Mathematical modeling of microbial growth in milk

A mathematical model to predict microbial growth in milk was developed and analyzed. The model consists of a system of two differential equations of first order. The equations are based on physical hypotheses of population growth. The model was applied to five different sets of data of microbial growth in dairy products selected from Combase, which is the most important database in the area with thousands of datasets from around the world, and the results showed a good fit. In addition, the model provides equations for the evaluation of the maximum specific growth rate and the duration of the lag phase which may provide useful information about microbial growth.

Visual Customer Segmentation and Data Exploration with the Self-Organizing Map

Advancements in information technology have made it possible for organizations to gather and store vast amounts of data of their customers. Information stored in databases can be highly valuable for organizations. However, analyzing large databases has proven to be difficult in practice. For companies in the retail industry, customer intelligence can be used to identify profitable customers, their characteristics, and behavior. By clustering customers into homogeneous groups, companies can more effectively manage their customer base and target profitable customer segments. This thesis will study the use of the self-organizing map (SOM) as a method for analyzing large customer datasets, clustering customers, and discovering information about customer behavior. Aim of the thesis is to find out whether the SOM could be a practical tool for retail companies to analyze their customer data.

Macro- and Microevolution of Languages: Exploring Linguistic Divergence with Approaches from Evolutionary Biology

There are more than 7000 languages in the world, and many of these have emerged through linguistic divergence. While questions related to the drivers of linguistic diversity have been studied before, including studies with quantitative methods, there is no consensus as to which factors drive linguistic divergence, and how. In the thesis, I have studied linguistic divergence with a multidisciplinary approach, applying the framework and quantitative methods of evolutionary biology to language data. With quantitative methods, large datasets may be analyzed objectively, while approaches from evolutionary biology make it possible to revisit old questions (related to, for example, the shape of the phylogeny) with new methods, and adopt novel perspectives to pose novel questions. My chief focus was on the effects exerted on the speakers of a language by environmental and cultural factors. My approach was thus an ecological one, in the sense that I was interested in how the local environment affects humans and whether this human-environment connection plays a possible role in the divergence process. I studied this question in relation to the Uralic language family and to the dialects of Finnish, thus covering two different levels of divergence. However, as the Uralic languages have not previously been studied using quantitative phylogenetic methods, nor have population genetic methods been previously applied to any dialect data, I first evaluated the applicability of these biological methods to language data. I found the biological methodology to be applicable to language data, as my results were rather similar to traditional views as to both the shape of the Uralic phylogeny and the division of Finnish dialects. I also found environmental conditions, or changes in them, to be plausible inducers of linguistic divergence: whether in the first steps in the divergence process, i.e. dialect divergence, or on a large scale with the entire language family. My findings concerning Finnish dialects led me to conclude that the functional connection between linguistic divergence and environmental conditions may arise through human cultural adaptation to varying environmental conditions. This is also one possible explanation on the scale of the Uralic language family as a whole. The results of the thesis bring insights on several different issues in both a local and a global context. First, they shed light on the emergence of the Finnish dialects. If the approach used in the thesis is applied to the dialects of other languages, broader generalizations may be drawn as to the inducers of linguistic divergence. This again brings us closer to understanding the global patterns of linguistic diversity. Secondly, the quantitative phylogeny of the Uralic languages, with estimated times of language divergences, yields another hypothesis as to the shape and age of the language family tree. In addition, the Uralic languages can now be added to the growing list of language families studied with quantitative methods. This will allow broader inferences as to global patterns of language evolution, and more language families can be included in constructing the tree of the world’s languages. Studying history through language, however, is only one way to illuminate the human past. Therefore, thirdly, the findings of the thesis, when combined with studies of other language families, and those for example in genetics and archaeology, bring us again closer to an understanding of human history.

Bioimage informatics in STED super-resolution microscopy

Optical microscopy is living its renaissance. The diffraction limit, although still physically true, plays a minor role in the achievable resolution in far-field fluorescence microscopy. Super-resolution techniques enable fluorescence microscopy at nearly molecular resolution. Modern (super-resolution) microscopy methods rely strongly on software. Software tools are needed all the way from data acquisition, data storage, image reconstruction, restoration and alignment, to quantitative image analysis and image visualization. These tools play a key role in all aspects of microscopy today – and their importance in the coming years is certainly going to increase, when microscopy little-by-little transitions from single cells into more complex and even living model systems. In this thesis, a series of bioimage informatics software tools are introduced for STED super-resolution microscopy. Tomographic reconstruction software, coupled with a novel image acquisition method STED< is shown to enable axial (3D) super-resolution imaging in a standard 2D-STED microscope. Software tools are introduced for STED super-resolution correlative imaging with transmission electron microscopes or atomic force microscopes. A novel method for automatically ranking image quality within microscope image datasets is introduced, and it is utilized to for example select the best images in a STED microscope image dataset.

