Exploring social representations of adapting to climate change using topic modeling and Bayesian networks

When something unfamiliar emerges or when something familiar does something unexpected people need to make sense of what is emerging or going on in order to act. Social representations theory suggests how individuals and society make sense of the unfamiliar and hence how the resultant social representations (SRs) cognitively, emotionally, and actively orient people and enable communication. SRs are social constructions that emerge through individual and collective engagement with media and with everyday conversations among people. Recent developments in text analysis techniques, and in particular topic modeling, provide a potentially powerful analytical method to examine the structure and content of SRs using large samples of narrative or text. In this paper I describe the methods and results of applying topic modeling to 660 micronarratives collected from Australian academics / researchers, government employees, and members of the public in 2010-2011. The narrative fragments focused on adaptation to climate change (CC) and hence provide an example of Australian society making sense of an emerging and conflict ridden phenomena. The results of the topic modeling reflect elements of SRs of adaptation to CC that are consistent with findings in the literature as well as being reasonably robust predictors of classes of action in response to CC. Bayesian Network (BN) modeling was used to identify relationships among the topics (SR elements) and in particular to identify relationships among topics, sentiment, and action. Finally the resulting model and topic modeling results are used to highlight differences in the salience of SR elements among social groups. The approach of linking topic modeling and BN modeling offers a new and encouraging approach to analysis for ongoing research on SRs.

Recognition of Activities of Daily Living from Topic Model

Research in ubiquitous and pervasive technologies have made it possible to recognise activities of daily living through non-intrusive sensors. The data captured from these sensors are required to be classified using various machine learning or knowledge driven techniques to infer and recognise activities. The process of discovering the activities and activity-object patterns from the sensors tagged to objects as they are used is critical to recognising the activities. In this paper, we propose a topic model process of discovering activities and activity-object patterns from the interactions of low level state-change sensors. We also develop a recognition and segmentation algorithm to recognise activities and recognise activity boundaries. Experimental results we present validates our framework and shows it is comparable to existing approaches.

Foresterie urbaine : recensement mondial des publications, situation actuelle et besoins de recherche des municipalités canadiennes

L’arbre en ville offre plusieurs bienfaits, et ce, même s’il est souvent confronté à des conditions de vie difficiles. Au cours des années, la foresterie urbaine a suscité plusieurs travaux de recherches. Ce projet visait à faire un recensement des publications scientifiques de recherche en foresterie urbaine, à faire l’état de la situation des municipalités en matière de foresterie urbaine et à identifier les besoins de recherche en foresterie urbaine des municipalités canadiennes de 5 000 habitants et plus. Le premier objectif a été réalisé en utilisant plusieurs bases de données d’articles scientifiques et des mots clefs en foresterie urbaine. Un sondage pancanadien a permis de réaliser les deux autres objectifs. Le recensement que nous avons effectué des publications en foresterie urbaine depuis 1800 a montré, comme pour les publications scientifiques en général, mais de façon encore plus marquée, un accroissement de leur nombre ces dernières années. Ce recensement a aussi permis de révéler que les préoccupations de recherche touchent surtout l’effet des arbres sur la santé humaine et la qualité de l’air. L’identification des besoins de recherche réalisée auprès des responsables des espaces verts a permis de constater qu’il y avait toujours place pour la recherche dans ce domaine. Les municipalités veulent connaître, entre autres, quelles espèces d’arbres sont adaptées à l’environnement urbain, quel est l’effet des arbres sur le ruissellement des eaux de pluie et sur la santé humaine. Nous avons aussi constaté qu’il y avait un manque de transfert de connaissances entre les chercheurs et les responsables d’espaces verts ou que les recherches menées ne répondent pas aux besoins exprimés par les répondants. Le présent mémoire a permis de mieux cerner les besoins de recherche des municipalités canadiennes et permettra aux chercheurs de mieux définir les priorités de recherche en foresterie urbaine.

