943 resultados para Dynamic User Modelling


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Transport is an essential sector in modern societies. It connects economic sectors and industries. Next to its contribution to economic development and social interconnection, it also causes adverse impacts on the environment and results in health hazards. Transport is a major source of ground air pollution, especially in urban areas, and therefore contributing to the health problems, such as cardiovascular and respiratory diseases, cancer, and physical injuries. This thesis presents the results of a health risk assessment that quantifies the mortality and the diseases associated with particulate matter pollution resulting from urban road transport in Hai Phong City, Vietnam. The focus is on the integration of modelling and GIS approaches in the exposure analysis to increase the accuracy of the assessment and to produce timely and consistent assessment results. The modelling was done to estimate traffic conditions and concentrations of particulate matters based on geo-references data. A simplified health risk assessment was also done for Ha Noi based on monitoring data that allows a comparison of the results between the two cases. The results of the case studies show that health risk assessment based on modelling data can provide a much more detail results and allows assessing health impacts of different mobility development options at micro level. The use of modeling and GIS as a common platform for the integration of different assessments (environmental, health, socio-economic, etc.) provides various strengths, especially in capitalising on the available data stored in different units and forms and allows handling large amount of data. The use of models and GIS in a health risk assessment, from a decision making point of view, can reduce the processing/waiting time while providing a view at different scales: from micro scale (sections of a city) to a macro scale. It also helps visualising the links between air quality and health outcomes which is useful discussing different development options. However, a number of improvements can be made to further advance the integration. An improved integration programme of the data will facilitate the application of integrated models in policy-making. Data on mobility survey, environmental monitoring and measuring must be standardised and legalised. Various traffic models, together with emission and dispersion models, should be tested and more attention should be given to their uncertainty and sensitivity

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Generally, smart campus applications do not consider the role of the user with his/her position in a university environment, consequently irrelevant information is delivered to the users. This dissertation proposes a location-based access control model, named Smart-RBAC, extending the functionality of Role-based Access Control Model (RBAC) by including user’s location as the contextual attribute, to solve the aforementioned problem. Smart-RBAC model is designed with a focus on content delivery to the user in order to offer a feasible level of flexibility, which was missing in the existing location-based access control models. An instance of the model, derived from Liferay’s RBAC, is implemented by creating a portal application to test and validate the Smart-RBAC model. Additionally, portlet-based applications are developed to assess the suitability of the model in a smart campus environment. The evaluation of the model, based on a popular theoretical framework, demonstrates the model’s capability to achieve some security goals like “Dynamic Separation of Duty” and “Accountability”. We believe that the Smart-RBAC model will improve the existing smart campus applications since it utilizes both, role and location of the user, to deliver content.

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In the last few years, we have observed an exponential increasing of the information systems, and parking information is one more example of them. The needs of obtaining reliable and updated information of parking slots availability are very important in the goal of traffic reduction. Also parking slot prediction is a new topic that has already started to be applied. San Francisco in America and Santander in Spain are examples of such projects carried out to obtain this kind of information. The aim of this thesis is the study and evaluation of methodologies for parking slot prediction and the integration in a web application, where all kind of users will be able to know the current parking status and also future status according to parking model predictions. The source of the data is ancillary in this work but it needs to be understood anyway to understand the parking behaviour. Actually, there are many modelling techniques used for this purpose such as time series analysis, decision trees, neural networks and clustering. In this work, the author explains the best techniques at this work, analyzes the result and points out the advantages and disadvantages of each one. The model will learn the periodic and seasonal patterns of the parking status behaviour, and with this knowledge it can predict future status values given a date. The data used comes from the Smart Park Ontinyent and it is about parking occupancy status together with timestamps and it is stored in a database. After data acquisition, data analysis and pre-processing was needed for model implementations. The first test done was with the boosting ensemble classifier, employed over a set of decision trees, created with C5.0 algorithm from a set of training samples, to assign a prediction value to each object. In addition to the predictions, this work has got measurements error that indicates the reliability of the outcome predictions being correct. The second test was done using the function fitting seasonal exponential smoothing tbats model. Finally as the last test, it has been tried a model that is actually a combination of the previous two models, just to see the result of this combination. The results were quite good for all of them, having error averages of 6.2, 6.6 and 5.4 in vacancies predictions for the three models respectively. This means from a parking of 47 places a 10% average error in parking slot predictions. This result could be even better with longer data available. In order to make this kind of information visible and reachable from everyone having a device with internet connection, a web application was made for this purpose. Beside the data displaying, this application also offers different functions to improve the task of searching for parking. The new functions, apart from parking prediction, were: - Park distances from user location. It provides all the distances to user current location to the different parks in the city. - Geocoding. The service for matching a literal description or an address to a concrete location. - Geolocation. The service for positioning the user. - Parking list panel. This is not a service neither a function, is just a better visualization and better handling of the information.

