A importância das histórias no desenvolvimento das crianças

Relatório de estágio para a obtenção do grau de mestre em Ensino de pré-escolar e de 1º ciclo do ensino básico

Contributo da utilização dos recursos educativos digitais no 1.º Ciclo do Ensino Básico

O estudo foi concretizado numa turma do 1.º ano de escolaridade do 1.º CEB, constituída por 27 alunos, numa Escola EB1, onde se realizou a Prática de Ensino Supervisionada (PES), no âmbito do Mestrado em Educação Pré-Escolar e Ensino do 1.º CEB.. O objetivo da investigação pretendeu averiguar quais os potenciais contributos que a utilização complementar de um recurso em formato digital pode contribuir para melhorar a motivação e o envolvimento dos alunos no sentido de promover mais e melhores aprendizagens. Relativamente ao tipo de investigação, optou-se por uma metodologia de natureza qualitativa que recaiu numa investigação-ação. Como técnicas de recolha de dados foram utilizadas as notas de campo, a observação participante, a entrevista semiestruturada, o inquérito por questionário e os registos fotográficos. Nesta recolha de dados houve a participação direta dos alunos da turma, da Orientadora Cooperante, do «Par Pedagógico» e dos professores titulares de turma da Escola EB1 Quinta da Granja. Os resultados obtidos após a análise e tratamento dos dados, permitiram concluir que ao utilizar este RED os alunos demonstraram terem adquirido aprendizagens mais significativas, pelo facto de se terem potenciado níveis de maior interesse, empenho, motivação, envolvimento e espírito de iniciativa no decorrer das atividades propostas.

An Estimation of Distribution Algorithm for Nurse Scheduling

Schedules can be built in a similar way to a human scheduler by using a set of rules that involve domain knowledge. This paper presents an Estimation of Distribution Algorithm (EDA) for the nurse scheduling problem, which involves choosing a suitable scheduling rule from a set for the assignment of each nurse. Unlike previous work that used Genetic Algorithms (GAs) to implement implicit learning, the learning in the proposed algorithm is explicit, i.e. we identify and mix building blocks directly. The EDA is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, a new rule string has been obtained. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed approach might be suitable for other scheduling problems.

Data warehouse para el Centro de Documentación Regional "Juan Bautista Vázquez"

El proceso de toma de decisiones en las bibliotecas universitarias es de suma importancia, sin embargo, se encuentra complicaciones como la gran cantidad de fuentes de datos y los grandes volúmenes de datos a analizar. Las bibliotecas universitarias están acostumbradas a producir y recopilar una gran cantidad de información sobre sus datos y servicios. Las fuentes de datos comunes son el resultado de sistemas internos, portales y catálogos en línea, evaluaciones de calidad y encuestas. Desafortunadamente estas fuentes de datos sólo se utilizan parcialmente para la toma de decisiones debido a la amplia variedad de formatos y estándares, así como la falta de métodos eficientes y herramientas de integración. Este proyecto de tesis presenta el análisis, diseño e implementación del Data Warehouse, que es un sistema integrado de toma de decisiones para el Centro de Documentación Juan Bautista Vázquez. En primer lugar se presenta los requerimientos y el análisis de los datos en base a una metodología, esta metodología incorpora elementos claves incluyendo el análisis de procesos, la calidad estimada, la información relevante y la interacción con el usuario que influyen en una decisión bibliotecaria. A continuación, se propone la arquitectura y el diseño del Data Warehouse y su respectiva implementación la misma que soporta la integración, procesamiento y el almacenamiento de datos. Finalmente los datos almacenados se analizan a través de herramientas de procesamiento analítico y la aplicación de técnicas de Bibliomining ayudando a los administradores del centro de documentación a tomar decisiones óptimas sobre sus recursos y servicios.

