Adopting the Vaadin Java framework to rebuild VTT’s Web Interfaces and Database Applications

With the growth in new technologies, using online tools have become an everyday lifestyle. It has a greater impact on researchers as the data obtained from various experiments needs to be analyzed and knowledge of programming has become mandatory even for pure biologists. Hence, VTT came up with a new tool, R Executables (REX) which is a web application designed to provide a graphical interface for biological data functions like Image analysis, Gene expression data analysis, plotting, disease and control studies etc., which employs R functions to provide results. REX provides a user interactive application for the biologists to directly enter the values and run the required analysis with a single click. The program processes the given data in the background and prints results rapidly. Due to growth of data and load on server, the interface has gained problems concerning time consumption, poor GUI, data storage issues, security, minimal user interactive experience and crashes with large amount of data. This thesis handles the methods by which these problems were resolved and made REX a better application for the future. The old REX was developed using Python Django and now, a new programming language, Vaadin has been implemented. Vaadin is a Java framework for developing web applications and the programming language is extremely similar to Java with new rich components. Vaadin provides better security, better speed, good and interactive interface. In this thesis, subset functionalities of REX was selected which includes IST bulk plotting and image segmentation and implemented those using Vaadin. A code of 662 lines was programmed by me which included Vaadin as the front-end handler while R language was used for back-end data retrieval, computing and plotting. The application is optimized to allow further functionalities to be migrated with ease from old REX. Future development is focused on including Hight throughput screening functions along with gene expression database handling

Design thinking framework of experience innovation

Innovation remains one of the key drivers of sustainable and successful business. The variety of innovation approaches such as open models, intersectional thinking and co-creation tackles the challenge of viable novel offerings across the world. These approaches have certain similarities and their elements constitute design thinking. Recent market and society trends such as technological advances and globalization have intensify companies’ interaction with customers. Emotional engagement, pleasing communication and delight have gained equal to functionality importance. The complex of these components constitutes consumer experience. Academic research conceptualizes these changes by introducing customer-centered innovation, which replaces product-oriented approaches. However, both methods omit experience concept and provide fragmented explanation of experience innovation. Experience is an essential process of offering perception, which drives customer decisions. Therefore, an agenda of experience innovation development can systemize and explain the mechanisms of experience innovation. The purpose of this study is to create and explain the stage process framework of experience innovation by the means of design thinking approach. The research proceeds in accordance with the following sub-objectives: 1. Conceptualization of consumer experience through customer value. 2. Creation of experience innovation framework by the means of design thinking. This study is conducted by the means of conceptual research methods. The main theoretical contribution of the study is creation of the integrated framework of consumer experience innovation. The elaboration of design thinking agenda and methods applied to experience design builds the guidelines of experience innovation development. This research synthesizes the conceptual elements of the framework that resolves inconsistencies and duplications of theories. This essential clarification simplifies application of the experience innovation agenda, which can be useful for the wide range of specialists, from marketing to strategists, and from managers to entrepreneurs, willing to offer compelling experience to customers. The study highlights the crucial role of consumer experience in maintaining customer loyalty and designs the roadmap of innovating experience through the communication with customers.

Exploring the impact of celebrity endorsement and product class involvement on advertising effectiveness: an eye tracking approach

