Schematic of the experimental setup.

<p>The blood sample is stirred continuously and perfused via pressure control. Focussed microstrobe illumination is used to acquire images of the flow with a CCD camera.</p

Parent branch haematocrit profiles.

<p>Sample haematocrit profiles in the parent branch for a range of . Lines show spline fits through the data and error bars show standard deviations. (a) Dextran data (b) PBS data. Uniform haematocrit distribution is indicated by the solid line. Insets show fits to Equation S14: axes on the insets are equal to the main figure.</p

Errors in RBC mass continuity and volumetric continuity, shown as weighted mean and standard deviation, with weights defined from the reciprocal of the variance for each data set, calculated from the cumulative errors.

<p>Errors in RBC mass continuity and volumetric continuity, shown as weighted mean and standard deviation, with weights defined from the reciprocal of the variance for each data set, calculated from the cumulative errors.</p

Flux-flow curves.

<p>Flux-flow curves with empirical fits (a) Bifurcation 1, Dextran (b) Bifurcation 2, Dextran (c) Bifurcation 1, PBS (d) Bifurcation 2, PBS. Error bars show one standard deviation. Parameter values and 95% confidence intervals are given for each fit.</p

Middle and outlet branch haematocrit profiles.

<p>(a) Sample haematocrit profiles in the middle branch and (b) in the outlet branch. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100473#pone-0100473-t002" target="_blank">Table 2</a> for details of flow ratios. Profiles are indicated with a smoothing spline for clarity and error bars show one standard deviation.</p

Skewness of the haematocrit profiles.

<p>Haematocrit skewness index, , as a function of flow ratio in (a) Bifurcation 1 (b) Bifurcation 2. Error bars show one standard deviation.</p

Daughter branch haematocrit profiles.

<p>Sample haematocrit profiles in (a) daughter branch 1 and (b) daughter branch 2. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100473#pone-0100473-t002" target="_blank">Table 2</a> for details of flow ratios. Profiles are indicated with a smoothing spline for clarity and error bars show one standard deviation.</p

Flow parameters for the selected cases analysed in Figures 5 and 6.

<p>Flow parameters for the selected cases analysed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100473#pone-0100473-g005" target="_blank">Figures 5</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100473#pone-0100473-g006" target="_blank">6</a>.</p

Metalliteollisuuden yritysten resurssitarvekartoitus

Tämän opinnäytetyön toimeksiantaja oli Kainuun Etu Oy. Opinnäytetyön tarkoituksena oli selvittää Kainuun ja lähialueiden metalliteollisuuden yritysten resurssitarpeita. Pääasiassa selvityksen alla oli yritysten erityiskoneistustarpeet ja suorittavan tason henkilökunnan koulutustarpeet. Resurssitarvekartoitus tehtiin osana Kajaanin Otanmäkeen suunnitteilla olleen koulutustehtaan perustamisselvitystä. Opinnäytetyön tavoite oli saada tietoa potentiaalisten asiakasyritysten tarpeista, että perustettavassa tehtaassa päätöksiä tekevät henkilöt saavat lisätietoa tai varmistavaa tietoa päätöksenteon tueksi. Tiedon pääasiallinen käyttötarkoitus oli tehtaan alkutuotannon suunnittelu asiakkaiden tarpeita varten. Kyselyn piiriin kuuluvilta Kainuulaisilta yrityksiltä tiedusteltiin myös yrityksen tai yrittäjän halukkuudesta lähteä osakkaaksi tehtaaseen. Resurssitarvekartoitus suoritettiin kvalitatiivisena tutkimuksena. Suunniteltu kyselylomake lähetettiin sähköpostilla ennakkoon päätettyihin yrityksiin, ja siten aineisto kerättiin kyselyyn vastanneiden yritysten vastausten pohjalta. Tutkimuksen tulokset heijastelevat koulutustarpeiden osalta toimialan työvoimapulan vaikutuksia. Tarvetta on etenkin joko suorittavan tason työntekijöistä, tai sitten halutaan tuotannon automaatioon liittyvää koulutusta. Koneistuspuolelta tarvetta löytyi lähinnä raskaasta aarporauksesta. Opinnäytetyön tuloksilla ei luultavasti ole myöhempiä käyttömahdollisuuksia muuten kuin opinnäytetyön toimeksiantajalle, tai vastaavanlaisen kartoituksen suunnittelijalle. Kaikki yritysten lähettämät vastaukset käsiteltiin opinnäytetyön raporttia tehdessä luottamuksellisesti ja nimettömänä.This thesis was commissioned by Kainuun Etu Oy. The purpose was to find out about the nature of resource demands at metal industry companies. The companies were mainly located in the Kainuu and Northern Ostrobothnia regions. The primary resource demands to be examined were the companies' special machining needs and training needs for the companies' executive personnel. The resource demand survey was made as a part of the foundation report for a training workshop that was planned to be founded in Otanmäki, Kajaani. The purpose was to gather information about the needs of the potential business clients, so that the workshop management would get information to support their decision making. The primary purpose of the information was the planning of the workshop production according to the clients' needs. The companies located in the Kainuu region were also asked about their interest in being a shareholder in the planned workshop. The resource demand survey was conducted as qualitative research. The questionnaire was e-mailed to the group of companies, which was decided beforehand. The data was gathered from the companies' answers to the questionnaire. The results of the survey seem to reflect the effects of the labor shortage in the metal industry, especially in the training needs. Companies seem to need either executive personnel or training associated with industrial automation. There were no major machining needs apart from reaming, especially when it comes to machining large and heavy objects. There are probably no later utilization possibilities for this thesis, apart from the client or someone who plans to conduct a similar survey. While writing this thesis report, all the companies' answers were reported with confidentiality and anonymously

Supplementary Material List from Computational tools for clinical support: a multi-scale compliant model for haemodynamic simulations in an aortic dissection based on multi-modal imaging data

Aortic dissection (AD) is a vascular condition with high morbidity and mortality rates. Computational fluid dynamics (CFD) can provide insight into the progression of AD and aid clinical decisions; however, oversimplified modelling assumptions and high computational cost compromise the accuracy of the information and impede clinical translation. To overcome these limitations, a patient-specific CFD multi-scale approach coupled to Windkessel boundary conditions and accounting for wall compliance was developed and used to study an AD patient. A new moving boundary algorithm was implemented to capture wall displacement and a rich <i>in vivo</i> clinical dataset was used to tune model parameters and for validation. Comparisons between <i>in silico</i> and <i>in vivo</i> data showed that this approach successfully captures flow and pressure waves for the patient-specific AD and is able to predict the pressure in the false lumen (FL), a critical variable for the clinical management of the condition. Results showed regions of low and oscillatory wall shear stress which, together with higher diastolic pressures predicted in the FL, may indicate risk of expansion. This study, at the interface of engineering and medicine, demonstrates a relatively simple and computationally efficient approach to account for arterial deformation and wave propagation phenomena in a three-dimensional model of AD, representing a step forward in the use of CFD as potential tool for AD management and clinical support