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The purpose of this study was to examine what kind of geomorphologic formations can be found in Hanko, Raasepori, Sipoo, Porvoo and Loviisa districts and which of them can be used for teaching purposes. Questions for the study were: 1) What interesting geomorphologic formations can be found in the areas?, 2) In what way could the formations be used by the geography- and biology teachers in the areas in their geography teaching?, 3) Which of the formations will only be thought through schoolbooks because they can't be found in the nearby grounds? The geomorphologic formations taken into account are the following: a) erractic block, b) roche moutonnée, c) drumlin, d) esker, e) terminal moraine, f) kettle, g) pothole. Besides these formations some nationally valuable formations are described as well. This study was done because other studies have shown that outdoor activities are seldom a part of the curriculum. Other studies showed that the reasons for a minor role of outdoor activities are that (the) teachers feel unsure of themselves about the subject, lack of time and too big groups of students. Studies have also shown that field trips stimulate deep learning by presenting a environment where the student can connect the learnt subject with reality. Field teaching isn't only a benefit when it comes to knowledge but also for the students self-confidence and motivation towards the subject. This is because the affective domain in the brain also influences on the deep learning by raising the motivation level and making the students believe in their own capacity. Map- and literature analysis where made to map to which places are worth visiting. Based on the analysis the field investigations were done at the chosen places and the formations were documented with camera and with GPS. Soilsamples were taken from three places for drysieving in the sedimentlaboratory in Kumpula. The morphoscopical features of these glaciofluvial soil samples were also analyzed. The results of the analysis and the field investigations showed that most of the geomorphologic formations that are describe in the schoolbooks for geography in high school can be found in the districts. Because of this there are possibilities for field trips as a part of the curriculum and there by a chance for a more deep understanding of the Finnish physical landscape and the geomorphologic formations by the students. In addition to the knowledge about the formations and the landscape the field trips also enables the student to get out of the classroom and explore the nature with their own senses instead of through a school book. The students' social skills also have a chance to develop as well as the use of working methods in science subjects.

This study addresses the ways in which environmental challenges and power relations are manifested through tourism in the easternmost province of Panama, Darién. Historically, the area of Darién has remained relatively isolated from the rest of the country and tourism in the area is of small-scale. However, the high biodiversity and natural resources have drawn increasing attention, thus resulting in tensions and competing interests between different stakeholders. Local perceptions of tourism bring insight about how people make sense of and engage with touristic activities, and how geopolitical and ecological discourses contribute to environmental inequalities. The theoretical background draws from geopolitical ecology, which states the role and impact of the environment in the shaping of political space and power relations. The research is based on 37 thematic interviews and participant observation carried out during a one-month ethnographic fieldwork in Darién. The findings indicate that tourism has contributed to exposing the environmental challenges in Darién, but also caused controversy over the use of resources for tourism. Tourism brings forward power relations and demonstrates that different players are in an uneven position. The results show that tourism in Darién has been influenced by its remoteness and the nowadays largely misleading assumption of its unstable security situation. Darién faces a broad range of environmental problems, resulting mainly from the State´s weak presence and poor environmental policies. However, tourism has been locally able to enhance environmental awareness and interest towards conservation. Different tourism actors have unequal possibilities in making use of natural resources depending largely on their wealth and social networks. Further geopolitical interests appear through territorial issues and questions concerning land ownership. The findings indicate that by looking at tourism, many underlying tensions related to existing social inequalities, power relations and distribution of ecological benefits can be revealed.

Ship traffic is a major source of global greenhouse gas emissions, and the pressure on the maritime industry to lower its carbon footprint is constantly growing. One easy way for ships to lower their emissions would be to lower their sailing speed. The global ship traffic has for ages followed a practice called "sail fast, then wait", which means that ships try to reach their destination in the fastest possible time regardless and then wait at an anchorage near the harbor for a mooring place to become available. This method is easy to execute logistically, but it does not optimize the sailing speeds to take into account the emissions. An alternative tactic would be to calculate traffic patterns at the destination and use this information to plan the voyage so that the time at anchorage is minimized. This would allow ships to sail at lower speeds without compromising the total length of the journey. To create a model to schedule arrivals at ports, traffic patterns need to be formed on how ships interact with port infrastructure. However, port infrastructure is not widely available in an easy-to-use form. This makes it difficult to develop models that are capable of predicting traffic patterns. However, ship voyage information is readily available from commercial Automatic Information System (AIS) data. In this thesis, I present a novel implementation, which extracts information on the port infrastructure from AIS data using the DBSCAN clustering algorithm. In addition to clustering the AIS data, the implementation presented in this thesis uses a novel optimization method to search for optimal hyperparameters for the DBSCAN algorithm. The optimization process evaluates possible solutions using cluster validity indices (CVI), which are metrics that represent the goodness of clustering. A comparison with different CVIs is done to narrow down the most effective way to cluster AIS data to find information on port infrastructure.

