Molecular mimicry between cardiac myosin and Trypanosoma cruzi antigen B13: identification of a B13-driven human T cell clone that recognizes cardiac myosin

Previous reports from our group have demonstrated the association of molecular mimicry between cardiac myosin and the immunodominant Trypanosoma cruzi protein B13 with chronic Chagas' disease cardiomyopathy at both the antibody and heart-infiltrating T cell level. At the peripheral blood level, we observed no difference in primary proliferative responses to T. cruzi B13 protein between chronic Chagas' cardiopathy patients, asymptomatic chagasics and normal individuals. In the present study, we investigated whether T cells sensitized by T. cruzi B13 protein respond to cardiac myosin. T cell clones generated from a B13-stimulated T cell line obtained from peripheral blood of a B13-responsive normal donor were tested for proliferation against B13 protein and human cardiac myosin. The results showed that one clone responded to B13 protein alone and the clone FA46, displaying the highest stimulation index to B13 protein (SI = 25.7), also recognized cardiac myosin. These data show that B13 and cardiac myosin share epitopes at the T cell level and that sensitization of a T cell with B13 protein results in response to cardiac myosin. It can be hypothesized that this also occurs in vivo during T. cruzi infection which results in heart tissue damage in chronic Chagas' disease cardiomyopathy

Entry Modes for Finnish Firms into the Zambian Renewable Energy Industry

The renewable energy industry in Zambia is poised for growth and offers many possibilities for Finnish firms willing to enter the market. The Zambian government’s deliberate policy measures aim at attracting foreign direct investment (FDI) into this sector. This study rationalises that this could be the pull factor for Finnish firms. The thesis gives an overview of the industry and investigates an appropriate mode of entry, basing its arguments on the comparison analysis of the two economies with the use of the world forum’s stages of economic development as a framework. The theoretical part of the study examines internationalisation theories, entry mode choice and factors influencing the choice. The multiple case study approach is implored, analysing four case companies from Finland with the use of extant literature on internationalisation relevant to the study. The research design involves the use of documentation, secondary data, interviews and observation. The results of the case analyses show that the Finnish firm’s most preferred entry mode initially is exporting because it is considered to be less risky. Additionally, the findings also reveal that the selection of a suitable mode of entry is dependent on the firms’ size, orientation and international experience and could therefore be considered to be subjective. Paramount is the act of gaining market knowledge. The study shows that only hydro-electrical, solar energies and biomass are by far the most used and known forms of renewable energy in Zambia, while other alternative sources still remain un-exploited thus highlighting a growth potential. However, policy formulation and the regulatory framework in the renewable energy sector were found to be wanting.

Numerical Studies of Dark Energy Models and Observations

The cosmological standard view is based on the assumptions of homogeneity, isotropy and general relativistic gravitational interaction. These alone are not sufficient for describing the current cosmological observations of accelerated expansion of space. Although general relativity is extremely accurately tested to describe the local gravitational phenomena, there is a strong demand for modifying either the energy content of the universe or the gravitational interaction itself to account for the accelerated expansion. By adding a non-luminous matter component and a constant energy component with negative pressure, the observations can be explained with general relativity. Gravitation, cosmological models and their observational phenomenology are discussed in this thesis. Several classes of dark energy models that are motivated by theories outside the standard formulation of physics were studied with emphasis on the observational interpretation. All the cosmological models that seek to explain the cosmological observations, must also conform to the local phenomena. This poses stringent conditions for the physically viable cosmological models. Predictions from a supergravity quintessence model was compared to Supernova 1a data and several metric gravity models were studied with local experimental results. Polytropic stellar configurations of solar, white dwarf and neutron stars were numerically studied with modified gravity models. The main interest was to study the spacetime around the stars. The results shed light on the viability of the studied cosmological models.

Fundraising and investor relations in social enterprise: Marketing your way out of the pioneer gap in African off-grid renewable energy

