A minha pesquisa debruça-se sobre as produções musicais e as performances de António Variações em Portugal e de José Pérez Ocaña em Espanha. Primeiramente, desenvolverei uma análise crítica sobre a sexualidade, o género e as construções binárias da masculinidade e da feminilidade. Explorarei como os atos sexuais entre pessoas do mesmo sexo começaram a ser legislados e medicalizados pelas sociedades ocidentais e de que maneiras é que a (homos)sexualidade foi sucumbida ao bas-fonds pelos regimes ditatoriais. Neste estudo oferecerei uma leitura atenta das criações culturais de Variações e de Ocaña. Para apoiar a análise textual, musical e visual das representações culturais selecionadas, a teoria queer será adoptada como modelo teórico. Os principais conceitos postulados e estabelecidos por Michel Foucault, Judith Butler, Eve Kosofsky Sedgwick, entre outros, serão aplicados às produções culturais para permitir uma deconstrução queer dos textos, das performances e das relações com o contexto histórico, social e cultural de Portugal e de Espanha. Para além deste objectivo geral, o principal objectivo desta pesquisa é demonstrar a importância que António Variações e José Perez Ocaña tiveram na construção de uma cultura queer nestes países, uma vez que ambos os países haviam sido “dominados” durante décadas pelos regimes ditatoriais Salazarista e Franquista. Estes regimes defendiam a ideia de que a homossexualidade era uma “perversão” e como tal, esta teria que ser ser exterminada, de maneira a não pôr em causa os bons valores e a moralidade destas nações. Portanto, o objectivo principal desta tese será a análise das performances destes artistas em contraponto às representações dominantes de género estabelecidos por estes dois regimes ditatorias e as razões que levaram a estes artistas a usarem a música e as performances para expressarem género não-normativo e identidades sexuais. Ao analisar estes períodos poderemos observar os motivos pelos os quais a cultura queer era inexistente, ou mais problematicamente, invisível.

The thesis deals with the design of aesthetic modifications of the forest road Šumbera. The literature review is focused on the functions of the forest road network, their division, historical development and current state and aesthetics of the landscape, basic compositional principles and changes in the perception of the landscape during the age. In the practical part there was explored wider territorial relations, with respect to natural conditions, historical attitude to aesthetic measures at ŠLP ML Křtiny and recreational potential of the area. The obtained informations became the base for creating of complete project documentation, which is located in separate bindings.

Accurate forecasts are vital in multiple areas of economic, scientific, commercial, and industrial activity. There are few previous studies on using forecasting methods for predicting serious events. This thesis set out to investigate two things, firstly whether machine learning models could be applied to the objective of forecasting serious events. Secondly, if the models could be made interpretable. Given these objectives, the approach was to formulate two forecasting tasks for the models and then use the Python framework SHAP to make them interpretable. The first task was to predict if a serious event will happen in the coming eight hours. The second task was to forecast how many serious events that will happen in the coming six hours. GBDT and LSTM models were implemented, evaluated, and compared on both tasks. Given the problem complexity of forecasting, the results match those of previous related research. On the classification task, the best performing model achieved an accuracy of 71.6%, and on the regression task, it missed by less than 1 on average. / Exakta prognoser är viktiga inom flera områden av ekonomisk, vetenskaplig, kommersiell och industriell verksamhet. Det finns få tidigare studier där man använt prognosmetoder för att förutsäga allvarliga händelser. Denna avhandling syftar till att undersöka två saker, för det första om maskininlärningsmodeller kan användas för att förutse allvarliga händelser. För det andra, om modellerna kunde göras tolkbara. Med tanke på dessa mål var metoden att formulera två prognosuppgifter för modellerna och sedan använda Python-ramverket SHAP för att göra dem tolkbara. Den första uppgiften var att förutsäga om en allvarlig händelse kommer att ske under de kommande åtta timmarna. Den andra uppgiften var att förutse hur många allvarliga händelser som kommer att hända under de kommande sex timmarna. GBDT- och LSTM-modeller implementerades, utvärderades och jämfördes för båda uppgifterna. Med tanke på problemkomplexiteten i att förutspå framtiden matchar resultaten de från tidigare relaterad forskning. På klassificeringsuppgiften uppnådde den bäst presterande modellen en träffsäkerhet på 71,6%, och på regressionsuppgiften missade den i genomsnitt med mindre än 1 i antal förutspådda allvarliga händelser.

Computational fluid dynamics (CFD) is the analytical process of predicting fluid flow, mass transfer, chemical reactions, and other related phenomena during the design or manufacturing process. Aggressive use of CFD provides drastic reductions in wind tunnel time and lowers the number of experimental rig tests. CFD saves hundreds of millions of dollars for industries, governments, and national laboratories, offering the potential to deliver superior understanding and insight into the critical physical phenomena limiting component performance. Thus, CFD opens new frontiers in many fields, especially vehicle design. One key strength of CFD is its ability to produce simulations useful in inverse design and optimization problems. However, a simulation in a conventional solver is considerably time-consuming to converge. To enable more efficient and scalable CFD simulations, we leverage the universal approximation property of machine learning using deep neural networks (DNNs) to estimate a surrogate solution to the CFD simulation. We present an implementation of this idea in two different models, one representing the eulerian model for compressible viscous flows and another representing the compressible Navier–Stokes equations. Lastly, we discuss the compressible Navier–Stokes network’s performance by implementing an inverse design problem to know if a gradient descent step of the model w.r.t the shape would grant the optimal solution. After training, predictions from these networks are faster than conventional solvers. The network predicts the flow fields hundreds of times faster than current conventional CFD solvers while maintaining good accuracy. Using the network’s predicted solutions to initialize a CFD solver sufficiently speeds up the simulation.

