Regional plan för utveckling av vattentjänster i Åboregionen 2011 - 2035 : Sammandrag

Den regionala utvecklingsplanen för vattentjänsterna i Åboregionen omfattar städerna S:t Karins, Nådendal, Pemar, Pargas, Reso och Åbo samt kommunerna Aura, Lundo, Masku, Virmo, Nousis, Rusko och Sagu. Målsättningen är att hitta de bästa möjliga lösningarna för vattenförsörjning och avloppsvattenhantering. Uppdragsgivare för planeringsarbetet har varit de ovan nämnda kommunerna samt ELY-centralen i Egentliga Finland och Egentliga Finlands förbund. Planen har utarbetats av AIRIX Ympäristö Oy. Under planeringsarbetet framkom det att man i utvecklingen av vattenförsörjningen bör fokusera på att trygga distributionsförbindelserna. På planeringsområdet kommer det att byggas flera förbindelser som tryggar vattenförsörjningen. Därtill kommer befintliga vattentäkter och reservanläggningar att saneras. Ytvattenverket i Hallis saneras så att det blir reservanläggning för vattenverket för konstgjort grundvatten. I Masku, Nousis och Virmo byggs enligt planen en förbindelse som gör det möjligt att delvis övergå till användning av konstgjort grundvatten. I Virmo byggs också en ny grundvattentäkt. Pargas stads centralort med omgivning övergår helt till att använda konstgjort grundvatten men tätorterna i Houtskär, Korpo och Nagu kommer fortsättningsvis att försörjas av de lokala vattentäkterna. Beredskapen att trygga vattentjänsterna i särskilda situationer ska utvecklas genom ökat samarbete. Inom beredskapsplaneringen kommer man att kartlägga möjligheterna till gemensamt anordnande av bl.a. reservkraftanläggningar och desinficeringsberedskap. Enligt planen kommer den kommunala avloppsreningsfunktionen att nästan helt överföras till avloppsreningsverket i Kakola fram till 2035. Därför kommer reningsverket också att byggas ut. De lokala reningsverken kommer att vara i användning endast i tätorterna i Houtskär, Korpo och Nagu inom Pargas stad. På grund av de stora mängderna läckvatten föreslås en omfattande sanering av avloppsnätet för hela planeringsområdet. Också mottagningen av avloppsvattenslam på glesbygden ska utvecklas regionalt. Byggkostnaderna för projekten som föreslås i planen är cirka 40 miljoner euro för vattenförsörjningen och cirka 31 miljoner euro för avloppsreningen fram till 2035. Utvecklingsplanen innehåller i sin nuvarande omfattning projektens dimensioneringsgrunder och grundläggande tekniska lösningar samt preliminära kostnadsberäkningar. Den fortsatta beredningen av projekten kräver projektspecifik utredningsplanering och byggplanering innan de kan genomföras. Samtidigt utreds hur det kommunala beslutsfattandet framskrider, finansieringen av projekten och kostnadsfördelningen. Förslagen i den regionala utvecklingsplanen tas också i beaktande då man utarbetar utvecklingsplaner för de kommunala vattentjänsterna.

Feature selection using Fuzzy Entropy measures with Yu's Similarity measure

In this study, feature selection in classification based problems is highlighted. The role of feature selection methods is to select important features by discarding redundant and irrelevant features in the data set, we investigated this case by using fuzzy entropy measures. We developed fuzzy entropy based feature selection method using Yu's similarity and test this using similarity classifier. As the similarity classifier we used Yu's similarity, we tested our similarity on the real world data set which is dermatological data set. By performing feature selection based on fuzzy entropy measures before classification on our data set the empirical results were very promising, the highest classification accuracy of 98.83% was achieved when testing our similarity measure to the data set. The achieved results were then compared with some other results previously obtained using different similarity classifiers, the obtained results show better accuracy than the one achieved before. The used methods helped to reduce the dimensionality of the used data set, to speed up the computation time of a learning algorithm and therefore have simplified the classification task

The Design and Implementation of a Power Management Feature to a Configuration Management System

Green IT is a term that covers various tasks and concepts that are related to reducing the environmental impact of IT. At enterprise level, Green IT has significant potential to generate sustainable cost savings: the total amount of devices is growing and electricity prices are rising. The lifecycle of a computer can be made more environmentally sustainable using Green IT, e.g. by using energy efficient components and by implementing device power management. The challenge using power management at enterprise level is how to measure and follow-up the impact of power management policies? During the thesis a power management feature was developed to a configuration management system. The feature can be used to automatically power down and power on PCs using a pre-defined schedule and to estimate the total power usage of devices. Measurements indicate that using the feature the device power consumption can be monitored quite precisely and the power consumption can be reduced, which generates electricity cost savings and reduces the environmental impact of IT.

