Supply chain operations of construction industry including road projects in disaster regions results in exceeding project budget and timelines. In road construction projects, supply chain with poor performance can affect efficiency and completion time of the project. This is also the case of the road projects in disaster areas. Disaster areas consider both natural and man-made disasters. Few examples of disaster zones are; Pakistan, Afghanistan, Iraq, Sri Lanka, India, Japan, Haiti and many other countries with similar environments. The key factors affecting project performance and execution are insecurity, uncertainties in demand and supply, poor communication and technology, poor infrastructure, lack of political and government will, unmotivated organizational staff, restricted accessibility to construction materials, legal hitches, multiple challenges of hiring labour force and exponential construction rates due to high risk environment along with multiple other factors. The managers at all tiers are facing challenges of overrunning time and budget of supply chain operations during planning as well as execution phase of development projects. The aim of research is to develop a Multiple Linear Regression Model (MLRM) and a Rule Based Decision Support System by incorporating various factors affecting supply chain management of road projects in disaster areas in the order of importance. This knowledge base (KB) (importance / coefficient of each factor) will assist infrastructure managers (road projects) and practitioners in disaster regions in decision making to minimize the effect of each factor which will further help them in project improvement. Conduct of Literature Review in the fields of disaster areas, supply chain operational environments of road project, statistical techniques, Artificial Intelligence (AI) and types of research approaches has provided deep insights to the researchers. An initial questionnaire was developed and distributed amongst participants as pilot project and consequently results were analysed. The results’ analysis enabled the researcher to extract key variables impacting supply chain performance of road project. The results of questionnaire analysis will facilitate development of Multiple Linear Regression Model, which will eventually be verified and validated with real data from actual environments. The development of Multiple Linear Regression Model and Rule Based Decision Support System incorporating all factors which affect supply chain performance of road projects in disastrous regions is the most vital contribution to the research. The significance and novelty of this research is the methodology developed that is the integration of those different methods which will be employed to measure the SCM performance of road projects in disaster areas.

Indiana University-Purdue University Indianapolis (IUPUI) / During a 2011 summer dominated by headlines about an earthquake and a hurricane along the East Coast, extreme heat that silently killed scores of Americans largely went unnoticed by the media and public. However, despite a violent spasm of tornadic activity that claimed over 500 lives during the spring of the same year, heat-related mortality annually ranks as the top cause of death incident to weather. Two major data groups used in researching vulnerability to extreme heat events (EHE) include socioeconomic indicators of risk and factors incident to urban living environments. Socioeconomic determinants such as household income levels, age, race, and others can be analyzed in a geographic information system (GIS) when formatted as vector data, while environmental factors such as land surface temperature are often measured via raster data retrieved from satellite sensors. The current research sought to combine the insights of both types of data in a comprehensive examination of heat susceptibility using knowledge-based classification. The use of knowledge classifiers is a non-parametric approach to research involving the creation of decision trees that seek to classify units of analysis by whether they meet specific rules defining the phenomenon being studied. In this extreme heat vulnerability study, data relevant to the deadly July 1995 heat wave in Chicago’s Cook County was incorporated into decision trees for 13 different experimental conditions. Populations vulnerable to heat were identified in five of the 13 conditions, with predominantly low-income African-American communities being particularly at-risk. Implications for the results of this study are given, along with direction for future research in the area of extreme heat event vulnerability.

