Phase-field analysis of finite-strain plates and shells including element subdivision

With the theme of fracture of finite-strain plates and shells based on a phase-field model of crack regularization, we introduce a new staggered algorithm for elastic and elasto-plastic materials. To account for correct fracture behavior in bending, two independent phase-fields are used, corresponding to the lower and upper faces of the shell. This is shown to provide a realistic behavior in bending-dominated problems, here illustrated in classical beam and plate problems. Finite strain behavior for both elastic and elasto-plastic constitutive laws is made compatible with the phase-field model by use of a consistent updated-Lagrangian algorithm. To guarantee sufficient resolution in the definition of the crack paths, a local remeshing algorithm based on the phase- field values at the lower and upper shell faces is introduced. In this local remeshing algorithm, two stages are used: edge-based element subdivision and node repositioning. Five representative numerical examples are shown, consisting of a bi-clamped beam, two versions of a square plate, the Keesecker pressurized cylinder problem, the Hexcan problem and the Muscat-Fenech and Atkins plate. All problems were successfully solved and the proposed solution was found to be robust and efficient.

Variational and learning models for image and time series inverse problems

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.

Crystal engineering strategies against antimicrobial resistance: a solid contribution to a contemporary problem

This PhD thesis sets its goal in the application of crystal engineering strategies to the design, formulation, synthesis, and characterization of innovative materials obtained by combining well established biologically active molecules and/or GRAS (generally recognized as safe) compounds with co-formers able to modulate specific properties of the molecule of interest. The solid-state association, via non-covalent interactions, of an active ingredient with another molecular component, a metal salt or a complex, may alter in a useful way the physicochemical properties of the active ingredient and/or may allow to explore new ways to enhance, in a synergistic way, the overall biological performance. More specifically this thesis will address the threat posed by the increasing antimicrobial resistance (AMR) developed by microorganisms, which call for novel therapeutic strategies. Crystal engineering provides new tools to approach this crisis in a greener and cost-effective way. This PhD work has been developed along two main research lines aiming to contribute to the search for innovative solutions to the AMR problem. Design, preparation and characterization of novel metal-based antimicrobials, whereby organic molecules with known antimicrobial properties are combined with metal atoms also known to exert antimicrobial action. Design, preparation and characterization of co-crystals obtained by combining antibacterial APIs (active pharmaceutical ingredients) with natural antimicrobials.

Generative Neural Networks for image iper-resolution and improvement of Optical Character Recognition's performances

The usage of Optical Character Recognition’s (OCR, systems is a widely spread technology into the world of Computer Vision and Machine Learning. It is a topic that interest many field, for example the automotive, where becomes a specialized task known as License Plate Recognition, useful for many application from the automation of toll road to intelligent payments. However, OCR systems need to be very accurate and generalizable in order to be able to extract the text of license plates under high variable conditions, from the type of camera used for acquisition to light changes. Such variables compromise the quality of digitalized real scenes causing the presence of noise and degradation of various type, which can be minimized with the application of modern approaches for image iper resolution and noise reduction. Oneclass of them is known as Generative Neural Networks, which are very strong ally for the solution of this popular problem.

Screening The Life Cycle Of Schistosoma Mansoni Using High-resolution Mass Spectrometry.

Schistosomiasis is a common tropical disease caused by Schistosoma species Schistosomiasis' pathogenesis is known to vary according to the worms' strain. Moreover, high parasitical virulence is directly related to eggs release and granulomatous inflammation in the host's organs. This virulence might be influenced by different classes of molecules, such as lipids. Therefore, better understanding of the metabolic profile of these organisms is necessary, especially for an increased potential of unraveling strain virulence mechanisms and resistance to existing treatments. In this report, direct-infusion electrospray high-resolution mass spectrometry (ESI(+)-HRMS) along with the lipidomic platform were employed to rapidly characterize and differentiate two Brazilian S. mansoni strains (BH and SE) in three stages of their life cycle: eggs, miracidia and cercariae, with samples from experimental animals (Swiss/SPF mice). Furthermore, urine samples of the infected and uninfected mice were analyzed to assess the possibility of direct diagnosis. All samples were differentiated using multivariate data analysis, PCA, which helped electing markers from distinct lipid classes; phospholipids, diacylglycerols and triacylglycerols, for example, clearly presented different intensities in some stages and strains, as well as in urine samples. This indicates that biochemical characterization of S. mansoni may help narrowing-down the investigation of new therapeutic targets according to strain composition and aggressiveness of disease. Interestingly, lipid profile of infected mice urine varies when compared to control samples, indicating that direct diagnosis of schistosomiasis from urine may be feasible.