Path Analysis of Online Users Using Clickstream Da-ta: Case Online Magazine Website

This paper explores behavioral patterns of web users on an online magazine web-site. The goal of the study is to first find and visualize user paths within the data generated during collection, and to identify some generic behavioral typologies of user behavior. To form a theoretical foundation for processing data and identifying behavioral ar-chetypes, the study relies on established consumer behavior literature to propose typologies of behavior. For data processing, the study utilizes methodologies of ap-plied cluster analysis and sequential path analysis. Utilizing a dataset of click stream data generated from the real-life clicks of 250 ran-domly selected website visitors over a period of six weeks. Based on the data collect-ed, an exploratory method is followed in order to find and visualize generally occur-ring paths of users on the website. Six distinct behavioral typologies were recog-nized, with the dominant user consuming mainly blog content, as opposed to editori-al content. Most importantly, it was observed that approximately 80% of clicks were of the blog content category, meaning that the majority of web traffic occurring in the site takes place in content other than the desired editorial content pages. The out-come of the study is a set of managerial recommendations for each identified behavioral archetype.

A year after Crimea: continuity and change of Finnish-Russian relations reflected in the views of the government officials of Finland and Russia

This thesis discusses the dynamism of bilateral relations between Finland and Russia and their interconnection with wider EU-Russia relations in the sight of the recent conflict in Ukraine. In particular, incorporation of Crimea in the territory of Russia in March 2014 is believed to have triggered a series of disputes between the European Union and Russia and thus, have impacted the course of the bilateral Finnish-Russian relations. The study leans on a premise that there are some historical traditions and regularities in the Finnish foreign policy course towards Russia which make the bilateral Finnish-Russian relations special. These traditions are distinguished and described in the book “Russia Forever? Towards Pragmatism in Finnish/Russian relations” (2008) edited by H. Rytövuori-Apunen. Assuming that the featured traditions take place in modern relations between Finland and Russia, the aim of the thesis is to find out how these traditions reappear during the year shaped by the events in Ukraine. In order to do that, author follows the timeline of happenings around the Ukraine crisis starting with Crimea’s referendum on independence, and exams the way these events were commented on and evaluated by the key government officials and political institutions of Finland and Russia. The main focus is given to the Finnish official discourse on Russia during the study period. The data collection, consisting of mostly primary sources (ministerial press releases and comments, statements, speeches and blog posts of individual policy makers) is processed using the thematic analysis supported by the content analysis. The study reveals that the consequences of the Ukraine crisis have brought, among others, complications to the economic cooperation between Finland and Russia, and have stimulated the increased attention of the Finnish decision makers to the country’s security questions. As a result, the character and importance of some historical regularities of the Finnish foreign policies on Russia, like the Continental Dilemma, have taken new shape.

Efficient Algorithms for Coastal Geographic Problems

The increasing performance of computers has made it possible to solve algorithmically problems for which manual and possibly inaccurate methods have been previously used. Nevertheless, one must still pay attention to the performance of an algorithm if huge datasets are used or if the problem iscomputationally difficult. Two geographic problems are studied in the articles included in this thesis. In the first problem the goal is to determine distances from points, called study points, to shorelines in predefined directions. Together with other in-formation, mainly related to wind, these distances can be used to estimate wave exposure at different areas. In the second problem the input consists of a set of sites where water quality observations have been made and of the results of the measurements at the different sites. The goal is to select a subset of the observational sites in such a manner that water quality is still measured in a sufficient accuracy when monitoring at the other sites is stopped to reduce economic cost. Most of the thesis concentrates on the first problem, known as the fetch length problem. The main challenge is that the two-dimensional map is represented as a set of polygons with millions of vertices in total and the distances may also be computed for millions of study points in several directions. Efficient algorithms are developed for the problem, one of them approximate and the others exact except for rounding errors. The solutions also differ in that three of them are targeted for serial operation or for a small number of CPU cores whereas one, together with its further developments, is suitable also for parallel machines such as GPUs.