Celebrity endorsement has increased in popularity over the past decades and companies are willing to spend increasingly excessive amounts of money into it. Even though multiple studies support celebrity endorsement, further research on its impact on advertising effectiveness is called for. Fur-ther, the role of consumers’ product class involvement in advertising needs to be further studied. The purpose of this study is to explore if consumers’ product class involvement and exposure to celebrity endorsers affect consumers brand recall. Supported by earlier studies, brand recall was used as a measure for advertising effectiveness in this study. In general, a psychological approach was chosen for building the theoretical framework. Concept of classical conditioning was presented in order to understand why people act how they do. Balanced theory and meaning transfer model were presented in order to study how celebrities can be used effectively in advertising context. Further, the importance of product class involvement in advertising effectiveness was evaluated. Hypotheses were formulated based on a literature review of the existing research. Because of the versatility of the research design, a mixed methods approach for this study was adopted. Empirical part of the study was conducted in three stages. First, a pre-test was conducted in order to choose suitable product endorsers for the advertisement stimuli used in the experiment. Second, an eye-tracking experiment with 30 test subjects was conducted in order to study how people view advertisements and whether the familiarity of the product endorser and consumers’ product class involvement affects brand recall. For the experiment, a fictional brand was created in order to avoid bias on brand recall. Third, qualitative interviews for 15 test subjects were conducted in the post-experiment stage in order to gain deeper understating of the phenomenon and to make sense of the findings from the experiment. Findings from this study support celebrity endorsement by suggesting that a famous spokesperson does not steal attention from brand information more than a non-celebrity product endorser. As a result, the use of a celebrity endorser did not decrease brand recall. Results support earlier research as consumer’ higher product class involvement resulted in a better brand recall. Findings from the interviews suggest that consumers have positive perceptions of celebrity endorsement in general. However, the celebrity–brand congruence is a crucial factor when creating attitudes towards the advertisement. Future research ideas were presented based on the limitations and results of this study

Modelling ecological values in heterogeneous and dynamic landscapes with geospatial data

Our surrounding landscape is in a constantly dynamic state, but recently the rate of changes and their effects on the environment have considerably increased. In terms of the impact on nature, this development has not been entirely positive, but has rather caused a decline in valuable species, habitats, and general biodiversity. Regardless of recognizing the problem and its high importance, plans and actions of how to stop the detrimental development are largely lacking. This partly originates from a lack of genuine will, but is also due to difficulties in detecting many valuable landscape components and their consequent neglect. To support knowledge extraction, various digital environmental data sources may be of substantial help, but only if all the relevant background factors are known and the data is processed in a suitable way. This dissertation concentrates on detecting ecologically valuable landscape components by using geospatial data sources, and applies this knowledge to support spatial planning and management activities. In other words, the focus is on observing regionally valuable species, habitats, and biotopes with GIS and remote sensing data, using suitable methods for their analysis. Primary emphasis is given to the hemiboreal vegetation zone and the drastic decline in its semi-natural grasslands, which were created by a long trajectory of traditional grazing and management activities. However, the applied perspective is largely methodological, and allows for the application of the obtained results in various contexts. Models based on statistical dependencies and correlations of multiple variables, which are able to extract desired properties from a large mass of initial data, are emphasized in the dissertation. In addition, the papers included combine several data sets from different sources and dates together, with the aim of detecting a wider range of environmental characteristics, as well as pointing out their temporal dynamics. The results of the dissertation emphasise the multidimensionality and dynamics of landscapes, which need to be understood in order to be able to recognise their ecologically valuable components. This not only requires knowledge about the emergence of these components and an understanding of the used data, but also the need to focus the observations on minute details that are able to indicate the existence of fragmented and partly overlapping landscape targets. In addition, this pinpoints the fact that most of the existing classifications are too generalised as such to provide all the required details, but they can be utilized at various steps along a longer processing chain. The dissertation also emphases the importance of landscape history as an important factor, which both creates and preserves ecological values, and which sets an essential standpoint for understanding the present landscape characteristics. The obtained results are significant both in terms of preserving semi-natural grasslands, as well as general methodological development, giving support to science-based framework in order to evaluate ecological values and guide spatial planning.

