Multivariable regression analysis and hot-spot stress approach into fatigue life estimations of welded joints

Kolmen eri hitsausliitoksen väsymisikä arvio on analysoitu monimuuttuja regressio analyysin avulla. Regression perustana on laaja S-N tietokanta joka on kerätty kirjallisuudesta. Tarkastellut liitokset ovat tasalevy liitos, krusiformi liitos ja pitkittäisripa levyssä. Muuttujina ovat jännitysvaihtelu, kuormitetun levyn paksuus ja kuormitus tapa. Paksuus effekti on käsitelty uudelleen kaikkia kolmea liitosta ajatellen. Uudelleen käsittelyn avulla on varmistettu paksuus effektin olemassa olo ennen monimuuttuja regressioon siirtymistä. Lineaariset väsymisikä yhtalöt on ajettu kolmelle hitsausliitokselle ottaen huomioon kuormitetun levyn paksuus sekä kuormitus tapa. Väsymisikä yhtalöitä on verrattu ja keskusteltu testitulosten valossa, jotka on kerätty kirjallisuudesta. Neljä tutkimustaon tehty kerättyjen väsymistestien joukosta ja erilaisia väsymisikä arvio metodeja on käytetty väsymisiän arviointiin. Tuloksia on tarkasteltu ja niistä keskusteltu oikeiden testien valossa. Tutkimuksissa on katsottu 2mm ja 6mm symmetristäpitkittäisripaa levyssä, 12.7mm epäsymmetristä pitkittäisripaa, 38mm symmetristä pitkittäisripaa vääntökuormituksessa ja 25mm/38mm kuorman kantavaa krusiformi liitosta vääntökuormituksessa. Mallinnus on tehty niin lähelle testi liitosta kuin mahdollista. Väsymisikä arviointi metodit sisältävät hot-spot metodin jossa hot-spot jännitys on laskettu kahta lineaarista ja epälineaarista ekstrapolointiakäyttäen sekä paksuuden läpi integrointia käyttäen. Lovijännitys ja murtumismekaniikka metodeja on käytetty krusiformi liitosta laskiessa.

Measuring Brand Equity and Analyzing Its Relationship with Key Marketing Mix Elements

The aim of this paper is to analyze the effect of price and advertising on brand equity. The dimensionality of brand equity is thoroughly examined, and the effect price, price deals, perceived advertising spending and advertising appeal have on the dimensions of brand equity are analyzed using multiple regression analysis as well as other supporting analyses. Price and advertising are found to be of great importance to brand equity. Arguably the most influential finding is the strong positive effect low prices – an integral brand element – have on the case company brand equity, even though a negative effect was hypothesized based on prior research. The results also support separating advertising appeal from perceived advertising spending, as well as linking service quality as part of the overall perceived quality in the context of service-intensive firms.

Ohutlevytuotteen valmistuskustannusten määrittely suunnitteluvaiheessa

Diplomityössä kehitetään ABB Oy Drives:lle menetelmää, jolla voidaan ennustaa ohutlevyosien ja niistä koostuvien kokoonpanojen hintaa ilman tarkkaa valmistuksellista geometriatietoa. Työ on osa Tekesin rahoittamaa Piirre 2.0 -projektia. Työn teoriaosa määrittelee lyhyesti ohutlevytuotteet ja niiden valmistusmenetelmät. Laajemmassa teoriatarkastelussa ovat erilaiset ohutlevytuotteiden valmistuskustannusten ennustamismenetelmät regressioanalyysin käyttöön painottuen. Käytännön osiossa määritetään Finn-Power LP6 -levytyökeskuksen suorituskyky ja muodostetaan työaikalaskuri kerättyyn tietoon perustuen. Lisäksi muodostetaan regressioanalyysit kahden eri alihankkijan valmistamien ohutlevytuotteiden pohjalta. Regressiotekniikoiden avulla etsitään kustannuksiin voimakkaasti vaikuttavat parametrit ja muodostetaan laskukaava valmistuskustannusten ennustamiseen. Lopuksi vertaillaan teorian ja käytännön osien yhteensopivuutta ja etsitään syitä havaittuihin eroihin. Tutkimustulosten hyödyntämismahdollisuuksien ohella esitetään myös eräitä jatkokehitysehdotuksia.