I Sverige har en negativ utveckling skett i vissa geografiskt avgränsade bostadsområden, så kallade utsatta områden. Att minska brottsligheten och öka tryggheten i utsatta områden är av stor vikt. Det är därför relevant att undersöka huruvida kommuner med utsatta områden bedriver ett programtroget brottsförebyggande arbete. Syftet med studien var därmed att undersöka hur väl tre kommuner (Helsingborg, Kristianstad och Landskrona) har implementerat den brottsförebyggande och trygghetsskapande metoden Effektiv samordning förtrygghet (EST), samt vilka faktorer som har påverkat implementeringprocessen. De två frågeställningarna var 1. I vilken utsträckning har de tre skånska kommunerna Helsingborg, Kristianstad och Landskrona implementerat EST? 2. Vilka faktorer har påverkat kommunernas implementeringsprocess av EST? Genom tematisk analys av semistrukturerade intervjuer har studien eftersträvat en inblick i flera aktörers (N=9) arbetsprocess kring och implementering av EST-metoden. Resultatet från intervjuerna indikerar att EST till viss del är implementerad i de tre kommunerna då kommunerna arbetar med samtliga steg i EST och metoden har bidragit till en god samverkan. Det framgår dock även att det finns ett behov av utbildning kring EST-metoden och ett mer metodtroget arbete. Av resultatet framgår även att ett antal faktorer har påverkat implementeringen av EST: metodens komplexitet; kommunikation och förankring av metoden; aktörernas lämplighet; aktörernas personliga egenskaper; graden av tillgänglig information om metoden; aktörernas delaktighet; strukturella omständigheter; metodtrogenhet samt uppföljning och återkoppling. Förhoppningen är att lagen “Kommuner mot brott” tydliggör hur arbetet ska se ut och fördelas. Resultatet kan således ge en vägledning för andra kommuner gällande vilka faser i implementeringen som kan utgöra en utmaning samt vilka faktorer som påverkar implementeringsprocessen. / In Sweden, a negative development has taken place in certain geographically delimited residential areas, so-called vulnerable areas. Reducing crime and increasing perceived safety and security in vulnerable areas is of great importance. It is therefore relevant to investigate whether municipalities with vulnerable areas carry out methodical crime prevention work. The purpose of the study was thus to investigate how well three municipalities (Helsingborg, Kristianstad and Landskrona) have implemented the crime prevention method Effective Coordination for Safety (EST), and which factors have affected the implementation process. The two main issues were 1. To what extent have the three Scanian municipalities of Helsingborg, Kristianstad and Landskrona implemented EST? 2. What factors have influenced the municipalities' implementation process of EST? Through thematic analysis of semi-structured interviews, the study has sought an insight into several actors' (N = 9) work process regarding, and implementation of, the method. The results indicate that EST is to some extent implemented, as the municipalities work with all steps in EST, and the method has contributed to a good collaboration amongst actors. However, it is also evident that there is a need for education regarding the method and more extensive methodical work. In addition, the results show that several factors have influenced the implementation of EST: the method’s complexity; communication and anchoring of the method; suitability of the actors; personal characteristics of the actors; information available on the method; stakeholder participation; structural circumstances, as well as follow-up and feedback. It is to be hoped that the law "Municipalities against crime" clarifies how the crime preventative work should be carried out and allocated. The results can thus provide guidance for other municipalities regarding which phases in the implementation might constitute a challenge and which factors affect the implementation process.

Air intakes are complex components that are critical for the propulsion of the aircraft. The design has to consider requirements from several different departments, often contradictory. Additionally, the air intakes need to cooperate with other critical components. This makes testing of the models crucial, hence time-demanding. Design automation is a growing field which aims at minimizing repetitive work during product concept development. To follow the increasing digitalization, further investigations of design automation applied on air intakes are significant.  The application Imagine and Shape in 3D Experience CATIA handles subdivided surfaces. These surfaces are both flexible and provide a high order of continuity, which is often desired. While design automation in CATIA is well investigated, design automation in Imagine and Shape is not.  Knowledge based engineering techniques are often used to implement design automation. The methodology MOKA is frequently used when developing knowledge based engineering applications. This master thesis has followed MOKA in combination with Scrum.  The master thesis has resulted in a method to allow automation in Imagine and Shape by linking mesh nodes on subdivided surfaces to reference points that are parameterized. Further, a method for generating air intake configurations as well as the integration with a fuselage has been developed. The method includes wireframe models in Generative Shape Design, subdivided surfaces in Imagine and Shape, scripts in EKL as well as UserForm and scripts in VBA. Additionally, the order of continuity for an integration between air intakes and fuselage has been analyzed using tools in 3D Experience CATIA.  A conclusion drawn is that the method for generating air intakes cannot be completely automated. Instantiation and dimension of components can be automated, but manual work is required when using tools in Imagine and Shape during the integration between the components and the fuselage.Two methods for linking mesh nodes to reference points have been identified, one manual and one semi-automatic. The automatic method saves time and mouse clicks by utilizing VBA scripts. Further, the achieved order of continuity of an integration between subdivided surfaces depends on the individual components.

