Thesis (M. Sc.) Nursing Sciences)) -- University of Limpopo, 2016

Intensivvård är en avancerad och resurskrävande form av vård och ställer stora krav på intensivvårdssjuksköterskans kompetens och kunskapsnivå. Tidigare studier inom området belyser i huvudsak den grundutbildade sjuksköterskans upplevelser av att vara ny på en intensivvårdsavdelning och deras erfarenheter av introduktionen. Däremot saknas kunskap som beskriver den nyutbildade IVA-sjuksköterskans upplevelser av att vara ny på en intensivvårdsavdelning samt deras erfarenheter av given introduktion. Syftet med studien var att beskriva nyutbildade intensivvårdssjuksköterskors upplevelser av att vara ny på en intensivvårdsavdelning samt belysa deras erfarenheter av given introduktion. Datamaterialet bestod av beskrivande berättelser som analyserades enligt kvalitativ innehållsanalys med en induktiv ansats. I resultatet framkom tre kategorier; ”Från osäkerhet till ansvarskännande”, ”Stöd under introduktionen gav trygghet” och ”Brist på tid, uppföljning och kontinuitet av handledare under introduktionen upplevdes otryggt”. Deltagarna upplevde en osäkerhet i sin nya roll som IVA-sjuksköterska kopplat till bristande klinisk kunskap och erfarenhet. Stödet de fick under introduktionen av en checklista, kollegor, handledare och mentorer gav trygghet. Brist på tid för handledning samt bristande uppföljning och kontinuitet av handledare under introduktionen upplevdes otryggt och negativt för utvecklingen som novis. Konklusionen av resultatet var att IVA-sjuksköterskan upplevde en osäkerhet i början av sin anställning vid intensivvårdsavdelningen men med en stöttande i / Intensive care is the most advanced and resourcing care and demands great competence and level of knowledge on the intensive care nurse. Previous research shows newly trained nurses experiences of starting to work in an intensive care unit and their experiences of the introduction. While there is a lack of knowledge that describes newly trained ICU-nurses experiences of being new in an intensive care unit and their experiences of the given introduction. The aim of this study was to describe newly trained ICU-nurses experiences of being new in an intensive care unit and their experiences of the given introduction. The data consisted of descriptive stories that were analyzed according to qualitative content analysis with an inductive approach. The result shows three categories; "From uncertainty to a sense of responsibility", "Support during the introduction gave sense of security" and "Lack of time, follow-up and continuity of supervisors during the introduction was experienced uncertain”. The participants experienced an uncertainty in their new role as an ICU-nurse linked to a lack of clinical knowledge and experience. The support they received during the introduction of a checklist, colleagues, supervisors and mentors provided security. Lack of time for supervision and lack of follow-up and continuity of supervisors during the introduction was perceived as insecure and negative for the progress as a novice. The conclusion of the result was that the ICU-nurses experienced insecurity in the beginning but with a supportive introduction the feeling of being a novice nurse became a feeling of being an advanced beginner.

Thesis focuses on disk arrays, where the goal is to design test scenarios to measure performance of disk array and use predictive analytics tools to train a model that will predict the selected performance parameter on a measured set of data. The implemented web application demonstrates the functionality of the trained model and shows estimate of the disk array performance.

Detta arbete undersöker hur språkmodellen BERT och en MaLSTM-arkitektur fungerar att för att identifiera parafraser ur 'Microsoft Paraphrase Research Corpus' (MPRC) om dessa tränats på automatiskt identifierade parafraser ur 'Paraphrase Database' (PPDB). Metoderna ställs mot varandra för att undersöka vilken som presterar bäst och metoden att träna på maskinklassificerad data för att användas på mänskligt klassificerad data utvärderas i förhållande till annan klassificering av samma dataset. Meningsparen som används för att träna modellerna hämtas från de högst rankade parafraserna ur PPDB och genom en genereringsmetod som skapar icke-parafraser ur samma dataset. I resultatet visar sig BERT vara kapabel till att identifiera en del parafraser ur MPRC, medan MaLSTM-arkitekturen inte klarade av detta trots förmåga att särskilja på parafraser och icke-parafraser under träning. Både BERT och MaLSTM presterade sämre på att identifiera parafraser ur MPRC än modeller som till exempel StructBERT, som tränat och utvärderats på samma dataset, presterar. Anledningar till att MaLSTM inte klarar av uppgiften diskuteras och främst lyfts att meningarna från icke-parafraserna ur träningsdatan är för olika varandra i förhållande till hur de ser ut i MPRC. Slutligen diskuteras vikten av att forska vidare på hur man kan använda sig av maskinframtagna parafraser inom parafraseringsrelaterad forskning.

Antalet cyberattacker ökar ständigt och därav också antalet angreppssätt och försvarstekniker. Detta innebär att personer verksamma inom cybersäkerhet behöver spendera mer och mer tid på att hålla sig uppdaterade om de senaste utvecklingarna i branschen. Det är därför av intresse att hitta sätt som kan påskynda denna inhämtning av information. I denna studie utvecklas en prototyp med målet att på ett nytt sätt automatiskt summera en av de många sorters nyhetskällor som finns inom cybersäkerhetsdiskussioner på onlineforum. Prototypen använder sig av abstraktiv textsummering med zero-shot modellen GPT-3. Prototypen som utvecklades utvärderades genom att mäta de summeringar som skapades med SUPERT. Resultatet från mätningen gav ett värde av 0,269 vid mätning mot de originella texterna och 0,358 vid mätning mot ett dataset som städats från text som inte rör cybersäkerhet. Från dessa värdet dras slutsatsen att utvecklingen av prototypen lyckades.

