Smart Principles for Knowledge-based Urban Development: Case Finnish Railway Station Areas

Cities undergo continuous transformation processes, which have unique characteristic manifestations over time. The changes in many Finnish cities currently focus on the vicinity of railway station areas due to changes in regional structures and rail transport, as well as the densification of city centres. The enthusiasm for this kind of development is also increased by the special features of railway station areas, which seem to provide opportunities for new kinds of local economic and innovation policies. Railway station areas are also favourable locations for the application of various smart city technologies and services. In this article, we analyse the development of Finnish railway station areas as part of a wider continuum of urban development where both economic and innovation policies unify with urban planning. Case studies confirm our outlook of knowledge-based urban development transitioning to a new phase. This provides the prerequisites for interesting connections between railway station areas, the concept of a smart city and open innovation. One of the aims of our article is to bring together various themes that are brought up in smart city discussions and urban planning by introducing new kinds of spatial planning principles, which can be placed in three categories: 1) smart profiling, 2) smart design and 3) smart innovation

Apukaasu virtauksen simulointi laser leikkauksessa.

Työn päämääränä oli esittää laskennallinen CFD-malli apukaasuvirtaukselle laser leikkauksessa. Mallin tarkoituksena on auttaa apukaasusuutinten suunnittelussa. Työssä käsiteltiin myös apukaasun roolia laser leikkauksessa, jotta lukija kykenisi paremmin ymmärtämään työn päämäärää. Simulaatiot suoritettiin Star-CCM+ CFD-ohjelmistolla. Simuloinnissa käytettiin tavanomaista eriytettyä ratkaisijaa yhdistetyn ratkaisijan sijasta, jotta laskenta voitaisiin pitää mahdollisimman kevyenä. Tästä on erityisesti hyötyä, kun monimutkaisempia malleja yhdistetään kaasuvirtaukseen. Työssä vertailtiin myös kahta eri viskositeetti mallia. Malleiksi valittiin Sutherlandin-laki ja vakio viskositeetti malli. Molemmat mallit antoivat samankaltaisia tuloksia, mutta Sutherlandinlain käyttö aiheutti numeerisia ongelmia. Tästä syystä vakio viskositeetti malli oli sopivampi kyseiseen ongelmaan. Laskentatulosten paikkansapitävyyttä arvioitiin Schelieren-kuvien avulla. Vertailussa tultiin lopputulokseen, että kyseinen malli pystyi ennustamaan kaasuvirtauksen Lavalsuuttimessa riittävän hyvin

Maaseutuyritykset ja yritykset monilla maaseuduilla

publishedVersionNon peer reviewe

A Hypothesis Testing Procedure Designed for Q-Matrix Validation of Diagnostic Classification Models

Cognitive diagnosis models have become very popular largely because these models provide educators with an explanation for a student not performing well based on skills that have not yet been mastered, making it possible for educators to provide targeted remediation and tailor instruction to address individual strengths and weaknesses. However, in order for these procedures to be effective, the Q-matrix which establishes the relationships between latent variables representing knowledge structures (columns) and individual items on an assessment (rows) must be carefully considered. The goal of this work is to develop a new test statistic for the detection of model misspecifications of the Q-matrix, which include both underfitting the Q-matrix and overfitting the Q-matrix. In addition to the development of this new test statistic, this dissertation evaluated the performance of this new test statistic and developed an estimator of the asymptotic variance based on the Fisher Information Matrix of the slip and guess parameters. The test statistic was evaluated by two simulation studies and also applied to the fraction subtraction dataset. The first simulation study investigated the true Type-I error rates for the test under four levels of sample size, three levels of correlation among attributes and three levels of item discrimination. Results showed that as the sample size increases the Type I error reduces to 5%. Surprisingly, the results for the relationship between Type I error and Item discrimination show that the most discriminating items (Item Discrimination of 4) have the largest Type I error rates. The power study showed that the statistic is very powerful in the detection of under-specification or over-specification of the Q-matrix with large sample sizes and/or when items are highly discriminating between students that have mastered or have not mastered a skill. Interestingly, the results when the Q matrix has multiple misspecifications the detection of under-specification is better than for over-specification when two misclassifications are being tested simultaneously. The analysis of the fraction subtraction dataset found 15% of the q-entries had enough evidence to reject the Null hypothesis. This clearly indicates that the test finds misfit in the original expert designed Q-matrix