No description available.

The research is conducted in the area of Software Engineering, with emphasis on the design phase of the Software Development Life Cycle (SDLC). The object-oriented paradigm is the point of departure. The investigation deals with the problem of creating support for the design phase of object-oriented system development. This support must be able to guide the system designer through the design process, according to a sound design method, highlight opportunities for prototyping and point out where to re-iterate a design step, for example. A solution is proposed in the form of a knowledge-based support system. In the prototype this support guides a designer partially through the first step of the System Design task for object-oriented design. The intention is that the knowledge-based system should capture the know-how of an expert system designer and assist an inexperienced system designer to create good designs. / Computing / M. Sc. (Information Systems)

This doctoral thesis presents the basic and the main developments of the theories of the firm rooted in Transaction Cost Theory (TCT). Since the article of Coase on the nature of the firm, this question has been elaborated by number of economists. In my point of view, I would like to introduce the last theory developed by the representative of the school called New Institutional Economics. The theory concentrates on the role of institutions. We distinguish institutions formal and informal. The formal are laws, constitution, regulations, contracts and other written rules. The informal don't require a written form and they represent informal restrictions such as rules of behaviour, conventions, traditions and habits. The main purpose of the institutions in their various forms is mainly to protect property rights, enforce voluntary contracts and establish the physical and regulatory infrastructure to facilitate economic activity. Generally, we can call them the rules of the game. They inform us about possible economic behaviour and give us basic restrictions in this sense. First -- the theoretical part of this thesis, we can find a simple model with human asset specificity based on TCT. The main proposition is that transactions with a high level of asset specificity are more probably internalised because the firm handles better such transactions compared to the market, even if asset specificity increases the cost of coordination in the firm. If the hierarchy, for example through the formation of routines, may enhance the efficiency compared to the market, we can modify the previous model. The model developed according to a Knowledge-based view assumes that asset specificity reduces transaction costs inside the firm and increases transaction costs on the market. In the conclusion of the theoretical part is discussed the reply on first formulate hypothesis if the TCT is still compatible with a newer Capability and Knowledge based view. In my opinion based on the presented arguments and views, both theories are compatible, they can answer more questions and explain more issues. The last step links the aforementioned theories into one Theory of firm boundaries. In the practical part of the thesis, I bring basic arguments about the institutional framework quality in the Czech Republic. These arguments rely on the latest studies of the international institutions such The Transparency International, The Heritage Foundation, The Kurtzman Group and mainly The World Bank. Studies reveal certain weak arrangements concerning for example the number of procedures when starting a business, complicated construction permits, rigidity of working hours, time-consuming and administrative demanding tax system, duration of judicial process which results in ineffective contract enforcement and mainly poor protection of property rights. Taking these disclosures into account we have to say that emergent transaction costs are enormous. These transaction costs influence negatively the competitiveness of the firms and the whole Czech economy. Therefore a negative response to the second hypothesis relating to the quality of the institutional framework in the Czech Republic.