Evolution Of The Deaths Registry System In Brazil: Associations With Changes In The Mortality Profile, Under-registration Of Death Counts, And Ill-defined Causes Of Death.

This paper examines the spatial pattern of ill-defined causes of death across Brazilian regions, and its relationship with the evolution of completeness of the deaths registry and changes in the mortality age profile. We make use of the Brazilian Health Informatics Department mortality database and population censuses from 1980 to 2010. We applied demographic methods to evaluate the quality of mortality data for 137 small areas and correct for under-registration of death counts when necessary. The second part of the analysis uses linear regression models to investigate the relationship between, on the one hand, changes in death counts coverage and age profile of mortality, and on the other, changes in the reporting of ill-defined causes of death. The completeness of death counts coverage increases from about 80% in 1980-1991 to over 95% in 2000-2010 at the same time the percentage of ill-defined causes of deaths reduced about 53% in the country. The analysis suggests that the government's efforts to improve data quality are proving successful, and they will allow for a better understanding of the dynamics of health and the mortality transition.

Biased Random-key Genetic Algorithms For The Winner Determination Problem In Combinatorial Auctions.

Abstract In this paper, we address the problem of picking a subset of bids in a general combinatorial auction so as to maximize the overall profit using the first-price model. This winner determination problem assumes that a single bidding round is held to determine both the winners and prices to be paid. We introduce six variants of biased random-key genetic algorithms for this problem. Three of them use a novel initialization technique that makes use of solutions of intermediate linear programming relaxations of an exact mixed integer-linear programming model as initial chromosomes of the population. An experimental evaluation compares the effectiveness of the proposed algorithms with the standard mixed linear integer programming formulation, a specialized exact algorithm, and the best-performing heuristics proposed for this problem. The proposed algorithms are competitive and offer strong results, mainly for large-scale auctions.

[balloon Laryngoplasty For Acquired Subglottic Stenosis In Children: Predictive Factors For Success].

The treatment of subglottic stenosis in children remains a challenge for the otorhinolaryngologist, and may involve both endoscopic and open surgery. To report the experience of two tertiary facilities in the treatment of acquired subglottic stenosis in children with balloon laryngoplasty, and to identify predictive factors for success of the technique and its complications. Descriptive, prospective study of children diagnosed with acquired subglottic stenosis and submitted to balloon laryngoplasty as primary treatment. Balloon laryngoplasty was performed in 37 children with an average age of 22.5 months; 24 presented chronic subglottic stenosis and 13 acute subglottic stenosis. Success rates were 100% for acute subglottic stenosis and 32% for chronic subglottic stenosis. Success was significantly associated with acute stenosis, initial grade of stenosis, children of a smaller age, and the absence of tracheostomy. Transitory dysphagia was the only complication observed in three children. Balloon laryngoplasty may be considered the first line of treatment for acquired subglottic stenosis. In acute cases, the success rate is 100%, and although the results are less promising in chronic cases, complications are not significant and the possibility of open surgery remains without prejudice.

High-resolution Transcript Profiling Of The Atypical Biotrophic Interaction Between Theobroma Cacao And The Fungal Pathogen Moniliophthora Perniciosa.

