When assessing network systems, security has always been one of the priorities.Cyber threat modelling is one of the most suitable methods. From a startingpoint to each valuable asset, the simulation can enable the users to explore certainsecurity weaknesses alongside the attack path. In the end, the time to compromiseshows the security level of the whole system.In principle, most cyber threat models can be built and simulated by attack graphswhere each point in the graph can stand for a certain asset in the network system.However, different systems have different infrastructures and implementations.As a result, it will be more suitable if engineers can develop a domain specificlanguage (DSL) which can be associated with a specific attack graph in order toimprove accuracy and efficiency.In this master thesis work, the final outcome is azureLang, a cyber threat modelinglanguage based on Meta Attack Language (MAL) for Microsoft Azure cloudcomputing environment. Compatible with securiCAD®, a CAD tool developedby Foreseeti AB, a threat model can be built and then be simulated. / Vid bedömning av nätverkssystem har säkerhet alltid varit en av prioriteringarna.Bland tusentals metoder är cyberhotsmodellering en av de mest lämpliga. Frånen startpunkt till varje värdefull tillgång kan simuleringen göra det möjligt föranvändare att utforska vissa säkerhetssvagheter längs attackvägen. I slutändanvisar tiden för kompromiss säkerhetsnivån för hela systemet.I princip kan de flesta cyberhotsmodeller byggas och simuleras med attackgraferdär varje punkt i diagrammet kan stå för en viss tillgång i nätverkssystemet. Menolika system har olika infrastrukturer och implementationer. Som ett resultatkommer det att vara mer lämpligt om ingenjörer kan utveckla ett domänspecifiktspråk (DSL) som kan associeras med en specifik attackgrafik för att förbättranoggrannhet och effektivitet.I det här examensarbetet är slutresultatet azureLang, ett språk för modelleringav hothot baserat på Meta Attack Language (MAL) för Microsoft Azure cloudcomputing-miljö. Kompatibel med securiCAD ®, ett CAD-verktyg utvecklat avForeseeti AB, en hotmodell kan byggas och sedan simuleras.

The shortcomings of current antidepressant agents prompts the design of novel multimodal antidepressants and the identification of new antidepressant targets, especially those located at sub-cellular level. Such antidepressants should possess improved response rates as well as safety profiles. Methylene blue (MB) is reported to possess diverse pharmacological actions and is attracting increasing attention for the treatment of a variety of disorders including Alzheimer’s disease, bipolar disorder, anxiety and depression. MB acts on both monoamine oxidase (MAO) and the nitric oxide (NO)-cGMP pathway, and possesses antidepressant activity in rodents. The principal goal of this study was to design a close structural analogue of MB and to evaluate the effects of these structural changes on MAO inhibition, a well-known antidepressant target. Furthermore, MAO inhibition is also responsible for cardiovascular toxicity in clinically used MAOI inhibitors. For this purpose we investigated the antidepressant properties of the synthetic MB analogue (ethyl-thioniniumchloride; ETC) as well as azure B, the major metabolite of MB, in the forced swim test (FST). ETC was synthesized with a high degree of purity from diethyl-p-phenylenediamine with 6% yield. ETC was firstly evaluated as a potential inhibitor of recombinant human MAO-A and MAO-B. Azure B and ETC were evaluated over a dosage range of 4-30 mg/kg for antidepressant-like activity in the acute FST in rats, and the results were compared to those obtained with saline, imipramine (15 mg/kg) and MB (15 mg/kg) treated rats. Locomotor activity was evaluated to ensure that changes in swim motivation are based on antidepressant response and not due to an indirect effect of the drug on locomotor activity. The results document that ETC inhibits MAO-A and MAO-B with IC50 values of 0.51 μM and 0.592 μM, respectively. Furthermore, ETC inhibits MAO-A and MAO-B reversibly, while the mode of inhibition is most likely competitive. In the acute FST, azure B and ETC were more effective than imipramine and MB in reversing immobility, without inducing locomotor effects. Azure B and ETC increased swimming behaviour during acute treatment, which is indicative of enhanced serotonergic neurotransmission. Azure B and ETC did not affect noradrenergicmediated climbing behaviour. These results suggest that azure B may be a contributor to the antidepressant effect of MB, and acts via increasing serotonergic transmission. Secondly, small structural changes made to MB do not abolish its antidepressant effect even though ETC is a less potent MAO-A inhibitor than MB. / MSc (Pharmaceutical Chemistry), North-West University, Potchefstroom Campus, 2014

