Evaluation of the spot urine sampling technique to assess urinary pseudouridine excretion in lactating dairy cows

Selostus: Mahdollisuus lyhytaikaisen virtsankeruun käyttöön lypsylehmien virtsan pseudouridiinin erityksen määrittämisessä

A simple device for sampling of volumetric forest soil cores

Summary

Variation in soil organic carbon and thickness of soil horizons within a boreal forest stand - effect of trees and implications for sampling

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Cut-off importance sampling of bole volume

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Inventory of sparse forest populations using adaptive cluster sampling

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Comparison of randomized branch sampling with and without replacement at the first stage

Abstract

Oops! Another medication error : a literature review of contributing factors and methods to prevent medication errors

The purpose of this bachelor's thesis was to chart scientific research articles to present contributing factors to medication errors done by nurses in a hospital setting, and introduce methods to prevent medication errors. Additionally, international and Finnish research was combined and findings were reflected in relation to the Finnish health care system. Literature review was conducted out of 23 scientific articles. Data was searched systematically from CINAHL, MEDIC and MEDLINE databases, and also manually. Literature was analysed and the findings combined using inductive content analysis. Findings revealed that both organisational and individual factors contributed to medication errors. High workload, communication breakdowns, unsuitable working environment, distractions and interruptions, and similar medication products were identified as organisational factors. Individual factors included nurses' inability to follow protocol, inadequate knowledge of medications and personal qualities of the nurse. Developing and improving the physical environment, error reporting, and medication management protocols were emphasised as methods to prevent medication errors. Investing to the staff's competence and well-being was also identified as a prevention method. The number of Finnish articles was small, and therefore the applicability of the findings to Finland is difficult to assess. However, the findings seem to fit to the Finnish health care system relatively well. Further research is needed to identify those factors that contribute to medication errors in Finland. This is a necessity for the development of methods to prevent medication errors that fit in to the Finnish health care system.

Coifman-aallokkeet ja skaalaussuotimen muodostus differentiaalievoluut ion avulla

Ortogonaalisen M-kaistaisen moniresoluutioanalyysin matemaattiset perusteet esitetään yksityiskohtaisesti. Coifman-aallokkeiden määritelmä yleistetään dilaatiokertoimelle M ja nollasta poikkeavalle häviävien momenttien keskukselle.Funktion approksimointia näytepisteistä aallokkeiden avulla pohditaan ja erityisesti esitetään approksimaation asymptoottinen virhearvio Coifman-aallokkeille. Skaalaussuotimelle osoitetaan välttämättömät ja riittävät ehdot, jotka johtavat yleistettyihin Coifman-aallokkeisiin. Moniresoluutioanalyysin tiheys todistetaansuoraan Lebesguen integraalin määritelmään perustuen yksikön partitio-ominaisuutta käyttäen. Todistus on riittävä sellaisenaan avaruudessa L2(Wd) käyttämättä Fourier-tason ominaisuuksia tai ehtoja. Mallatin algoritmi johdetaan M-kaistaisille aallokkeille ja moniuloitteisille signaaleille. Algoritmille esitetään myös rekursiivinen muoto. Differentiaalievoluutioalgoritmin avulla ratkaistaan Coifman-aallokkeisiin liittyvien skaalaussuotimien kertoimien arvoja useille skaalausfunktiolle. Approksimaatio- ja kuvanpakkausesimerkkejä esitetään menetelmien havainnollistamiseksi. Differentiaalievoluutioalgoritmin avulla etsitään myös referenssikuville optimoitu skaalaussuodin. Löydetty suodin on regulaarinen ja erittäinsymmetrinen.

Minimum error contrast enhancement

Contrast enhancement is an image processing technique where the objective is to preprocess the image so that relevant information can be either seen or further processed more reliably. These techniques are typically applied when the image itself or the device used for image reproduction provides poor visibility and distinguishability of different regions of interest inthe image. In most studies, the emphasis is on the visualization of image data,but this human observer biased goal often results to images which are not optimal for automated processing. The main contribution of this study is to express the contrast enhancement as a mapping from N-channel image data to 1-channel gray-level image, and to devise a projection method which results to an image with minimal error to the correct contrast image. The projection, the minimum-error contrast image, possess the optimal contrast between the regions of interest in the image. The method is based on estimation of the probability density distributions of the region values, and it employs Bayesian inference to establish the minimum error projection.

Origins of production errors and significance of employee empowerment in reducing production error amount in sheet metal fabricating industry

The market place of the twenty-first century will demand that manufacturing assumes a crucial role in a new competitive field. Two potential resources in the area of manufacturing are advanced manufacturing technology (AMT) and empowered employees. Surveys in Finland have shown the need to invest in the new AMT in the Finnish sheet metal industry in the 1990's. In this run the focus has been on hard technology and less attention is paid to the utilization of human resources. In manymanufacturing companies an appreciable portion of the profit within reach is wasted due to poor quality of planning and workmanship. The production flow production error distribution of the sheet metal part based constructions is inspectedin this thesis. The objective of the thesis is to analyze the origins of production errors in the production flow of sheet metal based constructions. Also the employee empowerment is investigated in theory and the meaning of the employee empowerment in reducing the overall production error amount is discussed in this thesis. This study is most relevant to the sheet metal part fabricating industrywhich produces sheet metal part based constructions for electronics and telecommunication industry. This study concentrates on the manufacturing function of a company and is based on a field study carried out in five Finnish case factories. In each studied case factory the most delicate work phases for production errors were detected. It can be assumed that most of the production errors are caused in manually operated work phases and in mass production work phases. However, no common theme in collected production error data for production error distribution in the production flow can be found. Most important finding was still that most of the production errors in each case factory studied belong to the 'human activity based errors-category'. This result indicates that most of the problemsin the production flow are related to employees or work organization. Development activities must therefore be focused to the development of employee skills orto the development of work organization. Employee empowerment gives the right tools and methods to achieve this.

Evaluation of Software Development Practices through Error Analysis

This thesis studies evaluation of software development practices through an error analysis. The work presents software development process, software testing, software errors, error classification and software process improvement methods. The practical part of the work presents results from the error analysis of one software process. It also gives improvement ideas for the project. It was noticed that the classification of the error data was inadequate in the project. Because of this it was impossible to use the error data effectively. With the error analysis we were able to show that there were deficiencies in design and analyzing phases, implementation phase and in testing phase. The work gives ideas for improving error classification and for software development practices.

Bayesian Estimation of Noise

In mathematical modeling the estimation of the model parameters is one of the most common problems. The goal is to seek parameters that fit to the measurements as well as possible. There is always error in the measurements which implies uncertainty to the model estimates. In Bayesian statistics all the unknown quantities are presented as probability distributions. If there is knowledge about parameters beforehand, it can be formulated as a prior distribution. The Bays’ rule combines the prior and the measurements to posterior distribution. Mathematical models are typically nonlinear, to produce statistics for them requires efficient sampling algorithms. In this thesis both Metropolis-Hastings (MH), Adaptive Metropolis (AM) algorithms and Gibbs sampling are introduced. In the thesis different ways to present prior distributions are introduced. The main issue is in the measurement error estimation and how to obtain prior knowledge for variance or covariance. Variance and covariance sampling is combined with the algorithms above. The examples of the hyperprior models are applied to estimation of model parameters and error in an outlier case.