A multibody dynamic model of the cross-country ski-skating technique

The objective of this thesis is the development of a multibody dynamic model matching the observed movements of the lower limb of a skier performing the skating technique in cross-country style. During the construction of this model, the formulation of the equation of motion was made using the Euler - Lagrange approach with multipliers applied to a multibody system in three dimensions. The description of the lower limb of the skate skier and the ski was completed by employing three bodies, one representing the ski, and two representing the natural movements of the leg of the skier. The resultant system has 13 joint constraints due to the interconnection of the bodies, and four prescribed kinematic constraints to account for the movements of the leg, leaving the amount of degrees of freedom equal to one. The push-off force exerted by the skate skier was taken directly from measurements made on-site in the ski tunnel at the Vuokatti facilities (Finland) and was input into the model as a continuous function. Then, the resultant velocities and movement of the ski, center of mass of the skier, and variation of the skating angle were studied to understand the response of the model to the variation of important parameters of the skate technique. This allowed a comparison of the model results with the real movement of the skier. Further developments can be made to this model to better approximate the results to the real movement of the leg. One can achieve this by changing the constraints to include the behavior of the real leg joints and muscle actuation. As mentioned in the introduction of this thesis, a multibody dynamic model can be used to provide relevant information to ski designers and to obtain optimized results of the given variables, which athletes can use to improve their performance.

Proactive and Preventive Student Welfare Activities in Finnish Preschool and Elementary School: Handling of Transition to Formal Schooling and a National Anti-Bullying Program as Examples

Prerequisites and effects of proactive and preventive psycho-social student welfare activities in Finnish preschool and elementary school were of interest in the present thesis. So far, Finnish student welfare work has mainly focused on interventions and individuals, and the voluminous possibilities to enhance well-being of all students as a part of everyday school work have not been fully exploited. Consequently, in this thesis three goals were set: (1) To present concrete examples of proactive and preventive psycho-social student welfare activities in Finnish basic education; (2) To investigate measurable positive effects of proactive and preventive activities; and (3) To investigate implementation of proactive and preventive activities in ecological contexts. Two prominent phenomena in preschool and elementary school years—transition to formal schooling and school bullying—were chosen as examples of critical situations that are appropriate targets for proactive and preventive psycho-social student welfare activities. Until lately, the procedures concerning both school transitions and school bullying have been rather problem-focused and reactive in nature. Theoretically, we lean on the bioecological model of development by Bronfenbrenner and Morris with concentric micro-, meso-, exo- and macrosystems. Data were drawn from two large-scale research projects, the longitudinal First Steps Study: Interactive Learning in the Child–Parent– Teacher Triangle, and the Evaluation Study of the National Antibullying Program KiVa. In Study I, we found that the academic skills of children from preschool–elementary school pairs that implemented several supportive activities during the preschool year developed more quickly from preschool to Grade 1 compared with the skills of children from pairs that used fewer practices. In Study II, we focused on possible effects of proactive and preventive actions on teachers and found that participation in the KiVa antibullying program influenced teachers‘ self-evaluated competence to tackle bullying. In Studies III and IV, we investigated factors that affect implementation rate of these proactive and preventive actions. In Study III, we found that principal‘s commitment and support for antibullying work has a clear-cut positive effect on implementation adherence of student lessons of the KiVa antibullying program. The more teachers experience support for and commitment to anti-bullying work from their principal, the more they report having covered KiVa student lessons and topics. In Study IV, we wanted to find out why some schools implement several useful and inexpensive transition practices, whereas other schools use only a few of them. We were interested in broadening the scope and looking at local-level (exosystem) qualities, and, in fact, the local-level activities and guidelines, along with teacherreported importance of the transition practices, were the only factors significantly associated with the implementation rate of transition practices between elementary schools and partner preschools. Teacher- and school-level factors available in this study turned out to be mostly not significant. To summarize, the results confirm that school-based promotion and prevention activities may have beneficial effects not only on students but also on teachers. Second, various top-down processes, such as engagement at the level of elementary school principals or local administration may enhance implementation of these beneficial activities. The main message is that when aiming to support the lives of children the primary focus should be on adults. In future, promotion of psychosocial well-being and the intrinsic value of inter- and intrapersonal skills need to be strengthened in the Finnish educational systems. Future research efforts in student welfare and school psychology, as well as focused training for psychologists in educational contexts, should be encouraged in the departments of psychology and education in Finnish universities. Moreover, a specific research centre for school health and well-being should be established.

