Background: Pediatric Autoimmune Neuropsychiatric Disorders Associated with Streptococcus (PANDAS) research is based on the hypothesis that infections trigger changes in behavior and movement in children. Methods: We enrolled 693 children (ages 3 to 12 years) into a systematic, longitudinal study. Data were collected monthly for 8 months (October-May) to determine point prevalence of Group A Streptococcal (GAS) infections, tics, behavior, and choreiform movements. Simultaneous throat cultures were obtained, and relational analyses were made between GAS and movement/observation ratings. Results: Combined behavior/GAS associations (concurrent with or 3 subsequent months to GAS) revealed a strong relationship, relative risk (RR) of 1.71 (p < .0001). Detailed analysis revealed that balance/swaying and non-tic grimacing were responsible for a significant proportion of this association (RR = 2.92, p < .0001). A strong seasonal pattern was found, with fall being more significant for GAS infections and observation ratings (p < .0001) compared with winter/spring. Children with repeated streptococcus (n = 64) showed higher rates of behavior and distal choreiform observations (p = .005). Conclusions: Motor/behavior changes were noted to occur in relationship to positive GAS culture with support that repeated GAS increases risk.

This thesis presents a domain knowledge based similarity measure for recommender systems, using Systembolaget's open API with product information as input data. The project includes the development of the similarity measure, implementing it in a content based recommender engine as well as evaluating the model and comparing it to an existing model which uses a bag-of-words based approach. The developed similarity measure uses domain knowledge to calculate the similarity of three feature, grapes, wine regions and production year, to attempt to improve the quality of recommendations. The result shows that the bag-of-words based model performs slightly better than the domain knowledge based model, in terms of coverage, diversity and correctness. However, the results are not conclusive enough to discourage from more investigation into using domain knowledge in recommender systems.

Sammanfattning/AbstractSandberg, Helén (2020). En livsberättelse om skolgången för en person med PANS. Specialpedagogprogrammet, Institutionen för skolutveckling och ledarskap, Lärande och samhälle, Malmö universitet, 90 hp.Förväntat kunskapsbidragPANS (Pediatric Acute-onset Neuropsychiatric Syndrome) eller PANDAS (Pediatric Autoimmune Neuropsychiatric Disorders Associated with Streptococcal infections) är en relativt ny och okänd diagnos inom skolan. Det är en autoimmun sjukdom som har neuropsykiatriska symtom men utlöses av infektion. Forskare menar att alla som arbetar inom skolan kommer att stöta på en eller flera elever som har PANS under sitt arbetsliv (Miglioretti, 2019, Doran, 2015). Därför är det av vikt att PANS uppmärksammas så att skolan kan ge bästa stöd för elever. Då det finns stor brist på information och forskning om PANS inom skolan är min förhoppning att genomförda studie ska kunna bidra med ökad kunskap om diagnosen och vara som en väckarklocka för skolväsendet att uppmärksamma elever med PANS.Syfte och frågeställningarSyftet med undersökningen är att genom livsberättelseanalys öka kunskapen om diagnosen PANS samt hur skolan på bästa sätt kan bemöta elever med PANS och anpassa skolgången.Frågeställningar:-Hur har en person med PANS upplevt sin skolgång?-Vilka lärdomar kan skolväsendet dra ifrån denna berättelse?TeoriStudien har vägletts av livsberättelseanalys som metod men har även intagit ett systemteoretiskt perspektiv. I resultat och diskussionsdelen har en livsberättelse analyserats med hjälp av systemteori. Bronfenbrenners utvecklingsekologiska teori redogör hur en individs utveckling alltid är beroende av samspelet med sin miljö och sitt sammanhang. Livsberättelsen har studerats i förhållande till omgivningens olika system: mikro-, meso-, exo-, och makrosystem.  MetodLivsberättelseintervju har använts för att studera en person med PANS subjektiva upplevelse av skolgången. Livsberättelse är en lämplig metod då upplevelser av ett fenomen ska beskrivas ur ett längre tidsperspektiv (Fejes & Thornberg, 2019). Intervjun blir då retrospektiv då informanten reflekterar över tidigare erfarenheter och minnen. Det är inte en exakt avbildning av verkligheten som eftersträvats utan personens egen berättelse om sin upplevelse av skolgången.ResultatResultatet visar att informantens upplevelse är att hon har bemötts av en oförstående skola och ett oförstående samhälle. Sjukdomen PANS bröt ut när hon var 7 år men det dröjde tills hon blev 23 år innan hon fick rätt diagnos och behandling. Under grundskolan har hon blivit utsatt för övergrepp som att bli fasthållen när hon varit i affekt samt blivit avstängd och nekad undervisning under ett helt läsår. Vidare har både skola och vård skuldbelagt henne och föräldrarna.Vad skolan kan lära sig av denna livsberättelse är vikten av att alltid ha ett relationellt perspektiv och inte lägga problemet hos eleven eller dess föräldrar. Att ha gott samarbete mellan skola och hem och lyssna på barnet och föräldrarna är avgörande för att eleven ska få rätt stöd i skolan. Vidare behöver skolan hålla sig uppdaterad om nya diagnoser och aktuell forskning för att förebygga att elever bemöts av en oförstående skola som inte ger rätt hjälp i tid.Specialpedagogiska implikationerDet är viktigt att specialpedagoger håller sig informerade om nya diagnoser och om aktuell forskning för att kunna ge eleverna bästa stöd i skolan. När skolan missförstår en elevs problematik kan det få förödande konsekvenser. Specialpedagogen kan verka för att skolan utgår ifrån ett relationellt perspektiv på skolsvårigheter där lärare alltid lyssnar på eleven och föräldrarnas bild av situationen (Nilholm, 2012). Att ha en positiv elevsyn och alltid utgå ifrån att eleven vill uppföra sig om den kan (Hejlskov et al. 2017) är av stor vikt för alla elever, men särskilt för elever med PANS. NyckelordLivsberättelse, PANS, PANDAS, skolgång, systemteori

