Foucault 1991c) which seek to render them self-regulating subjects. Using data from a longitudinal interview study, this paper explores how mothers who are made responsible in these medical discourses around child nutrition, engage with, resist and refuse expert advice. It examines, in particular, the rhetorical strategies which mothers use to defend themselves against the charges of maternal irresponsibility that arise when their practices do not conform to expert medical recommendations.
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Stochastic methods based on time-series modeling combined with geostatistics can be useful tools to describe the variability of water-table levels in time and space and to account for uncertainty. Monitoring water-level networks can give information about the dynamic of the aquifer domain in both dimensions. Time-series modeling is an elegant way to treat monitoring data without the complexity of physical mechanistic models. Time-series model predictions can be interpolated spatially, with the spatial differences in water-table dynamics determined by the spatial variation in the system properties and the temporal variation driven by the dynamics of the inputs into the system. An integration of stochastic methods is presented, based on time-series modeling and geostatistics as a framework to predict water levels for decision making in groundwater management and land-use planning. The methodology is applied in a case study in a Guarani Aquifer System (GAS) outcrop area located in the southeastern part of Brazil. Communication of results in a clear and understandable form, via simulated scenarios, is discussed as an alternative, when translating scientific knowledge into applications of stochastic hydrogeology in large aquifers with limited monitoring network coverage like the GAS.
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Traditional knowledge associated with genetic resources (TKaGRs) is acknowledged as a valuable resource. Its value draws from economic, social, cultural, and innovative uses. This value places TK at the heart of competing interests as between indigenous peoples who hold it and depend on it for their survival, and profitable industries which seek to exploit it in the global market space. The latter group seek, inter alia, to advance and maintain their global competitiveness by exploiting TKaGRs leads in their research and development activities connected with modern innovation. Biopiracy remains an issue of central concern to the developing world and has emerged in this context as a label for the inequity arising from the misappropriation of TKaGRs located in the South by commercial interests usually located in the North. Significant attention and resources are being channeled at global efforts to design and implement effective protection mechanisms for TKaGRs against the incidence of biopiracy. The emergence and recent entry into force of the Nagoya Protocol offers the latest example of a concluded multilateral effort in this regard. The Nagoya Protocol, adopted on the platform of the Convention on Biological Diversity (CBD), establishes an open-ended international access and benefit sharing (ABS) regime which is comprised of the Protocol as well as several complementary instruments. By focusing on the trans-regime nature of biopiracy, this thesis argues that the intellectual property (IP) system forms a central part of the problem of biopiracy, and so too to the very efforts to implement solutions, including through the Nagoya Protocol. The ongoing related work within the World Intellectual Property Organization (WIPO), aimed at developing an international instrument (or a series of instruments) to address the effective protection of TK, constitutes an essential complementary process to the Nagoya Protocol, and, as such, forms a fundamental element within the Nagoya Protocol’s evolving ABS regime-complex. By adopting a third world approach to international law, this thesis draws central significance from its reconceptualization of biopiracy as a trans-regime concept. By construing the instrument(s) being negotiated within WIPO as forming a central component part of the Nagoya Protocol, this dissertation’s analysis highlights the importance of third world efforts to secure an IP-based reinforcement to the Protocol for the effective eradication of biopiracy.
