The thesis is focused on a determination of heparin and chondroitin sulfate, using flow injection analysis with spectrofluorimetric detection. The determination is based on the interaction of negatively charged heparin, chondroitin sulfate resp., with a cationic dye (azure B or phenosafranine) which is manifested by the decrease in fluorescence intensity of the dye in its emission maximum. The optimal conditions for the determination in static mode were found, and calibration dependencies were measured. The conditions of FIA were optimized and following parameters were established: the volume of dispensed sample of 100 ml, the length of the reaction coil 60 cm, the flow rate 0.7 ml min-1 , the concentration of azure B 1.6×10-5 mol dm-3 , the concentration of phenosafranine 3.5×10-5 mol dm-3 . For the determination of heparin using azure B it was found: LOD = 0.023 IU ml-1 , LOQ = 0.186 IU ml-1 , and linear dynamic range 0.19-1.43 IU ml-1 . For the determination of heparin using phenosafranine it was found: LOD = 0.102 IU ml-1 , LOQ = 0.192 IU ml-1 , and linear dynamic range 0.19-1.79 IU ml-1 . For the determination of chondroitin sulfate using azure B it was found: LOD = 0.58 mg dm-3 , LOQ = 2.37 mg dm-3 , and linear dynamic range 2.37-8.32 mg dm-3 . The developed determination was applied to the...

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Cloud computing has been given a great deal of attention during recent years. Almost all the technology market leaders and leading hosting service providers (like IBM, Microsoft and Verizon) have entered into the Cloud market as Cloud Providers. Cloud computing promises to provide highly available, secure, low cost, agile and highly scalable solution to the consumers. Scania is a global company and one of the world’s leading heavy vehicle manufacturers with 35,000+ employees. All the large organizations such as Scania, aim to constantly update themselves with the latest technology in order to meet their business requirements but, these organizations must always be convinced that there is a strong reason(s) to implement new technology. This research provides the method and criteria in relation to initiating Cloud computing. A number of Scania’s specific business requirements that it is possible to map to the Cloud are addressed in this thesis. The methodology of research is split in two parts. Firstly, the identification of business cases at Scania and their requirements with the Cloud and Secondly, the evaluation and comparison of the functionalities and capabilities of different vendors. The accumulated data is then compared and suitable vendors, according to those business requirements are suggested. This thesis also shares the experience of moving on premise applications to the Cloud. These are Scania specific applications which are currently being hosted in-house. The research also addresses the possibilities of portability between the Cloud providers. Although there is no standardization in relation to Cloud computing, some initiatives such as OpenStack are available and its current position and some application and data migration tools are also discussed. The thesis concludes with a general discussion, recommendations in relation to adapting Cloud computing and selecting the Cloud provider. This recommendation applies to every organization including Scania.

With the advent of cloud computing, resizable scalable infrastructures for data processing is now available to everyone. Software platforms and frameworks that support data intensive distributed applications such as Amazon Web Services and Apache Hadoop enable users to the necessary tools and infrastructure to work with thousands of scalable computers and process terabytes of data. However writing scalable applications that are run on top of these distributed frameworks is still a demanding and challenging task. The thesis aimed to advance the core scientific and technological means of managing, analyzing, visualizing, and extracting useful information from large data sets, collectively known as “big data”. The term “big-data” in this thesis refers to large, diverse, complex, longitudinal and/or distributed data sets generated from instruments, sensors, internet transactions, email, social networks, twitter streams, and/or all digital sources available today and in the future. We introduced architectures and concepts for implementing a cloud-based infrastructure for analyzing large volume of semi-structured and unstructured data. We built and evaluated an application prototype for collecting, organizing, processing, visualizing and analyzing data from the retail industry gathered from indoor navigation systems and social networks (Twitter, Facebook etc). Our finding was that developing large scale data analysis platform is often quite complex when there is an expectation that the processed data will grow continuously in future. The architecture varies depend on requirements. If we want to make a data warehouse and analyze the data afterwards (batch processing) the best choices will be Hadoop clusters and Pig or Hive. This architecture has been proven in Facebook and Yahoo for years. On the other hand, if the application involves real-time data analytics then the recommendation will be Hadoop clusters with Storm which has been successfully used in Twitter. After evaluating the developed prototype we introduced a new architecture which will be able to handle large scale batch and real-time data. We also proposed an upgrade of the existing prototype to handle real-time indoor navigation data.

The usage of cloud computing in order to move away from local servers and infrastructure have grown enormously the last decade. The ability to quickly scale capacity of servers and their resources at once when needed is something that can both be a price saver for companies and help them deliver high end products that will function correctly at all times even under heavy load to their customers. To meet todays challenges, one of the strategic directions of Attentec, a software company located in Linköping, is to examine the world of cloud computing in order to deliver robust and scalable applications to their customers. This thesis investigates the usage of cloud services in order to deploy scalable applications which can adapt to usage peaks within minutes.