В данной дипломной работе была поставлена цель разработки эффективной рекомендательной системы для туристических достопримечательностей на основе графов и алгоритмов машинного обучения. Основная задача состояла в создании системы, которая может анализировать обширный набор данных о туристических достопримечательностях, извлекаемых из Википедии. Используя дампы Википедии, содержащие информацию о миллионах статей, был выполнен обзор существующих рекомендательных систем и методов машинного обучения, применяемых для предоставления рекомендаций в области туризма. Затем были выбраны определенные категории туристических достопримечательностей, которые были использованы для построения моделей рекомендаций. Для обработки и анализа данных из Википедии был использован современный технический стек инструментов, включающий Python, библиотеки networkx и pandas для работы с графами и данными, а также библиотеку scikit-learn для применения алгоритмов машинного обучения. Кроме того, для разработки интерактивного веб-интерфейса был использован фреймворк Streamlit. Процесс работы включал сбор и предварительную обработку данных из Википедии, включая информацию о достопримечательностях, связях между ними и характеристиках. Для создания графа данных на основе загруженных и обработанных данных были применены выбранные алгоритмы машинного обучения. Алгоритм PageRank был использован для определения важности каждой достопримечательности в графе и формирования персонализированных рекомендаций. Демонстрационный пользовательский интерфейс, разработанный на основе фреймворка Streamlit, позволяет пользователям взаимодействовать с системой, вводить запросы о местах и получать персонализированные рекомендации. С помощью выпадающего списка можно выбрать конкретную достопримечательность, к которой требуется получить рекомендации, а с помощью ползунка можно настроить количество рекомендаций. / This thesis aimed to develop an effective recommendation system for tourist attractions based on graphs and machine learning algorithms. The main challenge was to create a system that can analyze a large set of tourist attraction data extracted from Wikipedia. Using Wikipedia dumps containing information on millions of articles, a review of existing recommender systems and machine learning methods used to provide recommendations in the field of tourism was performed. Specific categories of tourist attractions were then selected and used to build recommendation models. To process and analyze data from Wikipedia, a modern technical stack of tools was used, including Python, the networkx and pandas libraries for working with graphs and data, as well as the scikit-learn library for applying machine learning algorithms. In addition, the Streamlit framework was used to develop an interactive web interface. The work process included the collection and preliminary processing of data from Wikipedia, including information about attractions, connections between them and characteristics. Selected machine learning algorithms were applied to create a data graph based on the downloaded and processed data. The PageRank algorithm was used to determine the importance of each point of interest in the graph and generate personalized recommendations. The demo user interface, developed using the Streamlit framework, allows users to interact with the system, enter queries about places and receive personalized recommendations. Using the drop-down list, you can select a specific attraction for which you want to receive recommendations, and using the slider, you can adjust the number of recommendations.