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Background: Falls are common events in older people, which cause considerable morbidity and mortality. Non-pharmacological interventions are an important approach to prevent falls. There are a large number of systematic reviews of non-pharmacological interventions, whose evidence needs to be synthesized in order to facilitate evidence-based clinical decision making. Objectives: To systematically examine reviews and meta-analyses that evaluated non-pharmacological interventions to prevent falls in older adults in the community, care facilities and hospitals. Methods: We searched the electronic databases Pubmed, the Cochrane Database of Systematic Reviews, EMBASE, CINAHL, PsycINFO, PEDRO and TRIP from January 2009 to March 2015, for systematic reviews that included at least one comparative study, evaluating any non-pharmacological intervention, to prevent falls amongst older adults. The quality of the reviews was assessed using AMSTAR and ProFaNE taxonomy was used to organize the interventions. Results: Fifty-nine systematic reviews were identified which consisted of single, multiple and multi-factorial non-pharmacological interventions to prevent falls in older people. The most frequent ProFaNE defined interventions were exercises either alone or combined with other interventions, followed by environment/assistive technology interventions comprising environmental modifications, assistive and protective aids, staff education and vision assessment/correction. Knowledge was the third principle class of interventions as patient education. Exercise and multifactorial interventions were the most effective treatments to reduce falls in older adults, although not all types of exercise were equally effective in all subjects and in all settings. Effective exercise programs combined balance and strength training. Reviews with a higher AMSTAR score were more likely to contain more primary studies, to be updated and to perform meta-analysis. Conclusions: The aim of this overview of reviews of non-pharmacological interventions to prevent falls in older people in different settings, is to support clinicians and other healthcare workers with clinical decision-making by providing a comprehensive perspective of findings.
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Inverse problems are at the core of many challenging applications. Variational and learning models provide estimated solutions of inverse problems as the outcome of specific reconstruction maps. In the variational approach, the result of the reconstruction map is the solution of a regularized minimization problem encoding information on the acquisition process and prior knowledge on the solution. In the learning approach, the reconstruction map is a parametric function whose parameters are identified by solving a minimization problem depending on a large set of data. In this thesis, we go beyond this apparent dichotomy between variational and learning models and we show they can be harmoniously merged in unified hybrid frameworks preserving their main advantages. We develop several highly efficient methods based on both these model-driven and data-driven strategies, for which we provide a detailed convergence analysis. The arising algorithms are applied to solve inverse problems involving images and time series. For each task, we show the proposed schemes improve the performances of many other existing methods in terms of both computational burden and quality of the solution. In the first part, we focus on gradient-based regularized variational models which are shown to be effective for segmentation purposes and thermal and medical image enhancement. We consider gradient sparsity-promoting regularized models for which we develop different strategies to estimate the regularization strength. Furthermore, we introduce a novel gradient-based Plug-and-Play convergent scheme considering a deep learning based denoiser trained on the gradient domain. In the second part, we address the tasks of natural image deblurring, image and video super resolution microscopy and positioning time series prediction, through deep learning based methods. We boost the performances of supervised, such as trained convolutional and recurrent networks, and unsupervised deep learning strategies, such as Deep Image Prior, by penalizing the losses with handcrafted regularization terms.
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Biomedicine is a highly interdisciplinary research area at the interface of sciences, anatomy, physiology, and medicine. In the last decade, biomedical studies have been greatly enhanced by the introduction of new technologies and techniques for automated quantitative imaging, thus considerably advancing the possibility to investigate biological phenomena through image analysis. However, the effectiveness of this interdisciplinary approach is bounded by the limited knowledge that a biologist and a computer scientist, by professional training, have of each other’s fields. The possible solution to make up for both these lacks lies in training biologists to make them interdisciplinary researchers able to develop dedicated image processing and analysis tools by exploiting a content-aware approach. The aim of this Thesis is to show the effectiveness of a content-aware approach to automated quantitative imaging, by its application to different biomedical studies, with the secondary desirable purpose of motivating researchers to invest in interdisciplinarity. Such content-aware approach has been applied firstly to the phenomization of tumour cell response to stress by confocal fluorescent imaging, and secondly, to the texture analysis of trabecular bone microarchitecture in micro-CT scans. Third, this approach served the characterization of new 3-D multicellular spheroids of human stem cells, and the investigation of the role of the Nogo-A protein in tooth innervation. Finally, the content-aware approach also prompted to the development of two novel methods for local image analysis and colocalization quantification. In conclusion, the content-aware approach has proved its benefit through building new approaches that have improved the quality of image analysis, strengthening the statistical significance to allow unveiling biological phenomena. Hopefully, this Thesis will contribute to inspire researchers to striving hard for pursuing interdisciplinarity.