Witches' broom disease (WBD), caused by the hemibiotrophic fungus Moniliophthora perniciosa, is one of the most devastating diseases of Theobroma cacao, the chocolate tree. In contrast to other hemibiotrophic interactions, the WBD biotrophic stage lasts for months and is responsible for the most distinctive symptoms of the disease, which comprise drastic morphological changes in the infected shoots. Here, we used the dual RNA-seq approach to simultaneously assess the transcriptomes of cacao and M. perniciosa during their peculiar biotrophic interaction. Infection with M. perniciosa triggers massive metabolic reprogramming in the diseased tissues. Although apparently vigorous, the infected shoots are energetically expensive structures characterized by the induction of ineffective defense responses and by a clear carbon deprivation signature. Remarkably, the infection culminates in the establishment of a senescence process in the host, which signals the end of the WBD biotrophic stage. We analyzed the pathogen's transcriptome in unprecedented detail and thereby characterized the fungal nutritional and infection strategies during WBD and identified putative virulence effectors. Interestingly, M. perniciosa biotrophic mycelia develop as long-term parasites that orchestrate changes in plant metabolism to increase the availability of soluble nutrients before plant death. Collectively, our results provide unique insight into an intriguing tropical disease and advance our understanding of the development of (hemi)biotrophic plant-pathogen interactions.

Ecological Literacy And Beyond: Problem-based Learning For Future Professionals.

Ecological science contributes to solving a broad range of environmental problems. However, lack of ecological literacy in practice often limits application of this knowledge. In this paper, we highlight a critical but often overlooked demand on ecological literacy: to enable professionals of various careers to apply scientific knowledge when faced with environmental problems. Current university courses on ecology often fail to persuade students that ecological science provides important tools for environmental problem solving. We propose problem-based learning to improve the understanding of ecological science and its usefulness for real-world environmental issues that professionals in careers as diverse as engineering, public health, architecture, social sciences, or management will address. Courses should set clear learning objectives for cognitive skills they expect students to acquire. Thus, professionals in different fields will be enabled to improve environmental decision-making processes and to participate effectively in multidisciplinary work groups charged with tackling environmental issues.

Detection Of Explosives On The Surface Of Banknotes By Raman Hyperspectral Imaging And Independent Component Analysis.

The aim of this study was to develop a methodology using Raman hyperspectral imaging and chemometric methods for identification of pre- and post-blast explosive residues on banknote surfaces. The explosives studied were of military, commercial and propellant uses. After the acquisition of the hyperspectral imaging, independent component analysis (ICA) was applied to extract the pure spectra and the distribution of the corresponding image constituents. The performance of the methodology was evaluated by the explained variance and the lack of fit of the models, by comparing the ICA recovered spectra with the reference spectra using correlation coefficients and by the presence of rotational ambiguity in the ICA solutions. The methodology was applied to forensic samples to solve an automated teller machine explosion case. Independent component analysis proved to be a suitable method of resolving curves, achieving equivalent performance with the multivariate curve resolution with alternating least squares (MCR-ALS) method. At low concentrations, MCR-ALS presents some limitations, as it did not provide the correct solution. The detection limit of the methodology presented in this study was 50μgcm(-2).

Profile Of Phenolic Compounds Of Brazilian Virgin Olive Oils By Rapid Resolution Liquid Chromatography Coupled To Electrospray Ionisation Time-of-flight Mass Spectrometry (rrlc-esi-tof-ms).

In recent years, agronomical researchers began to cultivate several olive varieties in different regions of Brazil to produce virgin olive oil (VOO). Because there has been no reported data regarding the phenolic profile of the first Brazilian VOO, the aim of this work was to determine phenolic contents of these samples using rapid-resolution liquid chromatography coupled to electrospray ionisation time-of-flight mass spectrometry. 25 VOO samples from Arbequina, Koroneiki, Arbosana, Grappolo, Manzanilla, Coratina, Frantoio and MGS Mariense varieties from three different Brazilian states and two crops were analysed. It was possible to quantify 19 phenolic compounds belonging to different classes. The results indicated that Brazilian VOOs have high total phenolic content because the values were comparable with those from high-quality VOOs produced in other countries. VOOs from Coratina, Arbosana and Grappolo presented the highest total phenolic content. These data will be useful in the development and improvement of Brazilian VOO.