Sistemas de Recomendação (SR) são softwares capazes de sugerir itens aos usuários com base no histórico de interações de usuários ou por meio de métricas de similaridade que podem ser comparadas por item, usuário ou ambos. Existem diferentes tipos de SR e dentre os que despertam maior interesse deste trabalho estão: SR baseados em conteúdo; SR baseados em conhecimento; e SR baseado em filtro colaborativo. Alcançar resultados adequados às expectativas dos usuários não é uma meta simples devido à subjetividade inerente ao comportamento humano, para isso, SR precisam de soluções eficientes e eficazes para: modelagem dos dados que suportarão a recomendação; recuperação da informação que descrevem os dados; combinação dessas informações dentro de métricas de similaridade, popularidade ou adequabilidade; criação de modelos descritivos dos itens sob recomendação; e evolução da inteligência do sistema de forma que ele seja capaz de aprender a partir da interação com o usuário. A tomada de decisão por um sistema de recomendação é uma tarefa complexa que pode ser implementada a partir da visão de áreas como inteligência artificial e mineração de dados. Dentro da área de inteligência artificial há estudos referentes ao método de raciocínio baseado em casos e da recomendação baseada em casos. No que diz respeito à área de mineração de dados, os SR podem ser construídos a partir de modelos descritivos e realizar tratamento de dados textuais, constituindo formas de criar elementos para compor uma recomendação. Uma forma de minimizar os pontos fracos de uma abordagem, é a adoção de aspectos baseados em uma abordagem híbrida, que neste trabalho considera-se: tirar proveito dos diferentes tipos de SR; usar técnicas de resolução de problemas; e combinar recursos provenientes das diferentes fontes para compor uma métrica unificada a ser usada para ranquear a recomendação por relevância. Dentre as áreas de aplicação dos SR, destaca-se a recomendação de notícias, sendo utilizada por um público heterogêneo, amplo e exigente por relevância. Neste contexto, a presente pesquisa apresenta uma abordagem híbrida para recomendação de notícias construída por meio de uma arquitetura implementada para provar os conceitos de um sistema de recomendação. Esta arquitetura foi validada por meio da utilização de um corpus de notícias e pela realização de um experimento online. Por meio do experimento foi possível observar a capacidade da arquitetura em relação aos requisitos de um sistema de recomendação de notícias e também confirmar a hipótese no que se refere à privilegiar recomendações com base em similaridade, popularidade, diversidade, novidade e serendipidade. Foi observado também uma evolução nos indicadores de leitura, curtida, aceite e serendipidade conforme o sistema foi acumulando histórico de preferências e soluções. Por meio da análise da métrica unificada para ranqueamento foi possível confirmar sua eficácia ao verificar que as notícias melhores colocadas no ranqueamento foram as mais aceitas pelos usuários / Recommendation Systems (RS) are software capable of suggesting items to users based on the history of user interactions or by similarity metrics that can be compared by item, user, or both. There are different types of RS and those which most interest in this work are content-based, knowledge-based and collaborative filtering. Achieving adequate results to user\'s expectations is a hard goal due to the inherent subjectivity of human behavior, thus, the RS need efficient and effective solutions to: modeling the data that will support the recommendation; the information retrieval that describes the data; combining this information within similarity, popularity or suitability metrics; creation of descriptive models of the items under recommendation; and evolution of the systems intelligence to learn from the user\'s interaction. Decision-making by a RS is a complex task that can be implemented according to the view of fields such as artificial intelligence and data mining. In the artificial intelligence field there are studies concerning the method of case-based reasoning that works with the principle that if something worked in the past, it may work again in a new similar situation the one in the past. The case-based recommendation works with structured items, represented by a set of attributes and their respective values (within a ``case\'\' model), providing known and adapted solutions. Data mining area can build descriptive models to RS and also handle, manipulate and analyze textual data, constituting one option to create elements to compose a recommendation. One way to minimize the weaknesses of an approach is to adopt aspects based on a hybrid solution, which in this work considers: taking advantage of the different types of RS; using problem-solving techniques; and combining resources from different sources to compose a unified