Gestação após gastroplastia para tratamento de obesidade mórbida: série de casos e revisão da literatura

Objetivo: relatar a evolução de uma série de casos de gestação em mulheres previamente submetidas à cirurgia de bypass gástrico para tratamento de obesidade grave. Métodos: cinco casos consecutivos de gravidez após gastroplastia ocorridos entre 2001 e 2004 foram avaliados. As pacientes tinham idade entre 30 e 34 anos e todas haviam sido submetidas à cirurgia de Capella. Aspectos clínicos, laboratoriais e do acompanhamento materno e fetal foram considerados, durante o período gestacional e após o parto. Foi realizada revisão da literatura internacional, por meio das bases de dados MEDLINE e Web of Science, utilizando os seguintes unitermos: gastroplasty, gastric bypass surgery, bariatric surgery e pregnancy. Resultados: todas as gestações observadas foram únicas e não ocorreram complicações obstétricas, durante o seguimento pré-natal e parto. Também não houve registro de recém-nascidos prematuros ou de baixo peso ao nascimento. Conclusão: nossos dados sugerem que a gravidez após gastroplastia é segura para a mãe e feto. Entretanto, em virtude do limitado volume de informação disponível sobre o tema, investigações adicionais são necessárias para estabelecer recomendações apropriadas com relação ao seguimento dessas gestações _________________________________________________ABSTRACT Purpose: we report a small series of pregnant women who underwent gastric bypass surgery for severe obesity, with a review of the literature on this topic. Methods: five consecutive cases of pregnancy after gastroplasty between 2001 and 2004 were evaluated, and clinical, laboratory and therapeutic features were considered. Patients were 30 to 34 years old and all had been submitted to gastroplasty by the Capella technique. The outcomes for both the pregnant woman and the fetus were evaluated. A search of the English language literature was done through MEDLINE and Web of Science databases with the following terms: gastroplasty, gastric bypass surgery, bariatric surgery, and pregnancy. Results: all 5 pregnancies were singleton. No major obstetric complications were observed and there were no premature or lowbirth weight infants. Conclusion: our data suggest that pregnancy following gastroplasty is safe for mother and fetus. However, since information about this topic is limited, further investigations are required to establish appropriate recommendations concerning the follow-up of these pregnancies

Uma abordagem sobre infecções endodônticas

As infeções endodônticas envolvem a invasão e multiplicação de microrganismos na polpa dentária e tecidos periapicais sendo responsáveis por dois tipos de patologias: as patologias pulpares e as patologias periapicais. Relativamente às patologias pulpares destacam-se a pulpite reversível, a pulpite irreversível e a necrose pulpar. Quanto às patologias periapicais, destacam-se o abcesso apical agudo, o abcesso apical crónico, a periodontite apical aguda, a periodontite apical crónica, o granuloma perirradicular e o quisto perirradicular. As doenças pulpares e periapicais apresentam manifestações clínicas diferentes que, em conjunto com os sinais e sintomas manifestados pelo paciente permitem diagnosticar o tipo de infeção endodôntica. As infeções endodônticas estão associadas a uma elevada diversidade de bactérias, sendo frequentemente intituladas de infeções endodônticas polimicrobianas. Sabe-se que os microrganismos são a causa principal das doenças pulpares e periapicais e, por esse motivo, o objetivo principal do Tratamento Endodôntico consiste na eliminação dos microrganismos e prevenção da re-infeção. O tratamento das infeções endodônticas baseia-se na preparação químico-mecânica do sistema de canais radiculares – instrumentação e irrigação – seguida da obturação e culminando com a restauração definitiva ou tratamento reabilitador. Este trabalho tem como objetivos adquirir um conhecimento mais amplo relativamente aos tipos de infeções endodônticas, à realização dos diversos diagnósticos e, principalmente, às várias opções de tratamento, disponíveis na área da Endodontia. Para tal foi realizada uma pesquisa bibliográfica baseada em artigos científicos, publicados nas bases de dados PubMed, Scielo e Science Direct bem como em alguns livros relacionados com o tema.

Computer-aided tool for the teaching of relational algebra in data base courses

This article describes the design and implementation of computer-aided tool called Relational Algebra Translator (RAT) in data base courses, for the teaching of relational algebra. There was a problem when introducing the relational algebra topic in the course EIF 211 Design and Implementation of Databases, which belongs to the career of Engineering in Information Systems of the National University of Costa Rica, because students attending this course were lacking profound mathematical knowledge, which led to a learning problem, being this an important subject to understand what the data bases search and request do RAT comes along to enhance the teaching-learning process.It introduces the architectural and design principles required for its implementation, such as: the language symbol table, the gramatical rules and the basic algorithms that RAT uses to translate from relational algebra to SQL language. This tool has been used for one periods and has demonstrated to be effective in the learning-teaching process.  This urged investigators to publish it in the web site: www.slinfo.una.ac.cr in order for this tool to be used in other university courses.