The thesis is focused on the evaluation of rainfall-runoff process on three stabilized micro-watersheds with different tree species in forest stand, which cover spruce, beech and mixed forest stand, located in the uplands. River basin has been investigated under the same climatic conditions during the growing season 2015. Examined micro-watersheds is located in the FTE Křtiny. This thesis include evaluation of basic statistical parameters and assessment of extreme event for particular watersheds. The work also includes a literature review on the topic parameterization rainfall-runoff process and a description of the experimental watershed. The results indicate that the highest water management efficiency (best parameters of the runoff process)obtains a catchment area of beech stand, while the worst parametrs obtains the spruce watershed.

An idea of storing contents, such as media files, music files, movie clips, etc. is simple yet challenging in terms of required effort to make it count. Some of the benefits of pre-storing the contents are reduced delay of accessing/downloading a content, reduced load to the centralized servers and of course, a higher data rate. However, several challenges need to be addressed to achieve these benefits. Among many, some of the fundamentals are limited storage capacity, storing the right content and minimizing the costs. This thesis aims to address these challenges. First, a framework for predicting the proper contents that need to be stored to the limited storage capacity is presented. Then, the cost is minimized considering several real-world scenarios. While doing that, all possible collaborations among the local nodes are performed to ensure high performance. Therefore, the goal of this thesis is to come up with a solution to the content storing problems so that the network cost is minimized.

This study aims to analyse the relationship between the design and implementation of an Advanced Certificate in Education (ACE) in Mathematical Literacy (ML) (reskilling) program and the development of teacher identities. This study confirms that teacher learning in an in‐service context is a social process that demands a social‐cultural perspective and therefore Wenger’s theory was used in this study. This study illustrates that teachers’ participation in an ACE ML community of practice involved the complex intersection of various components of learning: meaning (learning as experience), practice (learning as doing) and community (learning as belonging) when development of teacher identities takes place. The course was also designed in such a way as to promote a changing way of being. The emerging identities were different in each individual as identity is influenced by the past, the present and the future according to Wenger. The study reveals that when meaning of the subject ML is gained, the meaning can be translated into changed classroom practice. These result in fostering a specific identity influenced by the ACE ML course’s attempts to support the development of understanding in relation to the meaning of ML. This leads to a change in classroom practice and ultimately a change in teachers’ way of ‘being’. This resonates with Wenger’s claim that the four learning components are deeply interconnected and mutually defined. The new trajectories that teachers developed can be grouped into three categories: • Where teachers back grounded their previous identities and fore grounded their ML identities. • Where the teachers added their ML identity to their existing identity, leaving them with a dual identity, the one they had before their involvement in the ACE ML course and the ML identity. • Where those teachers whose existing identity stayed strong, and their ML identity was still developing or was less strong.

Background.In machine learning you let the computer play a scenario, often millions of times. When the computer plays it receives feedback based on preset guidelines. The computer then adjusts its behaviour based on that feedback. The way the computer stores its feedback is in its artificial neural network(ANN). The ANN consists of an input layer, a set amount of hidden layers and an output layer. The ANN calculates actions using weights between the nodes in each layer and modifies those weights when it receives feedback. ml-agents is Unity Technologies implementation of machine learning. Objectives.ml-agents is a complex system with many different configurations. This results in users needing sources on what configuration to use for the best results. Our thesis aimed to answer the question of how many hidden layers yield the best results.We did this by attempting to answer our research question "How many layers are required to make the network capable of capturing the complexities of the environ-ment?". Methods.We used a prebuilt environment provided by Unity, in which the agent aims to keep a ball on its head for as long as possible. The training was collected by Tensorflow, which then provided graphs for each training session. We used these graphs to evaluate the training sessions. We ran each training session several times to get more consistent results. To evaluate the training sessions we looked at the peak of their cumulative reward graph and secondarily on how fast they reached this peak. Results.We found that with just one layer, the agent could only get roughly a fifth of the way to capturing the complexity of the environment. However, with two and three layers the agent was capable of capturing the complexity of the environment.The three layered training sessions reached their cumulative reward peak 22 percent faster than the two layered. Conclusions.We managed to get an answer to our research question. The minimum amount of hidden layers required to capture the complexity of the environment is two. However, with an additional layer the agent was able to get the same result faster. Which is worth taking into consideration

Prediction of future stock markets has long been, and will continue to be a relevant topic. However, predicting markets is one of the most challenging areas to work with due to the unpredictability of the market. The extent to which markets can be predicted is a debated subject that has not yet been answered. A common approach is to use machine learning in combination with historical data to predict future stock prices. In this report, a classical machine learning method, LSTM, will be applied to nickel product prices to predict future product prices. The data used is provided by the company Harald Pihl, which has been trading various metals since the early 1900s. As a comparative material, the method is also applied to data on the nickel futures market. The results conclude that a larger number of data points are required for the prediction of nickel products to generate a credible result. In addition to this, there is a significant variation in the quality of the results depending on the dataset being used. The difference in results is due, among other things, to the number of data points, fluctuations in the dataset, and the regularity of the dataset.

A study of Richard Strauss' two concertos for horn.