Supervised localimage feature detection

This thesis is about detection of local image features. The research topic belongs to the wider area of object detection, which is a machine vision and pattern recognition problem where an object must be detected (located) in an image. State-of-the-art object detection methods often divide the problem into separate interest point detection and local image description steps, but in this thesis a different technique is used, leading to higher quality image features which enable more precise localization. Instead of using interest point detection the landmark positions are marked manually. Therefore, the quality of the image features is not limited by the interest point detection phase and the learning of image features is simplified. The approach combines both interest point detection and local description into one phase for detection. Computational efficiency of the descriptor is therefore important, leaving out many of the commonly used descriptors as unsuitably heavy. Multiresolution Gabor features has been the main descriptor in this thesis and improving their efficiency is a significant part. Actual image features are formed from descriptors by using a classifierwhich can then recognize similar looking patches in new images. The main classifier is based on Gaussian mixture models. Classifiers are used in one-class classifier configuration where there are only positive training samples without explicit background class. The local image feature detection method has been tested with two freely available face detection databases and a proprietary license plate database. The localization performance was very good in these experiments. Other applications applying the same under-lying techniques are also presented, including object categorization and fault detection.

Sentence Compression with Supervised Machine Learning

The subject of the thesis is automatic sentence compression with machine learning, so that the compressed sentences remain both grammatical and retain their essential meaning. There are multiple possible uses for the compression of natural language sentences. In this thesis the focus is generation of television program subtitles, which often are compressed version of the original script of the program. The main part of the thesis consists of machine learning experiments for automatic sentence compression using different approaches to the problem. The machine learning methods used for this work are linear-chain conditional random fields and support vector machines. Also we take a look which automatic text analysis methods provide useful features for the task. The data used for machine learning is supplied by Lingsoft Inc. and consists of subtitles in both compressed an uncompressed form. The models are compared to a baseline system and comparisons are made both automatically and also using human evaluation, because of the potentially subjective nature of the output. The best result is achieved using a CRF - sequence classification using a rich feature set. All text analysis methods help classification and most useful method is morphological analysis. Tutkielman aihe on suomenkielisten lauseiden automaattinen tiivistäminen koneellisesti, niin että lyhennetyt lauseet säilyttävät olennaisen informaationsa ja pysyvät kieliopillisina. Luonnollisen kielen lauseiden tiivistämiselle on monta käyttötarkoitusta, mutta tässä tutkielmassa aihetta lähestytään television ohjelmien tekstittämisen kautta, johon käytännössä kuuluu alkuperäisen tekstin lyhentäminen televisioruudulle paremmin sopivaksi. Tutkielmassa kokeillaan erilaisia koneoppimismenetelmiä tekstin automaatiseen lyhentämiseen ja tarkastellaan miten hyvin erilaiset luonnollisen kielen analyysimenetelmät tuottavat informaatiota, joka auttaa näitä menetelmiä lyhentämään lauseita. Lisäksi tarkastellaan minkälainen lähestymistapa tuottaa parhaan lopputuloksen. Käytetyt koneoppimismenetelmät ovat tukivektorikone ja lineaarisen sekvenssin mallinen CRF. Koneoppimisen tukena käytetään tekstityksiä niiden eri käsittelyvaiheissa, jotka on saatu Lingsoft OY:ltä. Luotuja malleja vertaillaan Lopulta mallien lopputuloksia evaluoidaan automaattisesti ja koska teksti lopputuksena on jossain määrin subjektiivinen myös ihmisarviointiin perustuen. Vertailukohtana toimii kirjallisuudesta poimittu menetelmä. Tutkielman tuloksena paras lopputulos saadaan aikaan käyttäen CRF sekvenssi-luokittelijaa laajalla piirrejoukolla. Kaikki kokeillut teksin analyysimenetelmät auttavat luokittelussa, joista tärkeimmän panoksen antaa morfologinen analyysi.

