Evaluation of noni (Morinda citrifolia) volatile profile by dynamic headspace and gas chromatography-mass spectrometry

Noni is a fruit that has interested the scientific community due to its medicinal and functional activities. Different products that contain noni are already in the market, but their consumption could be impaired by their distinctive unpleasant aroma and flavor. The aim of this work was to evaluate the noni pulp volatile profile by dynamic headspace and gas chromatography-mass spectrometry. Thirty seven volatile compounds were detected, mainly alcohols (63.3%), esters (26.9%), cetones (7.4%), and acids (1.2%).

A Project Portfolio Tool for Forecasting Small Projects Profitability

The case company in this study is a large industrial engineering company whose business is largely based on delivering a wide-range of engineering projects. The aim of this study is to create and develop a fairly simple Excel-based tool for the sales department. The tool’s main function is to estimate and visualize the profitability of various small projects. The study also aims to find out other possible and more long-term solutions for tackling the problem in the future. The study is highly constructive and descriptive as it focuses on the development task and in the creation of a new operating model. The developed tool focuses on estimating the profitability of the small orders of the selected project portfolio currently on the bidding-phase (prospects) and will help the case company in the monthly reporting of sales figures. The tool will analyse the profitability of a certain project by calculating its fixed and variable costs, then further the gross margin and operating profit. The bidding phase of small project is a phase that has not been covered fully by the existing tools within the case company. The project portfolio tool can be taken into use immediately within the case company and it will provide fairly accurate estimate of the profitability figures of the recently sold small projects.

Heat Demand Forecasting Models’ Development: Use of Data Mining Tools in SQL Server Analysis Services

This thesis introduces heat demand forecasting models which are generated by using data mining algorithms. The forecast spans one full day and this forecast can be used in regulating heat consumption of buildings. For training the data mining models, two years of heat consumption data from a case building and weather measurement data from Finnish Meteorological Institute are used. The thesis utilizes Microsoft SQL Server Analysis Services data mining tools in generating the data mining models and CRISP-DM process framework to implement the research. Results show that the built models can predict heat demand at best with mean average percentage errors of 3.8% for 24-h profile and 5.9% for full day. A deployment model for integrating the generated data mining models into an existing building energy management system is also discussed.

Dynamic Competitor Analysis Model Of Welding Quality Management Software

Tämä diplomityö arvioi hitsauksen laadunhallintaohjelmistomarkkinoiden kilpailijoita. Kilpailukenttä on uusi ja ei ole tarkkaa tietoa siitä minkälaisia kilpailijoita on markkinoilla. Hitsauksen laadunhallintaohjelmisto auttaa yrityksiä takaamaan korkean laadun. Ohjelmisto takaa korkean laadun varmistamalla, että hitsaaja on pätevä, hän noudattaa hitsausohjeita ja annettuja parametreja. Sen lisäksi ohjelmisto kerää kaiken tiedon hitsausprosessista ja luo siitä vaadittavat dokumentit. Diplomityön teoriaosuus muodostuu kirjallisuuskatsauksesta ratkaisuliike-toimintaan, kilpailija-analyysin ja kilpailuvoimien teoriaan sekä hitsauksen laadunhallintaan. Työn empiriaosuus on laadullinen tutkimus, jossa tutkitaan kilpailevia hitsauksen laadunhallintaohjelmistoja ja haastatellaan ohjelmistojen käyttäjiä. Diplomityön tuloksena saadaan uusi kilpailija-analyysimalli hitsauksen laadunhallintaohjelmistoille. Mallin avulla voidaan arvostella ohjelmistot niiden tarjoamien primääri- ja sekundääriominaisuuksien perusteella. Toiseksi tässä diplomityössä analysoidaan nykyinen kilpailijatilanne hyödyntämällä juuri kehitettyä kilpailija-analyysimallia.

