High-throughput Sequencing (HTS)-tekniker fortsätter att utvecklas snabbt, vilket ökar genomströmningen och minskar sannolikheten för fel. MGI Tech Co., Ltd. (MGI) är ett ledande HTS-varumärke som använder DNBSEQ-teknologi och finns i Center for Translational Microbiome (CTMR). MGI:s sequencers har en hög känslighet och det är viktigt att följa protokollen när proverna hanteras för att undvika introduktion av kontaminering. Detta projekt kommer att utforska tidigare genererade data vid CTMR för att fastställa hur och var i sekvenseringsprocessen kontaminering har introducerats. Data delas in i två huvudkategorier: primärdata, eller verkliga data (RD), och sekundära data, vidare uppdelad i Never Used Barcodes (NUB) och Non-Sequenced (NS). RD:n är sann mot provet, medan NUB och NS anses vara hämtade från bakgrundsbrus. RD, NUB och NS var föremål för taxonomiska analyser, på släkt- och artnivå, och streckkodsanalyser med hjälp av RStudio-gränssnittet för att identifiera och kontrastera de vanligaste i varje kategori. Dessutom var RD också föremål för dekontamineringsanalys på två databaser, VaMyGyn och KOLBIBAKT. Dekontaminering används för att identifiera förorenande arter i ett samhälle. Efter analysen fanns det inga starka bevis som tydde på laboratoriekontamination eller kontaminerade reagenser. Några av dessa NUB delade subsekvenser med RD barcodes, där antal reads för varje par var korrelerade mellan prover. Det kan vara en indikation på att RD barcoded med sekvenseringsfel blir inkorrekt tolkade som NUB. En djupare analys skulle krävas för att bekräfta det.CTMR är numera medveten om att kontaminering från laboratoriet, reagenser eller manipulation inte är orsaken till hämtning av bakgrundsljud. / High-throughput sequencing (HTS) technologies keep developing rapidly, increasing throughput and lowering probabilities of errors. MGI Tech Co., Ltd. (MGI) is a leading HTS brand that uses DNBSEQ technology and is present in the Centre for Translational Microbiome (CTMR). MGI’s sequencers have a high sensitivity and it is critical to follow the protocols when the samples are being handled to avoid introduction of contamination. This project will explore the previously generated data at CTMR to determine how and where in the sequencing process contamination has been introduced. Data is divided into two main categories: the primary data, or real data (RD), and secondary data, further divided into Never Used Barcodes (NUB) and Non-Sequenced (NS). The RD is true to the sample, while NUB and NS are considered background noise retrieved. The RD, NUB, and NS were subject to taxonomic analyses, at genus and species level, and barcode analyses using the RStudio interface to identify and contrast the most frequent in each category. Moreover, RD was also subject to decontam analysis on two databases, VaMyGyn and KOLBIBAKT. Decontam is used for the identification of contaminant species in a community. After the analysis, there was no strong evidence suggesting lab contamination or contaminated reagents. Some of the barcodes from NUB shared substrings with RD barcodes for which the amount of reads were correlated across samples. This may indicate that RD barcodes with sequencing errors are falsely identified as NUB, however, more analyses are needed to verify this. CTMR is now aware that contamination from the lab, reagents, or manipulation are not the causes for the background noise retrieval.