Puhdastilojen suunnitteluprosessi ja teknisten järjestelmien validointi

Puhdastilojen suunnittelussa pyritään saamaan hallittu ja valvottu ilmanpuhtaus luokiteltuun tilaan.Luokittelu tapahtuu puhdastilastandardeilla, lisäksi lääkevalmisteita valmistettavassa tilassa GMP -säädösten mukaisin luokituksin. Puhdastilastandardi ISO 14644 käsittää seitsemän osaa, jossa on käsitelty puhdastilaa koskevia määräyksiä suunnittelusta käyttöön ja testaukseen. GMP-säädökset sisältävät yhdeksän kappaletta, joista kappale 3: 'Tilat ja laitteet' on keskeinen osa lääkeainevalmistuksen puhdastilasuunnittelua. Puhtaan ilman aikaansaamiseksi puhdastilaan merkittävimmät roolit ovat ilmanvaihdolla, puhdastilarakenteilla ja rakennusautomaatiolla. Ilma voidaan tuoda tilaan kolmella eri periaatteella. Ilmaa tuodaan tilaan yhdensuuntaisesti, turbulenttisesti tai sekavirtauksena HEPA -suodattimien kautta, joilla varmistetaan epäpuhtauksien korkea suodatusaste. Ilmapoistetaan rei'itettyjen, korotettujen lattioiden kautta tai tilan alaosassa olevien poistoilmasäleikköjen kautta, josta se johdetaan noin 75-90%:sti kierrätettynä takaisin tilaan. Lääketeollisuudessa rei'itettyjä, korotettuja lattioita eivoida käyttää kontaminaatiovaaran, vuoksi. Tilaan suunniteltuja olosuhteita ylläpidetään rakennusautomaation avulla ja monitorointijärjestelmällä valvotaan tilassa olevan ilman laatua. Kaikki GMP-luokituksen mukaiset puhdastilat tulee validoida. Validointiin kuuluu teknisten järjestelmien kvalifiointi ja koko prosessin validointi. Teknisten järjestel-mien kvalifiointi käsittää suunnitelmien tarkastuksen (DQ), asennus - ja käyttöönotto tarkastukset (IQ), toiminnan testauksen (OQ) ja suorituksen testauksen (PQ). Kvali-fiointi kuuluu yhtenä osa-alueena validointiin. Prosessin validointi on osa yrityksen laadunvarmistusta. Validoinnilla hankitaan dokumentoidut todisteet siitä, että tila tai prosessi todella täyttää annetut vaatimukset. Tässä työssä laadittiin esimerkinomainen kvalifiointisuunnitelma puhdastilan tekni-sille järjestelmille. Suunnitelma sisältää asennus- ja käyttöönoton mukaiset tarkastukset (IQ)ja toiminnan aikaiset testaukset (OQ).

Nursing student-patient relationship and associated factors

The purpose of this study was to analyse the nursing student-patient relationship and factors associated with this relationship from the point of view of both students and patients, and to identify factors that predict the type of relationship. The ultimate goal is to improve supervised clinical practicum with a view to supporting students in their reciprocal collaborative relationships with patients, increase their preparedness to meet patients’ health needs, and thus to enhance the quality of patient care. The study was divided into two phases. In the first phase (1999-2005), a literature review concerning the student-patient relationship was conducted (n=104 articles) and semi-structured interviews carried out with nursing students (n=30) and internal medicine patients (n=30). Data analysis was by means of qualitative content analysis and Student-Patient Relationship Scales, which were specially developed for this research. In the second phase (2005-2007), the data were collected by SPR scales among nursing students (n=290) and internal medicine patients (n=242). The data were analysed statistically by SPSS 12.0 software. The results revealed three types of student-patient relationship: a mechanistic relationship focusing on the student’s learning needs; an authoritative relationship focusing on what the student assumes is in the patient’s best interest; and a facilitative relationship focusing on the common good of both student and patient. Students viewed their relationship with patients more often as facilitative and authoritative than mechanistic, while in patients’ assessments the authoritative relationship occurred most frequently and the facilitative relationship least frequently. Furthermore, students’ and patients’ views on their relationships differed significantly. A number of background factors, contextual factors and consequences of the relationship were found to be associated with the type of relationship. In the student data, factors that predicted the type of relationship were age, current year of study and support received in the relationship with patient. The higher the student’s age, the more likely the relationship with the patient was facilitative. Fourth year studies and the support of a person other than a supervisor were significantly associated with an authoritative relationship. Among patients, several factors were found to predict the type of nursing student-patient relationships. Significant factors associated with a facilitative relationship were university-level education, several previous hospitalizations, admission to hospital for a medical problem, experience of caring for an ill family member and patient’s positive perception of atmosphere during collaboration and of student’s personal and professional growth. In patients, positive perceptions of student’s personal and professional attributes and patient’s improved health and a greater commitment to self-care, on the other hand, were significantly associated with an authoritative relationship, whereas positive perceptions of one’s own attributes as a patient were significantly associated with a mechanistic relationship. It is recommended that further research on the student-patient relationship and related factors should focus on questions of content, methodology and education.