Social enterprises apply the best of business for the pursuit of social or environmental mission while also generating revenues. Globally, nearly 1,3 billion people lack access to electricity, as well as another billion having access to only low quality and infrequent electricity. Off-grid renewable energy, like solar, will increasingly have a key role in the solution of the energy access issue. The pioneer gap in off-grid renewable energy consists of financing (or funding) gaps and capacity gaps, to do with both the early stage of the enterprises in question, as well as the early stage of the whole industry. The gaps are emphasised by specific characteristics of off-grid renewable energy business models and the requirements of operating in bottom-of-the-pyramid markets. The marketing perspective to fundraising is chosen to uncover the possible role enterprises themselves have in bridging the pioneer gap. The purpose of this thesis is to study how social enterprises operating in off-grid renewable energy in Africa utilise marketing activities in their investor relations in bridging the pioneer gap. This main research question is divided into the following sub-questions:  How does the pioneer gap affect fundraising for these enterprises?  How are the funding needs for these enterprises characterised?  How do these enterprises build trust in their investor relations? The theoretic framework is built on relationship marketing and investor relations, with an emphasis on creation of trust. The research is conducted as a thematical case study. Primary data is gathered via semi-structured interviews with six solar energy companies and two accelerators. According to the findings, the main components affecting trust-creation are diminished information asymmetry and perceived risk, mission alignment as well as a personal fit or relationship with the investor. Therefore, an enterprise can utilise e.g. the following marketing activities in their investor relations to bridge the pioneer gap: ensuring investor material, the enterprise story and presenting of them is clear, concise and complete to “package” the enterprise as an investment; taking investor needs and motivations into account as well as utilising existing investors as ambassadors.

Legitimacy Building in Public Disclosures on Strategic Energy Investments

Renewable energy investments play a key role in energy transition. While studies have suggested that social acceptance may form a barrier for renewable energy investments, the ways in which companies perceive and attempt to gain the acceptance have received little attention. This study aims to fill the gap by exploring how large electric utilities justify their strategic investments in their press releases and how do the justifications differ between renewable and non-renewable energy investments. The study bases on legitimacy theory and aims at contributing to the research on legitimation in institutional change. As its research method, the study employs an inductive mixed method content analysis. The study has two parts: a qualitative content analysis that explores and identifies the themes and legitimation strategies of the press releases and a quantitative computer-aided analysis that compares renewable and non-renewable energy investments. The sample of the study consists of 396 press releases representing the strategic energy investments of 34 electric utilities from the list of the world’s 250 largest and financially most successful energy companies. The data is collected from the period of 2010–2014. The study reveals that most important justifications for strategic energy investments are fit with the strategy and environmental and social benefits. Justifications address especially the expectations of market. Investments into non-renewable energy are justified more and they use more arguments addressing the proprieties and performance of power plants whereas renewable energy investments are legitimized by references to past actions and commonly accepted morals and norms. The findings support the notion that validity-addressing and propriety-addressing legitimation strategies are used differently in stable and unstable institutional settings.

Comparison of Model-Based Flow Rate Estimation Methods in Frequency-Converter-Driven Pumps and Fans

Fluid handling systems such as pump and fan systems are found to have a significant potential for energy efficiency improvements. To deliver the energy saving potential, there is a need for easily implementable methods to monitor the system output. This is because information is needed to identify inefficient operation of the fluid handling system and to control the output of the pumping system according to process needs. Model-based pump or fan monitoring methods implemented in variable speed drives have proven to be able to give information on the system output without additional metering; however, the current model-based methods may not be usable or sufficiently accurate in the whole operation range of the fluid handling device. To apply model-based system monitoring in a wider selection of systems and to improve the accuracy of the monitoring, this paper proposes a new method for pump and fan output monitoring with variable-speed drives. The method uses a combination of already known operating point estimation methods. Laboratory measurements are used to verify the benefits and applicability of the improved estimation method, and the new method is compared with five previously introduced model-based estimation methods. According to the laboratory measurements, the new estimation method is the most accurate and reliable of the model-based estimation methods.

A Bayesian Approach for Forecasting Heat Load in a District Heating System

The growing population in cities increases the energy demand and affects the environment by increasing carbon emissions. Information and communications technology solutions which enable energy optimization are needed to address this growing energy demand in cities and to reduce carbon emissions. District heating systems optimize the energy production by reusing waste energy with combined heat and power plants. Forecasting the heat load demand in residential buildings assists in optimizing energy production and consumption in a district heating system. However, the presence of a large number of factors such as weather forecast, district heating operational parameters and user behavioural parameters, make heat load forecasting a challenging task. This thesis proposes a probabilistic machine learning model using a Naive Bayes classifier, to forecast the hourly heat load demand for three residential buildings in the city of Skellefteå, Sweden over a period of winter and spring seasons. The district heating data collected from the sensors equipped at the residential buildings in Skellefteå, is utilized to build the Bayesian network to forecast the heat load demand for horizons of 1, 2, 3, 6 and 24 hours. The proposed model is validated by using four cases to study the influence of various parameters on the heat load forecast by carrying out trace driven analysis in Weka and GeNIe. Results show that current heat load consumption and outdoor temperature forecast are the two parameters with most influence on the heat load forecast. The proposed model achieves average accuracies of 81.23 % and 76.74 % for a forecast horizon of 1 hour in the three buildings for winter and spring seasons respectively. The model also achieves an average accuracy of 77.97 % for three buildings across both seasons for the forecast horizon of 1 hour by utilizing only 10 % of the training data. The results indicate that even a simple model like Naive Bayes classifier can forecast the heat load demand by utilizing less training data.