People read for various purposes like learning specific skills, acquiring foreign languages, and enjoying the pure reading experience, etc. This kind of pure enjoyment may credit to many aspects, such as the aesthetics of languages, the beauty of rhyme, and the entertainment of being surprised by what will happen next, the last of which is typically featured in fictional narratives and is also the main topic of this project. In other words, “good” fiction may be better at entertaining readers by baffling and eluding their expectations whereas “normal” narratives may contain more cliches and ready-made sentences that are easy to predict. Therefore, this project examines whether “good” fiction is less predictable than “normal” fiction, the two of which are predefined as canonized and non-canonized.  The predictability can be statistically reflected by the probability of the next words being correctly predicted given the previous content, which is then further measured in the metric of perplexity. Thanks to recent advances in deep learning, language models based on neural networks with billions of parameters can now be trained on terabytes of text to improve their performance in predicting the next unseen texts. Therefore, the generative pre-trained modeling and the text generator are combined to estimate the perplexities of canonized literature and non-canonized literature.  Due to the potential risk that the terabytes of text on which the advanced models have been trained may contain book content within the corpus, two series of models are designed to yield non-biased perplexity results, namely the self-trained models and the generative pre-trained Transformer-2 models. The comparisons of these two groups of results set up the final hierarchy of architecture constituted by five models for further experiments.  Over the process of perplexity estimation, the perplexity variance can also be generated at the same time, which is then used to denote how predictability varies across sequences with a certain length within each piece of literature. Evaluated by the perplexity variance, the literature property of homogeneity can also be examined between these two groups of literature.  The ultimate results from the five models imply that there lie distinctions in both perplexity values and variances between the canonized literature and non-canonized literature. Besides, the canonized literature shows higher perplexity values and variances measured in both median and mean metrics, which denotes that it is less predictable and homogeneous than the non-canonized literature.  Obviously, the perplexity values and variances cannot be used to define the literary quality directly. However, they offer some signals that the metric of perplexity can be insightful in the literary quality analysis using natural language processing techniques.

In recent years, Generative Language Models have exploded into the mainstream with household names like BERT and ChatGPT, proving that text generation could have the potential to solve a variety of tasks. As the number of students enrolled into programming classes has increased significantly, providing adequate feedback for everyone has become a pressing logistical issue. In this work, we evaluate the ability of near state-of-the-art Generative Language Models to provide said feedback on an automated basis. Our results show that the latest publicly available model GPT-3.5 has a significant aptitude for finding errors in code while the older GPT-3 is noticeably more uneven in its analysis. It is our hope that future, potentially fine-tuned models could help fill the role of providing early feedback for beginners, thus significantly alleviating the pressure put upon instructors.

The service role and functions of school counseling and school psychology have been discussed extensively for several decades. The literature from each professional discipline is replete with calls to expand the training and service capacity of these school professionals. Simultaneously calls are made for even broader educational reform as it specifically relates to student pupil services. From within this context an integrated school counseling and school psychology program known as the School Counseling Psychology Program was organized and administered at Brigham Young University. This qualitative dissertation study highlights the perceived strengths and limitations of this integrated training program. The impact of integrated services and collaboration among student service professionals is highlighted from a regional perspective that reaffirms the value of grassroots level research. Lastly, the conceptual barriers and recommendations of administering an integrated school counseling and school psychology training program are presented and discussed.

The purpose of this research note was to examine whether relationships existed between anthropometrics (body mass, body fat percentage [BF%], and femur length) and descriptive characteristics (age and sex) with repetitions performed to failure at 70% of 1 repetition maximum (1RM) in the back squat. Fifty-eight subjects (males = 43, females = 15; age: 23 +/- 3 years, training age: 5.5 +/- 2.5 years, body mass: 80.65 +/- 16.34 kg, BF%: 10.98 +/- 3.53%, and femur length: 47.1 +/- 2.6 cm) completed a 1RM squat followed by one set to failure at 70% of 1RM. Total repetitions performed at 70% of 1RM were 14 +/- 4 (range: 6-26). Bivariate correlations showed significant inverse relationships between body mass (r = -0.352, p = 0.003), BF% (r = -0.278, p = 0.014), and femur length (r = -0.265, p = 0.019), with repetitions performed. No significant relationships existed between age and sex (p > 0.05), with repetitions performed. All these variables entered into a standard multivariate regression. The model R2 was 0.200, and body mass had the largest influence (p = 0.057) because relative importance analysis demonstrated body mass to contribute to 43.87% of the variance (of the R2) in repetitions performed. No other variable was significant or approached significance (p > 0.05). Our results reveal that body mass, BF%, and femur length all are inversely related to repetitions performed at 70% of 1RM in the back squat.

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24259号 / 情博第803号 / 新制||情||135(附属図書館) / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 吉川 正俊, 教授 田島 敬史, 教授 黒橋 禎夫, 特定准教授 LIN Donghui / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM