The performance of pilots in the aerospace environment is a critical factor in the success of modern air and space travel. Various methods of evaluating performances of pilots have been implemented and the search for improved means of evaluation is an ongoing process. Multiple factors influencing performance have been identified in the past. However, as the demands on the pilot’s performances varies with changing technology, so does the need to identify new risk factors, as well as ranking old and new factors in order of effect on performance. Aim The descriptive study aims to identify and rank risk factors affecting the performance of pilots as assessed by the Penalty Scoring System at a Precision World Flying Championship. Methods and materials Pilots participating at the 2008 World Precision Championship in Ried-Kircheim in Austria were requested to complete questionnaires regarding possible factors that could affect performance stress factors. Each questionnaire required the subject to answer 14 questions, relating to 17 possible factors. These questionnaires were linked to the participant’s individual score as per the official competition results. Results Out of a total number (n = 178) of pilot performances during a week period, 88 % (n=157) completed questionnaires. Only 57% (n=89) of these performances were included in the study, due to administrative difficulties preventing the accurate linking of performances to penalty scores. Out of the 17 possible risk factors, 4 factors (23 %) were identified as being significantly associated with the Penalty Scoring System. Age proved the most consistent factor, the younger pilots (youngest aged 21) performing consistently better than the older ones (oldest aged 67), even if the older pilots may have had more experience. Experience also proved reliable as a factor predicting outcome, as the performances of the moderate experienced group (having competed in 3 or less previous World championships) was associated with a lower penalty score. The mood of the pilots on the day of competing proved to be an effective way of predicting outcome, with a good mood associated with a lower penalty score. Any medical condition or medication used, were associated with a higher penalty score. The remaining factors (n=13) showed no association, although some (n=5) factors, like sleep deprivation and alcohol are known risk factors. Conclusions The study succeeds in showing an association between the Penalty Scoring System and 4 factors (Age, Experience, Mood and Medical conditions) affecting the performance of pilots. Although not the aim of this study, the conclusion can be made that the Penalty Scoring System may be a valuable tool in identifying risk factors affecting pilot’s performance. / Dissertation (MSc)--University of Pretoria, 2011. / School of Health Systems and Public Health (SHSPH) / Unrestricted

No autorizada por el autor para ser publicada a texto completo / El presente trabajo de título tuvo como objetivo desarrollar una herramienta de scoring crediticio dirigida a empresas Latinoamericanas emisoras de títulos con clasificación menor o igual a BB. Actualmente en la plaza local, el desconocimiento que existe en este tipo de instrumentos de renta fija se supedita, en su mayoría, a la compra de fondos elaborados por empresas externas y no al estudio y desarrollo de tecnologías in house, externalizando, de esta forma, el análisis crediticio. Por lo tanto, el interés de desarrollar herramientas que apoyen la toma de decisiones es imperante para instituciones como Asesorías e Inversiones Cruz del Sur que busca, evidentemente, obtener retornos por sobre la competencia. La metodología para el scoring consistió en un estudio de los reportes y recomendaciones de los principales bancos de inversión y compañías de servicios financieros, tanto nacional como internacional, que brindan fondos e investigación de empresas Latinoamericanas y mercados emergentes, de tal manera de crear un universo de las principales métricas que son utilizadas en sus análisis actualmente. De tal universo se derivaron, a través de un estudio de incidencias y juicio de expertos, 5 ratios que otorgaban un diagnóstico de la estructura de deuda y capacidad de cumplir con obligaciones en el corto plazo. Posteriormente, se le asignó a cada métrica un puntaje ajustado al percentil diez de la distribución que presentaba y luego, a través de una descomposición del rendimiento del instrumento, se realizaron ejercicios regresivos (lineal y de panel) que estimaron la importancia de cada métrica en la calibración final. La herramienta fue realizada en lenguaje VBA y su interfaz en Excel, otorgando, además del score crediticio, funcionalidades complementarias que incluyeran información de mercado de los títulos, gráficos y fácil manejo de una base de datos interna con objeto de disminuir tiempos asignados al proceso de manejo de información. El resultado, considerando todas las funcionalidades que abarca, fue una herramienta capaz de otorgar una opinión sobre las circunstancias de un emisor para cubrir sus compromisos financieros sujeta a la limitada posibilidad de automatización de las variables y presentar un punto de partida para el departamento de estudios.

Stage IV cancer is characterized by a cancer’s ability to metastasize, or spread throughout the body. Metastatic disease in bone is a devastating condition affecting hundreds of thousands of people each year. Stage IV cancer patients suffering from metastatic disease in the proximal femur are at high risk of catastrophic pathologic fracture, an event which severely impacts patient health. Although metrics have been created to assess the risk of impending fracture, they lack specificity in the proximal femoral region. Shortcomings of these metrics further complicate clinical decision making related to prophylactic fixation in these medically compromised individuals. Fortunately, by using computational modeling to study this at-risk patient population, the likelihood of fracture due to metastatic lesions in the proximal femur can be more accurately assessed to improve clinical decision making. Finite element analysis (FEA) is a computational modeling technique that can non-invasively provide mechanics information to better assess true fracture risk of a given metastatic lesion. Although FEA has previously been utilized to study metastatic disease, lesions were always modeled as spheres or ellipsoids, while true lesion shapes are far more amorphous. It was the focus of this study to validate FEA’s ability to predict fracture location in cadaveric femora with realistically shaped experimental metastatic lesions. Off-set torsion, or load applied off-set from the fixed long bone axis, was applied to cadaveric specimens with mechanically induced metastatic lesions, and the resultant fracture location was compared to specimen-specific FEA models replicating the mechanical test. FEA was able to correctly predict fracture locations in five models. Determining fracture risk based on objective mechanical data may more accurate and effective in this patient population.

