The portuguese pharmaceutial market in the near future - A time series exploration approach

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de InformaçãoUsing a novel exploratory technique for time series analysis, Single Spectrum Analysis (SSA), it was possible to develop a comprehensive study of the Portuguese pharmaceutical market. In the introductory chapter this technique is described in detail, for the decomposition step, homogeneity structure testing and forecasting. A bibliography review was conducted on the technique. To the best of our knowledge this was the first time that SSA was applied to any pharmaceutical market, so it was not possible to compare results with other published work. A detailed explanation on the Portuguese pharmaceutical market is provided in order to allow comprehensiveness of the work. The Portuguese pharmaceutical market is divided in 15 classes, which aggregates all drugs sold in the country. The technique was applied to those 15 time series plus the “Total Market” time series. Applying SSA, time series were decomposed in the respective components, which can be described as trend, cyclical movements and seasonality. The structure of all time series was tested for homogeneity. With those steps concluded, a monthly forecast, for the years 2008 and 2009 (with the respective confidence bounds) were produced for all the 16 time series. As a complex methodology, decisions need to be taken in several steps of the study. Even if not all possible choices are presented in the work, lengthy analyses were done to reach the best possible results. In fact, choosing between possible window lengths, Singular Value Decomposition (SVD) approaches, and eigentriples to be grouped together is sometimes more an “art” than a science; experience and previous knowledge of the actual phenomena can and should help. For confidentiality reasons the raw data is not provided in this work, but both forecast values and confidence bounds are presented

Myotonic dystrophy type 1 (DM1) and speech problems

Myotonic Dystrophy type 1(DM1), also called Steinert syndrome, is a multisystemic disorder transmitted in an autosomal dominant manner, characterized by myotonia. Muscles involved in voluntary movement are highly affected by myotonia especially distal muscles of upper limbs. Patients with DM1 present a myopathic face and oropharynx weakness. Reduced motor mobility and saliva flux can lead to gingival inflammation and periodontal disease together with other oral manifestations like disturbances at the temporomandibular articulation. Main causes of death are pneumonia and cardiac arrhythmias. Although the etiology of this syndrome is well known, a specific treatment for this disease is still not available. Nowadays, treatments consist on the relief of existing symptoms, in an attempt to give a better life quality to patients. It is very important to implement actions that can prevent complications and this is why treatments should be applied in an early stage of the disease. It is the aim of this paper to clarify the etiology, systemic characteristics of the syndrome and in particular discuss how myotonia can lead to speech disturbances and present strategies to deal with this particular problem.info:eu-repo/semantics/publishedVersio

A Gaussian random field model for similarity-based smoothing in Bayesian disease mapping

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Statistics and EconometricsConditionally specified Gaussian Markov random field (GMRF) models with adjacency- or distance-based neighbourhood weight matrix, commonly known as neighbourhood- based GMRF models, have been the mainstream approach to spatial smoothing in Bayesian Disease mapping (DM). In the present work, we propose a conditionally specified Gaussian random field (GRF) model with a similarity-based non-spatial weight matrix to facilitate non-spatial smoothing in Bayesian DM. The model, named similarity-based GRF, is motivated for modeling DM data in situations where the underlying small area relative risks and the associated determinant factors do not varying systematically in space, and the similarity is defined by “similarity” with respect to the associated disease determinant factors. The neighbourhood-based GMRF and the similarity-based GRF are compared and assessed via a simulation study and by two case studies, using new data on alcohol abuse in Portugal collected by the World Mental Health Survey Initiative (WMHSI) and the well-known lip cancer data in Scotland. In the presence of disease data with no evidence of positive spatial correlation, the simulation study showed a consistent gain in efficiency from the similarity-based GRF, compared with the adjacency-based GMRF with the determinant risk factors as covariate. This new approach broadens the scope of the existing Conditional autocorrelation (CAR) models

Scoping challenges and opportunities presented by COVID-19 for the development of sustainable short food supply chains

Over the past decades, short food supply chains attracted government and public support owing to their potential to mitigate some of the sustainability issues associated with the conventional globalized food supply system. The recent event of the coronavirus disease pandemic placed unprecedented pressure on food supply systems worldwide, and it constitutes a unique opportunity to evaluate the performance of food chains. Through a scoping review of the academic literature, this study provides a critical assessment of the implications of the pandemic on short food supply chains in multiple economies. Following the guidelines outlined in the PRISMA-ScR framework, the SCOPUS and ISI Web of Science databases were searched for the academic literature on the topic. The results of the review indicate that, besides the direct effects of the pandemic, the indirect effects resulting from public policies implemented to contain the spread of the virus affected all relevant dimensions of sustainability. Moreover, the consequences of the pandemic were more disruptive in the short food chains of low-income countries than in those of high-income countries. The main challenges and opportunities for the sustainable development of short food supply chains are identified, and recommendations for future research are outlined.info:eu-repo/semantics/publishedVersio