Implementing Green IT approach for transferring Big Data over Parallel Data Link

The whole research of the current Master Thesis project is related to Big Data transfer over Parallel Data Link and my main objective is to assist the Saint-Petersburg National Research University ITMO research team to accomplish this project and apply Green IT methods for the data transfer system. The goal of the team is to transfer Big Data by using parallel data links with SDN Openflow approach. My task as a team member was to compare existing data transfer applications in case to verify which results the highest data transfer speed in which occasions and explain the reasons. In the context of this thesis work a comparison between 5 different utilities was done, which including Fast Data Transfer (FDT), BBCP, BBFTP, GridFTP, and FTS3. A number of scripts where developed which consist of creating random binary data to be incompressible to have fair comparison between utilities, execute the Utilities with specified parameters, create log files, results, system parameters, and plot graphs to compare the results. Transferring such an enormous variety of data can take a long time, and hence, the necessity appears to reduce the energy consumption to make them greener. In the context of Green IT approach, our team used Cloud Computing infrastructure called OpenStack. It’s more efficient to allocated specific amount of hardware resources to test different scenarios rather than using the whole resources from our testbed. Testing our implementation with OpenStack infrastructure results that the virtual channel does not consist of any traffic and we can achieve the highest possible throughput. After receiving the final results we are in place to identify which utilities produce faster data transfer in different scenarios with specific TCP parameters and we can use them in real network data links.

Benefits of gas driven ground source heat pump systems

This thesis studies energy efficiencies and technical properties of gas driven ground source heat pumps and pump systems. The research focuses on two technologies: gas engine driven compressor heat pump and thermally driven gas absorption heat pump. System consist of a gas driven compressor or absorption ground source heat pump and a gas condensing boiler, which covers peak load. The reference system is a standard electrically powered compressor heat pump with electric heating elements for peak load. The systems are compared through primary energy ratios. Coefficient of performances of different heat pump technologies are also compared. At heat pump level, gas driven heat pumps are having lower coefficient of performances as compared with corresponding electric driven heat pump. However, gas heat pumps are competitive when primary energy ratios, where electricity production losses are counted in, are compared. Technically, gas heat pumps can potentially achieve a slightly higher temperatures with greater total energy efficiency as compared to the electric driven heat pump. The primary energy ratios of gas heat pump systems in relation to EHP-system improves when the share of peak load increases. Electric heat pump system's overall energy efficiency is heavily dependent on the electricity production efficiency. Economy as well as CO2-emissions were not examined in this thesis, which however, would be good topics for further study.

Energy efficiency and moisture diffusivity of apple slices during convective drying

Abstract The present study aimed at investigating the influences of drying air temperature and flow rate on energy parameters and dehydration behaviour of apple slices. For this purpose, apple slices were dried in a convective dryer at air temperatures of 50, 60 and 70 °C, and air velocities of 1, 1.5 and 2 m s–1. Dehydration rate increased as the air temperature and flow rate increased from 50 to 70 °C and 1 to 2 m s–1, respectively. The effective moisture diffusivity was determined to be in the range of 6.75×10–10-1.28×10–9 m2 s–1. Results of data analysis showed that the maximum energy consumption (23.94 kW h) belonged to 50 °C and 2 m s–1 and the minimum (13.89 kW h) belonged to 70 °C and 1 m s–1 treatment. Energy efficiency values were in the range of 2.87-9.11%. Moreover, the results indicated that any increment in the air temperature increases thermal and drying efficiencies while any increment in the air flow rate decreases both of them.