Due to increasing water treatment costs and conservation needs, traditional water loss analysis and acoustic leak detection methods are becoming heavily scrutinized by water utilities. This study explores water loss in Johnson City, Tennessee and how geospatial data analysis techniques improve water loss mitigation. This project uses sample water system pressure data and ordinary kriging spatial interpolation methods to identify leakage areas for further investigation. Analysis of existing geographic information system (GIS) water utility datasets with interpolated hydraulic grade values at sample water pressure points produce manageable survey areas that pinpoint areas with possible water leakage. Field detection methods, including ground-penetrating radar (GPR) and traditional acoustic methods, are employed to verify leakage predictions. Ten leakage areas are identified and verified using traditional acoustic detection methods, work order research, and GPR. The resulting data show that spatial analysis coupled with geospatial analysis of field pressure information improves water loss mitigation.

Stress detection based on non-EEG physiological data can be useful for monitoring drivers, pilots, and also for monitoring of people in ordinary situation, where standard EEG monitoring is unsuitable. This work uses Non-EEG database freely available from Physionet. The database contains records of heart rate, saturation of blood oxygen, motion, a conductance of skin and temperature recorded for 3 type of stress alternated with relax state. Two final models were created in this thesis. First model for Binary classification stress/relax, second for classification of 4 different type of psychical state. Best results were reached using model created by decision tree algorithm with 8 features for binary classification and with 8 features for classification of 4 psychical state. Accuracy of final models is aproximately 95 % for binary model and 99 % for classification of 4 psychical state. All algorithms were implemented in Python.

This diploma thesis presents a project documentation for construction of a zoo pavilion with a restaurant. The structure has to be designed in compliance with regulations for buildings with almost zero energy consumption. Its construction site is located within the existing compound of Prague’s zoo on plot no. 1491/1. The pavilion is proposed to house giant pandas. The building has three floors- one underground and two above. It is covered by a flat green roof. In terms of circulation, the structure is composed from two parts. The first one is formed by premises for the pandas, including necessary facilities for their wellbeing and their breeding; and the second part is visitors’ part, including restaurant, gift shop and amenities. The building is enclosed by two exterior expositions for pandas which are connected with the interior exposition premises by two ramps- tunnels. There is a roof terrace adjacent to the restaurant for visitors to enjoy the view of pandas in their exterior grounds. The vertical structure of the pavilion for giant pandas is a combination of load bearing brick walls and cast-in-place concrete walls- the underground floor and the ground floor is formed by the cast-in-place concrete walls and the upper floor is formed by the brick walls made out of autoclaved aerated concrete blocks. The horizontal structures are formed by prestressed concreted floor panels or in the case of the upper floor by cast-in-place reinforced concrete slabs.