The effectiveness of interpretive methods in informal educational facilities : an experimental study with reference to marine parks

Interpretation has been used in many tourism sectors as a technique in achieving building hannony between resources and human needs. The objectives of this study are to identify the types of the interpretive methods used, and to evaluate their effectiveness, in marine parks. This study reviews the design principles of an effective interpretation for marine wildlife tourism, and adopts Drams' five design principles (1997) into a conceptual framework. Enjoyment increase, knowledge gain, attitude and intention change, and behaviour modification were used as key indicators in the assessment of the interpretive effectiveness of the Vancouver Aquarium (VA) and Marineland Canada (MC). Since on-site research is unavailable, a virtual tour is created to represent the interpretive experiences in the two study sites. Self-administered questionnaires are used to measure responses. Through comparing responses to the questionnaires (pre-, post-virtual tours and follow-up), this study found that interpretation increased enjoyment and added to respondents' knowledge. Although the changes in attitudes and intentions are not significant, the findings indicate that attitude and intention changes did occur as a result of interpretation, but only to a limited extent. Overall results suggest that more techniques should be added to enhance the effectiveness of the interpretation in marine parks or self-guiding tours, and with careful design, virtual tours are the innovative interpretation techniques for marine parks or informal educational facilities.

Cognitive coaching : a multiple case study /

The purpose of this qualitative study was to investigate the application of Cognitive Coaching as a school-based professional development program to improve instructional thought and decision making as well as to enhance staff perceptions, coUegiality and school culture. This topic emerged from personal and professional issues related to the role ofthe reflective practitioner in improving the quality of education, yet cognizant of the fact that little professional development was available to train teachers to become reflective. This case study, positioned within the interpretive sciences, focused on three teachers and how their experiences with cognitive coaching affected their teaching practices. Their knowledge, understanding and use of the four stages of instructional thought (preactive, interactive, reflective and projective) were tested before and at the end of eight coaching cycles, and again after two months to determine whether they had continued to use the reflective process. They were also assessed on whether their attitude towards peer coaching had changed, whether their feelings about teaching had become more positive and whether their professional dialogue had increased. Three methods of data collection were selected to assess growth: interviews, observations and joumaling. Analysis primarily consisted of coding and organizing data according to emerging themes. Although the professed aim of cognitive coaching was to teach the process in order that the teachers would become self-analytical and self-modifying, this study found that the value of the coaching, after trust had been established in both the coach and the process, was in the dialoguing and the time set aside to do it. Once the coaching stopped providing the time to dialogue, to examine one's meanings and beliefs, so did the critical self-reflection. As a result ofthe cognitive coaching experience though, all participants grew in their feelings of efficacy, craftsmanship, flexibility, consciousness and interdependence. The actual and potential significance ofthis study was discussed according to implications for teacher supervision, professional development, school culture, further areas of research and to my personal growth and development.

Improving the use of the nursing process: the impact of charting methodology developed from King's conceptual framework on the use of the nursing process

The purpose of this study was to assess the effects of changing a nursing documentation system, developed from King's Conceptual Framework, on the use of the nursing process. The null hypothesis was that there would be no significant increase in the reflection of the use of the nursing process on the nursing care plan or nurses' notes, as a result of using a nursing documentation system developed using King's Conceptual Framework (1981). The design involved the development of a questionnaire that was used to review health records pre and post implementation of a documentation system developed based on King's Conceptual Framework and Theory of Goal Attainment (1981). A Record Completeness Score was obtained from some of the questions. The null hypothesis was rejected. The results of the study have implications for nursing administration and the evaluation of nursing practice. If the use of a documentation system developed from a conceptual framework increases the reflection of the nursing process on the patient's health record, nursing will have the means to measure patient outcomes/goal attainment. All health care organizations and levels of government are focusing on methods to monitor and control the health-care dollar. In order for nursing to clearly determine the costs associated with nursing care, measurement of patient outcomes/goal attainment will need to be possible. In order to measure patient outcomes/goals attainment nurses will need to be able to collect data on their practice. It will be critical that nursing have a documentation system in place which facilitates the reflection of the nursing process within a theoretical framework.