La Thaïlande est un État unitaire et depuis 1892 son administration déconcentrée est assurée par la présence de gouverneurs provinciaux. Or, malgré de nombreuses attributions législatives, l'exercice des fonctions du chef de la province se heurte à plusieurs obstacles dans la pratique. A titre d'exemple, l'unité de commandement du gouverneur provincial était souvent contestée, les pouvoirs qui lui ont été délégués par les autorités centrales étaient inappropriés tant à sa fonction qu'à ses responsabilités. Ceci était une source de lenteur de l'administration provinciale dans son ensemble. Puis, comme la province ne pouvait pas bénéficier directement du budget provenant de l'État car la demande de budget provincial était effectuée au nom de la Direction, les projets réalisés sur place ne convenaient donc pas aux besoins des habitants locaux. Enfin, l'absence de coopération entre les fonctionnaires des différents organes dans la province contribuait alors aux chevauchements des travaux de ces organes et causait par la suite une perte de temps et d'argent pour l'État. Ainsi, lors de la réforme en 2001, le gouvernement de l'époque a introduit au niveau provincial le concept du gouverneur « CEO » et la méthode de la gestion d'intégration (MGI). Le chef de la province endosse alors le rôle du Chief Executive Officer (CEO), de ce fait ses pouvoirs ont été renforcés afin qu'il puisse prendre une décision rapide et efficace en réponse aux problèmes survenus dans sa province à l'instar des CEO dans leurs entreprises. Quant à la province, elle applique la MGI qui est une nouvelle façon de travailler visant à rendre de meilleurs services rendus à la population. / Thailand is a unitary state and since 1892 its deconcentrated administration is ensured by the presence of provincial governors. However, despite numerous attributions recognized by the laws, the provincial governor faces several obstacles in practice. For example, his Unity of Command was often challenged, the powers delegated to him by the central authorities were not only inappropriate to his function but also to his responsibilities. This was actually a source of slowness in provincial administration. Then, because the province could not directly benefit the budget from the State, as the process of demand for provincial budget was conducted on behalf of the Department, the projects that were carried out did not necessarily match up with local's need. Finally, the lack of cooperation between officials of different organs who work in the province contributed to an overlap of works and caused thereafter a waste of time and money to the State. Therefore, during the reform in 2001, the then government introduced the concept of "CEO" provincial governor and the system of "Province's Integrated Management - PIM". The provincial governor endorses hereupon the role of Chief Executive Officer (CEO), for this reason, his powers have been reinforced so that he can make a fast and effective decision in response to problems occurring in his province like the CEOs to their companies. As for the province, the system of PIM is applied and it is a new way of working which aims to provide a better service for the population.

AlNiCo magnets are known for high-temperature stability and superior corrosion resistance and have been widely used for various applications. Reported magnetic energy density ((BH) max) for these magnets is around 10 MGOe. Theoretical calculations show that ((BH) max) of 20 MGOe is achievable which will be helpful in covering the gap between AlNiCo and Rare-Earth Elements (REE) based magnets. An extended family of AlNiCo alloys was studied in this dissertation that consists of eight elements, and hence it is important to determine composition-property relationship between each of the alloying elements and their influence on the bulk properties. In the present research, we proposed a novel approach to efficiently use a set of computational tools based on several concepts of artificial intelligence to address a complex problem of design and optimization of high temperature REE-free magnetic alloys. A multi-dimensional random number generation algorithm was used to generate the initial set of chemical concentrations. These alloys were then examined for phase equilibria and associated magnetic properties as a screening tool to form the initial set of alloy. These alloys were manufactured and tested for desired properties. These properties were fitted with a set of multi-dimensional response surfaces and the most accurate meta-models were chosen for prediction. These properties were simultaneously extremized by utilizing a set of multi-objective optimization algorithm. This provided a set of concentrations of each of the alloying elements for optimized properties. A few of the best predicted Pareto-optimal alloy compositions were then manufactured and tested to evaluate the predicted properties. These alloys were then added to the existing data set and used to improve the accuracy of meta-models. The multi-objective optimizer then used the new meta-models to find a new set of improved Pareto-optimized chemical concentrations. This design cycle was repeated twelve times in this work. Several of these Pareto-optimized alloys outperformed most of the candidate alloys on most of the objectives. Unsupervised learning methods such as Principal Component Analysis (PCA) and Heirarchical Cluster Analysis (HCA) were used to discover various patterns within the dataset. This proves the efficacy of the combined meta-modeling and experimental approach in design optimization of magnetic alloys.