The case for social support in social marketing

Purpose – This paper aims to reinforce the arguments for applying the social support concept in social marketing. Design/methodology/approach – This paper aims to conceptually outline the potential positive contribution of social support for social marketing practice as a tool to induce behavior change. Findings – This paper focuses on the philosophical principle of social exchange, highlights the consumercentered perspective of social marketing, which implies the natural evaluation of the social networks of influence and support and presents social support as a mechanism to induce long-term behavior change. Research limitations/implications – No empirical (qualitative or quantitative) investigations were used to test the application of the concept in practical interventions. Practical implications – This paper provides significant insights for intervention developers that can be used to program and theoretically justify future social marketing interventions applying the social support concept. Social implications – Empirical research concluded for a positive relation between social support and human health and well-being. Thus, increasing the use of the concept in social marketing can serve to attain these social goals. Originality/value – The concept of social support has gained considerable interest in the areas of behavioral medicine and health psychology. Despite such interest, it is still not clear how it can be approached in social marketing as there is a lack of conceptual literature discussing social support from a social marketing perspective, the number of social marketing interventions operationalizing the concept is limited and, till date, no research has focused in comprehensively establishing a theoretical rationale to operationalize the concept in social marketin

Comparing two models for disease mapping data not varying systematically in space

Baptista, H., Mendes, J., & Congdon, P. (2018). Comparing two models for disease mapping data not varying systematically in space. In METMA 9: Book of Extended Abstracts (pp. 68-71). [METMA IX, 9th Workshop on spatio-temporal modeling, 13-15 june, 2018, Montepellier, France].Conditionally specified Gaussian Markov random field (GMRF) models with adjacencybased neighborhood weight matrix, commonly known as eighborhood-based GMRF models, have been the mainstream approach to spatial smoothing in Bayesian disease mapping. However, there are cases when there is no evidence of positive spatial correlation or the appropriate mix between local and global smoothing is not constant across the region being study. Two models have been proposed for those cases, a conditionally specified Gaussian random field (GRF) model using a similarity-based non-spatial weight matrix to facilitate non-spatial smoothing in Bayesian disease mapping, and a spatially adaptive conditional autoregressive prior model. The former model, named similarity-based GRF, is motivated for modeling disease mapping data in situations where the underlying small area relative risks and the associated determinant factors do not varying systematically in space, and the similarity is defined by similarity with respect to the associated disease determinant factors. In the presence of disease data with no evidence of positive spatial correlation, a simulation study showed a consistent gain in efficiency from the similarity-based GRF, compared with the adjacency-based GMRF with the determinant risk factors as covariate. The latter model considers a spatially adaptive extension of Leroux et al. [9] prior to reflect the fact that the appropriate mix between local and global smoothing may not be constant across the region being studied. Local smoothing will not be indicated when an area is disparate from its neighbours (e.g. in terms of social or environmental risk factors for the health outcome being considered). The prior for varying spatial correlation parameters may be based on a regression structure which includes possible observed sources of disparity between neighbours. We will compare the results of the two models.publishersversionpublishe

A problem-solving experience: The teacher’s perspective

Problem solving is a skill that can be developed by students. For this to happen, the teacher must be prepared to teach classes in which they proposes that their students solve one or more problems. Teacher do not always feel ready and confident to have a class where the focus is on solving a problem. In this communication, the focus is on the planning and implementation of a class in which one intends to solve a mathematical problem. Thus, the experience of the first author is reported when planning a class, passing through the five practices that facilitate the discussion of mathematical tasks: anticipation, monitoring, selection, sequencing and connection.La résolution de problèmes est une compétence qui doit être développée par les étudiants. Pour ce faire, l’enseignant doit être prêt à donner des cours dans lesquels il/elle propose à ses élèves de résoudre un ou plusieurs problèmes. Les enseignants ne se sentient pas toujours prêts et confiants d’avoir une classe où l’accent est mis sur la résolution d’un problème. Dans cette communication, l'accent est mis sur la planification et la concrétisation d'une classe dans laquelle on entend résoudre un problème mathématique. Ainsi, l'expérience du premier auteur est rapportée lors de la planification d'un cours, en passant par les cinq pratiques qui facilitent la discussion des tâches mathématiques: anticipation, surveillance, sélection, séquence et connexion.This paper is a result of the project SmartEGOV: Harnessing EGOV for Smart Governance (Foundations, Methods, Tools) NORTE-01-0145-FEDER-000037, supported by Norte Portugal Regional Operational Programme (NORTE 2020),under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (EFDR). Further support by CIEd –Research Centre on Education, projects UID/CED/1661/2013 and UID/CED/1661/2016, Institute of Education, University of Minho, through national funds of FCT/MCTES-PT

Written resolution of a mathematical problem by 11th grade students

[Excerpt] Problem solving and written communication are strongly connected, since the resolution of a problem presupposes the use of written communication to record the reasoning, either to communicate with another person or to review the resolution in the future. Bearing in mind this relation, and also considering the relevance of both in learning mathematics, our research question is: how students communicate their resolutions of a mathematical problem in writing? To answer this, we made a qualitative research with an interpretative paradigm. The participants were 29 students of 11th grade, divided into six working groups, who voluntarily signed up for a problem-solving project developed online and in an extracurricular format