This diploma thesis deals with design and creation of a software application in a particular company. Application is created for needs of IT department and it is used to simplify and streamline the processes related to user accounts in Active Directory. This thesis describes the whole process of application creation from analysis of current status through analysis of requirements and creation of application structure to the creation of application in programming language C#.

abstract: The intent of this study was to identify the most viable among a proposive sample of emerging sustainable construction technologies with respect to the Twin Cities Metropolitan Geographic Area. With space heating and space cooling accounting for such a significant portion of energy consumption in Twin Cities homes, a representative sample of homes was analyzed for annual heating and cooling loads. For each home a series of heating, ventilation, air conditioning (HVAC) and envelope equipment was modeled in order to provide data for various sustainable home construction technologies. The result was a specific amount of energy savings from baseline construction methods for each sustainable technology. The study found that integrated geothermal heat pump and radiant conditioning systems have a far greater impact on energy savings than the construction methods evaluated. Nevertheless, insulated concrete forms provided the greatest energy savings within the proposive set of construction methods. The greatest amount of space conditioning energy savings of all configurations tested was 73.48% using an integrated geothermal heat pump and radiant conditioning system, structural insulated panel wall construction, aerosol air infiltration prevention, and insulated concrete form basement construction. The results of the study were used to determine areas for further research and to provide awareness within the Twin Cities construction enterprise to determine the most viable technologies that contractors, municipalities, and citizens should prioritize moving forward. / Dissertation/Thesis / Masters Thesis Civil, Environmental and Sustainable Engineering 2019

OBJECTIVE: In recent decades scientific advancements and improvements in treatment options have significantly reduced breast cancer mortality. Unfortunately, not all have experienced these improvements; there is a widening gap in mortality rates between Black and Non-Hispanic, White women. While race may be one factor in this disparity, the interactions between social, environmental, and economic circumstances and their differential impact across racial and ethnic groups invariably contribute to observed mortality differences. Socio-legal concerns are experienced often by patients at safety-net hospitals and have the potential to become barriers to care and impact the receipt of timely care. Regarded as basic life needs, socio-legal barriers are amenable to legal intervention but if the needs remain unmet, negative health outcomes may results. Understanding the burden of socio-legal concerns in at-risk populations is necessary to address these barriers to care. To date, systematic assessment of socio-legal concerns among newly diagnosed breast cancer patients has not been well documented. METHODS: We conducted a secondary analysis of I-HELP survey data collected for Project SUPPORT, an RCT comparative effectiveness study that compared standard navigation to legally enhanced navigation at Boston Medical Center. The survey, administered in English, Spanish or Haitian Creole by trained Research Assistants at baseline, 3 and 6 months, systematically assessed participants socio-legal concerns within four categories: Employment, Disability Benefits, Housing and Utilities and Personal and Family Stability. Demographic information was extracted from participants’ electronic medical records, supplemented by surveys on health literacy and employment. Means, medians, frequencies and percentages were used to describe and quantify the burden, type and change in concern at each follow-up point. T-tests and Fischer exact tests were used to evaluate differences in socio-demographic characteristics on the presence, type and trends among the concerns. RESULTS: Overall 262 breast cancer patients enrolled in Project SUPPORT and completed the baseline I-HELP survey. The racial distribution of the participants reflects the diverse population served by Boston Medical Center with the majority of the participants (n=200, 76%) being non-white. Overall, 77% (201/262) of participants reported 1 or more concerns across all categories with 44% (115/262) reporting 3 or more concerns. Among the four categories of concern, the most frequently reported concern was related to housing with 61% (161/262) reporting at least one housing concern. Participants most likely to express any concern were 50 years old or younger, non-English speaking and employed. For the majority of participants, the burden of concern remained constant or increased during the first 6-months of treatment. Non-white women were most likely to experience a persistent concern over the first six months. CONCLUSIONS: Our findings confirmed that the majority of breast cancer patients experience some kind of socio-legal concern at the time of diagnosis. Also, socio-legal concerns persist throughout the course of treatment. The reported results emphasize the continued need for systematic assessment during cancer treatment. Additionally, collaboration between the medical and legal sectors in cancer care delivery at safety-net hospitals offers a means to address barriers to care and improve health outcomes.