Heart failure in young adults

Heart failure (HF) is a major health concern affecting 15 million people in Europe and around 900 000 people in the U.K. HF predominantly affects the elderly, with the mean age of patients with a diagnosis of HF between 70 and 80 years. Most previous HF studies have accordingly focused on older patients. Although HF is less common in younger adults (<65 years), 15% to 20% of patients hospitalised with HF are younger than 60 years of age. Very few studies have described the characteristics of younger adults with HF and its outcome. The aims of this thesis are to describe the clinical characteristics of younger adults with HF, explore the epidemiology of HF in younger adults and determine their short- and long-term outcomes. This was made possible by access multiple databases consisting of large patient cohorts with HF. The first chapter is a systematic literature review of younger adults with HF. Gaps in the current literature were identified and the thesis focused on some of these. The CHARM study allows detail characterisations of younger adults with HF. It recorded characteristics of patients with HF, including symptoms and signs of HF, electrocardiographic changes, chest radiographic findings, and also left ventricular ejection fraction. HF hospitalisations and its precipitating factors were also recorded systematically. Younger adults were more likely to have a third heart sound and hepatomegaly, but less likely to have pulmonary crackles and peripheral oedema. Similarly, radiological findings in younger adults were less likely to show interstitial pulmonary oedema or pleural effusion. Interestingly, younger adults aged <40 years not only have similar HF hospitalisation rate to older patients, however during their presentation with decompensated HF, they were less likely to have clinical pulmonary oedema and radiological signs of HF. Physicians managing younger adults with HF need to be aware of this. Younger adults were also less compliant with medications and lifestyle restriction resulting in hospitalisation with decompensated HF. Fortunately, despite these challenges, mortality rates in younger adults with HF were lower compared to older patients. To further substantiate the findings from the CHARM study, the MAGGIC study, a meta-analysis consists of over 40 000 patients with HF from large observational studies and randomised controlled trials, was examined. In both databases, the commonest aetiology of HF in younger adults was dilated cardiomyopathy. The ejection fraction was the lowest in younger adults. Similar to the CHARM study, mortality rates in younger adults were lower compared to older patients. However, in the MAGGIC study, by stratifying mortality into patients with preserved ejection fraction and with reduced ejection fraction, younger patients with preserved ejection fraction have a much lower mortality rate compared to patients with reduced ejection fraction. Findings from clinical trials are not always reflective of the real life clinical practice. The U.K. Clinical Practice Research Datalink (CPRD), a large and well-validated primary care database with 654 practices contributing information into the database representing approximated 8% of the U.K. population, is a rich dataset offering a unique opportunity to examine the characteristics, treatments, and outcomes of younger adults with HF in the community. In contrast to the CHARM and MAGGIC studies, younger adults aged <40 years were stratified into 20-29 and 30-39 years in the CPRD analysis. This is possible due to the larger number of younger adults with HF. Further stratifying the younger age groups demonstrated heterogeneity among younger adults with HF. In contrast to previous data showing younger adults have lower co-morbidities, the proportions of depression, chronic kidney disease, asthma, and any connective tissue disease were high among patients aged 20-29 years in the analysis from the CPRD. Surprisingly, the treatment rates for angiotensin converting enzyme (ACE) inhibitor, and aldosterone antagonist were the lowest in patients aged 20-29 years. With the exception of patients aged ≥80 years, treatment rate with beta-blocker was also the lowest in patients aged 20-29 years. With over two decades of follow up, long-term mortality rates in younger adults with HF can be determined. The mortality rates continued to decline from 1988 to 2011. Physicians managing younger adults with HF can now use this contemporary data to provide prognostic information to patients and their family. A hospital administrative database is the logical next platform to explore younger adults with HF. The Alberta Ministry of Health database links an outpatient database to a hospitalisation database providing ample data to examine the relationship between outpatient clinic visits and hospital admissions in younger adults with HF. Following a diagnosis of HF in the outpatient setting, younger adults were admitted to the hospital with decompensated HF much sooner than older patients. Younger adults also presented to emergency department more frequently following their first hospitalisation for HF. In conclusion, this thesis presented the characteristics and outcomes of younger adults with HF, and helped to extend our current understanding on this important topic. I hope the data presented here will benefit not only physicians looking after younger adults with HF, but also patients and their family.

