Regulation of adenoviral gene expression is a complex process directed by viral proteins controlling a multitude of different activities at distinct phases of the virus life cycle. This thesis discusses adenoviral regulation of transcription and splicing by two proteins expressed at the late phase: L4-22K and L4-33K. These are closely related with a common N-terminus but unique C-terminal domains. The L4-33K protein is an alternative RNA splicing factor inducing L1-IIIa mRNA splicing, while L4-22K is stimulating transcription from the major late promoter (MLP). The L4-33K protein contains a tiny RS-repeat in its unique C-terminal end that is essential for the splicing enhancer function of the protein. Here we demonstrate that the tiny RS-repeat is required for localization of the protein to the nucleus and viral replication centers. Further, we describe an auto-regulatory loop where L4-33K enhances splicing of its own intron. The preliminary characterization of the responsive RNA-element suggests that it differs from the previously defined L4-33K-responsive element activating L1-IIIa mRNA splicing. L4-22K lacks the ability to enhance L1-IIIa splicing in vivo, and here we show that the protein is defective in L1-IIIa or other late pre-mRNA splicing reactions in vitro. Interestingly, we found a novel function for the L4-22K and L4-33K proteins as regulators of E1A alternative splicing. Both proteins selectively upregulated E1A-10S mRNA accumulation in transfection experiments, by a mechanism independent of the tiny RS-repeat. Although L4-22K is reported to be an MLP transcriptional enhancer protein, here we show that L4-22K also functions as a repressor of MLP transcription. This novel activity depends on the integrity of the major late first leader 5’ splice site. The model suggests that at low concentrations L4-22K activates MLP transcription while at high concentrations L4-22K represses transcription. So far, characterizations of the L4-22K and L4-33K proteins have been limited to human adenoviruses 2 or 5 (HAdV-2/5). We expanded our experiments to include HAdV-3, HAdV-4, HAdV-9, HAdV-11 and HAdV-41. The results demonstrated that the transcription- or splicing-enhancing properties of L4-22K and L4-33K, respectively, are evolutionarily conserved and non-overlapping. Thus, the sequence-based conservation is mirrored by the functions, as expected for functionally important proteins.

A capacidade de prever resultados futuros, ao se analisar uma série de dados, é uma importante ferramenta para o planejamento de qualquer empresa ou indústria. Porém, a literatura oferece muitas opções de ferramentas e modelos estatísticos que permitem obter estas previsões. Cada qual com suas características e recomendações. Dentre estes modelos, destacam-se os modelos de Box e Jenkins, e os modelos de Redes Neurais Artificiais (RNA) - com destaque aos modelos de perceptron de múltiplas camadas (MLP). Estas duas diferentes abordagens são comparadas nesta dissertação com relação a sua capacidade de obter previsões acuradas em séries de dados não lineares quanto a sua média. As abordagens foram comparadas utilizando-se a série mensal do índice de produção física industrial do Estado do Rio Grande do Sul. Bem como a série anual de manchas solares, sendo a segunda utilizada como caso-controle para as comparações, devido ao fato de que as suas propriedades já foram amplamente estudadas. No estudo da série do índice de produção física mensal, os modelos de Box e Jenkins obtiveram melhor rendimento. Na série das manchas solares foram os modelos MLP que se destacaram. Desta forma, não é possível afirmar se alguma das abordagens é superior - tratando-se de séries de dados não lineares quanto a sua média. / The capacity to preview future outcomes on the time series analysis is an important tool for any business and industry planning. However, the literature offers many options on statistical tools and models which allow to obtain these forecasts. Each one with their features and recommendations. 1n these models, the Box and Jenkins and Artificial Neural Networks (ANN) models, with the multilayer perceptron (MLP) highlighted, stand out. These two different approaches are compared in this thesis related to the capacity to obtain accurate forecasts in mean related non-linear time series analysis. These approaches were compared using the monthly physical production index of Rio Grande do Sul time series and the sunspot series, being the second one used as a case-control to the comparisons, due the fact of its properties are already widely studied. 1n the monthly physical production index series study, t,he Box and Jenkins models obtained better efficiency. 1n the sunspot series, the MLP models were highlighted. So, it isn't possible to affirm if any of the approaches is superior, in the case of mean related non-linear time series.