metric to be used to rank the recommendation by relevance. Among the RS application areas, news recommendation stands out, being used by a heterogeneous public, ample and demanding by relevance. In this context, the this work shows a hybrid approach to news recommendations built through a architecture implemented to prove the concepts of a recommendation system. This architecture has been validated by using a news corpus and by performing an online experiment. Through the experiment it was possible to observe the architecture capacity related to the requirements of a news recommendation system and architecture also related to privilege recommendations based on similarity, popularity, diversity, novelty and serendipity. It was also observed an evolution in the indicators of reading, likes, acceptance and serendipity as the system accumulated a history of preferences and solutions. Through the analysis of the unified metric for ranking, it was possible to confirm its efficacy when verifying that the best classified news in the ranking was the most accepted by the users

Made available in DSpace on 2016-04-25T16:44:59Z (GMT). No. of bitstreams: 1 Gilmar Lima Hilario.pdf: 4391683 bytes, checksum: 9b2bffb0c354d5d73021c08c4604cee2 (MD5) Previous issue date: 2007-10-24 / The recognition that internal factors of the organization, among them knowledge, can act much more on its performance than the external factors, is collaborating to consolidate the use of new approaches for enterprise administration. Among these, are pointed out in this paper the Knowledge-Based View (KBV) and the Resource-Based View (RBV). In the information technology (IT) sector, in which the knowledge is used in an intensive way, the adoption of those approaches can be expressed through the increasing of the knowledge to the condition of an important asset for the company. Consequently, the adoption of knowledge management practices aims at producing better performance results and quality in the processes of products generation and services offerings. In this paper - drewed out from a Case Study with a Research-Action carried through in a business unit of a company supplier of specialized IT services the evaluation was focused on the potential contributions and main challenges for the adoption of knowledge management practices. As result, a knowledge management method was produced, whose application can contribute for knowledge mapping and dissemination in projects of same scope / O reconhecimento de que fatores internos da organização, entre eles o conhecimento, exercem uma influência significativamente maior sobre seu desempenho do que os fatores externos estão colaborando para consolidar a utilização de novas abordagens para gestão empresarial. Entre estas abordagens destacam-se neste trabalho a Visão da Empresa Baseado no Conhecimento (KBV Knowledge-Based View) e a Visão da Empresa Baseada em Recursos (RBV Resource-Based View). No setor de tecnologia da informação TI, em que o conhecimento é utilizado de maneira intensiva, a adoção dessas abordagens podem se traduzir na elevação do conhecimento à condição de importante ativo da empresa. Por conseqüência, a adoção de praticas de gestão do conhecimento tem como objetivo produzir melhores resultados de desempenho e qualidade nos processos de geração de produtos e prestação de serviços. Neste trabalho a partir de um estudo de caso com pesquisa ação - realizada em uma unidade de negócios de uma empresa prestadora de serviços especializados de TI busca-se avaliar as potenciais contribuições e principais desafios inerentes a adoção de práticas de gestão do conhecimento. Como resultado, produziu-se um método para gestão do conhecimento cuja aplicação pode trazer contribuições para mapeamento e disseminação de conhecimentos em projetos de mesmo teor

Sistemas de Recomendação (SR) são softwares capazes de sugerir itens aos usuários com base no histórico de interações de usuários ou por meio de métricas de similaridade que podem ser comparadas por item, usuário ou ambos. Existem diferentes tipos de SR e dentre os que despertam maior interesse deste trabalho estão: SR baseados em conteúdo; SR baseados em conhecimento; e SR baseado em filtro colaborativo. Alcançar resultados adequados às expectativas dos usuários não é uma meta simples devido à subjetividade inerente ao comportamento humano, para isso, SR precisam de soluções eficientes e eficazes para: modelagem dos dados que suportarão a recomendação; recuperação da informação que descrevem os dados; combinação dessas informações dentro de métricas de similaridade, popularidade ou adequabilidade; criação de modelos descritivos dos itens sob recomendação; e evolução da inteligência do sistema de forma que ele seja capaz de aprender a partir da interação com o usuário. A tomada de decisão por um sistema de recomendação é uma tarefa complexa que pode ser implementada