Intelligent Internet Data Searching

In this thesis author approaches the problem of automated text classification, which is one of basic tasks for building Intelligent Internet Search Agent. The work discusses various approaches to solving sub-problems of automated text classification, such as feature extraction and machine learning on text sources. Author also describes her own multiword approach to feature extraction and pres-ents the results of testing this approach using linear discriminant analysis based classifier, and classifier combining unsupervised learning for etalon extraction with supervised learning using common backpropagation algorithm for multilevel perceptron.

Sähkönjakeluverkon komponenttien pitoajat

Suomessa sähkönjakeluverkkoyhtiöt toimivat verkkovastuualueillaan yksinoikeudella. Verkkovastuualuiden ominaispiirteet voivat olla hyvin erilaiset. Energiamarkkinavirasto valvoo sähkömarkkinalainsäädännön noudattamista jakeluverkkotoiminnassa. Jakeluverkonhaltijat on velvoitettu Energiamarkkinaviraston valvontamallin kautta määrittämään tiettyjen rajoitusten mukaisesti verkkokomponenteillensa sopivimmat teknistaloudelliset pitoajat. Nämä pitoajat vaikuttavat varsinkin verkkoyhtiön tuottomahdollisuuksiin ja asiakkaiden siirtohintoihin. Lisäksi huomioon on otettava jaettavan sähkön laatu, verkon käyttövarmuus sekä vaikutukset ympäristöön ja turvallisuuteen. Pitoaikojen matemaattinen mallintaminen on usein monimutkaista. Teknistaloudellinen pitoaika valitaankin monesti kokemuksen ja harkinnan perusteella. Tärkeimmät reunaehdot jakeluverkkokomponenttien teknistaloudellisten pitoaikojen valinnalle muodostavat verkkovastuualueen sähkönkulutuksen kasvun sekä infrastruktuurin muutoksen nopeudet. Hitaan muutoksen alueilla verkkokomponenttien teknistaloudelliset pitoajat lähenevät teknisiä pitoaikoja, joihin vaikuttavat voimakkaasti verkkovastuualueen maantieteelliset ja ilmastolliset ominaispiirteet. Yhtiöittäin vaihtelevat verkon rakennus- ja ylläpitomenetelmät tulee myös huomioida. Tässä diplomityössä keskitytään pääosin sähkönjakeluverkon komponenttien teknistaloudelliseen pitoaikaan verkon ja verkkovastuualueen ominaispiirteiden kautta. Aluksi määritellään jakeluverkon pitoaika usealla eri tavalla, sekä selvitetään pitoajan merkitystä nykytilanteessa. Lisäksi työn alkuosassa esitellään Energiamarkkinaviraston vuoden 2005 alusta käyttöönotettu jakeluverkkotoiminnan hinnoittelun kohtuullisuuden valvontamalli ja käydään läpi teknistaloudellisen pitoajan merkitys siinä. Sen jälkeen tarkastellaan jakeluverkkokomponenttien ja niiden osien tekniseen pitoaikaan vaikuttavia tekijöitä. Erityisesti puupylväisiin ja niihin liittyviin ajankohtaisiin asioihin kiinnitetään huomiota, koska puupylväät määräävät monesti koko ilmajohtorakenteen uusimisajankohdan. Lisäksi suolakyllästeiselle puupylväälle esitetään yleinen rappeutumismalli ja jakelumuuntajan rappeutumistapahtumaa tutkitaan. Lopuksi tarkastellaan Graninge Kainuu Oy:tä jakeluverkonhaltijana sekä määritetään sen verkkovastuualueelle ominaisia komponenttien teknisiä ja teknistaloudellisia pitoaikoja haastattelujen, tuoreimpien lähteiden, tutkimustulosten, vertailun ja harkinnan avulla.