A capacidade de prever resultados futuros, ao se analisar uma série de dados, é uma importante ferramenta para o planejamento de qualquer empresa ou indústria. Porém, a literatura oferece muitas opções de ferramentas e modelos estatísticos que permitem obter estas previsões. Cada qual com suas características e recomendações. Dentre estes modelos, destacam-se os modelos de Box e Jenkins, e os modelos de Redes Neurais Artificiais (RNA) - com destaque aos modelos de perceptron de múltiplas camadas (MLP). Estas duas diferentes abordagens são comparadas nesta dissertação com relação a sua capacidade de obter previsões acuradas em séries de dados não lineares quanto a sua média. As abordagens foram comparadas utilizando-se a série mensal do índice de produção física industrial do Estado do Rio Grande do Sul. Bem como a série anual de manchas solares, sendo a segunda utilizada como caso-controle para as comparações, devido ao fato de que as suas propriedades já foram amplamente estudadas. No estudo da série do índice de produção física mensal, os modelos de Box e Jenkins obtiveram melhor rendimento. Na série das manchas solares foram os modelos MLP que se destacaram. Desta forma, não é possível afirmar se alguma das abordagens é superior - tratando-se de séries de dados não lineares quanto a sua média. / The capacity to preview future outcomes on the time series analysis is an important tool for any business and industry planning. However, the literature offers many options on statistical tools and models which allow to obtain these forecasts. Each one with their features and recommendations. 1n these models, the Box and Jenkins and Artificial Neural Networks (ANN) models, with the multilayer perceptron (MLP) highlighted, stand out. These two different approaches are compared in this thesis related to the capacity to obtain accurate forecasts in mean related non-linear time series analysis. These approaches were compared using the monthly physical production index of Rio Grande do Sul time series and the sunspot series, being the second one used as a case-control to the comparisons, due the fact of its properties are already widely studied. 1n the monthly physical production index series study, t,he Box and Jenkins models obtained better efficiency. 1n the sunspot series, the MLP models were highlighted. So, it isn't possible to affirm if any of the approaches is superior, in the case of mean related non-linear time series.

A capacidade de prever resultados futuros, ao se analisar uma série de dados, é uma importante ferramenta para o planejamento de qualquer empresa ou indústria. Porém, a literatura oferece muitas opções de ferramentas e modelos estatísticos que permitem obter estas previsões. Cada qual com suas características e recomendações. Dentre estes modelos, destacam-se os modelos de Box e Jenkins, e os modelos de Redes Neurais Artificiais (RNA) - com destaque aos modelos de perceptron de múltiplas camadas (MLP). Estas duas diferentes abordagens são comparadas nesta dissertação com relação a sua capacidade de obter previsões acuradas em séries de dados não lineares quanto a sua média. As abordagens foram comparadas utilizando-se a série mensal do índice de produção física industrial do Estado do Rio Grande do Sul. Bem como a série anual de manchas solares, sendo a segunda utilizada como caso-controle para as comparações, devido ao fato de que as suas propriedades já foram amplamente estudadas. No estudo da série do índice de produção física mensal, os modelos de Box e Jenkins obtiveram melhor rendimento. Na série das manchas solares foram os modelos MLP que se destacaram. Desta forma, não é possível afirmar se alguma das abordagens é superior - tratando-se de séries de dados não lineares quanto a sua média. / The capacity to preview future outcomes on the time series analysis is an important tool for any business and industry planning. However, the literature offers many options on statistical tools and models which allow to obtain these forecasts. Each one with their features and recommendations. 1n these models, the Box and Jenkins and Artificial Neural Networks (ANN) models, with the multilayer perceptron (MLP) highlighted, stand out. These two different approaches are compared in this thesis related to the capacity to obtain accurate forecasts in mean related non-linear time series analysis. These approaches were compared using the monthly physical production index of Rio Grande do Sul time series and the sunspot series, being the second one used as a case-control to the comparisons, due the fact of its properties are already widely studied. 1n the monthly physical production index series study, t,he Box and Jenkins models obtained better efficiency. 1n the sunspot series, the MLP models were highlighted. So, it isn't possible to affirm if any of the approaches is superior, in the case of mean related non-linear time series.