Sex Talk: A Multiple Case Study to Explore and Understand Parent-Child Sexual Health Communication in Chinese Immigrant Families

Parent-child sexual health communication can be beneficial. Many factors affect such communication in Chinese immigrant families. This qualitative study explored the influences of acculturation, parenting, and parental participation in the Raising Sexually Healthy Children Program (RSHC) on such communication. With a hermeneutic framework, the purpose was to develop understanding based on the topic, context, and researcher interpretations. Twelve interviews elicited data from six parent-child dyads, three from the RSHC. Analysis involved coding processes; data were compared repeatedly and organized into themes. Perceived personality differences between generations were confounded with cultural communicative differences. Parents used implicitness observed in Chinese culture to establish "open" communication; children expected explicitness observed in Western culture. Post- RSHC, parents perceived themselves as more open to talking about sex; children did not perceive such parental changes. Future research should include joint interviews and longitudinal program evaluation. Future practice should focus on cross-cultural communication and involving children in RSHC.

A Systematic Framework for Analyzing the Dynamic Effects of Permanent and Transitory Shocks.

This paper proposes a systematic framework for analyzing the dynamic effects of permanent and transitory shocks on a system of \"n\" economic variables.

Computation and Analysis of Multiple Structural-Change Models

In a recent paper, Bai and Perron (1998) considered theoretical issues related to the limiting distribution of estimators and test statistics in the linear model with multiple structural changes. In this companion paper, we consider practical issues for the empirical applications of the procedures. We first address the problem of estimation of the break dates and present an efficient algorithm to obtain global minimizers of the sum of squared residuals. This algorithm is based on the principle of dynamic programming and requires at most least-squares operations of order O(T 2) for any number of breaks. Our method can be applied to both pure and partial structural-change models. Secondly, we consider the problem of forming confidence intervals for the break dates under various hypotheses about the structure of the data and the errors across segments. Third, we address the issue of testing for structural changes under very general conditions on the data and the errors. Fourth, we address the issue of estimating the number of breaks. We present simulation results pertaining to the behavior of the estimators and tests in finite samples. Finally, a few empirical applications are presented to illustrate the usefulness of the procedures. All methods discussed are implemented in a GAUSS program available upon request for non-profit academic use.

Finite-Sample Inference Methods for Simultaneous Equations and Models with Unobserved and Generated Regressors

We propose finite sample tests and confidence sets for models with unobserved and generated regressors as well as various models estimated by instrumental variables methods. The validity of the procedures is unaffected by the presence of identification problems or \"weak instruments\", so no detection of such problems is required. We study two distinct approaches for various models considered by Pagan (1984). The first one is an instrument substitution method which generalizes an approach proposed by Anderson and Rubin (1949) and Fuller (1987) for different (although related) problems, while the second one is based on splitting the sample. The instrument substitution method uses the instruments directly, instead of generated regressors, in order to test hypotheses about the \"structural parameters\" of interest and build confidence sets. The second approach relies on \"generated regressors\", which allows a gain in degrees of freedom, and a sample split technique. For inference about general possibly nonlinear transformations of model parameters, projection techniques are proposed. A distributional theory is obtained under the assumptions of Gaussian errors and strictly exogenous regressors. We show that the various tests and confidence sets proposed are (locally) \"asymptotically valid\" under much weaker assumptions. The properties of the tests proposed are examined in simulation experiments. In general, they outperform the usual asymptotic inference methods in terms of both reliability and power. Finally, the techniques suggested are applied to a model of Tobin’s q and to a model of academic performance.

Semi-Parametric Weak Instrument Regressions with an Application to the Risk-Return Trade-off

Recent work shows that a low correlation between the instruments and the included variables leads to serious inference problems. We extend the local-to-zero analysis of models with weak instruments to models with estimated instruments and regressors and with higher-order dependence between instruments and disturbances. This makes this framework applicable to linear models with expectation variables that are estimated non-parametrically. Two examples of such models are the risk-return trade-off in finance and the impact of inflation uncertainty on real economic activity. Results show that inference based on Lagrange Multiplier (LM) tests is more robust to weak instruments than Wald-based inference. Using LM confidence intervals leads us to conclude that no statistically significant risk premium is present in returns on the S&P 500 index, excess holding yields between 6-month and 3-month Treasury bills, or in yen-dollar spot returns.