The influence of strategic entrepreneurship on firm performance

In the study the recently developed concept of strategic entrepreneurship was addressed with the aim to investigate the underlying factors and components constituting the concept and their influence on firm performance. As the result of analysis of existing literature and empirical studies the model of strategic entrepreneurship for the current study is developed with the emphasis on exploration and exploitation parts of the concept. The research model is tested on the data collected in the project ―Factors of growth and success of entrepreneurial firms in Russia‖ by Center for Entrepreneurship of GSOM in 2007 containing answers of owners and managers of 500 firms operating in St. Petersburg and Moscow. Multiple regression analysis showed that exploration and exploitation presented by entrepreneurial values, investments in internal resources, knowledge management and developmental changes are significant factors constituting strategic entrepreneurship and having positive relation to firm performance. The theoretical contribution of the work is linked to development and testing of the model of strategic entrepreneurship. The results can be implemented in management practices of companies willing to engage in strategic entrepreneurship and increase their firm performance.

Exploring the feasibility of applying data mining for library reference service improvement : a case study of Turku Main Library

Data mining, as a heatedly discussed term, has been studied in various fields. Its possibilities in refining the decision-making process, realizing potential patterns and creating valuable knowledge have won attention of scholars and practitioners. However, there are less studies intending to combine data mining and libraries where data generation occurs all the time. Therefore, this thesis plans to fill such a gap. Meanwhile, potential opportunities created by data mining are explored to enhance one of the most important elements of libraries: reference service. In order to thoroughly demonstrate the feasibility and applicability of data mining, literature is reviewed to establish a critical understanding of data mining in libraries and attain the current status of library reference service. The result of the literature review indicates that free online data resources other than data generated on social media are rarely considered to be applied in current library data mining mandates. Therefore, the result of the literature review motivates the presented study to utilize online free resources. Furthermore, the natural match between data mining and libraries is established. The natural match is explained by emphasizing the data richness reality and considering data mining as one kind of knowledge, an easy choice for libraries, and a wise method to overcome reference service challenges. The natural match, especially the aspect that data mining could be helpful for library reference service, lays the main theoretical foundation for the empirical work in this study. Turku Main Library was selected as the case to answer the research question: whether data mining is feasible and applicable for reference service improvement. In this case, the daily visit from 2009 to 2015 in Turku Main Library is considered as the resource for data mining. In addition, corresponding weather conditions are collected from Weather Underground, which is totally free online. Before officially being analyzed, the collected dataset is cleansed and preprocessed in order to ensure the quality of data mining. Multiple regression analysis is employed to mine the final dataset. Hourly visits are the independent variable and weather conditions, Discomfort Index and seven days in a week are dependent variables. In the end, four models in different seasons are established to predict visiting situations in each season. Patterns are realized in different seasons and implications are created based on the discovered patterns. In addition, library-climate points are generated by a clustering method, which simplifies the process for librarians using weather data to forecast library visiting situation. Then the data mining result is interpreted from the perspective of improving reference service. After this data mining work, the result of the case study is presented to librarians so as to collect professional opinions regarding the possibility of employing data mining to improve reference services. In the end, positive opinions are collected, which implies that it is feasible to utilizing data mining as a tool to enhance library reference service.

Vinouden huomioiva arvopapereiden hinnoittelumalli ja sen empiirinen testaaminen Suomen markkinoilla

Tutkimuksen tarkoituksena on tutkia osaketuottojen jakauman vinoutta ja sen mahdollisia vaikutuksia osakkeiden hinnoitteluun Suomen markkinoilla. Aineistona käytetään kuutta portfoliota jotka on muodostettu Suomen markkinoilla noteerattavista osakkeista ajanjaksolla 1.1.1987–31.12.2004. Osakkeet on jaettu portfolioihin markkina-arvon mukaan. Empiiriset tulokset osoittavat, että osaketuotot Suomen markkinoilla ovat positiivisesti vinoja mutta pääosin eivät merkitsevästi. Teoreettisen taustan perusteella olisi ollut odotettavaa, että vinoutta olisi ollut enemmän. Regressioanalyysillä ja kahta artikkelia replikoiden tutkittiin perinteisen ja vinouden sisältäviä CAPM-malleja. Odotettavissa oli, että perinteinen CAPM-malli suoriutuu huonommin kuin vinouden sisältävä. Regressio-analyysillä testatessa molemmat mallit suoriutuivat hyvin tuottojen selittämisessä, mutta vakiotermien perusteella kolmimomenttinen malli suoriutuisi paremmin. Regressiomallin ja artikkelin perusteella saadut betat olivat yhteneväisiä. Regressiomallin ja artikkelin perusteella saaduissa gam-moissa oli kuitenkin eroja ja niiden perusteella ei voida tehdä johtopäätöksiä. Regressiomalli näyttäisi kuitenkin huomioivan vinouden.