Gesture recognition is a fundamental technology for understanding human behavior and providing natural human-computer interaction, of which the need has been boosted by the popularity of ubiquitous systems and applications. Currently, there are mainly two classes of gesture recognition approaches: parametric methods and the template matching methods. Although both approaches provide accurate recognition performance, the former is usually highly constrained in time and resources, while the latter is limited within a small range of gesture types that are simple and distinct from each other. The objective of the thesis is to present a novel stroke-based gesture recognition solution, Gestimator, that recognizes dynamic hand gestures with high accuracy, run-time efficiency, and ease for customization, considering a wide range of complexity, ambiguity, and difficulty for user to perform the gesture. A stroke-wise elastic gesture matching framework alongside an adaptive sequence segmentation technique were developed for improving recognition accuracy. We conducted extensive evaluations using three datasets that included pen-based command gestures, character gestures, and mid-air gestures collected from a user authentication experiment. Results from extensive benchmark evaluations prove that Gestimator achieves higher overall accuracy, compared with three state-of-the-art gesture recognizers, dealing with both touch-screen based gestures (98:9%) and spatial gestures (96:61%). Results also show that Gestimator outperforms baseline methods on recognizing ambiguous gestures.

SQLite has been called the most widely deployed database system but its use in web services has been somewhat limited compared to client/server database engines. Today due to the continued development of SQLite it has the needed features to be a serious option for certain kinds of web services. SQLite is also the technology behind many emerging globally distributed database technologies. In a case study, an existing web application that is backed by centralized SQLite is evaluated in the context of what trade-offs would have to be made when switching to a globally distributed database. This is done by benchmarking the difference in latency users experience depending on their geographical location. Outside of that known challenges in the context of distributed computing as well as challenges specific to migrating from a centralized embedded database to a globally distributed one are evaluated as well. In the results, we found out that there are latency improvements to be made with the globally distributed approach. That said optimizing application code is likely to be the most effective latency improvement for many projects. In addition, especially the increased complexity of running a distributed system compared to a centralized one was in our estimations a big rea- son why the application being studied ultimately decided not to migrate towards a globally distributed deployment. Our findings relate primarily to this one application and other applications with different cir- cumstances could come to a different answer. These technologies are still rapidly advancing so it is likely we will see continued development and properties of these globally distributed database technologies evolving.

The topography of the Earth's surface is the result of the interaction of tectonics, erosion and climate. Thus, topography should contain a record of these processes that can be extracted by topographic analysis. The question considered in this study is whether the spatial variations in erosion that have sculpted the modern topography are representative of the long-term erosion rates in mountainous regions. We compare long-term erosion rates derived from low-temperature thermochronometry to erosional proxies calculated from topographic and climatic data analysis. The study has been performed on a global scale including six orogens: The Himalaya, Andes, Taiwan, Olympic Mountains, Southern Alps in New Zealand and European Alps. The data was analyzed using a new swath profile analysis tool for ArcGIS called ArcSwath to determine the correlations between the long-term erosion rates and modern elevations, slope angles, relief in 2.5-km- and 5-km-diameter circles, erosion potential, normalized channel steepness index ksn, and annual rainfall. ArcSwath uses a Python script that has been incorporated into an ArcMap 10.2 add-in tool, extracting swath profiles in about ten seconds compared to earlier workflows that could take more than an hour. Swath profile analysis is a relatively common method for geomorphological research. A swath profile is a rectangular extraction of a digital model to a cross-section, where statistical parameters (minimum, mean and maximum) are presented along the profile length. In previous studies swath profiles have been used to identify relationships between topography and major structures, to compare various climatic, erosional and topographic data across a given orogen and along strike, and to recognize fluvially and glacially eroded forms. An unambiguous correlation between the topographic or climatic metrics and long-term erosion rates was not found. Fitting of linear regression lines to the topographic/climatic metric data and the long-term erosion rates shows that 86 of 288 plots (30%) have 'good' R2 values (> 0.35) and 135 of 288 (47%) have an 'acceptable' R2 value (> 0.2). The 'acceptable' and 'good' values have been selected on the basis of visual fit to the regression line. The majority of the plots with a 'good' correlation value have positive correlations, while 11/86 plots have negative slopes for the regression lines. Interestingly, two topographic profile shapes were clear in swath profiles: Concave-up (e.g., the central-western Himalaya and the northern Bolivian Andes) and concave-down or straight (e.g., the eastern Himalayas and the southern Bolivian Andes). On the orogen scale, the concave-up shape is often related to relatively high precipitation and erosion rates on the slopes of steep topography. The concave-down/straight profiles seem to occur in association of low rainfall and/or erosion rates. Though we cannot say with confidence, the lack of a clear correlation between long-term erosion rates and climate or topography may be due to the difference in their respective timescales as climate can vary over shorter timescales than 10^5-10^7 years. In that case, variations between fluvial and glacial erosion may have overprinted the erosional effects of one another.