Testing Mean-Variance Efficiency in CAPM with Possibly Non-Gaussian Errors : An Exact Simulation-Based Approach

In this paper we propose exact likelihood-based mean-variance efficiency tests of the market portfolio in the context of Capital Asset Pricing Model (CAPM), allowing for a wide class of error distributions which include normality as a special case. These tests are developed in the frame-work of multivariate linear regressions (MLR). It is well known however that despite their simple statistical structure, standard asymptotically justified MLR-based tests are unreliable. In financial econometrics, exact tests have been proposed for a few specific hypotheses [Jobson and Korkie (Journal of Financial Economics, 1982), MacKinlay (Journal of Financial Economics, 1987), Gib-bons, Ross and Shanken (Econometrica, 1989), Zhou (Journal of Finance 1993)], most of which depend on normality. For the gaussian model, our tests correspond to Gibbons, Ross and Shanken’s mean-variance efficiency tests. In non-gaussian contexts, we reconsider mean-variance efficiency tests allowing for multivariate Student-t and gaussian mixture errors. Our framework allows to cast more evidence on whether the normality assumption is too restrictive when testing the CAPM. We also propose exact multivariate diagnostic checks (including tests for multivariate GARCH and mul-tivariate generalization of the well known variance ratio tests) and goodness of fit tests as well as a set estimate for the intervening nuisance parameters. Our results [over five-year subperiods] show the following: (i) multivariate normality is rejected in most subperiods, (ii) residual checks reveal no significant departures from the multivariate i.i.d. assumption, and (iii) mean-variance efficiency tests of the market portfolio is not rejected as frequently once it is allowed for the possibility of non-normal errors.

Methods to Estimate Dynamic Stochastic General Equilibrium Models

This paper employs the one-sector Real Business Cycle model as a testing ground for four different procedures to estimate Dynamic Stochastic General Equilibrium (DSGE) models. The procedures are: 1 ) Maximum Likelihood, with and without measurement errors and incorporating Bayesian priors, 2) Generalized Method of Moments, 3) Simulated Method of Moments, and 4) Indirect Inference. Monte Carlo analysis indicates that all procedures deliver reasonably good estimates under the null hypothesis. However, there are substantial differences in statistical and computational efficiency in the small samples currently available to estimate DSGE models. GMM and SMM appear to be more robust to misspecification than the alternative procedures. The implications of the stochastic singularity of DSGE models for each estimation method are fully discussed.

Exact Multivariate Tests of Asset Pricing Models with Stable Asymmetric Distributions

In this paper, we propose exact inference procedures for asset pricing models that can be formulated in the framework of a multivariate linear regression (CAPM), allowing for stable error distributions. The normality assumption on the distribution of stock returns is usually rejected in empirical studies, due to excess kurtosis and asymmetry. To model such data, we propose a comprehensive statistical approach which allows for alternative - possibly asymmetric - heavy tailed distributions without the use of large-sample approximations. The methods suggested are based on Monte Carlo test techniques. Goodness-of-fit tests are formally incorporated to ensure that the error distributions considered are empirically sustainable, from which exact confidence sets for the unknown tail area and asymmetry parameters of the stable error distribution are derived. Tests for the efficiency of the market portfolio (zero intercepts) which explicitly allow for the presence of (unknown) nuisance parameter in the stable error distribution are derived. The methods proposed are applied to monthly returns on 12 portfolios of the New York Stock Exchange over the period 1926-1995 (5 year subperiods). We find that stable possibly skewed distributions provide statistically significant improvement in goodness-of-fit and lead to fewer rejections of the efficiency hypothesis.