GIS-based approach for optimization of onshore wind park infrastructure alignment in Finland

Wind power is a rapidly developing, low-emission form of energy production. In Fin-land, the official objective is to increase wind power capacity from the current 1 005 MW up to 3 500–4 000 MW by 2025. By the end of April 2015, the total capacity of all wind power project being planned in Finland had surpassed 11 000 MW. As the amount of projects in Finland is record high, an increasing amount of infrastructure is also being planned and constructed. Traditionally, these planning operations are conducted using manual and labor-intensive work methods that are prone to subjectivity. This study introduces a GIS-based methodology for determining optimal paths to sup-port the planning of onshore wind park infrastructure alignment in Nordanå-Lövböle wind park located on the island of Kemiönsaari in Southwest Finland. The presented methodology utilizes a least-cost path (LCP) algorithm for searching of optimal paths within a high resolution real-world terrain dataset derived from airborne lidar scannings. In addition, planning data is used to provide a realistic planning framework for the anal-ysis. In order to produce realistic results, the physiographic and planning datasets are standardized and weighted according to qualitative suitability assessments by utilizing methods and practices offered by multi-criteria evaluation (MCE). The results are pre-sented as scenarios to correspond various different planning objectives. Finally, the methodology is documented by using tools of Business Process Management (BPM). The results show that the presented methodology can be effectively used to search and identify extensive, 20 to 35 kilometers long networks of paths that correspond to certain optimization objectives in the study area. The utilization of high-resolution terrain data produces a more objective and more detailed path alignment plan. This study demon-strates that the presented methodology can be practically applied to support a wind power infrastructure alignment planning process. The six-phase structure of the method-ology allows straightforward incorporation of different optimization objectives. The methodology responds well to combining quantitative and qualitative data. Additional-ly, the careful documentation presents an example of how the methodology can be eval-uated and developed as a business process. This thesis also shows that more emphasis on the research of algorithm-based, more objective methods for the planning of infrastruc-ture alignment is desirable, as technological development has only recently started to realize the potential of these computational methods.

Mandy Klein fonds

Mandy Klein was diagnosed with Asperger’s syndrome as an adult. Her husband also has Asperger’s syndrome, and together they have a daughter with autism. She lives in Ontario and writes about her family’s experiences with autism on her blog, Tales from an Autism Family, http://talesfromanaustismfamily.blogspot.ca.

Hyperspectral Mineral Identification using SVM and SOM

Remote sensing techniques involving hyperspectral imagery have applications in a number of sciences that study some aspects of the surface of the planet. The analysis of hyperspectral images is complex because of the large amount of information involved and the noise within that data. Investigating images with regard to identify minerals, rocks, vegetation and other materials is an application of hyperspectral remote sensing in the earth sciences. This thesis evaluates the performance of two classification and clustering techniques on hyperspectral images for mineral identification. Support Vector Machines (SVM) and Self-Organizing Maps (SOM) are applied as classification and clustering techniques, respectively. Principal Component Analysis (PCA) is used to prepare the data to be analyzed. The purpose of using PCA is to reduce the amount of data that needs to be processed by identifying the most important components within the data. A well-studied dataset from Cuprite, Nevada and a dataset of more complex data from Baffin Island were used to assess the performance of these techniques. The main goal of this research study is to evaluate the advantage of training a classifier based on a small amount of data compared to an unsupervised method. Determining the effect of feature extraction on the accuracy of the clustering and classification method is another goal of this research. This thesis concludes that using PCA increases the learning accuracy, and especially so in classification. SVM classifies Cuprite data with a high precision and the SOM challenges SVM on datasets with high level of noise (like Baffin Island).

New Contig Creation Algorithm for the de novo DNA Assembly Problem

DNA assembly is among the most fundamental and difficult problems in bioinformatics. Near optimal assembly solutions are available for bacterial and small genomes, however assembling large and complex genomes especially the human genome using Next-Generation-Sequencing (NGS) technologies is shown to be very difficult because of the highly repetitive and complex nature of the human genome, short read lengths, uneven data coverage and tools that are not specifically built for human genomes. Moreover, many algorithms are not even scalable to human genome datasets containing hundreds of millions of short reads. The DNA assembly problem is usually divided into several subproblems including DNA data error detection and correction, contig creation, scaffolding and contigs orientation; each can be seen as a distinct research area. This thesis specifically focuses on creating contigs from the short reads and combining them with outputs from other tools in order to obtain better results. Three different assemblers including SOAPdenovo [Li09], Velvet [ZB08] and Meraculous [CHS+11] are selected for comparative purposes in this thesis. Obtained results show that this thesis’ work produces comparable results to other assemblers and combining our contigs to outputs from other tools, produces the best results outperforming all other investigated assemblers.