Scaling up stepwise feature introduction to construction of large software systems

Developing software is a difficult and error-prone activity. Furthermore, the complexity of modern computer applications is significant. Hence,an organised approach to software construction is crucial. Stepwise Feature Introduction – created by R.-J. Back – is a development paradigm, in which software is constructed by adding functionality in small increments. The resulting code has an organised, layered structure and can be easily reused. Moreover, the interaction with the users of the software and the correctness concerns are essential elements of the development process, contributing to high quality and functionality of the final product. The paradigm of Stepwise Feature Introduction has been successfully applied in an academic environment, to a number of small-scale developments. The thesis examines the paradigm and its suitability to construction of large and complex software systems by focusing on the development of two software systems of significant complexity. Throughout the thesis we propose a number of improvements and modifications that should be applied to the paradigm when developing or reengineering large and complex software systems. The discussion in the thesis covers various aspects of software development that relate to Stepwise Feature Introduction. More specifically, we evaluate the paradigm based on the common practices of object-oriented programming and design and agile development methodologies. We also outline the strategy to testing systems built with the paradigm of Stepwise Feature Introduction.

Aligning Regional Repository Networks

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

HIMALDOC: A Tool for a Regional Open Repository on Sustainable Mountain Development Issues in the Hindu Kush Himalaya

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Biogas production in regional integrated biodegradable waste treatment – Possibilities for improving energy performance and reducing GHG emissions

The greatest threat that the biodegradable waste causes on the environment is the methane produced in landfills by the decomposition of this waste. The Landfill Directive (1999/31/EC) aims to reduce the landfilling of biodegradable waste. In Finland, 31% of biodegradable municipal waste ended up into landfills in 2012. The pressure of reducing disposing into landfills is greatly increased by the forthcoming landfill ban on biodegradable waste in Finland. There is a need to discuss the need for increasing the utilization of biodegradable waste in regional renewable energy production to utilize the waste in a way that allows the best possibilities to reduce GHG emissions. The objectives of the thesis are: (1) to find important factors affecting renewable energy recovery possibilities from biodegradable waste, (2) to determine the main factors affecting the GHG balance of biogas production system and how to improve it and (3) to find ways to define energy performance of biogas production systems and what affects it. According to the thesis, the most important factors affecting the regional renewable energy possibilities from biodegradable waste are: the amount of available feedstock, properties of feedstock, selected utilization technologies, demand of energy and material products and the economic situation of utilizing the feedstocks. The biogas production by anaerobic digestion was seen as the main technology for utilizing biodegradable waste in agriculturally dense areas. The main reason for this is that manure was seen as the main feedstock, and it can be best utilized with anaerobic digestion, which can produce renewable energy while maintaining the spreading of nutrients on arable land. Biogas plants should be located close to the heat demand that would be enough to receive the produced heat also in the summer months and located close to the agricultural area where the digestate could be utilized. Another option for biogas use is to upgrade it to biomethane, which would require a location close to the natural gas grid. The most attractive masses for biogas production are municipal and industrial biodegradable waste because of gate fees the plant receives from them can provide over 80% of the income. On the other hand, directing gate fee masses for small-scale biogas plants could make dispersed biogas production more economical. In addition, the combustion of dry agricultural waste such as straw would provide a greater energy amount than utilizing them by anaerobic digestion. The complete energy performance assessment of biogas production system requires the use of more than one system boundary. These can then be used in calculating output–input ratios of biogas production, biogas plant, biogas utilization and biogas production system, which can be used to analyze different parts of the biogas production chain. At the moment, it is difficult to compare different biogas plants since there is a wide variation of definitions for energy performance of biogas production. A more consistent way of analyzing energy performance would allow comparing biogas plants with each other and other recovery systems and finding possible locations for further improvement. Both from the GHG emission balance and energy performance point of view, the energy consumption at the biogas plant was the most significant factor. Renewable energy use to fulfil the parasitic energy demand at the plant would be the most efficient way to reduce the GHG emissions at the plant. The GHG emission reductions could be increased by upgrading biogas to biomethane and displacing natural gas or petrol use in cars when compared to biogas CHP production. The emission reductions from displacing mineral fertilizers with digestate were seen less significant, and the greater N2O emissions from spreading digestate might surpass the emission reductions from displacing mineral fertilizers.

