Devaluation, competitiveness and new business formation in emerging countries

In September 2010, Brazil’s Finance Minister, Guido Mantega, used the term “currency war” with reference to monetary policies implemented by different countries to generate an artificial devaluation of their currency and achieve a cheaper, more competitive domestic economy that may be attractive to foreign investors. Similar cases have been documented since the 1930s Great Depression, when several countries abandoned the gold standard as backing for their currencies. More recently, a large-scale asset purchase by Japan’s Central Bank in 2013 was singled out as a strategy aimed at generating devaluation of the yen. This research uses statistics of new business formation density reported by Doing Business for 30 emerging countries in the period 2004-2011 to evaluate the impact of devaluation measured by the behavior of the real effective exchange rate (REER) on the rate of new business formation (NBF). It is determined how variables associated with competitiveness affect the relationship between devaluation and business formation. Results show that devaluation has a positive effect on NBF in the short term, which gets diluted in the long term. Countries with greater competitiveness have less dependence on devaluation to increase the number of businesses.

'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.

'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,

Crescer a par em educação : os desafios da dupla profissionalização

Relatório de Estágio apresentado à Escola Superior de Educação de Paula Frassinetti para obtenção do grau de Mestre em Educação Pré-Escolar e Ensino do 1º Ciclo do Ensino Básico,sob Orientação de Mestre Ana Maria Ambrósio Serapicos de Borda Cardoso.

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.

Recrear «el verdadero lío de la vida»: programa estético y estrategia editorial detrás del ciclo ramoniano de las novelas de la nebulosa

[es] Teniendo presente que las cuatro obras que Ramón identifica en 1947 como «novelas de la nebulosa» abarcan más de veinticinco años y se definen como ciclo a posteriori, el presente artículo propone una aproximación al ciclo «nebúlico» desde la construcción discursiva metatextual que lo rodeó (en prólogos, ensayos y epistolarios) para demostrar que nació de una voluntad de Ramón de construir para sí mismo cierta imagen social de escritor y así consolidar, desde el exilio, su figura de novelista de vanguardia. [en] Only in 1947 did Ramón define as a series his “Novels of the Nebula”, a literary cycle written over twenty five years. This paper focuses on the metatextual discourse surrounding the whole series (in prologues, essays or letters). By doing so, it aims at showing how the design of the series was born from Ramón’s desire to build a certain social image of himself as an avant-garde writer.

Docente generalista: complementaridades, diferenças e desafios de dois níveis educativos

Relatório de estágio apresentado à Escola Superior de Educação Paula Frassinetti para obtenção de grau de Mestre em Educação Pré – Escolar e 1º Ciclo do Ensino Básico

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.