Estee Klar fonds, 2005-2014

Estee Klar is the founder and executive director of The Autism Acceptance Project, an organization that strives to support people with autism by promoting acceptance and inclusion of these individuals. She is the mother of a son, Adam, who has autism, and writes about her experiences with him on her blog, found at http://www.esteeklar.com. She also writes about issues concerning autism in the area of human rights, law, and social justice, and has contributed to several books, including The Thinking Person's Guide to Autism, Between Interruptions: Thirty Women Tell the Truth about Motherhood, and Concepts of Normality: The Autistic and Typical Spectrum. Currently, she is a Ph.D. candidate at York University, Critical Disability Studies, as well as a writer and freelance curator of art.

Enhancing Student Learning: Study of a Motivational Resource for Educators

This study surveyed practicing classroom teacher’s perceptions of a proposed educational resource “Avatar Academy” designed to enhance students’, particularly young boys, motivation and general attitude towards learning. The Avatar Academy resource is an instructional guide for implementing a classroom reward system based on common game mechanics. The resource emphasizes the modification of current pedagogies to exploit the use of game design to engage boys. A survey of recent literature indicated an opportunity to study teachers’ perceptions of the possible applications of game design mechanics to support the enhancement of student motivation and learning in the classroom. As a result the Avatar Academy handbook and blog resource were developed to assist teachers with the integration and administration of a program designed to enhance student motivation, especially boys, using avatars and a point based reward system. The resources were initially distributed to several practicing teachers for their review, and their feedback formed the basis for revisions of the Avatar Academy resource. After implementing changes to the resource based on initial teacher feedback, an updated Avatar Academy was redistributed and teacher opinions and perceptions of the tool’s possible impacts on classroom learning were collected.

Raising Creativity: Exploring How Creativity Can be Nurtured in Educational Contexts

The question of how we can encourage creative capacities in young people has never been more relevant than it is today (Pink, 2006; Robinson as cited in TEDtalksDirector, 2007; Eisner as cited in VanderbiltUniversity, 2009). While the world is rapidly evolving, education has the great challenge of adapting to keep up. Scholars say that to meet the needs of 21st century learners, pedagogy must focus on fostering creative skills to enable students to manage in a future we cannot yet envision (Robinson as cited in TEDtalksDirector, 2007). Further, research demonstrates that creativity thrives with autonomy, support, and without judgment (Amabile, 1996; Codack [Zak], 2010; Harrington, Block, & Block, 1987; Holt, 1989; Kohn, 1993). So how well are schools doing in this regard? How do alternative models of education nurture or neglect creativity, and how can this inform teaching practice all around? In other words, ultimately, how can we nurture creativity in education? This documentary explores these questions from a scholarly art-based perspective. Artist/researcher/teacher Rebecca Zak builds on her experience in the art studio, academia, and the art classroom to investigate the various philosophies and strategies that diverse educational models implement to illuminate the possibilities for educational and paradigmatic transformation. The Raising Creativity documentary project consists of multiple parts across multiple platforms. There are five videos in the series that answer the why, who, how, what, and now what about creativity in education respectively (i.e., why is this topic important, who has spoken/written on this topic already, how will this issue be investigated this time, what was observed during the inquiry, and now what will this mean going forward?). There is also a self-reflexive blog that addresses certain aspects of the topic in greater depth (located here, on this website) and in the context of Rebecca's lived experience to complement the video format. Together, all video and blog artifacts housed on this website function as a polyptych, wherein the pieces can stand alone individually yet are intended to work together and fulfill the dissertation requirements for Rebecca's doctorate degree in education in reimagined ways.

Cynthia Kim fonds, 2012-2015

Cynthia Kim was diagnosed with Asperger’s syndrome when she was 42 years old. She has contributed articles to Autism Parenting magazine, Thinking Person’s Guide to Autism, and Autism West Midlands’ magazine. She has also written two books on autism, I Think I Might Be Autistic: A Guide to Autism Spectrum Disorder Diagnosis and Self-Discovery for Adults, and Nerdy, Shy, and Socially Inappropriate: A User Guide to an Asperger Life.