Aikasarjoissa ilmenevien ei-normaalisten piirteiden mallintamiseen voidaan käyttää epälineaarisia aikasarjamalleja, joista erityisesti tutkielmassa tarkastellaan autoregressiivisia sekoitusmalleja. Autoregressiiviset sekoitusmallit määritellään sekoituksena lineaarisista autoregressiivisista malleista ja erona eri sekoitusmallien välillä on niiden sekoitussuhteiden määrittely. Autoregressiivisella GMAR (Gaussian Mixture Autoregressive)-sekoitusmallilla on houkuttelevia teoreettisia ominaisuuksia, sillä sen stationaarinen jakauma tunnetaan ja sen stationaarisuusehto ja ergodisuus voidaan johtaa ilman lisärajoituksia parametreille. Kuitenkin sekoitussuhteiden monimutkaisesta määrittelystä johtuen sen parametrien estimointi käyttäen kirjallisuudessa usein käytettyä EM-algoritmia on hankalaa. Tästä syystä tutkielmassa selvitetään mahdollisuutta käyttää parametrien estimoinnissa kaksivaiheista menetelmää, jossa geneettisen algoritmin avulla etsitään alkuarvoja gradienttiperusteiselle optimointialgoritmille. Parametrien estimoinnin lisäksi tutkielmassa tarkastellaan mallinvalintaa osana estimointiprosessia. Tarkasteltavia työkaluja sopivan mallin etsinnässä ovat informaatiokriteerit sekä erilaiset kvantiiliresiduaaleihin perustuvat testit, joiden avulla voidaan tehdä mallidiagnostiikkaa tavallisten residuaalien tapaan myös silloin, kun tavallisia residuaaleja ei voida käyttää. Lisäksi tarkastellaan ennusteiden laskemista simulaatioiden avulla ja esitetään miten GMAR-mallia voidaan simuloida. Tutkielman empiirisessä osassa tarkastellaan kahta esimerkkiä, joista ensimmäisessä keskitytään estimointiin, mallinvalitaan ja diagnostiikkaan. Tässä esimerkissä aineistona käytetään yhdysvaltain kuukausittaista inflaatiota vuodesta 1975 vuoteen 2015. Toisessa empiirisessä esimerkissä tarkastellaan tuulen nopeuksia päivittäisen aineiston avulla ja keskitytään erityisesti ennusteiden laskemiseen. Tuulen nopeutta mittaava aineisto on ei-negatiivinen aikasarja ja siksi esimerkissä tarkastellaan estimointia logaritmoidun sarjan avulla ja alkuperäisen sarjan ennustamista. Tutkielman tulosten perusteella kaksivaiheinen estimointi käyttäen geneettistä algoritmia toimii GMAR-mallin tapauksessa hyvin ja kohtuullisessa ajassa.