Hydrometallurgical recovery of valuable metals from secondary raw materials

Wastes and side streams in the mining industry and different anthropogenic wastes often contain valuable metals in such concentrations their recovery may be economically viable. These raw materials are collectively called secondary raw materials. The recovery of metals from these materials is also environmentally favorable, since many of the metals, for example heavy metals, are hazardous to the environment. This has been noticed in legislative bodies, and strict regulations for handling both mining and anthropogenic wastes have been developed, mainly in the last decade. In the mining and metallurgy industry, important secondary raw materials include, for example, steelmaking dusts (recoverable metals e.g. Zn and Mo), zinc plant residues (Ag, Au, Ga, Ge, In) and waste slurry from Bayer process alumina production (Ga, REE, Ti, V). From anthropogenic wastes, waste electrical and electronic equipment (WEEE), among them LCD screens and fluorescent lamps, are clearly the most important from a metals recovery point of view. Metals that are commonly recovered from WEEE include, for example, Ag, Au, Cu, Pd and Pt. In LCD screens indium, and in fluorescent lamps, REEs, are possible target metals. Hydrometallurgical processing routes are highly suitable for the treatment of complex and/or low grade raw materials, as secondary raw materials often are. These solid or liquid raw materials often contain large amounts of base metals, for example. Thus, in order to recover valuable metals, with small concentrations, highly selective separation methods, such as hydrometallurgical routes, are needed. In addition, hydrometallurgical processes are also seen as more environmental friendly, and they have lower energy consumption, when compared to pyrometallurgical processes. In this thesis, solvent extraction and ion exchange are the most important hydrometallurgical separation methods studied. Solvent extraction is a mainstream unit operation in the metallurgical industry for all kinds of metals, but for ion exchange, practical applications are not as widespread. However, ion exchange is known to be particularly suitable for dilute feed solutions and complex separation tasks, which makes it a viable option, especially for processing secondary raw materials. Recovering valuable metals was studied with five different raw materials, which included liquid and solid side streams from metallurgical industries and WEEE. Recovery of high purity (99.7%) In, from LCD screens, was achieved by leaching with H2SO4, extracting In and Sn to D2EHPA, and selectively stripping In to HCl. In was also concentrated in the solvent extraction stage from 44 mg/L to 6.5 g/L. Ge was recovered as a side product from two different base metal process liquors with Nmethylglucamine functional chelating ion exchange resin (IRA-743). Based on equilibrium and dynamic modeling, a mechanism for this moderately complex adsorption process was suggested. Eu and Y were leached with high yields (91 and 83%) by 2 M H2SO4 from a fluorescent lamp precipitate of waste treatment plant. The waste also contained significant amounts of other REEs such as Gd and Tb, but these were not leached with common mineral acids in ambient conditions. Zn was selectively leached over Fe from steelmaking dusts with a controlled acidic leaching method, in which the pH did not go below, but was held close as possible to, 3. Mo was also present in the other studied dust, and was leached with pure water more effectively than with the acidic methods. Good yield and selectivity in the solvent extraction of Zn was achieved by D2EHPA. However, Fe needs to be eliminated in advance, either by the controlled leaching method or, for example, by precipitation. 100% Pure Mo/Cr product was achieved with quaternary ammonium salt (Aliquat 336) directly from the water leachate, without pH adjustment (pH 13.7). A Mo/Cr mixture was also obtained from H2SO4 leachates with hydroxyoxime LIX 84-I and trioctylamine (TOA), but the purities were 70% at most. However with Aliquat 336, again an over 99% pure mixture was obtained. High selectivity for Mo over Cr was not achieved with any of the studied reagents. Ag-NaCl solution was purified from divalent impurity metals by aminomethylphosphonium functional Lewatit TP-260 ion exchange resin. A novel preconditioning method, named controlled partial neutralization, with conjugate bases of weak organic acids, was used to control the pH in the column to avoid capacity losses or precipitations. Counter-current SMB was shown to be a better process configuration than either batch column operation or the cross-current operation conventionally used in the metallurgical industry. The raw materials used in this thesis were also evaluated from an economic point of view, and the precipitate from a waste fluorescent lamp treatment process was clearly shown to be the most promising.