Protecting databases from schema disclosure: a CRUD-based protection model

Database schemas, in many organizations, are considered one of the critical assets to be protected. From database schemas, it is not only possible to infer the information being collected but also the way organizations manage their businesses and/or activities. One of the ways to disclose database schemas is through the Create, Read, Update and Delete (CRUD) expressions. In fact, their use can follow strict security rules or be unregulated by malicious users. In the first case, users are required to master database schemas. This can be critical when applications that access the database directly, which we call database interface applications (DIA), are developed by third party organizations via outsourcing. In the second case, users can disclose partially or totally database schemas following malicious algorithms based on CRUD expressions. To overcome this vulnerability, we propose a new technique where CRUD expressions cannot be directly manipulated by DIAs any more. Whenever a DIA starts-up, the associated database server generates a random codified token for each CRUD expression and sends it to the DIA that the database servers can use to execute the correspondent CRUD expression. In order to validate our proposal, we present a conceptual architectural model and a proof of concept.

Relations among Topic Knowledge, Individual Interest, and Relational Reasoning, and Critical Thinking in Maternity Nursing

Critical thinking in learners is a goal of educators and professional organizations in nursing as well as other professions. However, few studies in nursing have examined the role of the important individual difference factors topic knowledge, individual interest, and general relational reasoning strategies in predicting critical thinking. In addition, most previous studies have used domain-general, standardized measures, with inconsistent results. Moreover, few studies have investigated critical thinking across multiple levels of experience. The major purpose of this study was to examine the degree to which topic knowledge, individual interest, and relational reasoning predict critical thinking in maternity nurses. For this study, 182 maternity nurses were recruited from national nursing listservs explicitly chosen to capture multiple levels of experience from prelicensure to very experienced nurses. The three independent measures included a domain-specific Topic Knowledge Assessment (TKA), consisting of 24 short-answer questions, a Professed and Engaged Interest Measure (PEIM), with 20 questions indicating level of interest and engagement in maternity nursing topics and activities, and the Test of Relational Reasoning (TORR), a graphical selected response measure with 32 items organized in scales corresponding to four forms of relational reasoning: analogy, anomaly, antithesis, and antinomy. The dependent measure was the Critical Thinking Task in Maternity Nursing (CT2MN), composed of a clinical case study providing cues with follow-up questions relating to nursing care. These questions align with the cognitive processes identified in a commonly-used definition of critical thinking in nursing. Reliable coding schemes for the measures were developed for this study. Key findings included a significant correlation between topic knowledge and individual interest. Further, the three individual difference factors explained a significant proportion of the variance in critical thinking with a large effect size. While topic knowledge was the strongest predictor of critical thinking performance, individual interest had a moderate significant effect, and relational reasoning had a small but significant effect. The findings suggest that these individual difference factors should be included in future studies of critical thinking in nursing. Implications for nursing education, research, and practice are discussed.

Information Analysis and Databases

Means to automate the fact replace the man in their job functions for a man and machines automatic mechanism, ie documentary specialists in computer and computers are the cornerstone of any modern system of documentation and information. From this point of view immediately raises the problem of deciding what resources should be applied to solve the specific problem in each specific case. We will not let alone to propose quick fixes or recipes in order to decide what to do in any case. The solution depends on repeat for each particular problem. What we want is to move some points that can serve as a basis for reflection to help find the best solution possible, once the problem is defined correctly. The first thing to do before starting any automated system project is to define exactly the domain you want to cover and assess with greater precision possible importance.

A Fuzzy Decision Strategy for Topic Identification and Dynamic Selection of Language Models

International audience

Different Aspects of General Anesthesia in Pediatric Dentistry: A Review

Context:Most child population is able to undergo dental treatment in the conventional setting. However, some children fail to cope with in-office conscious state and cannot respond to usual management modalities. This review aims to discuss the topic further. Evidence Acquisition: A computerized search in databases PubMed, MEDLINE, EMBASE, Google Scholar and Google were performed using dental general anesthesia related keywords. Original and review English-written articles that were limited to child population were retrieved without any limitation of publication date. The suitable papers were selected and carefully studied. A data form designed by author was used to write relevant findings. Results: Preoperative oral examination and comprehensive evaluation of treatment needs is only possible after clinical and radiographic oral examination. Effective collaboration in dental GA team should be made to minimize psychological trauma of children who undergo dental GA. Before conducting comprehensive dental treatment under GA, the general health of the child and the success rate of procedures provided needs to be accurately evaluated. It is noteworthy that determination of the optimal timing for GA dental operation is of great importance. Providing safety with pediatric dental rehabilitation under GA is critical. Conclusions: Besides criteria for case selection of dental GA, some degree of dental practitioner’s judgment is required to make decision. Pre- and post-operative instructions to parents or caregiver decrease the risk of complications. However, trained resuscitation providers, careful monitoring and advanced equipment minimize adverse outcomes.