Manual deployments of software is a tedious, repetitive and non-scaling method of deploying software.Continuous Delivery is a practice that enables automated deployment of software in a rapid fashion at the click of a button.When deciding whether to start using a new practice, software companies need to make an assessment from a cost-benefit perspective.This thesis compares automated deployments through Continuous Delivery with manual deployments from a cost perspective.The comparison is done at a small software company where two tools for Continuous Delivery are chosen based on requirements imposed by the company. The tools, Octopus Deploy and Azure DevOps, are cost efficient to different degrees.Octopus is cost efficient if several deployments per week are necessary, particularly if many deployment targets are involved.Azure DevOps is quickly cost efficient in most cases due to its pricing scheme, only needing roughly one deployment per week for few deployment targets, and a couple of deployments per year for many deployment targets.The initial cost of having a paid employee set up the tool needs to be paid off, but is easily done within a year using weekly deployments with a small number of deployment targets.

Major advances in machine learning have made image recognition applications, with Artificial Neural Network, blossom over the recent years. The aim of this thesis was to find a solution to recognize digits from a number sequence on an ID tag, used to identify farm animals, with the help of image recognition. A Recurrent Convolutional Neural Network solution called PPNet was proposed and tested on a data set called Animal Identification Tags. A transfer learning method was also used to test if it could help PPNet generalize and better recognize digits. PPNet was then compared against Microsoft Azures own image recognition API, to determine how PPNet compares to a general solution. PPNet, while not performing as good, still managed to achieve competitive results to the Azure API.

As the IoT has grown in popularity, modern Cloud Providers have begun offering IoT-specific features. These features could provide IoT developers with a lot of aid, but they are new and relatively unexplored. This study aims to explore these IoT-specific features in order to build a better foundation for developers looking to leverage them to improve their IoT systems or development process. To do this, a literature study combined with a case study was carried out. The literature study gives insight into the current research around IoT and IoT cloud providers, for example with common issues and possible applications. The case study provides a reference of how a simple proof-of-concept system can be developed using two of the available providers: AWS IoT and Azure IoT.

Cloud computing is emerging as a promising platform for compute and data intensive scientific applications. Thanks to the on-demand elastic provisioning capabilities, cloud computing has instigated curiosity among researchers from a wide range of disciplines. However, even though many vendors have rolled out their commercial cloud infrastructures, the service offerings are usually only best-effort based without any performance guarantees. Utilization of these resources will be questionable if it can not meet the performance expectations of deployed applications. Additionally, the lack of the familiar development tools hamper the productivity of eScience developers to write robust scientific high performance computing (HPC) applications. There are no standard frameworks that are currently supported by any large set of vendors offering cloud computing services. Consequently, the application portability among different cloud platforms for scientific applications is hard. Among all clouds, the emerging Azure cloud from Microsoft in particular remains a challenge for HPC program development both due to lack of its support for traditional parallel programming support such as Message Passing Interface (MPI) and map-reduce and due to its evolving application programming interfaces (APIs). We have designed newer frameworks and runtime environments to help HPC application developers by providing them with easy to use tools similar to those known from traditional parallel and distributed computing environment set- ting, such as MPI, for scientific application development on the Azure cloud platform. It is challenging to create an efficient framework for any cloud platform, including the Windows Azure platform, as they are mostly offered to users as a black-box with a set of application programming interfaces (APIs) to access various service components. The primary contributions of this Ph.D. thesis are (i) creating a generic framework for bag-of-tasks HPC applications to serve as the basic building block for application development on the Azure cloud platform, (ii) creating a set of APIs for HPC application development over the Azure cloud platform, which is similar to message passing interface (MPI) from traditional parallel and distributed setting, and (iii) implementing Crayons using the proposed APIs as the first end-to-end parallel scientific application to parallelize the fundamental GIS operations.