Variance reduction properties of dynamic hedging models - empirical evidence from BRICS countries

This Master’s Thesis analyses the effectiveness of different hedging models on BRICS (Brazil, Russia, India, China, and South Africa) countries. Hedging performance is examined by comparing two different dynamic hedging models to conventional OLS regression based model. The dynamic hedging models being employed are Constant Conditional Correlation (CCC) GARCH(1,1) and Dynamic Conditional Correlation (DCC) GARCH(1,1) with Student’s t-distribution. In order to capture the period of both Great Moderation and the latest financial crisis, the sample period extends from 2003 to 2014. To determine whether dynamic models outperform the conventional one, the reduction of portfolio variance for in-sample data with contemporaneous hedge ratios is first determined and then the holding period of the portfolios is extended to one and two days. In addition, the accuracy of hedge ratio forecasts is examined on the basis of out-of-sample variance reduction. The results are mixed and suggest that dynamic hedging models may not provide enough benefits to justify harder estimation and daily portfolio adjustment. In this sense, the results are consistent with the existing literature.

The Dynamic Relationship between Finnish Stock Market and Commodities

Since different stock markets have become more integrated during 2000s, investors need new asset classes in order to gain diversification benefits. Commodities have become popular to invest in and thus it is important to examine whether the investors should use commodities as a part for portfolio diversification. This master’s thesis examines the dynamic relationship between Finnish stock market and commodities. The methodology is based on Vector Autoregressive models (VAR). The long-run relationship between Finnish stock market and commodities is examined with Johansen cointegration while short-run relationship is examined with VAR models and Granger causality test. In addition, impulse response test and forecast error variance decomposition are employed to strengthen the results of short-run relationship. The dynamic relationships might change under different market conditions. Thus, the sample period is divided into two sub-samples in order to reveal whether the dynamic relationship varies under different market conditions. The results show that Finnish stock market has stable long-run relationship with industrial metals, indicating that there would not be diversification benefits among the industrial metals. The long-run relationship between Finnish stock market and energy commodities is not as stable as the long-run relationship between Finnish stock market and industrial metals. Long-run relationship was found in the full sample period and first sub-sample which indicate less room for diversification. However, the long-run relationship disappeared in the second sub-sample which indicates diversification benefits. Long-run relationship between Finnish stock market and agricultural commodities was not found in the full sample period which indicates diversification benefits between the variables. However, long-run relationship was found from both sub-samples. The best diversification benefits would be achieved if investor invested in precious metals. No long-run relationship was found from either sample. In the full sample period OMX Helsinki had short-run relationship with most of the energy commodities and industrial metals and the causality was mostly running from equities to commodities. During the first sub period the number of short-run relationships and causality shrunk but during the crisis period the number of short-run relationships and causality increased. The most notable result found was unidirectional causality from gold to OMX Helsinki during the crisis period.

Internal Changes in International New Ventures During Initial Internationalization: Finnish SMEs Operating in Dynamic High-tech Industries

The purpose of this qualitative research is to study how international new ventures change internally during initial internationalization. Based on the analysis of seven INV firms, a framework illustrating this change process, will be developed. This research will also develop earlier theories, and create a solid combination of existing theories to explain the phenomenon. INV firms internationalize more rapidly and aggressively than traditional MNEs. At the same, external and internal drivers cause changes in INVs culture, resources, capabilities, strategic management, and output decisions inside the company. Organizational learning and resource acquisition through international business networks explain how INVs are able to cope with the dynamic high-technology industry and be able to adapt. Internationalization of INVs proceeds through several phases, which may be gone through rapidly due to the network effects and INVs’ special characteristics. The results of this research revealed that INVs internal change process proceeds through four phases; pre-incorporation phase, product development phase, internationalization and growth phase, and maturation phase. INVs culture, resources, capabilities, strategic management, and outputs change significantly during initial internationalization, and INVs develop from small start-ups into fully established companies.

Dynamic modeling and simulation of the airflow cleaning device

The objective of the work is to study the flow behavior and to support the design of air cleaner by dynamic simulation.In a paper printing industry, it is necessary to monitor the quality of paper when the paper is being produced. During the production, the quality of the paper can be monitored by camera. Therefore, it is necessary to keep the camera lens clean as wood particles may fall from the paper and lie on the camera lens. In this work, the behavior of the air flow and effect of the airflow on the particles at different inlet angles are simulated. Geometries of a different inlet angles of single-channel and double-channel case were constructed using ANSYS CFD Software. All the simulations were performed in ANSYS Fluent. The simulation results of single-channel and double-channel case revealed significant differences in the behavior of the flow and the particle velocity. The main conclusion from this work are in following. 1) For the single channel case the best angle was 0 degree because in that case, the air flow can keep 60% of the particles away from the lens which would otherwise stay on lens. 2) For the double channel case, the best solution was found when the angle of the first inlet was 0 degree and the angle of second inlet was 45 degree . In that case, the airflow can keep 91% of particles away from the lens which would otherwise stay on lens.