Verkkoliiketoiminnan tehokkuusmittauksen ohjausvaikutusten analysointijärjestelmä

Tässä diplomityössä oli tavoitteena suunnitella ja toteuttaa verkkoliiketoiminnan tehokkuusmittauksen ohjausvaikutusten analysointijärjestelmä. Verkkoliiketoiminta on monopoliasemassa olevaa liiketoimintaa, jossa ei ole kilpailusta johtuvaa pakotetta pitää liiketoimintaa tehokkaana ja hintoja alhaisina. Tämän vuoksi verkkoliiketoiminnan hinnoittelua ja toiminnan tehokkuutta tulee valvoa viranomaisen toimesta. Tehokkuusmittauksessa käytettäväksi menetelmäksi on valittu DEA-menetelmä (Data Envelopment Analysis). Tässä työssä on esitelty DEA-menetelmän teoreettiset perusteet sekä verkkoliiketoiminnan tehokkuusmittauksessa havaitut ongelmat. Näiden perusteella on määritelty analysointijärjestelmältä vaadittavat ominaisuudet sekä kehitetty kyseinen järjestelmä. Tärkeimmiksi järjestelmän ominaisuuksiksi osoittautuivat herkkyysanalyysin tekeminen ja etenkin sitä kautta suoritettava keskeytysten hinnan laskeminen sekä mahdollisuudet painokertoimien rajoittamiselle. Työn loppuosassa on esitelty järjestelmästä saatavia konkreettisia tuloksia, joiden avulla on pyritty havainnollistamaan järjestelmän käyttömahdollisuuksia.

Environmental Monitoring using Image Analysis

Tärkeä tehtävä ympäristön tarkkailussa on arvioida ympäristön nykyinen tila ja ihmisen siihen aiheuttamat muutokset sekä analysoida ja etsiä näiden yhtenäiset suhteet. Ympäristön muuttumista voidaan hallita keräämällä ja analysoimalla tietoa. Tässä diplomityössä on tutkittu vesikasvillisuudessa hai vainuja muutoksia käyttäen etäältä hankittua mittausdataa ja kuvan analysointimenetelmiä. Ympäristön tarkkailuun on käytetty Suomen suurimmasta järvestä Saimaasta vuosina 1996 ja 1999 otettuja ilmakuvia. Ensimmäinen kuva-analyysin vaihe on geometrinen korjaus, jonka tarkoituksena on kohdistaa ja suhteuttaa otetut kuvat samaan koordinaattijärjestelmään. Toinen vaihe on kohdistaa vastaavat paikalliset alueet ja tunnistaa kasvillisuuden muuttuminen. Kasvillisuuden tunnistamiseen on käytetty erilaisia lähestymistapoja sisältäen valvottuja ja valvomattomia tunnistustapoja. Tutkimuksessa käytettiin aitoa, kohinoista mittausdataa, minkä perusteella tehdyt kokeet antoivat hyviä tuloksia tutkimuksen onnistumisesta.

Opinion Mining

In this thesis we study the field of opinion mining by giving a comprehensive review of the available research that has been done in this topic. Also using this available knowledge we present a case study of a multilevel opinion mining system for a student organization's sales management system. We describe the field of opinion mining by discussing its historical roots, its motivations and applications as well as the different scientific approaches that have been used to solve this challenging problem of mining opinions. To deal with this huge subfield of natural language processing, we first give an abstraction of the problem of opinion mining and describe the theoretical frameworks that are available for dealing with appraisal language. Then we discuss the relation between opinion mining and computational linguistics which is a crucial pre-processing step for the accuracy of the subsequent steps of opinion mining. The second part of our thesis deals with the semantics of opinions where we describe the different ways used to collect lists of opinion words as well as the methods and techniques available for extracting knowledge from opinions present in unstructured textual data. In the part about collecting lists of opinion words we describe manual, semi manual and automatic ways to do so and give a review of the available lists that are used as gold standards in opinion mining research. For the methods and techniques of opinion mining we divide the task into three levels that are the document, sentence and feature level. The techniques that are presented in the document and sentence level are divided into supervised and unsupervised approaches that are used to determine the subjectivity and polarity of texts and sentences at these levels of analysis. At the feature level we give a description of the techniques available for finding the opinion targets, the polarity of the opinions about these opinion targets and the opinion holders. Also at the feature level we discuss the various ways to summarize and visualize the results of this level of analysis. In the third part of our thesis we present a case study of a sales management system that uses free form text and that can benefit from an opinion mining system. Using the knowledge gathered in the review of this field we provide a theoretical multi level opinion mining system (MLOM) that can perform most of the tasks needed from an opinion mining system. Based on the previous research we give some hints that many of the laborious market research tasks that are done by the sales force, which uses this sales management system, can improve their insight about their partners and by that increase the quality of their sales services and their overall results.