Submitted by Marta Toyoda (1144061@mackenzie.br) on 2018-10-29T17:09:12Z No. of bitstreams: 2 VALTER PEREIRA DE CARVALHO.pdf: 1797453 bytes, checksum: 0371ebaa50b7784190cf403272d8e502 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Paola Damato (repositorio@mackenzie.br) on 2018-10-31T14:51:00Z (GMT) No. of bitstreams: 2 VALTER PEREIRA DE CARVALHO.pdf: 1797453 bytes, checksum: 0371ebaa50b7784190cf403272d8e502 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2018-10-31T14:51:00Z (GMT). No. of bitstreams: 2 VALTER PEREIRA DE CARVALHO.pdf: 1797453 bytes, checksum: 0371ebaa50b7784190cf403272d8e502 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2018-08-03 / This work proposes a study of the forecast of time series with the use of data obtained from BOVESPA the basis of the values of the shares at the closing of the trading session. For the forecast, an arti_cial neural network (RNA) with MLP (MultiLayer Perceptron) architecture will be used. It will be shown through this prediction study of the financial market how the neural network behaves and how it can be of great value for forecasts with time series data. The analysis comprises the comparison between the forecast and the efective closing price within established periods. The paper compares the MLP network with the Random Walk Hypothesis. At the end of the study it is concluded that the artificial neural network used for stock market forecasting is able to show results very close to reality, and that this methodology can be used by individual and collective investors to understand the behavior of the actions and to orient themselves on the possible investment hypotheses. / Este trabalho propõe um estudo de previsão de séries temporais com o uso dos dados obtidos da BOVESPA (Bolsa de Valores de São Paulo) tomando-se por base os valores das ações no fechamento do pregão. Para a previsão será utilizada uma rede neural artificial (RNA) com arquitetura MLP (MultiLayer Perceptron). Será mostrado através desse estudo de previsão do mercado financeiro como a rede neural se comporta e como ela pode ser de grande valia para previsões com séries de dados temporais. A análise compreende a comparação entre a previsão e o preço de fechamento efetivo dentro de períodos estabelecidos. O trabalho faz um comparativo entre a rede MLP e a Hipótese de Random Walk. Ao final do trabalho conclui-se que a rede neural artificial utilizada para previsão de mercado acionário é capaz de mostrar resultados muito próximos da realidade, e que essa metodologia pode ser utilizada por investidores individuais e coletivos para compreenderem o comportamento das ações e se orientarem sobre as possíveis hipóteses de investimentos.