An investigation of imputation methods for discrete databases and multi-variate time series

When it comes to information sets in real life, often pieces of the whole set may not be available. This problem can find its origin in various reasons, describing therefore different patterns. In the literature, this problem is known as Missing Data. This issue can be fixed in various ways, from not taking into consideration incomplete observations, to guessing what those values originally were, or just ignoring the fact that some values are missing. The methods used to estimate missing data are called Imputation Methods. The work presented in this thesis has two main goals. The first one is to determine whether any kind of interactions exists between Missing Data, Imputation Methods and Supervised Classification algorithms, when they are applied together. For this first problem we consider a scenario in which the databases used are discrete, understanding discrete as that it is assumed that there is no relation between observations. These datasets underwent processes involving different combina- tions of the three components mentioned. The outcome showed that the missing data pattern strongly influences the outcome produced by a classifier. Also, in some of the cases, the complex imputation techniques investigated in the thesis were able to obtain better results than simple ones. The second goal of this work is to propose a new imputation strategy, but this time we constrain the specifications of the previous problem to a special kind of datasets, the multivariate Time Series. We designed new imputation techniques for this particular domain, and combined them with some of the contrasted strategies tested in the pre- vious chapter of this thesis. The time series also were subjected to processes involving missing data and imputation to finally propose an overall better imputation method. In the final chapter of this work, a real-world example is presented, describing a wa- ter quality prediction problem. The databases that characterized this problem had their own original latent values, which provides a real-world benchmark to test the algorithms developed in this thesis.

The representation of engineering education as a social media topic on Twitter

Online social media systems have created new ways for individuals to communicate, share information and interact with a wide audience. For organisations, social media provide new avenues for communication and collaboration with their stakeholders. The potential value of social media tools to assist in the successful communication and marketing inside and outside of engineering organisations has been identified. In the context of engineering education, the potential of social media to open new modes of communication, interaction and experimentation between students and teachers has also been identified, and a limited number of examples can be found documented in the literature. One of the most widely-used social media tools is the ‘microblogging’ service Twitter. This research presents an analysis of nearly 19,000 tweets relating to ‘engineering education’ collected over a period of almost a year. Social network analysis is used to visualise the Twitter data. The Twitter social media communication is examined to identify who is active on this topic, who is influential, and what is the structure of the online conversations relating to engineering education. This work provides insights regarding how engineering education is currently represented in social media internationally, and offers a methodology to those interested in related future research.

Implementazione e benchmarking dell'algoritmo QDANet PRO per l'analisi di big data genomici

Dato il recente avvento delle tecnologie NGS, in grado di sequenziare interi genomi umani in tempi e costi ridotti, la capacità di estrarre informazioni dai dati ha un ruolo fondamentale per lo sviluppo della ricerca. Attualmente i problemi computazionali connessi a tali analisi rientrano nel topic dei Big Data, con databases contenenti svariati tipi di dati sperimentali di dimensione sempre più ampia. Questo lavoro di tesi si occupa dell'implementazione e del benchmarking dell'algoritmo QDANet PRO, sviluppato dal gruppo di Biofisica dell'Università di Bologna: il metodo consente l'elaborazione di dati ad alta dimensionalità per l'estrazione di una Signature a bassa dimensionalità di features con un'elevata performance di classificazione, mediante una pipeline d'analisi che comprende algoritmi di dimensionality reduction. Il metodo è generalizzabile anche all'analisi di dati non biologici, ma caratterizzati comunque da un elevato volume e complessità, fattori tipici dei Big Data. L'algoritmo QDANet PRO, valutando la performance di tutte le possibili coppie di features, ne stima il potere discriminante utilizzando un Naive Bayes Quadratic Classifier per poi determinarne il ranking. Una volta selezionata una soglia di performance, viene costruito un network delle features, da cui vengono determinate le componenti connesse. Ogni sottografo viene analizzato separatamente e ridotto mediante metodi basati sulla teoria dei networks fino all'estrapolazione della Signature finale. Il metodo, già precedentemente testato su alcuni datasets disponibili al gruppo di ricerca con riscontri positivi, è stato messo a confronto con i risultati ottenuti su databases omici disponibili in letteratura, i quali costituiscono un riferimento nel settore, e con algoritmi già esistenti che svolgono simili compiti. Per la riduzione dei tempi computazionali l'algoritmo è stato implementato in linguaggio C++ su HPC, con la parallelizzazione mediante librerie OpenMP delle parti più critiche.