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Most of today’s systems, especially when related to the Web or to multi-agent systems, are not standalone or independent, but are part of a greater ecosystem, where they need to interact with other entities, react to complex changes in the environment, and act both over its own knowledge base and on the external environment itself. Moreover, these systems are clearly not static, but are constantly evolving due to the execution of self updates or external actions. Whenever actions and updates are possible, the need to ensure properties regarding the outcome of performing such actions emerges. Originally purposed in the context of databases, transactions solve this problem by guaranteeing atomicity, consistency, isolation and durability of a special set of actions. However, current transaction solutions fail to guarantee such properties in dynamic environments, since they cannot combine transaction execution with reactive features, or with the execution of actions over domains that the system does not completely control (thus making rolling back a non-viable proposition). In this thesis, we investigate what and how transaction properties can be ensured over these dynamic environments. To achieve this goal, we provide logic-based solutions, based on Transaction Logic, to precisely model and execute transactions in such environments, and where knowledge bases can be defined by arbitrary logic theories.

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INTRODUCTION: Sylvatic yellow fever (SYF) is enzootic in Brazil, causing periodic outbreaks in humans living near forest borders or in rural areas. In this study, the cycling patterns of this arbovirosis were analyzed. METHODS: Spectral Fourier analysis was used to capture the periodicity patterns of SYF in time series. RESULTS: SYF outbreaks have not increased in frequency, only in the number of cases. There are two dominant cycles in SYF outbreaks, a seven year cycle for the central-western region and a 14 year cycle for the northern region. Most of the variance was concentrated in the central-western region and dominated the entire endemic region. CONCLUSIONS: The seven year cycle is predominant in the endemic region of the disease due the greater contribution of variance in the central-western region; however, it was possible identify a 14 cycle that governs SYF outbreaks in the northern region. No periodicities were identified for the remaining geographical regions.

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Fundação para a Ciência e a Tecnologia (FCT) - SFRH/BD/64337/2009 ; projects PTDC/ECM/70652/2006, PTDC/ECM/117660/2010 and RECI/ECM-HID/0371/2012

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This work tests different delta hedging strategies for two products issued by Banco de Investimento Global in 2012. The work studies the behaviour of the delta and gamma of autocallables and their impact on the results when delta hedging with different rebalancing periods. Given its discontinuous payoff and path dependency, it is suggested the hedging portfolio is rebalanced on a daily basis to better follow market changes. Moreover, a mixed strategy is analysed where time to maturity is used as a criterion to change the rebalancing frequency.

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Field lab: Entrepreneurial and innovative ventures

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In this research we conducted a mixed research, using qualitative and quantitative analysis to study the relationship and impact between mobile advertisement and mobile app user acquisition and the conclusions companies can derive from it. Data was gathered from management of mobile advertisement campaigns of a portfolio of three different mobile apps. We found that a number of implications can be extracted from this intersection, namely to product development, internationalisation and management of marketing budget. We propose further research on alternative app users sources, impact of revenue on apps and exploitation of product segments: wearable technology and Internet of Things.

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There has been an increase in the use of telephone-based services and internet throughout the years and, therefore, the Saúde 24 Hotline has become an important service in Portugal. This service aims to screen, counsel and refer the patient in order to avoid unnecessary visits to health institutions and also to indicate the most appropriate resource according to the illness. This work has two different questions: the first one examines the determinants of satisfaction that have more influence on the overall satisfaction of the Saúde 24 Hotline users. The second one aims to analyze if the confidence level of the users is increasing over time, measured by following the recommendation. The first study was conducted on a random sample collected from June to October 2014, which was taken from the User Satisfaction Survey. The second approach includes data from January 2008 to December 2014 from the Clinical Data Base of all users who have called the Hotline. Findings suggest that the majority of users are very satisfied with the service and the variables with more impact on the overall satisfaction are commitment and availability from the nurse, adequacy of call duration and quick identification of the problem. The survey indicates that 94% of respondents follow the recommendation and on average people have called the hotline 3 times in the previous year. The results from the Clinical Database show that people who were recommended to go to the emergency room are more likely to follow the advice than the people who were recommended to book routine appointments