The ongoing transformation of the energy system along a more sustainable trajectory requires advancements in a range of technological fields, as well as active involvement of different societal groups. Integration of photovoltaic (PV) systems in the built environment in particular is expected to play a crucial long-term role in the deployment of renewable energy technologies in urban areas, demanding the successful cooperation of planners, architects, engineers, scientists and users. The realisation of that technological change will require innovation at both an individual (within firms and organisations) and a collective (sector) level, giving rise to systemic approaches for its characterisation and analysis of its drivers. This study investigates the processes that either accelerate or hinder the development and diffusion of Building-Integrated PV (BIPV) applications into the market. Affected by developments in both the renewable energy and construction industries, the BIPV innovation system is a multi-sectoral case that has been explored only partially up to now. Acknowledging the fact that drivers of innovation span the globalised BIPV supply chain, this research adopts both an international and a national spatial perspective focusing on the UK. The analysis is based on a novel analytical framework which was developed in order to capture innovation dynamics at different levels, including technological advancements within firms, competition and synergy with other emerging and established innovation systems and pressures from the wider socio-economic configuration. This hybrid functional framework was conceived by combining elements from three academic strands: Technological Innovation Systems, the Multi-Level Perspective and Business Studies. The empirical research is based on various methods, including desktop research, semi-structured interviews and in-depth firm-level case studies. A thorough market assessment provides the techno-economic background for the research. The hybrid framework is used as a guide throughout the empirical investigation and is also implemented in the analytical part of the study to organise and interpret the findings, in order to assess the overall functionality of the innovation system. The analysis has underlined a range of processes that affect the development and diffusion of BIPV applications including inherent technological characteristics, societal factors and wider transitions within the energy and construction sectors. Future approaches for the assessment and governance of BIPV innovation will need to address its hybrid character and disruptiveness with regards to incumbent configurations, in order to appreciate its significance over the short and long term. Methodological and conceptual findings show that the combination of insights from different analytical perspectives offers a broader understanding of the processes affecting innovation dynamics in emerging technologies. Different approaches can be used in tandem to overcome methodological weaknesses, provide different analytical perspectives and assess the performance of complex innovation systems, which may span multiple countries and sectors. By better reflecting complexities, tensions and synergies, the framework developed here offers a promising way forward for the analysis of emerging sustainable technologies.

Autoscalers handle the scaling of instances in a system automatically based on specified thresholds such as CPU utilization. Reactive autoscalers do not take the delay of initiating a new instance into account, which may lead to overutilization. By applying machine learning methodology to predict future loads and the desired number of instances, it is possible to preemptively initiate scaling such that new instances are available before demand occurs. Leveraging efficient scaling policies keeps the costs and energy consumption low while ensuring the availability of the system. In this thesis, the predictive capability of different multilayer perceptron configurations is investigated to elicit a suitable model for a telecom support system. The results indicate that it is possible to accurately predict future load using a multilayer perceptron regressor model. However, the possibility of reproducing the results in a live environment is questioned as the dataset used is derived from a simulation.

Increased strain on incumbent wastewater distribution networks originating from population increases as well as climate change calls for enhanced resource utilization. Accurately being able to predict network interconnectivity is vital within the wastewater industry to enable operational management strategies that optimizes the performance of the wastewater system. In this thesis, an evaluation of the network interconnectivity prediction performance of two machine learning models, the multilayer perceptron (MLP) and the support vector machine (SVM), utilizing supervisory control and dataacquisition (SCADA) system data for a wastewater system is presented. Results of the thesis imply that the MLP achieves the best predictions of the network interconnectivity. The thesis concludes that the MLP is the superior model and that the highest achievable network interconnectivity accuracy is 56% which is attained by the MLP model. / Den ökade påfrestningen på nuvarande avloppsnät till följd av befolkningstillväxt och klimatförändringar medför att det finns behov för optimerad resursförbrukning. Att korrekt kunna predicera ett avloppsnät är önskvärt då det möjliggör för effektivitetshöjande operativ förvaltning av avloppssystemet. I denna avhandling evalueras hur väl två maskininlärningsmodeller kan predicera nätverketssammankoppling med data från ett system för övervakning och kontroll av data (SCADA) genererat av ett avloppsnätverk. De två modellerna som testas är en multilagersperceptron (MLP) och en stödvektormaskin (SVM). Resultaten av avhandlingen visar på att MLP modellen uppnår den bästa prediktionen av nätverketssammankoppling. Avhandlingen konkluderar att MLP modellen är den bästa modellen för att predicera nätverkets sammankoppling samt att den högsta nåbara korrektheten var 56% vilket uppnåddes av MLP modellen.