Tämä Pro Gradu -tutkielma käsittelee työntekijän eläkelain (TyEL) nykyisin käytössä olevan Gompertz-kuolevuusmallin sopivuutta kuvaamaan TyEL:n selektiä kuolevuutta vanhuuseläkeliikkeessä. Tutkielman tarkoitus on nostaa esiin nykymallin ongelmakohdat ja esitellä Gompertz-kuolevuusmallin laajennus, joka ainakin osin korjaisi nykymallin puutteita pysymällä kuitenkin järjestelmätekniseltä kannalta tarpeeksi yksinkertaisena. Keskeisiksi teemoiksi tutkielmassa nousee elämänvaravakuutuksen yleisen teorian sekä työntekijän eläkelain vanhuuseläkeliikkeen vakuutustekniikan lisäksi toteutuvan kuolevuuden ennustaminen Lee-Miller-mallin sovelluksella ja laajennetun Gompertz-kuolevuusmallin parametrien estimointi havaintoaineistosta. Havaintoaineistona tutkielmassa käytetään sekä Tilastokeskuksen väestökuolevuuksia että TyEL:n riskiperusteanalyysien mukaisia toteutuneita rahapainotettuja kuolevuuksia. Ennustemalli on laadittu käyttäen R- ja Excel-ohjelmistoja (ml. VBA). Kuolevuusmallin laajennusta on kehitetty työeläkejärjestelmässä vuodesta 2014 laskuperusteasiain neuvottelukunnan alaisessa kuolevuusperustejaoksessa, jonka sihteerinä tutkielman kirjoittaja on toiminut vuosina 2014 ja 2015. Puheenjohtajayhtiönä kyseisinä vuosina on toiminut Keskinäinen Työeläkevakuutusyhtiö Elo ja puheenjohtajana tämän tutkielman toinen tarkastaja Tuomas Hakkarainen. Kuolevuusperustejaoksessa on edustus jokaisen työeläkeyhtiön lisäksi eläkekassoilla ja eläkesäätiöillä, Sosiaali- ja terveysministeriöllä, Eläketurvakeskuksella sekä Kevalla (julkisen puolen eläkkeet). Kuolevuusmallin valinnalla ja osuvuudella on merkitystä vanhuuseläkeliikkeessä, sillä se määrää pääoma-arvokertoimet, joilla varaudutaan vastaisten ja alkaneiden vanhuuseläkkeiden suorituksiin tulevaisuudessa. Tutkielmassa esitelty uusi kuolevuusmalli otetaan käyttöön vuoden 2017 eläkeuudistuksen yhteydessä, eli ensimmäisen kerran vanhuuseläkeliikkeen vanhuuseläkevastuut lasketaan sen mukaisina vuoden 2016 lopussa. Vanhuuseläkemaksu määräytyy uuden mallin mukaisesti vuodesta 2017 alkaen.

This thesis examines the implementation of general purpose graphics processing unit (GPGPU) acceleration to a non-equilibrium Green’s function (NEGF) equation solver in the context of a computational photoelectrochemical (PEC) cell model. The goal is to find out whether GPGPU acceleration of the NEGF equation solver is a viable option. The current model does not yet have electron-photon scattering, but from the results it is possible to assess the viability of GPGPU acceleration in the case of a complete PEC cell model. The viability of GPGPU acceleration was studied by comparing the performance difference of two graphics processing unit (GPU) solutions against a multi core central processing unit (CPU) solution. The difference between the two GPU solutions was in the used floating-point precision. The GPU solutions would use LU factorization to solve the NEGF equations, and the CPU solution a banded solver (Gauss tridiagonal) provided by Scipy Python package. The performance comparison was done on multiple different GPU and CPU hardware. The electrical transport properties of the PEC cell were modeled by a self-consistent process in which the NEGF and Poisson equations were solved iteratively. The PEC cell was described as a semiconductor device connected with a metal and electrolyte contacts. The device was assumed to be a simple one dimensional tight-binding atom chain made of GaAs, where the transverse modes in the y–z plane are treated with a logarithmic function. The computational model did lack electron-photon scattering, which would be implemented in the future. From the benchmark results, it can be concluded that the GPGPU acceleration via LU factorization is not a viable option in the current code or in the complete model with electron-photon scattering and the assumed approximations. The parallel multi-core CPU code generally outperformed the GPU codes. The key weakness of the GPU code was the usage of LU factorization. Despite of this, there could be an opportunity for GPGPU acceleration if a more complex lattice structure and more exact scattering terms would be used. Also, a GPU accelerated tridiagonal solver could be a possible solution.

Learning a model over possible actions and using the learned model to maximize the obtained reward is an integral part of many applications. Trying to simultaneously learn the model by exploring state space and maximize the obtained reward using the learned model is an exploitation-exploitation tradeoff. Gaussian process upper confidence bound (GB-UCB) algorithm is an effective method for balancing between exploitation and exploration when exploring spatially dependent data in n-dimensional space. The balance between exploration and exploitation is required to limit the amount of user feedback required to achieve good prediction result in our context-based image retrieval system. The system starts with high amount of exploration and — as the confidence in the model increases — it starts exploiting the gathered information to direct the search towards better results. While the implementation of the GP-UCB is quite straightforward, it has time complexity of O(n^3) which limits its use in near real-time applications. In this thesis I present our reinforcement learning image retrieval system based on GP-UCB, with the focus on speed requirements for interactive applications. I also show simple methods to speed up the algorithm running time by doing some of the Gaussian process calculations on the GPU.