A computer aided machining system for the manufacture of three-dimensional surfaces

Includes bibliographical references.This thesis describes the design of a computerised system for machining three-dimensional surfaces, more specifically "sculptured surfaces", using an IBM PC and a Bridgeport 3 axis Computer Numerically Controlled (CNC) Milling Machine. There was a twofold objective to the project: To design and develop a milling system; To provide a teaching tool for undergraduate students who wish to further their studies in the concepts of Computer Aided Design and Manufacture (CAD/CAM). Five broad areas are covered: 1. The development of a mathematical algorithm to approximate a sculptured surf ace represented by a set, of surface datapoints in the form of XYZ coordinates. 2. The development of an algorithm to calculate the toolpath to machine the sculptured surface. 3. The development of an algorithm to display the surface and toolpath in three dimensions on the computer screen. 4. The development of an algorithm (the postprocessor) which translates the toolpath into a program suitable for use with the CNC milling machine. 5. The development of a communications algorithm for the direct transmission of the CNC program from the IBM microcomputer to the memory of the CNC computer

Predictors of Refugee Adjustment: The Importance of Cognitive Skills and Personality

In light of the recent worldwide migration of refugees, determinants of a more or less successful integration are heavily discussed, but reliable empirical investigations are scarce and have often focused on sociodemographic factors. In the present study, we explore the role of several individual characteristics for refugee adjustment in the areas of (a) institutional, (b) interpersonal and (c) intrapersonal adaptation. In a sample of 4,527 refugees (M = 33.6 years, 38% women), we investigated the effect of sociodemographic characteristics (age, gender, months in Germany, religious affiliation), cognitive factors (cognitive ability, educational history, language skills, integration-course participation), and personality (locus of control, risk appetite, willingness to reciprocity) on adjustment parameters. Both, cognitive skills (especially language skills) and personality, showed incremental validity beyond sociodemographic factors for refugee adjustment comparable across contextual factors. Even with respect to contextual factors such as residency status and living situation, results remained largely stable. The study provides first hints on the importance of personality, thereby providing important implications for understanding integration processes and optimizing interventions on personal, social, and societal levels

Hydrogeology, Digital Solute-transport Simulation of Nitrate and Geochemistry of Fluoride in Ground Waters of Cohanche County Oklah0ma

Geolog

The personality traits of self-made and inherited millionaires

Very wealthy people influence political and societal processes by wielding their economic power through foundations, lobbying groups, media campaigns, as investors and employers. Because personality shapes goals, attitudes, and behaviour, it is important to understand the personality traits that characterize the rich. We used representative survey data to construct two large samples, one from the general population and one consisting of individuals with at least 1 million euros in individual net wealth, to analyse what personality traits characterize the wealthy and why their traits differ from those of the general population. High wealth was associated with higher Risk tolerance, Emotional Stability, Openness, Extraversion, and Conscientiousness. This “rich” personality profile was more prominent among individuals who had accumulated wealth through their own efforts (“self-mades”) than among individuals who had been born into wealth (“inheritors”). Thus, our evidence is suggestive of a unique configuration of personality traits contributing to self-made millionaires’ economic success

Decision support by machine learning systems for acute management of severely injured patients: A systematic review

Introduction Treating severely injured patients requires numerous critical decisions within short intervals in a highly complex situation. The coordination of a trauma team in this setting has been shown to be associated with multiple procedural errors, even of experienced care teams. Machine learning (ML) is an approach that estimates outcomes based on past experiences and data patterns using a computer-generated algorithm. This systematic review aimed to summarize the existing literature on the value of ML for the initial management of severely injured patients. Methods We conducted a systematic review of the literature with the goal of finding all articles describing the use of ML systems in the context of acute management of severely injured patients. MESH search of Pubmed/Medline and Web of Science was conducted. Studies including fewer than 10 patients were excluded. Studies were divided into the following main prediction groups: (1) injury pattern, (2) hemorrhage/need for transfusion, (3) emergency intervention, (4) ICU/length of hospital stay, and (5) mortality. Results Thirty-six articles met the inclusion criteria; among these were two prospective and thirty-four retrospective case series. Publication dates ranged from 2000 to 2020 and included 32 different first authors. A total of 18,586,929 patients were included in the prediction models. Mortality was the most represented main prediction group (n = 19). ML models used were artificial neural network ( n = 15), singular vector machine (n = 3), Bayesian network (n = 7), random forest (n = 6), natural language processing (n = 2), stacked ensemble classifier [SuperLearner (SL), n = 3], k-nearest neighbor (n = 1), belief system (n = 1), and sequential minimal optimization (n = 2) models. Thirty articles assessed results as positive, five showed moderate results, and one article described negative results to their implementation of the respective prediction model. Conclusions While the majority of articles show a generally positive result with high accuracy and precision, there are several requirements that need to be met to make the implementation of such models in daily clinical work possible. Furthermore, experience in dealing with on-site implementation and more clinical trials are necessary before the implementation of ML techniques in clinical care can become a reality

COVID-19 treatment in patients with comorbidities: Awareness of drug-drug interactions

Contains fulltext : 232386.pdf (Publisher’s version ) (Open Access

Survey of e-learning implementation and faculty support strategies in a cluster of mid-European medical schools

Background The use of electronic learning formats (e-learning) in medical education is reported mainly from individual specialty perspectives. In this study, we analyzed the implementation level of e-learning formats and the institutional support structures and strategies at an institutional level in a cluster of mid-European medical schools. Methods A 49-item online questionnaire was send to 48 medical schools in Austria, Germany and Switzerland using SurveyMonkey®. Data were collected between February and September of 2013 and analyzed using quantities, statistical and qualitative means. Results The response rate was 71 %. All schools had implemented e-learning, but mainly as an optional supplement to the curriculum. E-learning involved a wide range of formats across all disciplines. Online learning platforms were used by 97 % of the schools. Full-time e-learning staff was employed by 50 %, and these had a positive and significant effect on the presence of e-learning in the corresponding medical schools. In addition, 81 % offered training programs and qualifications for their teachers and 76 % awarded performance-oriented benefits, with 17 % giving these for e-learning tasks. Realization of e-learning offers was rewarded by 33 %, with 27 % recognizing this as part of the teaching load. 97 % would use curriculum- compatible e-learning tools produced by other faculties. Conclusions While all participating medical schools used e-learning concepts, this survey revealed also a reasonable support by institutional infrastructure and the importance of staff for the implementation level of e-learning offerings. However, data showed some potential for increasing tangible incentives to motivate teachers to engage in further use of e-learning. Furthermore, the use of individual tools and the distribution of e-learning presentations in various disciplines were quite inhomogeneous. The willingness of the medical schools to cooperate should be capitalized for the future, especially concerning the provision of e-learning tools and concepts