'Bayesian Optimisation for Nurse Scheduling'

A Bayesian optimisation algorithm for a nurse scheduling problem is presented, which involves choosing a suitable scheduling rule from a set for each nurse's assignment. When a human scheduler works, he normally builds a schedule systematically following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not yet completed, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this paper, we design a more human-like scheduling algorithm, by using a Bayesian optimisation algorithm to implement explicit learning from past solutions. A nurse scheduling problem from a UK hospital is used for testing. Unlike our previous work that used Genetic Algorithms to implement implicit learning [1], the learning in the proposed algorithm is explicit, i.e. we identify and mix building blocks directly. The Bayesian optimisation algorithm is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, new rule strings have been obtained. Sets of rule strings are generated in this way, some of which will replace previous strings based on fitness. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. For clarity, consider the following toy example of scheduling five nurses with two rules (1: random allocation, 2: allocate nurse to low-cost shifts). In the beginning of the search, the probabilities of choosing rule 1 or 2 for each nurse is equal, i.e. 50%. After a few iterations, due to the selection pressure and reinforcement learning, we experience two solution pathways: Because pure low-cost or random allocation produces low quality solutions, either rule 1 is used for the first 2-3 nurses and rule 2 on remainder or vice versa. In essence, Bayesian network learns 'use rule 2 after 2-3x using rule 1' or vice versa. It should be noted that for our and most other scheduling problems, the structure of the network model is known and all variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus, learning can amount to 'counting' in the case of multinomial distributions. For our problem, we use our rules: Random, Cheapest Cost, Best Cover and Balance of Cost and Cover. In more detail, the steps of our Bayesian optimisation algorithm for nurse scheduling are: 1. Set t = 0, and generate an initial population P(0) at random; 2. Use roulette-wheel selection to choose a set of promising rule strings S(t) from P(t); 3. Compute conditional probabilities of each node according to this set of promising solutions; 4. Assign each nurse using roulette-wheel selection based on the rules' conditional probabilities. A set of new rule strings O(t) will be generated in this way; 5. Create a new population P(t+1) by replacing some rule strings from P(t) with O(t), and set t = t+1; 6. If the termination conditions are not met (we use 2000 generations), go to step 2. Computational results from 52 real data instances demonstrate the success of this approach. They also suggest that the learning mechanism in the proposed approach might be suitable for other scheduling problems. Another direction for further research is to see if there is a good constructing sequence for individual data instances, given a fixed nurse scheduling order. If so, the good patterns could be recognized and then extracted as new domain knowledge. Thus, by using this extracted knowledge, we can assign specific rules to the corresponding nurses beforehand, and only schedule the remaining nurses with all available rules, making it possible to reduce the solution space. Acknowledgements The work was funded by the UK Government's major funding agency, Engineering and Physical Sciences Research Council (EPSRC), under grand GR/R92899/01. References [1] Aickelin U, "An Indirect Genetic Algorithm for Set Covering Problems", Journal of the Operational Research Society, 53(10): 1118-1126,

As tecnologias de informação e comunicação (TIC) como ferramentas cognitivas

A utilização das Tecnologias da Informação e Comunicação (TIC) como ferramentas cognitivas foi o propósito deste projeto. A problemática da introdução das TIC na sala de aula numa perspetiva construtivista levou-nos ao nosso estudo empírico, onde utilizamos uma metodologia de investigação quantitativa e qualitativa numa triangulação que nos pareceu mais adequada a este estudo. Como método foi adotado o estudo de caso e utilizamos como fontes de recolha de dados o inquérito por questionário a professores e alunos e uma entrevista a um especialista na área das TIC. O resultado do estudo efetuado permitiu-nos verificar que da parte dos docentes existe uma necessidade de formação e informação na área das TIC como ferramentas cognitivas, mais partilha de boas práticas e também mais utilização de meios tecnológicos na sua ação de ensino e aprendizagem. Quanto aos alunos, pensamos que com a mudança de atitude dos professores, mudaremos também a sua forma de utilizar as TIC na sua aprendizagem. Na operacionalização do plano de ação, criamos três etapas de trabalho: a) Oficina de Formação, b) Informação e c) promoção de atividades online. Pensamos que com este plano daremos resposta à nossa questão de partida, e criaremos um ambiente de ensino/aprendizagem onde as TIC se transformem em ferramentas cognitivas de apoio à construção das aprendizagens significativas dos alunos.

'A Bayesian Optimisation Algorithm for the Nurse Scheduling Problem'

Abstract- A Bayesian optimization algorithm for the nurse scheduling problem is presented, which involves choosing a suitable scheduling rule from a set for each nurse's assignment. Unlike our previous work that used GAs to implement implicit learning, the learning in the proposed algorithm is explicit, i.e. eventually, we will be able to identify and mix building blocks directly. The Bayesian optimization algorithm is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, a new rule string has been obtained. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed approach might be suitable for other scheduling problems.