This thesis investigates the prerequisites needed for the Swedish real estate company Fabege to create useful machine learning models for classification and prediction of error reports from tenants. These error reports are regarding cold indoor climates and bad indoor air quality. By analyzing the available data, that consists of error reporting data, weather data and indoor climate data, the thesis investigates the different correlations between the sensor data and the error reports. By using an algorithm called decision jungle, two machine learning models have been trained in Microsoft Azure Machine Learning Studio. The main model, trained on error reporting data and weather data, shows the possibilities to classify data instances as a part of different error reporting classes. The model proves that it is possible to predict the emergence of future error reports of different classes with an average accuracy of 78%. The complementary model, trained on a small but more richly annotated dataset consisting of one year of indoor sensor data as well as the above-mentioned data, shows that there is a possibility to improve the main model by using indoor climate data. The thesis has shown that for Fabege to expand and improve these models, the amount of data collected from the indoor sensors needs to be largely increased. Fabege also needs to improve the quality of the error reporting data, which could be achieved by improving the error reporting form used by the tenants.

The main objective of this diploma thesis is to design and develop a cloud hosted service that allows developers of games for Windows Phone 7 platform to extend their products with social and competitive aspect using Platform as a Service solution Windows Azure for hosting the backend of this system. The first part of the thesis analyses systems that are available on the market and that are providing similar services for the target platform. Based on this analysis, functional and general requirements for the future application are specified in the second part. Based on these requirements, use case analysis is made. The third part of the thesis is dedicated to description of the most important features, APIs and specifics of the Windows Phone 7 software platform. The fourth part is dedicated to Windows Azure as a Cloud platform both in terms of most important services, their characteristics and APIs used to utilise them and in terms of their business and pricing conditions. The fifth part addresses the architecture and the most important implementation details of the application. The final part contains the user guide for both the Windows Phone 7 application and for developers interested in using the library containing the API during the development of their game. The output of this thesis is the designed and implemented application with client-server architecture ready to have its backend deployed in Windows Azure environment and to have its frontend deployed on Windows Phone 7 devices. The client part of the system is comprised of a library intended for developers of games, which encapsulates the API needed to access the backend on the Internet, and a graphic frontend intended for end users. The server part, which contains most of the application logic, has REST interface. The applications architecture allows future development in terms of new functionality and expansion on more client platforms.

El presente proyecto desarrolla una propuesta de implementación de arquitectura empresarial integrada con tecnologías de inteligencia artificial. El resultado es proveer una propuesta de solución enfocada a la problemática identificada en los procesos comerciales del área de Soporte a la Operación: Identificar Oportunidades Comerciales y Gestionar Oportunidades Comerciales, los cuales actualmente son realizados de manera manual por especialistas del negocio, y la ejecución de tareas operativas o de análisis que, en diversos casos, se convierten en cuellos de botella, conlleva a la pérdida de oportunidades comerciales. Para ello, se ha propuesto un cambio en los procesos de negocio, con el objetivo de automatizar las actividades que demandan más tiempo y tienen alto impacto en el desarrollo de las oportunidades comerciales. El presente documento está compuesto por seis capítulos. Se inicia con la presentación de la empresa objeto de estudio, la problemática que se desea resolver, los objetivos del proyecto, incluyendo cómo medir su cumplimiento, el impacto que tendrá el proyecto en la empresa objeto de estudio y el análisis de factibilidad, tanto técnica como económica. Luego, se presenta el marco teórico, que contiene los conceptos claves que han brindado soporte a la solución propuesta, y bajo los marcos de trabajo TOGAF y ZACHMAN, utilizando la metodología ADM, se desarrolla el análisis del negocio y la identificación de requerimientos. Por último, se presenta el resultado del proyecto, que contiene la definición de la arquitectura del sistema propuesto y el plan de dirección del proyecto, que recoge las buenas prácticas del PMBOK® sexta edición y la metodología desarrollada por la empresa objeto de estudio. / This project develops a proposal to implement a business architecture integrated with artificial intelligence technologies. The result is to provide a solution proposal focused on the problem identified in the business processes of the Operation Support area: Identify Business Opportunities and Manage Business Opportunities, which are currently performed manually by business specialists, and the execution of operational or analytical tasks that, in some cases, become bottlenecks leading to the loss of business opportunities. In order to achieve this, a change in business processes has been planned, with the objective of automating activities that require more time and have a high impact on the development of business opportunities. This document is comprised of six chapters. It begins with the presentation of the company under study, the problem to be solved, the objectives of the project, including how to measure compliance, technical and economic impact the project will have on the company under study and the feasibility analysis. Then, the theoretical framework which contains the key concepts that have provided support to the proposed solution is presented and the business analysis and the identification of requirements are developed under the TOGAF and ZACHMAN frameworks, using the ADM methodology. Finally, the result of the project which contains the definition of the architecture of the proposed system and the project management plan is provided, which includes good practices of the PMBOK®️ sixth edition and the methodology developed by the company under study. / Tesis