Learning Preferences with Kernel-Based Methods

Learning of preference relations has recently received significant attention in machine learning community. It is closely related to the classification and regression analysis and can be reduced to these tasks. However, preference learning involves prediction of ordering of the data points rather than prediction of a single numerical value as in case of regression or a class label as in case of classification. Therefore, studying preference relations within a separate framework facilitates not only better theoretical understanding of the problem, but also motivates development of the efficient algorithms for the task. Preference learning has many applications in domains such as information retrieval, bioinformatics, natural language processing, etc. For example, algorithms that learn to rank are frequently used in search engines for ordering documents retrieved by the query. Preference learning methods have been also applied to collaborative filtering problems for predicting individual customer choices from the vast amount of user generated feedback. In this thesis we propose several algorithms for learning preference relations. These algorithms stem from well founded and robust class of regularized least-squares methods and have many attractive computational properties. In order to improve the performance of our methods, we introduce several non-linear kernel functions. Thus, contribution of this thesis is twofold: kernel functions for structured data that are used to take advantage of various non-vectorial data representations and the preference learning algorithms that are suitable for different tasks, namely efficient learning of preference relations, learning with large amount of training data, and semi-supervised preference learning. Proposed kernel-based algorithms and kernels are applied to the parse ranking task in natural language processing, document ranking in information retrieval, and remote homology detection in bioinformatics domain. Training of kernel-based ranking algorithms can be infeasible when the size of the training set is large. This problem is addressed by proposing a preference learning algorithm whose computation complexity scales linearly with the number of training data points. We also introduce sparse approximation of the algorithm that can be efficiently trained with large amount of data. For situations when small amount of labeled data but a large amount of unlabeled data is available, we propose a co-regularized preference learning algorithm. To conclude, the methods presented in this thesis address not only the problem of the efficient training of the algorithms but also fast regularization parameter selection, multiple output prediction, and cross-validation. Furthermore, proposed algorithms lead to notably better performance in many preference learning tasks considered.

Analysis of Satellite Data for Forest Segmentation

In this work we study the classification of forest types using mathematics based image analysis on satellite data. We are interested in improving classification of forest segments when a combination of information from two or more different satellites is used. The experimental part is based on real satellite data originating from Canada. This thesis gives summary of the mathematics basics of the image analysis and supervised learning , methods that are used in the classification algorithm. Three data sets and four feature sets were investigated in this thesis. The considered feature sets were 1) histograms (quantiles) 2) variance 3) skewness and 4) kurtosis. Good overall performances were achieved when a combination of ASTERBAND and RADARSAT2 data sets was used.

Sairaanhoitajaopiskelijoiden ohjattujen käytännön harjoittelujen aikana ilmenneet eettiset ongelmat