Toimittajayhteistyö rakennusalalla: Yhteistyön kehittäminen ja haasteet nykypäivän rakennusyhtiössä

Tämän kandidaatin tutkielman tarkoituksena on perehtyä suomalaisen rakennusyhtiön toimittajayhteistyön nykytilaan, yhteistyön tiivistämiseen liittyviin mahdollisuuksiin ja haasteisiin sekä toimittajakannan segmentointiin. Työ pyrkii antamaan kokonaiskuvan yrityksen yhteistyösuhteiden tilasta sekä muodostamaan näkemyksiä mahdollisista ongelmakohdista. Näin ollen pyritään tarjoamaan yritykselle myös mahdollisuus tarttua toimittajasuhteisiin liittyviin asioihin, jotka eivät ole vielä yrityksen haluamalla tasolla. Tutkimuksen tulokset perustuvat aikaisemmista tutkimuksista muodostuvaan teoriaosuuteen sekä haastatteluiden avulla kerättyyn empiriaosioon. Haastattelut on suoritettu puolistrukturoituina ja materiaali on analysoitu teemoittelua hyödyntäen. Tutkimus osoitti, että kehittämällä toimittajasuhteita oikeanlaisten toimittajien kanssa, on yrityksen mahdollista saavuttaa paljon kaivattua kilpailuetua. Hyötyjen tavoitteleminen ei kuitenkaan ole ongelmatonta projektiluontoisella rakennusalalla, jossa toimittajasuhteista on haastavaakehittää jatkuvia suhteita vaihtuvien projektien vuoksi. Kohdeyrityksessä pyritään jo nyt hyödyntämään toimittajayhteistyöstä kumpuavia etuja, mutta paljon on vielä tekemättä. Nykyään hinta vaikuttaa vielä liikaa toimittajavalintaan, yhteistyön kehittämiselle ei ole olemassa tunnettua järjestelmää ja yhteistyösuhteiden merkitystä tulisi läpi organisaation painottaa vahvemmin. Yrityksen henkilöstö tiedostaa haasteet, joita yhteistyösuhteiden kehittämiseen liittyy, mutta yleinen mielipide on kuitenkin se, että toimittajasuhteiden kehittäminen on kannattavaa haasteellisuudesta huolimatta. Tulevaisuudessa yrityksen kannattaa yhä vahvemmin lähteä tavoittelemaan yhteistyösuhteita kehittämällä saavutettavissa olevia kilpailullisia etuja, koska osaamista ja resursseja siihen löytyy. Ennen kehittämisprosessia johdon kannattaa kuitenkin kiinnittää huomiota tutkimuksessa ilmenneisiin eriäviin mielipiteisiin muun muassa henkilösuhteiden vaikutuksesta ja organisaatiotason ymmärryksestä, jotta toimittajayhteistyön hyödyt saadaan realisoitumaan halutulla tavalla.

An Estimation of Distribution Algorithm for Nurse Scheduling

Schedules can be built in a similar way to a human scheduler by using a set of rules that involve domain knowledge. This paper presents an Estimation of Distribution Algorithm (EDA) for the nurse scheduling problem, which involves choosing a suitable scheduling rule from a set for the assignment of each nurse. Unlike previous work that used Genetic Algorithms (GAs) to implement implicit learning, the learning in the proposed algorithm is explicit, i.e. we identify and mix building blocks directly. The EDA is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, a new rule string has been obtained. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed approach might be suitable for other scheduling problems.