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With the projection of an increasing world population, hand-in-hand with a journey towards a bigger number of developed countries, further demand on basic chemical building blocks, as ethylene and propylene, has to be properly addressed in the next decades. The methanol-to-olefins (MTO) is an interesting reaction to produce those alkenes using coal, gas or alternative sources, like biomass, through syngas as a source for the production of methanol. This technology has been widely applied since 1985 and most of the processes are making use of zeolites as catalysts, particularly ZSM-5. Although its selectivity is not especially biased over light olefins, it resists to a quick deactivation by coke deposition, making it quite attractive when it comes to industrial environments; nevertheless, this is a highly exothermic reaction, which is hard to control and to anticipate problems, such as temperature runaways or hot-spots, inside the catalytic bed. The main focus of this project is to study those temperature effects, by addressing both experimental, where the catalytic performance and the temperature profiles are studied, and modelling fronts, which consists in a five step strategy to predict the weight fractions and activity. The mind-set of catalytic testing is present in all the developed assays. It was verified that the selectivity towards light olefins increases with temperature, although this also leads to a much faster catalyst deactivation. To oppose this effect, experiments were carried using a diluted bed, having been able to increase the catalyst lifetime between 32% and 47%. Additionally, experiments with three thermocouples placed inside the catalytic bed were performed, analysing the deactivation wave and the peaks of temperature throughout the bed. Regeneration was done between consecutive runs and it was concluded that this action can be a powerful means to increase the catalyst lifetime, maintaining a constant selectivity towards light olefins, by losing acid strength in a steam stabilised zeolitic structure. On the other hand, developments on the other approach lead to the construction of a raw basic model, able to predict weight fractions, that should be tuned to be a tool for deactivation and temperature profiles prediction.

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Aspergillosis is an infection caused by saprophytic fungi of the genus Aspergillus, which typically occurs in immunosuppressed individuals, but has also been reported in immunocompetent patients. The main routes of entry are the respiratory tract, skin, cornea, and ear, and the infection may be localized or disseminated by contiguity or vascular invasion. We report a severe case of rhinosinusitis with cutaneous involvement, caused by invasive aspergillosis, in an immunocompetent user of inhaled cocaine. Invasive aspergillosis related to cocaine abuse has not yet been reported in the literature. After itraconazole treatment and surgical debridement, complete clinical remission was achieved. Nasal reconstruction with a skin graft over a silicone prosthesis resulted in a satisfactory esthetic outcome.

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Nestlé’s Dynamic Forecasting Process: Anticipating Risks and Opportunities This Work Project discusses the Nestlé’s Dynamic Forecasting Process, implemented within the organization as a way of reengineering its performance management concept and processes, so as to make it more flexible and capable to react to volatile business conditions. When stressing the importance of demand planning to reallocate resources and enhance performance, Nescafé Dolce Gusto comes as way of seeking improvements on this forecasts’ accuracy and it is thus, by providing a more accurate model on its capsules’ sales, as well as recommending adequate implementations that positively contribute to the referred Planning Process, that value is brought to the Project

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User-generated advertising changed the world of advertising and changed the strategies used by marketers. Many researchers explored the dimensions of source credibility in traditional media and online advertising. However, little previous research explored the dimensions of source credibility in the context of user-generated advertising. This exploratory study aims to investigate the different dimensions of source credibility in the case of user-generated advertising. More precisely, this study will explore the following factors: (1) objectivity, (2) trustworthiness, (3) expertise, (4) familiarity, (5) attractiveness and (6) frequency. The results suggest that some of the dimensions of source credibility (objectivity, trustworthiness, expertise, familiarity and attractiveness) remain the same in the case of user-generated advertising. Additionally, a new dimension is added to the factors that explain source credibility (reputation). Furthermore, the analysis suggests that the dimension “frequency” is not an explanatory factor of credibility in the case of user-generated advertisement. The study also suggests that companies using user-generated advertisement as part of their overall marketing strategy should focus on objectivity, trustworthiness, expertise, attractiveness and reputation when selecting users that will communicate sponsored user-generated advertisements.