The term Rogue Trading is defined as the activity of someone at a financial organisation losing a large amount of money in bad or illegal transactions and trying to hide this. The activity of Rogue traders exposes financial organisations to huge risks and may lead to the organisation collapsing, which will affect other stakeholders like, for example, the customers. In order to detect potential Rogue Trading cases, Control Systems that monitor the employees and the positions they take on financial markets must exist. In this study, a two-step control system is suggested to monitor the margins on Foreign exchange (FX) Forwards traded by employees at the Swedish bank Skandinaviska Enskilda Banken (SEB). The first step in the control system uses a Deep Learning neural network trained on transactional data to predict the margin. The errors of the predictions versus the actual values are then in the second step of the control system used to find outliers which should be flagged for further investigation due to a too high deviation. The results show that the model hopefully can decrease the number of false positives yielded by the current Control Systems at SEB and thus reduce manual inspection of flagged transactions. / Termen Rouge Trading definieras som en aktivitet där någon på en finansiell institution förlorar stora mängder pengar i dåliga eller illegala transaktioner och försöker dölja detta. Detta är något som skapar enorma risker för finansiella institutioner och som kan förorsaka organisationens kollaps, som kan påverka intressenter som till exempel kunder. För att upptäcka potentiella företeelser av Rouge Trading så måste kontrollsystem som övervakar anställda och deras positioner existera. I denna studie föreslås och presenteras ett tvåstegs-system för att övervaka marginaler vid terminsaffärer i utländsk valuta vid Skandinaviska Enskilda Banken (SEB). Det första steget i kontrollsystemet använder ett neuralt närverk tränat på data från transaktioner för att prediktera en marginal. Differenserna mellan prediktionen och det faktiska värdet används för att finna outliers vilka borde flaggas för vidare undersökning. Resultaten visar att modellen förhoppningsvis kan minska antalet falska positiva som det nuvarande kontrollsystemet ger på SEB, något som således kan minska den manuella inspektionen av flaggade transaktioner.

Vehicle Action Intention Prediction plays a more and more crucial role in automated driving and traffic safety. It allows automated vehicles to comprehend the other road participants’ current actions, and foresee the upcoming actions, which can significantly reduce the likelihood of traffic accidents, so as to enhance overall road safety. Meanwhile, by anticipating other vehicles’ movements on the road, the ego vehicle can plan its velocity and trajectory in advance, and make more smooth and finer adjustments during the whole driving process, contributing to a more safe and efficient traffic. Furthermore, the intention prediction enables vehicles to respond proactively rather than reactively in traditional ADAS (Advanced Driver Assistance Systems), such as AEB (Automatic Emergency Braking), which facilitates a more preventive and early intervention approach to traffic safety. In normal conditions, traffic behavior is controlled by traffic rules. This thesis explores vehicle behavior using intention prediction models in scenarios where there are no traffic rules. At hand, we have a unique dataset containing vehicle trajectories, collected from 2 cameras installed overhead on a 1-lane narrowing street, where the vehicles need to negotiate their right of way. After pre-processing these data to form specific input structures, we use different classifier models including both traditional methods and deep learning methods to make vehicle action intention predictions. The data was organized in 3-second windows and contained vehicle position and distance to the center of the intersection along with the speed of both vehicles. We compared and evaluated the model performances and found that MLPs (Multi-Layer Perceptrons) and LSTM (Long Short Term Memory) yield the best performance. Furthermore, a feature selection method and features’ importance analysis are also applied to explore which variables influence the model most in order to explain the internal principle of the prediction model. It was found that close to the narrowing street the first and last samples of the position and distance in the time window and the last sample of the speed of both vehicles were found to influence the model performance the most. Further away from the narrowing street, the first and last samples of the position of the vehicle have a higher influence on the model.