Le cycle hydrosocial et la fluoration de l'eau : la production des eaux fluorées

La fluoration artificielle de l’eau est une méthode employée en tant que moyen de prévention de la carie dentaire. Il s’agit d’un traitement de l’eau dont le but est d’ajuster de façon « optimale » la concentration en fluorure dans l’eau potable pour la prévention de la carie dentaire, par l’ajout d’un composé fluoré. La fluoration de l’eau fait l’objet d’un débat de société depuis le début des années 1950. La théorie du cycle hydrosocial nous invite à réfléchir sur la manière dont l’eau et la société se définissent et se redéfinissent mutuellement dans le temps et dans l’espace. Cette théorie nous permet d’aborder l’étude du sujet de la fluoration avec une nouvelle perspective d’analyse. Il y a peu d’études en sciences sociales qui portent sur le sujet de la fluoration, généralement abordé d’un point de vue des sciences de la santé. Nous proposons de décrire le processus de production des eaux fluorées dans un contexte hydrosocial. Ce mémoire est structuré en quatre chapitres. Nous commençons par familiariser le lecteur avec la théorie du cycle hydrosocial. Ensuite, nous faisons une mise en contexte de la fluoration de l’eau, d’une part en présentant un état des lieux, et d’autre part en présentant ce en quoi consiste la pratique de la fluoration de l’eau. Après avoir familiarisé le lecteur avec les thèmes généraux concernant la fluoration de l’eau, nous proposons de reconstituer une histoire hydrosociale de la fluoration. Cette histoire nous permet de mettre en évidence les relations hydrosociales desquelles découle la production des eaux fluorées. L’histoire hydrosociale de la fluoration comporte une phase contemporaine que nous abordons en présentant les principales idées de l’opposition à la fluoration artificielle de l’eau à l’aide notamment d’une analyse iconographique d’images portant sur le thème de la fluoration. Finalement, nous discutons des implications de la théorie du cycle hydrosocial pour étudier la problématique de la fluoration.

Microfluidics-based single-cell functional proteomics microchip for portraying protein signal transduction networks within the framework of physicochemical principles, with applications in fundamental and translational cancer research

Single-cell functional proteomics assays can connect genomic information to biological function through quantitative and multiplex protein measurements. Tools for single-cell proteomics have developed rapidly over the past 5 years and are providing unique opportunities. This thesis describes an emerging microfluidics-based toolkit for single cell functional proteomics, focusing on the development of the single cell barcode chips (SCBCs) with applications in fundamental and translational cancer research.

The microchip designed to simultaneously quantify a panel of secreted, cytoplasmic and membrane proteins from single cells will be discussed at the beginning, which is the prototype for subsequent proteomic microchips with more sophisticated design in preclinical cancer research or clinical applications. The SCBCs are a highly versatile and information rich tool for single-cell functional proteomics. They are based upon isolating individual cells, or defined number of cells, within microchambers, each of which is equipped with a large antibody microarray (the barcode), with between a few hundred to ten thousand microchambers included within a single microchip. Functional proteomics assays at single-cell resolution yield unique pieces of information that significantly shape the way of thinking on cancer research. An in-depth discussion about analysis and interpretation of the unique information such as functional protein fluctuations and protein-protein correlative interactions will follow.

The SCBC is a powerful tool to resolve the functional heterogeneity of cancer cells. It has the capacity to extract a comprehensive picture of the signal transduction network from single tumor cells and thus provides insight into the effect of targeted therapies on protein signaling networks. We will demonstrate this point through applying the SCBCs to investigate three isogenic cell lines of glioblastoma multiforme (GBM).

The cancer cell population is highly heterogeneous with high-amplitude fluctuation at the single cell level, which in turn grants the robustness of the entire population. The concept that a stable population existing in the presence of random fluctuations is reminiscent of many physical systems that are successfully understood using statistical physics. Thus, tools derived from that field can probably be applied to using fluctuations to determine the nature of signaling networks. In the second part of the thesis, we will focus on such a case to use thermodynamics-motivated principles to understand cancer cell hypoxia, where single cell proteomics assays coupled with a quantitative version of Le Chatelier's principle derived from statistical mechanics yield detailed and surprising predictions, which were found to be correct in both cell line and primary tumor model.

The third part of the thesis demonstrates the application of this technology in the preclinical cancer research to study the GBM cancer cell resistance to molecular targeted therapy. Physical approaches to anticipate therapy resistance and to identify effective therapy combinations will be discussed in detail. Our approach is based upon elucidating the signaling coordination within the phosphoprotein signaling pathways that are hyperactivated in human GBMs, and interrogating how that coordination responds to the perturbation of targeted inhibitor. Strongly coupled protein-protein interactions constitute most signaling cascades. A physical analogy of such a system is the strongly coupled atom-atom interactions in a crystal lattice. Similar to decomposing the atomic interactions into a series of independent normal vibrational modes, a simplified picture of signaling network coordination can also be achieved by diagonalizing protein-protein correlation or covariance matrices to decompose the pairwise correlative interactions into a set of distinct linear combinations of signaling proteins (i.e. independent signaling modes). By doing so, two independent signaling modes – one associated with mTOR signaling and a second associated with ERK/Src signaling have been resolved, which in turn allow us to anticipate resistance, and to design combination therapies that are effective, as well as identify those therapies and therapy combinations that will be ineffective. We validated our predictions in mouse tumor models and all predictions were borne out.