Framework for Regional Heating Energy Efficiency Assessment

The aim of this project was to develop general framework for systematic assessment of energy efficiency of heating on regional level in Russia. The framework created during this project includes two main instruments, namely: general regional heating energy efficiency assessment model (REEMod) and general regional heating energy efficiency assessment criteria for housing areas (REECrit). Framework pays extreme attention to realization of energy saving, overall cost efficiency and comfortable indoor climate. Life-cycle ideology was applied during creation of the framework. Application of the framework can provide decision-making process with systematically collected and processed information on current state of areas energy efficiency. Such information will help decision makers to evaluate current situation of the whole energy chain, to compare different development scenarios and to identify the most efficient improvement methods, thus supporting realization of regions efficient energy management. Simultaneous pursuit of energy savings, cost efficiency and indoor air quality can contribute to development of sustainable community. Presented instruments should be continuously developed further as an iterative process based on knew experience, development of technology and overall understanding of energy efficiency issues.

Sponsor Satisfaction in a Regional Charity Sport Event

The purpose of this Master’s thesis is to study sponsor satisfaction in charity sport events. Lack of research in regional charity sport events, emergence of corporate social responsibility and increasing popularity of charity sport events have created a research gap to be further explored. Theoretical part of the thesis focuses in development of sponsorships, charity sport event sponsorships and sponsorship as a marketing tool. Concept of satisfaction is discussed by implementing marketing theories to weight options on measuring sponsor satisfaction as a part of sponsorship evaluation process. Empirical analysis of the thesis was conducted in a regional charity sport event – Maailman Pisin Salibandyottelu. Evidences were collected in qualitative research method through semi-structured theme interviews. Altogether 12 major and minor sponsors were selected for the primary source of data. The data was analyzed by comparing sponsors’ expectations and experiences, and by displaying sponsors’ perceived satisfaction. The results indicated that sponsors were involved by partly altruistic and partly selfish motives as suggested by previous research. Respondents expressed very few, mainly non-financial expectations, yet were hoping to gain positive image association via event exposure. Negative experiences appear to have relatively small impact in overall satisfaction. Exceeding or fulfilling expectations appears to increase perceived satisfaction which was mainly driven by contribution towards the goodwill, perceived success of the event (successful record attempt, visibility (on- and off-line) and event execution.

Scalable Feature Selection Applications for Genome-Wide Association Studies of Complex Diseases

Personalized medicine will revolutionize our capabilities to combat disease. Working toward this goal, a fundamental task is the deciphering of geneticvariants that are predictive of complex diseases. Modern studies, in the formof genome-wide association studies (GWAS) have afforded researchers with the opportunity to reveal new genotype-phenotype relationships through the extensive scanning of genetic variants. These studies typically contain over half a million genetic features for thousands of individuals. Examining this with methods other than univariate statistics is a challenging task requiring advanced algorithms that are scalable to the genome-wide level. In the future, next-generation sequencing studies (NGS) will contain an even larger number of common and rare variants. Machine learning-based feature selection algorithms have been shown to have the ability to effectively create predictive models for various genotype-phenotype relationships. This work explores the problem of selecting genetic variant subsets that are the most predictive of complex disease phenotypes through various feature selection methodologies, including filter, wrapper and embedded algorithms. The examined machine learning algorithms were demonstrated to not only be effective at predicting the disease phenotypes, but also doing so efficiently through the use of computational shortcuts. While much of the work was able to be run on high-end desktops, some work was further extended so that it could be implemented on parallel computers helping to assure that they will also scale to the NGS data sets. Further, these studies analyzed the relationships between various feature selection methods and demonstrated the need for careful testing when selecting an algorithm. It was shown that there is no universally optimal algorithm for variant selection in GWAS, but rather methodologies need to be selected based on the desired outcome, such as the number of features to be included in the prediction model. It was also demonstrated that without proper model validation, for example using nested cross-validation, the models can result in overly-optimistic prediction accuracies and decreased generalization ability. It is through the implementation and application of machine learning methods that one can extract predictive genotype–phenotype relationships and biological insights from genetic data sets.