Forecasting with social media – Using Facebook likes to predict the results of the 2015 Finnish Parliamentary elections

Already one-third of the human population uses social media on a daily basis. The biggest social networking site Facebook has over billion monthly users. As a result, social media services are now recording unprecedented amount of data on human behavior. The phenomenon has certainly caught the attention of scholars, businesses and governments alike. Organizations around the globe are trying to explore new ways to benefit from the massive databases. One emerging field of research is the use of social media in forecasting. The goal is to use data gathered from online services to predict offline phenomena. Predicting the results of elections is a prominent example of forecasting with social media, but regardless of the numerous attempts, no reliable technique has been established. The objective of the research is to analyze how accurately the results of parliament elections can be forecasted using social media. The research examines whether Facebook “likes” can be effectively used for predicting the outcome of the Finnish parliament elections that took place in April 2015. First a tool for gathering data from Facebook was created. Then the data was used to create an electoral forecast. Finally, the forecast was compared with the official results of the elections. The data used in the research was gathered from the Facebook walls of all the candidates that were running for the parliament elections and had a valid Facebook page. The final sample represents 1131 candidates and over 750000 Facebook “likes”. The results indicate that creating a forecast solely based on Facebook “likes” is not accurate. The forecast model predicted very dramatic changes to the Finnish political landscape while the official results of the elections were rather moderate. However, a clear statistical relationship between “likes” and votes was discovered. In conclusion, it is apparent that citizens and other key actors of the society are using social media in an increasing rate. However, the volume of the data does not directly increase the quality of the forecast. In addition, the study faced several other limitations that should be addressed in future research. Nonetheless, discovering the positive correlation between “likes” and votes is valuable information that can be used in future studies. Finally, it is evident that Facebook “likes” are not accurate enough and a meaningful forecast would require additional parameters.

Dynamic Modelling of Circulating Fluidized Bed Plant with Gas Turbine Repowering

Increasing amount of renewable energy source based electricity production has set high load control requirements for power grid balance markets. The essential grid balance between electricity consumption and generation is currently hard to achieve economically with new-generation solutions. Therefore conventional combustion power generation will be examined in this thesis as a solution to the foregoing issue. Circulating fluidized bed (CFB) technology is known to have sufficient scale to acts as a large grid balancing unit. Although the load change rate of the CFB unit is known to be moderately high, supplementary repowering solution will be evaluated in this thesis for load change maximization. The repowering heat duty is delivered to the CFB feed water preheating section by smaller gas turbine (GT) unit. Consequently, steam extraction preheating may be decreased and large amount of the gas turbine exhaust heat may be utilized in the CFB process to reach maximum plant electrical efficiency. Earlier study of the repowering has focused on the efficiency improvements and retrofitting to maximize plant electrical output. This study however presents the CFB load change improvement possibilities achieved with supplementary GT heat. The repowering study is prefaced with literature and theory review for both of the processes to maximize accuracy of the research. Both dynamic and steady-state simulations accomplished with APROS simulation tool will be used to evaluate repowering effects to the CFB unit operation. Eventually, a conceptual level analysis is completed to compare repowered plant performance to the state-of-the-art CFB performance. Based on the performed simulations, considerably good improvements to the CFB process parameters are achieved with repowering. Consequently, the results show possibilities to higher ramp rate values achieved with repowered CFB technology. This enables better plant suitability to the grid balance markets.

Dynamic nature of human rights: Rawls's critique of moral universalism

Human rights do not represent an absolute truth. Otherwise, they would represent ideology, which is contradictory to the basic idea of human rights itself. Consequently, there is a need for redefinition of the main presuppositions of modern conception of human rights represented in the Universal Declaration of Human Rights. This paper argues that Rawls's conception of human rights is significant for the refiguration of human rights. It represents the path towards postmodern idea of human rights and the recognition of difference.