Ethical problems occurring during the practical training period of Finnish nursing students The present study focused on nursing students adopting the professional code of conduct during their supervised practical training. The study was carried out in two phases. During the first phase, the objective was to survey ethical problems occurring in practical training as well as how these problems are detected and resolved by nursing students and their supervisors at different stages of their studies. In the second phase, the capability of the nursing students about to graduate to detect and resolve ethical problems was described and analyzed. The students’ capacity for self-instruction, independent search for information as well as factors related to teaching of ethics were determined within this phase. Further, an extensive literature review was carried out to complement the study. Thus, the main objective of the thesis was to make suggestions for the development of the teaching of ethics and supervision in nursing studies and in practice. In the first part of the empirical phase (2002–2005), the views of the nursing students (n =18) were clarified with themed open essay questions. Furthermore, the views of the supervising nurses (n = 115) were established by utilizing a series of themed questions and group interviews. During the second phase (2006–2007), the data for the analyses were collected from nursing students in their graduating stage (n = 319) by a national Internet-based questionnaire. The results of the first phase were examined with contentanalysis and those of the second phase both statistically and by using content analysis. Ethical problems occurring during supervised practical training were typically connected to a patient or a client, a member of the nursing staff or to a student, while solutions were connected to preparation and the action to solve the problem in question. Ethical dilemmas were classified as legal, ethical comportment and uncertainty problems as well as personal and institutional ones. The solutions for these problems were further grouped as based on facts, instructor/staff/member/specialist or patient/client/relative. The results showed that although the nursing students about to graduate had detected many ethical problems both independently as well as together with the nursing staff during every practical training period, they were able to resolve only few of them. Ethical problems were most frequently encountered during training in psychiatric nursing. On the grounds of their own impressions, the nursing students stated that their ability to detect and solve ethical problems improved during their training period. The primary factors related to this enhancement of their skills were teaching and the students’ readiness for selfinstruction. Gender, orientation of the studies and age were observed to be the most important among the underlying factors influencing the capability to detect and solve ethical problems as well as to engage in self-instruction. Based on the results obtained, suggestions for development as well as topics for further studies are presented through teaching of professional ethics and supervision during practical training.

Machine Learning and Clinical Text. Supporting Health Information Flow

Fluent health information flow is critical for clinical decision-making. However, a considerable part of this information is free-form text and inabilities to utilize it create risks to patient safety and cost-­effective hospital administration. Methods for automated processing of clinical text are emerging. The aim in this doctoral dissertation is to study machine learning and clinical text in order to support health information flow.First, by analyzing the content of authentic patient records, the aim is to specify clinical needs in order to guide the development of machine learning applications.The contributions are a model of the ideal information flow,a model of the problems and challenges in reality, and a road map for the technology development. Second, by developing applications for practical cases,the aim is to concretize ways to support health information flow. Altogether five machine learning applications for three practical cases are described: The first two applications are binary classification and regression related to the practical case of topic labeling and relevance ranking.The third and fourth application are supervised and unsupervised multi-class classification for the practical case of topic segmentation and labeling.These four applications are tested with Finnish intensive care patient records.The fifth application is multi-label classification for the practical task of diagnosis coding. It is tested with English radiology reports.The performance of all these applications is promising. Third, the aim is to study how the quality of machine learning applications can be reliably evaluated.The associations between performance evaluation measures and methods are addressed,and a new hold-out method is introduced.This method contributes not only to processing time but also to the evaluation diversity and quality. The main conclusion is that developing machine learning applications for text requires interdisciplinary, international collaboration. Practical cases are very different, and hence the development must begin from genuine user needs and domain expertise. The technological expertise must cover linguistics,machine learning, and information systems. Finally, the methods must be evaluated both statistically and through authentic user-feedback.

Verenpaineen mittaamisen opettaminen sairaanhoitaja- ja terveydenhoitajaopiskelijoille