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Natural Language Processing (NLP) has seen tremendous improvements over the last few years. Transformer architectures achieved impressive results in almost any NLP task, such as Text Classification, Machine Translation, and Language Generation. As time went by, transformers continued to improve thanks to larger corpora and bigger networks, reaching hundreds of billions of parameters. Training and deploying such large models has become prohibitively expensive, such that only big high tech companies can afford to train those models. Therefore, a lot of research has been dedicated to reducing a model’s size. In this thesis, we investigate the effects of Vocabulary Transfer and Knowledge Distillation for compressing large Language Models. The goal is to combine these two methodologies to further compress models without significant loss of performance. In particular, we designed different combination strategies and conducted a series of experiments on different vertical domains (medical, legal, news) and downstream tasks (Text Classification and Named Entity Recognition). Four different methods involving Vocabulary Transfer (VIPI) with and without a Masked Language Modelling (MLM) step and with and without Knowledge Distillation are compared against a baseline that assigns random vectors to new elements of the vocabulary. Results indicate that VIPI effectively transfers information of the original vocabulary and that MLM is beneficial. It is also noted that both vocabulary transfer and knowledge distillation are orthogonal to one another and may be applied jointly. The application of knowledge distillation first before subsequently applying vocabulary transfer is recommended. Finally, model performance due to vocabulary transfer does not always show a consistent trend as the vocabulary size is reduced. Hence, the choice of vocabulary size should be empirically selected by evaluation on the downstream task similar to hyperparameter tuning.
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This study investigates the practices involved in the production of knowledge about menopause at Caism, Unicamp, a reference center for public policies for women's health. Gynecological appointments and psychological support meetings were observed, and women and doctors were interviewed in order to identify what discourse circulates there and how different actors are brought in to ensure that the knowledge produced attains credibility and travels beyond the boundaries of the teaching hospital to become universal. The analysis is based on localized studies aligned with social studies of science and technology.
Resumo:
This study investigates the practices involved in the production of knowledge about menopause at Caism, Unicamp, a reference center for public policies for women's health. Gynecological appointments and psychological support meetings were observed, and women and doctors were interviewed in order to identify what discourse circulates there and how different actors are brought in to ensure that the knowledge produced attains credibility and travels beyond the boundaries of the teaching hospital to become universal. The analysis is based on localized studies aligned with social studies of science and technology.
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To describe maternal and neonatal outcomes in pregnant women undergoing hemodialysis in a referral center in Brazilian Southeast side. Retrospective and descriptive study, with chart review of all pregnancies undergoing hemodialysis that were followed-up at an outpatient clinic of high- risk prenatal care in Southeast Brazil. Among the 16 women identified, 2 were excluded due to follow-up loss. In 14 women described, hypertension was the most frequent cause of chronic renal failure (half of cases). The majority (71.4%) had performed hemodialysis treatment for more than one year and all of them underwent 5 to 6 hemodialysis sessions per week. Eleven participants had chronic hypertension, 1 of which was also diabetic, and 6 of them were smokers. Regarding pregnancy complications, 1 of the hypertensive women developed malignant hypertension (with fetal growth restriction and preterm delivery at 29 weeks), 2 had acute pulmonary edema and 2 had abruption placenta. The mode of delivery was cesarean section in 9 women (64.3%). All neonates had Apgar score at five minutes above 7. To improve perinatal and maternal outcomes of women undergoing hemodialysis, it is important to ensure multidisciplinary approach in referral center, strict control of serum urea, hemoglobin and maternal blood pressure, as well as close monitoring of fetal well-being and maternal morbidities. Another important strategy is suitable guidance for contraception in these women.
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Universidade Estadual de Campinas . Faculdade de Educação Física
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Universidade Estadual de Campinas . Faculdade de Educação Física
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Universidade Estadual de Campinas . Faculdade de Educação Física