In the last part, some preliminary results about the clinical translation of single-cell proteomics chips will be presented. The successful demonstration of our work on human-derived xenografts provides the rationale to extend our current work into the clinic. It will enable us to interrogate GBM tumor samples in a way that could potentially yield a straightforward, rapid interpretation so that we can give therapeutic guidance to the attending physicians within a clinical relevant time scale. The technical challenges of the clinical translation will be presented and our solutions to address the challenges will be discussed as well. A clinical case study will then follow, where some preliminary data collected from a pediatric GBM patient bearing an EGFR amplified tumor will be presented to demonstrate the general protocol and the workflow of the proposed clinical studies.

A Bayesian optimization algorithm for the nurse scheduling problem

A Bayesian optimization algorithm for the nurse scheduling problem is presented, which involves choosing a suitable scheduling rule from a set for each nurse’s assignment. Unlike our previous work that used GAs to implement implicit learning, the learning in the proposed algorithm is explicit, i.e. eventually, we will be able to identify and mix building blocks directly. The Bayesian optimization algorithm is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, a new rule string has been obtained. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed approach might be suitable for other scheduling problems.

Implicações da reforma da previdência na seguridade social brasileira

A Seguridade Social aprovada na Constituição Brasileira de 1988 não foi implementada conforme previsto na Carta Magna. As sucessivas reformas do Estado e, sobretudo, da previdência social,1 implementadas ao longo da década de 1990, justificadas sob a alegação de um suposto déficit entre receita e despesa, vêm contribuindo para descaracterizá-la enquanto sistema de proteção social, além de favorecer a fragmentação das políticas sociais que a integram: previdência, saúde e assistência. Ao tratar a previdência como seguro e não como política social, estas reformas tendem a minar e corroer as bases conceituais e financeiras da seguridade social, solapando a possibilidade de sua consolidação como propriedade social. __________________________________________________________________________________________________ ABSTRACT

Análise comparativa entre as propriedades do gesso obtido de rejeito da produção de sal e gesso comerciais

The environmental impacts, caused by the solid residues generation, are an often quoted concern nowadays. Some of these residues, which are originated from different human activities, can be fully reused, reducing the effects of the poor waste management on the environment. During the salt production process, the first formed crystals are discarded as industrial waste. This is mainly made of gypsum that is a calcium sulfate dihydrate (CaSO4.2H2O). The gypsum in question may go through a calcination process due to the plaster (CaSO4.0,5H2O) production and then the application on the cement industry. Considering the necessity of development and application for these industrial wastes, this paper aims to analyze the plaster, called Salgesso, from the gypsum that was generated during the salt production, and its use viability on the civil construction industry in order to create environmental and economical benefits. For characterization, the following experiments were performed: X-ray Fluorescence (XRF), X-ray Diffraction (XRD), thermal analysis (TG/DTG) and Scanning Electron Microscopy (SEM) with EDS. The following tests were also performed to obtain the mechanical characteristics: Thinness Modulus, Unit Mass, Setting Time and Compressive Resistance. Three commercial plasters used on civil construction were taken as references. All of these tests were performed according to the current standards. It was noticed that although there were some conflicting findings between the salt and commercial plasters in all of the studied properties, the Salgesso has its values within the standard limits. However, there is the possibility to improve them by doing a more effective calcination process. Three commercial plasters, used in construction, were used as reference material. All tests were performed according to standards in force. It was observed that although some tests present conflicting findings between the salt and gypsum plasters commercial properties in all of the studied Salgesso have values within the limits imposed by the standard, but can be improved simply by calcination process more effective

Do livro ao fantoche, reciclando palavras. Desenvolvimento da comunicação oral

Dissertação de Mestrado, Educação Pré-Escolar, Escola Superior de Educação e Comunicação, Universidade do Algarve, 2016