Este proyecto tiene como objeto de estudio a una empresa pesquera nacional líder en su sector, dedicada a la extracción, transformación y comercialización de recursos hidrobiológicos para consumo humano indirecto y directo. La presente tesis propone el análisis, diseño e implementación de un asistente virtual para gestionar permisos y atención de consultas para reducir los tiempos en los cierres de planillas semanales de tripulantes y obreros. Los procesos de estudio son la gestión de permisos y la gestión de consultas del área de Gestión Humana. En el primer proceso, las solicitudes del personal tripulante y obrero se realizan con formatos físicos y son registrados de forma manual en el sistema por los responsables de administración de personal, lo que está sujeto a errores de digitación y a que no se realicen en el tiempo oportuno lo que produce pagos incorrectos en la nómina y reprocesos. En el segundo proceso, las consultas que los colaboradores realizan a los responsables de administración de personal sobre temas relacionados a sus pagos son recurrentes todas las semanas debido a que necesitan conocer cómo se calculan sus pagos semanales que dependen de factores variables derivados de la producción. También, realizan consultas variadas como los tipos de beneficios, seguros y otras gestiones personales. Bajo esta premisa, nuestra propuesta de solución en este proyecto se basa en un asistente virtual con reconocimiento de lenguaje natural utilizando inteligencia cognitiva, con acceso mediante una aplicación móvil, para que los tripulantes y obreros puedan auto gestionar sus descansos y consultas de manera oral o escrita mediante un asistente virtual tal como si lo hicieran con una persona real. Además, dejamos abierto este canal para que pueda ser usado por otros procesos del área y de otras áreas de la empresa. Todo esto, ayudará a minimizar el tiempo de horas hombre que se invierte en registrar descansos y gestionar las consultas para que el área de Gestión Humana se pueda enfocar al cálculo correcto y oportuno de los cierres de planillas semanales, y en otras funciones propias del área. / This project aims to study a leading national fishing company, a leader in its sector, dedicated to the extraction, transformation, and commercialization of hydrobiological resources for indirect and direct human consumption. This thesis proposes the analysis, design, and implementation of a virtual assistant to manage permits and attend inquiries to reduce the closing times of weekly payroll for crew and workers. The study processes are the management of permits and the management of queries from the Human Management area. In the first process, the requests of the crew and worker personnel are made with physical formats and are manually registered in the system by those responsible for personnel administration, which is subject to typing errors since they are not always carried out in a timely fashion which produces incorrect payroll payments and the need for corrections. In the second process, the consultations that collaborators make to those responsible for personnel administration on issues related to their payments are recurring every week because they need to know how their weekly payments are calculated, which depend on variable factors derived from production. Also, they carry out various queries such as types of benefits, insurance, and other personal procedures. Under this premise, our solution proposal in this project is based on a virtual assistant with natural language recognition using cognitive intelligence, with access through a mobile app, so that crew members and workers can self-manage their breaks and consultations orally or in writing. using a virtual assistant as if they were doing it with a real person. In addition, we leave this channel open so that it can be used by other processes in the area and other areas of the company. All this will help to minimize the man-hour time that is invested in recording breaks and managing inquiries so that the Human Management area can focus on the correct and timely calculation of weekly payroll closings, and on other functions of the area. / Tesis

Klassificering av dagliga aktiviteter (ADL) kan användas i system som bevakar människors aktiviteter i olika syften. T.ex., i nödsituationssystem. Med machine learning och bärbara sensor som samlar in data kan ADL klassificeras med hög noggrannhet. I detta arbete, ett proof-of-concept system med tre olika machine learning algoritmer utvärderas och jämförs mellan tre olika dataset, ett som är allmänt tillgängligt på (Ugulino, et al., 2012), och två som har samlats in i rapporten med hjälp av en android enhet. Algoritmerna som har använts är: Multiclass Decision Forest, Multiclass Decision Jungle and Multiclass Neural Network. Sensorerna som har använts är en accelerometer och ett gyroskop. Resultatet visar hur ett konceptuellt system kan byggas i Azure Machine Learning Studio, och hur tre olika algoritmer presterar vid klassificering av tre olika dataset. En algoritm visar högre precision vid klassning av Ugolino’s dataset, jämfört med machine learning modellen som ursprungligen används i rapporten. / Classifying Activities of daily life (ADL) can be used in a system that monitor people’s activities for different purposes. For example, in emergency systems. Machine learning is a way to classify ADL with high accuracy, using wearable sensors as an input. In this paper, a proof-of-concept system consisting of three different machine learning algorithms is evaluated and compared between tree different datasets, one publicly available at (Ugulino, et al., 2012), and two collected in this paper using an android device’s accelerometer and gyroscope sensor. The algorithms are: Multiclass Decision Forest, Multiclass Decision Jungle and Multiclass Neural Network. The two sensors used are an accelerometer and a gyroscope. The result shows how a system can be implemented using Azure Machine Learning Studio, and how three different algorithms performs when classifying three different datasets. One algorithm achieves a higher accuracy compared to the machine learning model initially used with the Ugolino data set.