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 Patterns in Electricity Spot Market Time Series

Due to its non-storability, electricity must be produced at the same time that it is consumed, as a result prices are determined on an hourly basis and thus analysis becomes more challenging. Moreover, the seasonal fluctuations in demand and supply lead to a seasonal behavior of electricity spot prices. The purpose of this thesis is to seek and remove all causal effects from electricity spot prices and remain with pure prices for modeling purposes. To achieve this we use Qlucore Omics Explorer (QOE) for the visualization and the exploration of the data set and Time Series Decomposition method to estimate and extract the deterministic components from the series. To obtain the target series we use regression based on the background variables (water reservoir and temperature). The result obtained is three price series (for Sweden, Norway and System prices) with no apparent pattern.

Motivational Influences on Transfer: Dimensions and Boundary Conditions

The focus of this dissertation is the motivational influences on transfer in higher education and professional training contexts. To estimate these motivational influences, the dissertation includes seven individual studies that are structured in two parts. Part I, Dimensions, aims at identifying the dimensionality of motivation to transfer and its structural relations with training-related antecedents and outcomes. Part II, Boundary Conditions, aims at testing the predictive validity of motivation theories used in contemporary training research under different study conditions. Data in this dissertation was gathered from multi-item questionnaires, which were analyzed differently in Part I and Part II. Studies in Part I employed exploratory and confirmatory factor analysis, structural equation modeling, partial least squares (PLS) path modeling, and mediation analysis. Studies in Part II used artifact distribution meta-analysis, (nested) subgroup analysis, and weighted least squares (WLS) multiple regression. Results demonstrate that motivation to transfer can be conceptualized as a three-dimensional construct, including autonomous motivation to transfer, controlled motivation to transfer, and intention to transfer, given a theoretical framework informed by expectancy theory, self-determination theory, and the theory of planned behavior. Results also demonstrate that a range of boundary conditions moderates motivational influences on transfer. To test the predictive validity of expectancy theory, social cognitive theory, and the theory of goal orientations under different study settings, a total of 17 boundary conditions were meta-analyzed, including age; assessment criterion; assessment source; attendance policy; collaboration among trainees; computer support; instruction; instrument used to measure motivation; level of education; publication type; social training context; SS/SMC bias; study setting; survey modality; type of knowledge being trained; use of a control group; and work context. Together, the findings cumulated in this thesis support the basic premise that motivation is centrally important for transfer, but that motivational influences need to be understood from a more differentiated perspective than commonly found in the literature, in order to account for several dimensions and boundary conditions. The results of this dissertation across the seven individual studies are reflected in terms of their implications for theory development and their significance for training evaluation and the design of training environments. Limitations and directions to take in future research are discussed.

Validation and Selection of Transportation Cost Drivers with Advanced Methods

In today's logistics environment, there is a tremendous need for accurate cost information and cost allocation. Companies searching for the proper solution often come across with activity-based costing (ABC) or one of its variations which utilizes cost drivers to allocate the costs of activities to cost objects. In order to allocate the costs accurately and reliably, the selection of appropriate cost drivers is essential in order to get the benefits of the costing system. The purpose of this study is to validate the transportation cost drivers of a Finnish wholesaler company and ultimately select the best possible driver alternatives for the company. The use of cost driver combinations as an alternative is also studied. The study is conducted as a part of case company's applied ABC-project using the statistical research as the main research method supported by a theoretical, literature based method. The main research tools featured in the study include simple and multiple regression analyses, which together with the literature and observations based practicality analysis forms the basis for the advanced methods. The results suggest that the most appropriate cost driver alternatives are the delivery drops and internal delivery weight. The possibility of using cost driver combinations is not suggested as their use doesn't provide substantially better results while increasing the measurement costs, complexity and load of use at the same time. The use of internal freight cost drivers is also questionable as the results indicate weakening trend in the cost allocation capabilities towards the end of the period. Therefore more research towards internal freight cost drivers should be conducted before taking them in use.