The shortcomings of current antidepressant agents prompts the design of novel multimodal antidepressants and the identification of new antidepressant targets, especially those located at sub-cellular level. Such antidepressants should possess improved response rates as well as safety profiles. Methylene blue (MB) is reported to possess diverse pharmacological actions and is attracting increasing attention for the treatment of a variety of disorders including Alzheimer’s disease, bipolar disorder, anxiety and depression. MB acts on both monoamine oxidase (MAO) and the nitric oxide (NO)-cGMP pathway, and possesses antidepressant activity in rodents. The principal goal of this study was to design a close structural analogue of MB and to evaluate the effects of these structural changes on MAO inhibition, a well-known antidepressant target. Furthermore, MAO inhibition is also responsible for cardiovascular toxicity in clinically used MAOI inhibitors. For this purpose we investigated the antidepressant properties of the synthetic MB analogue (ethyl-thioniniumchloride; ETC) as well as azure B, the major metabolite of MB, in the forced swim test (FST). ETC was synthesized with a high degree of purity from diethyl-p-phenylenediamine with 6% yield. ETC was firstly evaluated as a potential inhibitor of recombinant human MAO-A and MAO-B. Azure B and ETC were evaluated over a dosage range of 4-30 mg/kg for antidepressant-like activity in the acute FST in rats, and the results were compared to those obtained with saline, imipramine (15 mg/kg) and MB (15 mg/kg) treated rats. Locomotor activity was evaluated to ensure that changes in swim motivation are based on antidepressant response and not due to an indirect effect of the drug on locomotor activity. The results document that ETC inhibits MAO-A and MAO-B with IC50 values of 0.51 μM and 0.592 μM, respectively. Furthermore, ETC inhibits MAO-A and MAO-B reversibly, while the mode of inhibition is most likely competitive. In the acute FST, azure B and ETC were more effective than imipramine and MB in reversing immobility, without inducing locomotor effects. Azure B and ETC increased swimming behaviour during acute treatment, which is indicative of enhanced serotonergic neurotransmission. Azure B and ETC did not affect noradrenergicmediated climbing behaviour. These results suggest that azure B may be a contributor to the antidepressant effect of MB, and acts via increasing serotonergic transmission. Secondly, small structural changes made to MB do not abolish its antidepressant effect even though ETC is a less potent MAO-A inhibitor than MB. / MSc (Pharmaceutical Chemistry), North-West University, Potchefstroom Campus, 2014

The Internet of Things (IoT) is rapidly gaining ground in our daily life. There is a trend that the number of devices and data increases rapidly in the future. Meanwhile, IoT platforms are emerging to enable people conveniently deal with the IoT and these huge amount of data and devices. Therefore, the goal in this paper is to perform a quantitative evaluation on Microsoft Azure, one of the IoT platform, about its advantages and disadvantages under press to determine if Azure IoT is fit for future IoT. In order to reach this goal, Azure IoT Hub is used as a bridge to connect and manage lots of IoT devices which send and receive huge amount of data. .NET is used to simulate devices and connect them to the IoT Hub. The two-way communication from sensor to cloud and from cloud to actuator is implemented through MQTT protocol. This paper makes measurements on three metrics including response time from sensor sending messages to actuator receiving messages, scalability and cost and analyzes them in detail. Besides, the analysis is also made in a specific scenario which has high demand on sensor update to see how Azure IoT performs. Finally, conclusion is made on Microsoft Azure IoT's advantages and disadvantages under stress.

The Internet of Things market grows at an extreme rate each passing year. Devices will gather more data, which puts a lot of pressure on the communication between the devices and the cloud. The protocols used needs to be fast, secure, reliable and send any type of content. This thesis work conducts a research of the three most popular application level protocols ; MQTT, HTTP and AMQP, to examine which is best suited in an Internet of Things environment. The project is made with Axians AB to provide insight regarding the protocols, so that the company can decide which protocol will be best suited for their projects. A theoretical study of the performance was made, followed by case studies on different aspects of the protocol. The case studies were made using a Dell gateway and a 4G connection to mimic a real world project. Scripts were developed to measure different performance attributes of the protocols. The analysis and discussion of the results proved that MQTT or AMQP is the best protocols, depending on the project.

Windows Azure Platform is product of the Microsoft company aimed to host web applications. With this product, Microsoft is reacting on the new trend in IT -- cloud computing. This diploma thesis deals with all the aspects of developing and running PHP applications on Windows Azure Platform. Thesis is divided into two parts -- theoretical and practical. In the theoretical part, Windows Azure Platform is described in detail. The description consists of it's main parts, position in the world of cloud computing, price and main competitors. In the practical part, migration of school PHP application developed during 4IT445 course from LAMP platform to Windows Azure Platform is described. Except the migration itself, the benefits of new platform to application are described. In the thesis conclusion, development and running applications on both platforms is compared. The target of the thesis is to write first complete Czech text about running PHP applications on Windows Azure Platform and to help PHP developers to chose which way to go.