Application Of Laser Microdissection Icp-ms For High Resolution Elemental Mapping In Mouse Brain Tissue: A Comparative Study With Laser Ablation Icp-ms.

Mapping of elements in biological tissue by laser induced mass spectrometry is a fast growing analytical methodology in life sciences. This method provides a multitude of useful information of metal, nonmetal, metalloid and isotopic distribution at major, minor and trace concentration ranges, usually with a lateral resolution of 12-160 µm. Selected applications in medical research require an improved lateral resolution of laser induced mass spectrometric technique at the low micrometre scale and below. The present work demonstrates the applicability of a recently developed analytical methodology - laser microdissection associated to inductively coupled plasma mass spectrometry (LMD ICP-MS) - to obtain elemental images of different solid biological samples at high lateral resolution. LMD ICP-MS images of mouse brain tissue samples stained with uranium and native are shown, and a direct comparison of LMD and laser ablation (LA) ICP-MS imaging methodologies, in terms of elemental quantification, is performed.

Programas de educação de jovens e adultos e pesquisa acadêmica: a contribuição dos estudos do letramento

The purpose of this article is to introduce elements that allow building an interface between the academic research and the programs of basic education for youngsters and adults. It discusses contributions to these programs that can be found in the results of qualitative research studies. To this end, results of a five-year long project on teacher education are used, which aim was that of analyzing the interaction between teacher and student in youngster and adult literacy classes. The research project was conducted in natural contexts with the purpose of understanding a given social reality, and not of establishing general laws. Therefore, the credibility of its results was built through the observation of multiple contexts, and the gathering of data was made through various methods, from the perspective of several participants observed during a prolonged period of time. This empirical basis was used to evaluate recommendations contained in the report commissioned by Unesco to the International Literacy Institute for presentation at the World Forum on Education held in 2000 in Dakar. This report proposed that the continuous attendance of students to basic education programs is one of the great challenges of the new millennium. With respect to the problem of adult evasion from courses and programs, the article discusses the motivation and accessibility factors, pointed out in official documents as relevant factors to the success or failure of the programs.

Success attitudes of young ophthalmologists in the first decade of their career

PURPOSE: To describe the main success attitudes of young ophthalmologists in the first decade of their career. METHODS: This descriptive study comprised subjects selected from a sample of ophthalmologists who were participating in a congress, using a semi-structured questionnaire. The inclusion criteria were as follows: ophthalmologists under the age of 40 years, within 5-10 years from ophthalmology residency conclusion. The subjects were asked about the three main success attitudes in their personal experience during the first years of ophthalmology practice. After the initial results, the 10 most frequently mentioned attitudes were listed and volunteers were again interviewed to choose, within the latter list, the three main attitudes. RESULTS: Forty-eight ophthalmologists were interviewed, 24 (50%) were male; the mean age was 37 years (SD: 2 years, range: 33-40 years) and the mean time from ophthalmology residency conclusion was 8 years (SD: 1 year, range: 5-10 years). The frequency of such mentioned success attitudes were as follows: to invest in professional updating (22.9%), to have a good relationship with patients and professional partners (18.8%), to prioritize individual and family happiness (12.5%), initially to work in an established group (11.1%), to work in public service (9.7%), to have their own business with a homogeneous group (7.6%), to save money (7.6%), to be ready to resume work (4.2%), to get business administration skills (4.2%), and to have professional insurance (0.7%). CONCLUSIONS: The three main success attitudes consisted in investing in professional updating (22.9%), maintaining a good relationship with patients and professional partners (18.8%), and prioritizing individual and family happiness (12.5%). Although these results should not be generalized, they are helpful not only for those ophthalmologists at the beginning of a career but also those who want to reflect on what to prioritize in their professional practice.