Teaching the measurement of blood pressure for both nursing and public health nursing students The purpose of this two-phase study was to develop the teaching of blood pressure measurement within the nursing degree programmes of the Universities of Applied Sciences. The first survey phase described what and how blood pressure measurement was taught within nursing degree programmes. The second intervention phase (2004-2005) evaluated first academic year nursing and public health nursing students’ knowledge and skills results for blood pressure measurement. Additionally, the effect on the Taitoviikko experimental group students’ blood pressure measurement knowledge and skills level. A further objective was to construct models for an instrument (RRmittTest) to evaluate nursing students measurement of blood pressure (2003-2009). The research data for the survey phase were collected from teachers (total sampling, N=107, response rate 77%) using a specially developed RRmittopetus-questionnaire. Quasi-experimental study data on the RRmittTest-instrument was collected from students (purposive sampling, experimental group, n=29, control group, n=44). The RRmittTest consisted of a test of knowledge (Tietotesti) and simulation-based test (TaitoSimkäsi and Taitovideo) of skills. Measurements were made immediately after the teaching and in clinical practice. Statistical methods were used to analyse the results and responses to open-ended questions were organised and classified. Due to the small amount of materials involved and the results of distribution tests of the variables, non-parametric analytic methods were mainly used. Experimental group and control group similar knowledge and skills teaching was based on the results of the national survey phase (RRmittopetus) questionnaire results. Experimental group teaching includes the supervised Taitoviikko teaching method. During Taitoviikko students studied blood pressure measurement at the municipal hospital in a real nursing environment, guided by a teacher and a clinical nursing professional. In order to evaluate both learning and teaching the processes and components of blood pressure measurement were clearly defined as follows: the reliability of measurement instruments, activities preceding blood pressure measurement, technical execution of the measurement, recording, lifestyle guidance and measurement at home (self-monitoring). According to the survey study, blood pressure measurement is most often taught at Universities of Applied Sciences, separately, as knowledge (teaching of theory, 2 hours) and skills (classroom practice, 4 hours). The teaching was implemented largely in a classroom and was based mainly on a textbook. In the intervention phase the students had good knowledge of blood pressure measurement. However, their blood pressure measurement skills were deficient and the control group students, in particular, were highly deficient. Following in clinical practice the experimental group and control group students’ blood pressure measurement recording knowledge improve and experimental groups declined lifestyle guidance. Skills did not improve within any of the components analysed. The control groups` skills on the whole, declined statistically.There was a significant decline amongst the experimental group although only in one component measured. The results describe the learning results for first academic year students and no parallel conclusions should be drawn when considering any learning results for graduating students. The results support the use and further development of the Taitoviiko teaching method. The RRmittTest developed for the study should be assessed and the results seen from a negative perspective. This evaluation tool needs to be developed and retested.

Kaupunkialueella toimivan sähkönjakeluverkkoyhtiön siirtohinnoittelun kehittäminen

Sähkönjakeluverkkoyhtiöllä on sähkön siirtopalveluun alueellinen monopoli. Siirtohinnoittelun reunaehdot tulevat sähkömarkkinalaista ja hintojen kohtuullisuutta valvoo ja sääntelee Energiamarkkinavirasto. Tässä työssä selvitetään Turku Energia Sähköverkot Oy:lle kustannusvastaavat aiheuttamisperiaatteen mukaiset siirtohinnat. Samalla muodostetaan yleis-, yö- ja kausituotteiden perusmaksuihin asiakkaan liittymispisteen pääsulakkeen kokoon perustuva porrastus. Sulakeporrastuksen käyttöönotolla pystytään kehittämään siirtohinnoittelun kustannusvastaavuutta ja noudattamaan aiheuttamisperiaatteen mukaista hinnoittelupolitiikkaa entistä paremmin. Työssä tutkitaan siirtohinnoittelun kehitysnäkymiä myös pidemmällä tähtäimellä. Älykkäiden sähköverkkojen kehittyminen ja erityisesti etäluettavien mittareiden käyttöönotto tulevat luomaan kehitysmahdollisuuksia siirtohinnoitteluun tulevaisuudessa. Laitteista saatavia tarkkoja sähkönkulutus- ja tehotietoja voidaan käyttää kustannusvastaavuuslaskennan taustalla. Jotta verkon käyttö olisi sen mitoitukseen nähden mahdollisimman tehokasta, tulisi kuorman jakautua mahdollisimman tasaisesti ajan suhteen. Verkossa esiintyviä kulutushuippuja pystytään tulevaisuudessa mahdollisesti tasoittamaan esimerkiksi kuorman ohjauksen avulla, jonka tehokeinona voidaan käyttää siirtohinnoittelua. Siirtohinnoilla voidaan mahdollisesti vaikuttaa myös esimerkiksi kulutuksen ja tuotannon ajoittamiseen sekä kannusta asiakkaita loistehon kompensointiin. Siirtotuotteiden täytyy myös kehittyä asiakkaiden tarpeiden kehittymisen rinnalla ja esimerkiksi sähkön laadun tuotteistaminen voi olla eräs keino vastaamaan tiukentuneisiin vaatimuksiin.