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Inom denna rapport så kommer det gås igenom vilka tekniker och metoder som har används för att skapa en implementation en ny sorts webbaserat API. Det nya API't heter GraphQL och upfyller samma syfte som det mer traditionella REST API som används mycket utav företag och organisationer i dagsläget. Det som skiljer GraphQL mot REST är att GraphQL använder sig av "Query Language" för att kommunicera med API't genom att hämta eller skicka information. Implementationen har utförts inom .NET Framework hos företaget Quicksearch. Utvecklingen har skett inom företagets datasystem och API't kan hämta information från deras databas och skicka det vidare till användaren. Verktygen som har använts för att skapa applikationen är utvecklingsprogrammet Visual Studio tillsammans med programmeringsspråket C\#. Programmet Postman har använts till hjälp för utveckling och testning av API't. Relevanta metoder för att skapa API't inkluderar bibliotek riktat mot GraphQL och sammankoppling mot databasen. Designmönster har varit viktiga i projektet och dessa är "Dependency Injection" och "Model-View-Controller" för att skapa en web applikation. Slutresultatet är ett GraphQL API som kan forma enkla förfrågningar riktat mot en data-modell eller objekt. Även en jämförelse av GraphQL mot REST API har utförts med resultatet att det finns distinkta skillnader mellan de två konceptenför men för att komma fram till en övergripandeslutsats över vilket API som är ett bättre alternativ så behövs ytterligare tester. / This report will walkthrough several techniques and methods that have been used to create a new kind of web-based API. This new kind of API is called GraphQL and fullfils the same purpose as the more traditionally used REST API. What differs between these two types of API is that GraphQL uses a ”Query Language” when the user communicates with the API. The implementation is done in .NET Framework together with a company named Quicksearch. The development has been done within Quicksearch’s computer system and the API can request information from the company’s database and send it back to the user of the API. The tools that have been used to develop the API are Visual Studio as the development enviroment, along with the programming language C#. Postman is a software that have also been used to help develop and test the GraphQL API. Relevant methods used in the development includes libraries directed to create a GraphQL API and libraries used to create the connection to the database. Design patterns have also be important in the development and the most noticeable ones have been ”Dependency Injection” and ”Model-View-Controller” to create the application. The end result is an API that is able to recieve simple queries directed against a simple data-model or object. Aditionally a comparison between GraphQL and REST have been done and the result is that there are distinct differences between the two types of API but there is a lack of scientific reasarch and knowledge to make the conclusion of which is the better alternative and additional tests are required.

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.

京都大学 / 0048 / 新制・課程博士 / 博士(社会健康医学) / 甲第19639号 / 社医博第72号 / 新制||社医||9(附属図書館) / 32675 / 京都大学大学院医学研究科社会健康医学系専攻 / (主査)教授 今中 雄一, 教授 佐藤 俊哉, 教授 木原 正博 / 学位規則第4条第1項該当 / Doctor of Public Health / Kyoto University / DFAM

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Background: These days when developing applications for mobile platforms, therehave been two main branches to pick between as a developer. The first option– denoted native development – targets only one specific mobile operating system(OS). The other is cross-platform development which can target multiple platformssimultaneously. A common concern with cross-platform development, however, is itsperformance when compared to native frameworks. Objectives: In this thesis, the objective is to compare the performance of applica-tions created for Android using the native framework and the cross-platform .NETMAUI framework by Microsoft. Methods: The method used in this thesis was a combination of a literature (map-ping) study and an experiment. The purpose of the mapping study was to identifyrelevant performance metrics. The experiment then observed those how those met-rics, CPU and memory (RAM) usage, differed between the applications created usingboth frameworks. Results: Overall, the .NET MAUI framework was significantly worse than the na-tive framework on both measured performance metrics in 9 out of 12 tasks tested.The .NET MAUI framework was significantly better in 1 task, while 2 other tasksended up with differences that were statistically insignificant. Conclusions: An experiment was conducted to compare CPU usage in % andmemory (RAM) usage in MB between the native framework and the .NET MAUIframework for Android development on a variety of tasks. The outcome of the ex-periment significantly favored the native framework statistically. However, due to ofthe small sample size in selected tasks as well as specific design choices it is unclearhow real-world use would compare.