Modeling Energy Futures Returns With Markov Regime Switching Model

Financial time series have a tendency of abruptly changing their behavior and maintain this behavior for several consecutive periods, and commodity futures returns are not an exception. This quality proposes that nonlinear models, as opposed to linear models, can more accurately describe returns and volatility. Markov regime switching models are able to match this behavior and have become a popular way to model financial time series. This study uses Markov regime switching model to describe the behavior of energy futures returns on a commodity level, because studies show that commodity futures are a heterogeneous asset class. The purpose of this thesis is twofold. First, determine how many regimes characterize individual energy commodities’ returns in different return frequencies. Second, study the characteristics of these regimes. We extent the previous studies on the subject in two ways: We allow for the possibility that the number of regimes may exceed two, as well as conduct the research on individual commodities rather than on commodity indices or subgroups of these indices. We use daily, weekly and monthly time series of Brent crude oil, WTI crude oil, natural gas, heating oil and gasoil futures returns over 1994–2014, where available, to carry out the study. We apply the likelihood ratio test to determine the sufficient number of regimes for each commodity and data frequency. Then the time series are modeled with Markov regime switching model to obtain the return distribution characteristics of each regime, as well as the transition probabilities of moving between regimes. The results for the number of regimes suggest that daily energy futures return series consist of three to six regimes, whereas weekly and monthly returns for all energy commodities display only two regimes. When the number of regimes exceeds two, there is a tendency for the time series of energy commodities to form groups of regimes. These groups are usually quite persistent as a whole because probability of a regime switch inside the group is high. However, individual regimes in these groups are not persistent and the process oscillates between these regimes frequently. Regimes that are not part of any group are generally persistent, but show low ergodic probability, i.e. rarely prevail in the market. This study also suggests that energy futures return series characterized with two regimes do not necessarily display persistent bull and bear regimes. In fact, for the majority of time series, bearish regime is considerably less persistent. Rahoituksen aikasarjoilla on taipumus arvaamattomasti muuttaa käyttäytymistään ja jatkaa tätä uutta käyttäytymistä useiden periodien ajan, eivätkä hyödykefutuurien tuotot tee tähän poikkeusta. Tämän ominaisuuden johdosta lineaaristen mallien sijasta epälineaariset mallit pystyvät tarkemmin kuvailemaan esimerkiksi tuottojen jakauman parametreja. Markov regiiminvaihtomallit pystyvät vangitsemaan tämän ominaisuuden ja siksi niistä on tullut suosittuja rahoituksen aikasarjojen mallintamisessa. Tämä tutkimus käyttää Markov regiiminvaihtomallia kuvaamaan yksittäisten energiafutuurien tuottojen käyttäytymistä, sillä tutkimukset osoittavat hyödykefutuurien olevan hyvin heterogeeninen omaisuusluokka. Tutkimuksen tarkoitus on selvittää, kuinka monta regiimiä tarvitaan kuvaamaan energiafutuurien tuottoja eri tuottofrekvensseillä ja mitkä ovat näiden regiimien ominaisuudet. Aiempaa tutkimusta aiheesta laajennetaan määrittämällä regiimien lukumäärä tilastotieteellisen testauksen menetelmin sekä tutkimalla energiafutuureja yksittäin; ei indeksi- tai alaindeksitasolla. Tutkimuksessa käytetään päivä-, viikko- ja kuukausiaikasarjoja Brent-raakaöljyn, WTI-raakaöljyn, maakaasun, lämmitysöljyn ja polttoöljyn tuotoista aikaväliltä 1994–2014, siltä osin kuin aineistoa on saatavilla. Likelihood ratio -testin avulla estimoidaan kaikille aikasarjoille regiimien määrä,jonka jälkeen Markov regiiminvaihtomallia hyödyntäen määritetään yksittäisten regiimientuottojakaumien ominaisuudet sekä regiimien välinen transitiomatriisi. Tulokset regiimien lukumäärän osalta osoittavat, että energiafutuurien päiväkohtaisten tuottojen aikasarjoissa regiimien lukumäärä vaihtelee kolmen ja kuuden välillä. Viikko- ja kuukausituottojen kohdalla kaikkien energiafutuurien prosesseissa regiimien lukumäärä on kaksi. Kun regiimejä on enemmän kuin kaksi, on prosessilla taipumus muodostaa regiimeistä koostuvia ryhmiä. Prosessi pysyy ryhmän sisällä yleensä pitkään, koska todennäköisyys siirtyä ryhmään kuuluvien regiimien välillä on suuri. Yksittäiset regiimit ryhmän sisällä eivät kuitenkaan ole kovin pysyviä. Näin ollen prosessi vaihtelee ryhmän sisäisten regiimien välillä tiuhaan. Regiimit, jotka eivät kuulu ryhmään, ovat yleensä pysyviä, mutta prosessi ajautuu niihin vain harvoin, sillä todennäköisyys siirtyä muista regiimeistä niihin on pieni. Tutkimuksen tulokset osoittavat myös, että prosesseissa, joita ohjaa kaksi regiimiä, nämä regiimit eivät välttämättä ole pysyvät bull- ja bear-markkinatilanteet. Tulokset osoittavat sen sijaan, että bear-markkinatilanne on energiafutuureissa selvästi vähemmän pysyvä.