<p>The broad implementation of three-dimensional full-core modeling, with pin-resolved detail, for computational simulation and analysis of nuclear reactors highlights the importance of accuracy and efficiency in simulation codes for accurate and precise analysis. The primary objective of this dissertation is to develop a high-fidelity code capable of solving time-dependent neutron transport problems with 3D whole-core pin-resolved detail in nuclear reactor cores. Additionally, the dissertation explores the optimization of the code's parallelism to enhance its computational efficiency. To reduce the computational intensity associated with the direct 3D calculation of the neutron transport equation, a high-fidelity neutron transport code called PANDAS-MOC is developed using the 2D/1D approach. The 2D radial solution is obtained using the 2D Method of Characteristics (MOC), the axial 1D solution is determined through the Nodal Expansion Method (NEM), and then two solutions are coupled using transverse leakages to find the 3D solution. The convergence of the iterative scheme is accelerated using the multi-level coarse finite different mesh (ML-CMFD) technique. The code's validation and verification are carried out using the C5G7-TD benchmark exercises.</p> <p><br></p> <p>The significant and innovative aspect of this work involves parallelizing and optimizing the PANDAS-MOC code. Three parallel models are developed and evaluated based on the distributed memory and shared memory architecture: MPI parallel model (PMPI), Segment OpenMP threading hybrid model (SGP), and Whole-code OpenMP threading hybrid model (WCP). When computing the steady state of the C5G7 3D core with the same resources, the obtained speedup relationship between the three models is PMPI \(>\) WCP \(>\) SGP, whereas the WCP model only consumed 60\% of the memory of the PMPI model. Furthermore, the hybrid reduction in the ML-CMFD solver and the parallelism design of the MOC sweep are significant issues that decreased the speedup of WCP. Therefore, this study also addresses further optimizations of these two modules.</p> <p><br></p> <p>Concerning the MOC parallelism, two improvements are discussed: No-atomic schedule and Additional Axial Decomposition (AAD) parallelism. The No-atomic schedule evenly distributed the workload among threads and removes the \textit{omp atomic} clause from the code by predefining the MOC calculation sequence for each launched OpenMP thread while ensuring a thread-safe parallel environment. It can significantly reduce the calculation time and improve parallel efficiency. Furthermore, AAD divides the axial layers and OpenMP threads into multiple groups and restricts each thread to work on the layers designated to the same group. </p> <p>Meanwhile, Flag-Save-Update reduction is designed to increase the computational efficiency of the hybrid MPI/OpenMP reduction operations in the ML-CMFD module. It is accomplished by using the global arrays and status flags and establishing a tree configuration of all threads, and it includes no implicit and explicit barriers. In the case of the C5G7 3D core, the parallel efficiency of the MOC solver is about 0.872 when using 32 threads (=\#MPI \(\times\)\#OpenMP), and the Flag-Save-Update reduction yielded better speedup than the traditional hybrid MPI/OpenMP reduction, and its superiority is more obvious as more OpenMP threads are utilized. As a result, the WCP model outperforms the PMPI model for the overall steady-state calculation.</p> <p><br></p> <p>This research also investigates parallelizable preconditioners to accelerate the convergence of the generalized minimal residual method (GMRES) in the CMFD solver. Preconditioners such as Incomplete LU factorization (ILU), Symmetric Successive Over-relaxation (SOR), and Reduced Symmetric Successive Over-Relaxation (RSOR), are implemented in PANDAS-MOC. Except for RSOR, others are unsuitable for hybrid MPI/OpenMP parallel machines due to their inherent sequential nature and dependency on computation order. Their counterparts using the Red-Black ordering algorithm, namely RB-SOR, RB-RSOR, and RB-ILU, are formatted and examined on benchmark reactors such as TWIGL-2D, C5G7-2D, C5G7-3D, and their corresponding subplane models (TWIGL-2D(5S), C5G7-2D(5S), C5G7-3D(5S)), with relaxed convergence criteria (\(10^{-3}\)). Results show that all preconditioners significantly reduce the required number of iterations to converge the GMRES solutions, and RB-SOR is the best one for most reactors. In the case of C5G7-3D(5S), preconditioners exhibit similar sublinear speedup but demonstrate varying runtimes across all tests for both MG-GMRES and 1G-GMRES. However, the speedup results in 1G-GMRES are more than twice as high as those in MG-GMRES. RB-RSOR has an optimal efficiency of 0.6967 at (4,8), while RB-SOR and RB-ILU have optimal efficiencies of 0.6855 and 0.7275 at (32,1), respectively.</p>