Infertility is a widespread problem around the world, although the available treatment options constantly improve through research and methodological development. A common treatment option mainly for biologically caused infertility is in-vitro fertilization, where in a menstrual cycle multiple oocytes are stimulated to mature and are then fertilized in a laboratory. This often results in multiple good quality embryos which can be cryopreserved through vitrification and used in a later cycle to increase the success chance of the treatment. The purpose of this pilot study was to evaluate vitrification and warming kits from Kitazato and Irvine Scientific regarding results and procedures. Human cleavage stage embryos (n=76) were thawed and cultured to the blastocyst stage. The blastocysts were scored according to Gardner and cryopreserved with vitrification and warming kits from either Kitazato (n=20) or Irvine Scientific (n=20). The warmed blastocysts were controlled after 2 and 4 hours for re-expansion and freeze injuries. The data was analysed with Fisher’s exact test and considered statistically significant if two-tailed p-value <0.05. The results showed no significant difference between the kits after 2 respectively 4 hours regarding re-expansion (p=0.432; p=0.492) or freeze injury (p=1.000; p=0.476). A significant difference was observed between group AB (with higher Gardner-score) and group C (with lower Gardner-score) in the degree of freeze injury (p=0.048; p=0.034), regardless of vitrification kit used. The details of the procedures differed somewhat between the kits, both having pros and cons, although overall procedures were equivalent. Further evaluation is needed before a change in method can be conducted.

This master thesis work presents a novel method of automatic essay scoring for Swedish national tests written by upper secondary high school students by deploying neural network architectures and linguistic feature extraction in the framework of Swegram. There are four sorts of linguistic aspects involved in our feature extraction: count-based,lexical morphological and syntactic. One of the three variants of recurrent network, vanilla RNN, GRU and LSTM, together with the specific model parameter setting, is implemented in the Automatic Essay Scoring (AES) modelling with extracted features measuring the linguistic complexity as text representation. The AES model is evaluated through interrater agreement with human assigned grade as target label in terms of quadratic weighted kappa (QWK) and exact percent agreement. Our best observed averaged QWK and averaged exact percent agreement is 0.50 and 52% over 10 folds among our all experimented models.

The diploma thesis deals with the analysis of current situation of information systém Hutchhouse’s company. Due to the analysis of the current situation of the system and due to suggestions of preventions for the information system should the compan achieves better results. The subject of the thesis is mainly a analysis of the company, then the analysis of the information system and identification of risks. Output of this diploma thesis are preventions which eliminate these risks accordingly.

Written skills are an essential evaluation criterion for a student’s creativity, knowledge, and intellect. Consequently, academic writing is a common part of university and college admissions applications, standardized tests, and classroom assessments. However, the task for teachers is quite daunting when it comes to essay scoring. Then Automated Essay Scoring may be a helpful tool in the decision-making by the teacher.  There have been many successful models with supervised or unsupervised machine learning algorithms in the eld of Automated Essay Scoring. This thesis work makes a comparative study among various neural network models with supervised machine learning algorithms and different linguistic feature combinations. It also proves that the same linguistic features are applicable to more than one language.  The models studied in this experiment include TextCNN, TextRNN_LSTM, Tex- tRNN_GRU, and TextRCNN trained with the essays from the Automated Student Assessment Prize (ASAP) from Kaggle competitions. Each essay is represented with linguistic features measuring linguistic complexity. Those features are divided into four groups: count-based, morphological, syntactic, and lexical features, and the four groups of features can form a total of 14 combinations.  The models are evaluated via three measurements: Accuracy, F1 score, and Quadratic Weighted Kappa. The experimental results show that models trained only with count-based features outperform the models trained using other feature combinations. In addition, TextRNN_LSTM performs best, with an accuracy of 54.79%, an F1 score of 0.55, and a Quadratic Weighted Kappa of 0.59, which beats the statistically-based baseline models.

Thesis deals with the problems of life insurance. On the basis of comparison of life insurance products for children of chosen commercial insurance companies, it contains evaluation and proposals for improvement of insurance products for children of Generali pojišťovna, a. s.

This diploma thesis analyses problems connected with motor insurance of the company Česká pojišťovna a.s. and contains the project to improvement the offer of this product in order to be competitive and corresponding to the requirements of client.

This master’s thesis deals with using Text mining as a method to predict tags of articles. It describes the iterative way of handling big data files, parsing the data, cleaning the data and scoring of terms in article using TF-IDF. It describes in detail the flow of program written in programming language Python 3.4.3. The result of processing more than 1 million articles from Wikipedia database is a dictionary of English terms. By using this dictionary one is capable of determining the most important terms from article in corpus of articles. Relevancy of consequent tags proves the method used in this case.