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.

Energy aware software for many-core systems

Many-core systems provide a great potential in application performance with the massively parallel structure. Such systems are currently being integrated into most parts of daily life from high-end server farms to desktop systems, laptops and mobile devices. Yet, these systems are facing increasing challenges such as high temperature causing physical damage, high electrical bills both for servers and individual users, unpleasant noise levels due to active cooling and unrealistic battery drainage in mobile devices; factors caused directly by poor energy efficiency. Power management has traditionally been an area of research providing hardware solutions or runtime power management in the operating system in form of frequency governors. Energy awareness in application software is currently non-existent. This means that applications are not involved in the power management decisions, nor does any interface between the applications and the runtime system to provide such facilities exist. Power management in the operating system is therefore performed purely based on indirect implications of software execution, usually referred to as the workload. It often results in over-allocation of resources, hence power waste. This thesis discusses power management strategies in many-core systems in the form of increasing application software awareness of energy efficiency. The presented approach allows meta-data descriptions in the applications and is manifested in two design recommendations: 1) Energy-aware mapping 2) Energy-aware execution which allow the applications to directly influence the power management decisions. The recommendations eliminate over-allocation of resources and increase the energy efficiency of the computing system. Both recommendations are fully supported in a provided interface in combination with a novel power management runtime system called Bricktop. The work presented in this thesis allows both new- and legacy software to execute with the most energy efficient mapping on a many-core CPU and with the most energy efficient performance level. A set of case study examples demonstrate realworld energy savings in a wide range of applications without performance degradation.

Distributed energy resources in an electricity retailer’s short-term profit optimization

Liberalization of electricity markets has resulted in a competed Nordic electricity market, in which electricity retailers play a key role as electricity suppliers, market intermediaries, and service providers. Although these roles may remain unchanged in the near future, the retailers’ operation may change fundamentally as a result of the emerging smart grid environment. Especially the increasing amount of distributed energy resources (DER), and improving opportunities for their control, are reshaping the operating environment of the retailers. This requires that the retailers’ operation models are developed to match the operating environment, in which the active use of DER plays a major role. Electricity retailers have a clientele, and they operate actively in the electricity markets, which makes them a natural market party to offer new services for end-users aiming at an efficient and market-based use of DER. From the retailer’s point of view, the active use of DER can provide means to adapt the operation to meet the challenges posed by the smart grid environment, and to pursue the ultimate objective of the retailer, which is to maximize the profit of operation. This doctoral dissertation introduces a methodology for the comprehensive use of DER in an electricity retailer’s short-term profit optimization that covers operation in a variety of marketplaces including day-ahead, intra-day, and reserve markets. The analysis results provide data of the key profit-making opportunities and the risks associated with different types of DER use. Therefore, the methodology may serve as an efficient tool for an experienced operator in the planning of the optimal market-based DER use. The key contributions of this doctoral dissertation lie in the analysis and development of the model that allows the retailer to benefit from profit-making opportunities brought by the use of DER in different marketplaces, but also to manage the major risks involved in the active use of DER. In addition, the dissertation introduces an analysis of the economic potential of DER control actions in different marketplaces including the day-ahead Elspot market, balancing power market, and the hourly market of Frequency Containment Reserve for Disturbances (FCR-D).

Control of multi-actuator system by an electrically driven hydraulic pump-motor

The awareness and concern of our environment together with legislation have set more and more tightening demands for energy efficiency of non-road mobile machinery (NRMM). Integrated electro-hydraulic energy converter (IEHEC) has been developed in Lappeenranta University of Technology (LUT). The elimination of resistance flow, and the recuperation of energy makes it very efficient alternative. The difficulties of IEHEC machine to step to the market has been the requirement of one IEHEC machine per one actuator. The idea is to switch IEHEC between two actuators of log crane using fast on/off valves. The control system architecture is introduced. The system has been simulated in co-simulation using two different software. The simulated responses of pump-controlled system is compared to the responses of the conventional valve-controlled system.