This paper focuses on the translation of audiovisual humour. I have analysed Petr Palouš's translation of Monty Python's Flying Circus. The translator of audiovisual humour has to deal with the same issues and problems as the translator of literature, that is he has to find equivalents to word plays, idioms, register and so on, but at the same time he also has to comply with the restrictions of audiovisual medium. I have identified the most common translation problems when translating humour and audiovisual texts and possible strategies of their translation into the target language, and then analysed how Palouš dealt with these cruces translatorum when translating the Flying Circus.

In this work we apply data assimilation onto meteorological model WRF for local domain. We use bayesian statistics, namely Sequential Monte Carlo method combined with particle filtering. Only surface wind data are considered. An application written in Python programming language is also part of this work. This application forms interface with WRF, performs data assimilation and provides set of charts as output of data assimilation. In case of stable wind conditions, wind predictions of assimilated WRF are significantly closer to measured data than predictions of non-assimilated WRF. In this kind of conditions, this assimilated model can be used for more accurate short-term local weather predictions. Powered by TCPDF (www.tcpdf.org)

This thesis is concerned by programming language Python and its suitability of his assignment to the tuition. The work is divided into three dominant parts. The first part describes programming language Python, its elementary characteristics and features. The purpose is to introduce its properties to the reader so that he could estimate if there is point in the deeper concern. There are elements of object-oriented programming in description, too. The second part analyses programming language Python on the basis of ten criteria which are defined considering applicability of the language in preliminary courses of programming. The purpose is to review if the Python is appropriate in these courses. The third part considers the possibilities of the Python tuition at the University of Economics in Prague. The main contribution of the thesis is to give opinion on the fundamental description of the language, to define framework and to pass judgment on the potential chance of use in preliminary courses of programming.

This thesis aids at comparing two frameworks based on Python and Ruby used for agile web development. Besides the basic description, history and philosophy of both Django and Ruby on Rails, all criteria used for their evaluation are laid down, described and put into wider context. In addition, the implementation of the MVC architecture in both frameworks is described along with the basic project structure and tools available.

More and more devices around us are connected to the internet and communicate to each other. This includes devices such as radon sensors. Radon is a radio active gas and is the cause of several hundred cases of lung cancer. Smart connected radon sensors can be helpful to reduce the levels of radon as they provide remote access to the user. This study examines the opportunity to connect an already existing radon sensor to the “Internet of Things”. The aim of this study has been to answer the problem “find a better solution for the IoT system and develop it”. The study was performed with a literature study of three Internet of Things platforms. This resulted in one Internet of Thing platform being used throughout the study. A database system and a system with the chosen platform was implemented and a time measurement of the different systems was performed. The study has shown that a less secured system is faster and it is also shown that the Amazon Web Service IoT Core is fast with respect to the many features offered. The study concludes that the choice of system depends on where and how the system is supposed to be implement. If the system just needs to send and store data, a regular MySQL database is enough. If the system in the future is supposed to be able to communicate with other devices, a IoT platform should be used.

Today at various locations and factories we have a lot of sensors and actuators that interact with each other and a control-unit. The control-unit is in most cases a cloud-based solution. This is in most cases a good solution. However, there is a rise in expected devices and sensors which will most likely be too much data for the existing network to handle. This paper researches if a fog-unit might be the solution to this problem. The setup of the fog-unit in the network is a unit between the cloud and the sensors and actuators. In this paper the fog-unit and sensors/actuators have been emulated on Raspberry Pi’s. The sensors are emulated using python-threads and communicate with the fog-unit using the UDP-based protocol CoAP and the fog communicates to the cloud using the TCP- based protocol MQTT. After a prototype was built it using said Raspberry Pi’s it was sent through a few measurements in the fields of bandwidth, cloud-utilization and response times. This was later compared to another setup without the fog-unit as the control setup. The result with this kind of setup was that a fog-unit lowers the cloud-utilization and use of bandwidth, however it increases the round trip time of a request from the cloud by a large amount. Which leads to the conclusion that a fog-unit in this kind of setup might be a good network solution if the response time to the cloud isn’t important.

Today, the Internet of Things is widely used in various fields, such as factories, public facilities, and even homes. The use of the Internet of Things involves a large number of sensor devices that collect various types of data in real time, such as machine voltage, current, and temperature. These devices will generate a large amount of streaming sensor data. These data can be used to make the data analysis, which can discover hidden relation such as monitoring operating status of a machine, detecting anomalies and alerting the company in time to avoid significant losses. Therefore, the application of anomaly detection in the field of data mining is very extensive. This paper proposes an anomaly detection method based on multiple streaming sensor data and performs anomaly detection on three data sets which are from the real company. First, this project proposes the state transition detection algorithm, state classification algorithm, and the correlation analysis method based on frequency. Then two algorithms were implemented in Python, and then make the correlation analysis using the results from the system to find some possible meaningful relations which can be used in the anomaly detection. Finally, calculate the accuracy and time complexity of the system, and then evaluated its feasibility and scalability. From the evaluation result, it is concluded that the method

Knowing which database management system to use for a project is difficult to know in advance. Luckily, there are tools that can help the developer apply the same database design on multiple different database management systems without having to change the code. In this thesis, we investigate the strengths of SQLAlchemy, which is an SQL toolkit for Python. We compared SQLite, PostgreSQL and MySQL using SQLAlchemy as well as compared a pure MySQL implementation against the results from SQLAlchemy. We conclude that, for our database design, PostgreSQL was the best database management system and that for the average SQL-user, SQLAlchemy is an excellent substitution to writing regular SQL.

Advances in water resources modeling are improving the information that can be supplied to support decisions that affect the safety and sustainability of society, but these advances result in models being more computationally demanding. To facilitate the use of cost- effective computing resources to meet the increased demand through high-throughput computing (HTC) and cloud computing in modeling workflows and web applications, I developed a comprehensive Python toolkit that provides the following features: (1) programmatic access to diverse, dynamically scalable computing resources; (2) a batch scheduling system to queue and dispatch the jobs to the computing resources; (3) data management for job inputs and outputs; and (4) the ability for jobs to be dynamically created, submitted, and monitored from the scripting environment. To compose this comprehensive computing toolkit, I created two Python libraries (TethysCluster and CondorPy) that leverage two existing software tools (StarCluster and HTCondor). I further facilitated access to HTC in web applications by using these libraries to create powerful and flexible computing tools for Tethys Platform, a development and hosting platform for web-based water resources applications. I tested this toolkit while collaborating with other researchers to perform several modeling applications that required scalable computing. These applications included a parameter sweep with 57,600 realizations of a distributed, hydrologic model; a set of web applications for retrieving and formatting data; a web application for evaluating the hydrologic impact of land-use change; and an operational, national-scale, high- resolution, ensemble streamflow forecasting tool. In each of these applications the toolkit was successful in automating the process of running the large-scale modeling computations in an HTC environment.

With the amount of programming languages currently available there is a high risk of confusion anddoubtfulness in aspiring programmers of which to choose. It may be motivating for a beginner tochoose “the perfect language” when starting, to avoid learning multiple languages. This thesiscompares three popular languages on three separate aspects, their syntax, usefulness in differentareas, and performance in terms of speed. Syntax wise the results varied with some aspects beingvery similar across all three languages to completely different in others. In terms of usefulness inspecific areas the languages flexibility allowed them to develop applications in most fields, while beingdominant in different areas. The speed comparison resulted in python being the slowest across alltests, with Java and Javascript (running inside Nodejs) competing for first place.