a partir da visão de áreas como inteligência artificial e mineração de dados. Dentro da área de inteligência artificial há estudos referentes ao método de raciocínio baseado em casos e da recomendação baseada em casos. No que diz respeito à área de mineração de dados, os SR podem ser construídos a partir de modelos descritivos e realizar tratamento de dados textuais, constituindo formas de criar elementos para compor uma recomendação. Uma forma de minimizar os pontos fracos de uma abordagem, é a adoção de aspectos baseados em uma abordagem híbrida, que neste trabalho considera-se: tirar proveito dos diferentes tipos de SR; usar técnicas de resolução de problemas; e combinar recursos provenientes das diferentes fontes para compor uma métrica unificada a ser usada para ranquear a recomendação por relevância. Dentre as áreas de aplicação dos SR, destaca-se a recomendação de notícias, sendo utilizada por um público heterogêneo, amplo e exigente por relevância. Neste contexto, a presente pesquisa apresenta uma abordagem híbrida para recomendação de notícias construída por meio de uma arquitetura implementada para provar os conceitos de um sistema de recomendação. Esta arquitetura foi validada por meio da utilização de um corpus de notícias e pela realização de um experimento online. Por meio do experimento foi possível observar a capacidade da arquitetura em relação aos requisitos de um sistema de recomendação de notícias e também confirmar a hipótese no que se refere à privilegiar recomendações com base em similaridade, popularidade, diversidade, novidade e serendipidade. Foi observado também uma evolução nos indicadores de leitura, curtida, aceite e serendipidade conforme o sistema foi acumulando histórico de preferências e soluções. Por meio da análise da métrica unificada para ranqueamento foi possível confirmar sua eficácia ao verificar que as notícias melhores colocadas no ranqueamento foram as mais aceitas pelos usuários / Recommendation Systems (RS) are software capable of suggesting items to users based on the history of user interactions or by similarity metrics that can be compared by item, user, or both. There are different types of RS and those which most interest in this work are content-based, knowledge-based and collaborative filtering. Achieving adequate results to user\'s expectations is a hard goal due to the inherent subjectivity of human behavior, thus, the RS need efficient and effective solutions to: modeling the data that will support the recommendation; the information retrieval that describes the data; combining this information within similarity, popularity or suitability metrics; creation of descriptive models of the items under recommendation; and evolution of the systems intelligence to learn from the user\'s interaction. Decision-making by a RS is a complex task that can be implemented according to the view of fields such as artificial intelligence and data mining. In the artificial intelligence field there are studies concerning the method of case-based reasoning that works with the principle that if something worked in the past, it may work again in a new similar situation the one in the past. The case-based recommendation works with structured items, represented by a set of attributes and their respective values (within a ``case\'\' model), providing known and adapted solutions. Data mining area can build descriptive models to RS and also handle, manipulate and analyze textual data, constituting one option to create elements to compose a recommendation. One way to minimize the weaknesses of an approach is to adopt aspects based on a hybrid solution, which in this work considers: taking advantage of the different types of RS; using problem-solving techniques; and combining resources from different sources to compose a unified metric to be used to rank the recommendation by relevance. Among the RS application areas, news recommendation stands out, being used by a heterogeneous public, ample and demanding by relevance. In this context, the this work shows a hybrid approach to news recommendations built through a architecture implemented to prove the concepts of a recommendation system. This architecture has been validated by using a news corpus and by performing an online experiment. Through the experiment it was possible to observe the architecture capacity related to the requirements of a news recommendation system and architecture also related to privilege recommendations based on similarity, popularity, diversity, novelty and serendipity. It was also observed an evolution in the indicators of reading, likes, acceptance and serendipity as the system accumulated a history of preferences and solutions. Through the analysis of the unified metric for ranking, it was possible to confirm its efficacy when verifying that the best classified news in the ranking was the most accepted by the users