Scheduling dynamic dataflow graphs with model checking

With the shift towards many-core computer architectures, dataflow programming has been proposed as one potential solution for producing software that scales to a varying number of processor cores. Programming for parallel architectures is considered difficult as the current popular programming languages are inherently sequential and introducing parallelism is typically up to the programmer. Dataflow, however, is inherently parallel, describing an application as a directed graph, where nodes represent calculations and edges represent a data dependency in form of a queue. These queues are the only allowed communication between the nodes, making the dependencies between the nodes explicit and thereby also the parallelism. Once a node have the su cient inputs available, the node can, independently of any other node, perform calculations, consume inputs, and produce outputs. Data ow models have existed for several decades and have become popular for describing signal processing applications as the graph representation is a very natural representation within this eld. Digital lters are typically described with boxes and arrows also in textbooks. Data ow is also becoming more interesting in other domains, and in principle, any application working on an information stream ts the dataflow paradigm. Such applications are, among others, network protocols, cryptography, and multimedia applications. As an example, the MPEG group standardized a dataflow language called RVC-CAL to be use within reconfigurable video coding. Describing a video coder as a data ow network instead of with conventional programming languages, makes the coder more readable as it describes how the video dataflows through the different coding tools. While dataflow provides an intuitive representation for many applications, it also introduces some new problems that need to be solved in order for data ow to be more widely used. The explicit parallelism of a dataflow program is descriptive and enables an improved utilization of available processing units, however, the independent nodes also implies that some kind of scheduling is required. The need for efficient scheduling becomes even more evident when the number of nodes is larger than the number of processing units and several nodes are running concurrently on one processor core. There exist several data ow models of computation, with different trade-offs between expressiveness and analyzability. These vary from rather restricted but statically schedulable, with minimal scheduling overhead, to dynamic where each ring requires a ring rule to evaluated. The model used in this work, namely RVC-CAL, is a very expressive language, and in the general case it requires dynamic scheduling, however, the strong encapsulation of dataflow nodes enables analysis and the scheduling overhead can be reduced by using quasi-static, or piecewise static, scheduling techniques. The scheduling problem is concerned with nding the few scheduling decisions that must be run-time, while most decisions are pre-calculated. The result is then an, as small as possible, set of static schedules that are dynamically scheduled. To identify these dynamic decisions and to find the concrete schedules, this thesis shows how quasi-static scheduling can be represented as a model checking problem. This involves identifying the relevant information to generate a minimal but complete model to be used for model checking. The model must describe everything that may affect scheduling of the application while omitting everything else in order to avoid state space explosion. This kind of simplification is necessary to make the state space analysis feasible. For the model checker to nd the actual schedules, a set of scheduling strategies are de ned which are able to produce quasi-static schedulers for a wide range of applications. The results of this work show that actor composition with quasi-static scheduling can be used to transform data ow programs to t many different computer architecture with different type and number of cores. This in turn, enables dataflow to provide a more platform independent representation as one application can be fitted to a specific processor architecture without changing the actual program representation. Instead, the program representation is in the context of design space exploration optimized by the development tools to fit the target platform. This work focuses on representing the dataflow scheduling problem as a model checking problem and is implemented as part of a compiler infrastructure. The thesis also presents experimental results as evidence of the usefulness of the approach.

Collectiveness within startup teams – Leading the way to initiating and managing collective pursuit of opportunities in organizational contexts

While traditional entrepreneurship literature addresses the pursuit of entrepreneurial opportunities to a solo entrepreneur, scholars increasingly agree that new ventures are often founded and operated by entrepreneurial teams as collective efforts especially in hightechnology industries. Researchers also suggest that team ventures are more likely to survive and succeed than ventures founded by the individual entrepreneur although specific challenges might relate to multiple individuals being involved in joint entrepreneurial action. In addition to new ventures, entrepreneurial teams are seen central for organizing work in established organizations since the teams are able to create major product and service innovations that drive organizational success. Acknowledgement of the entrepreneurial teams in various organizational contexts has challenged the notion on the individual entrepreneur. However, considering that entrepreneurial teams represent a collective-level phenomenon that bases on interactions between organizational members, entrepreneurial teams may not have been studied as indepth as could be expected from the point of view of the team-level, rather than the individual or the individuals in the team. Many entrepreneurial team studies adopt the individualized view of entrepreneurship and examine the team members’ aggregate characteristics or the role of a lead entrepreneur. The previous understandings might not offer a comprehensive and indepth enough understanding of collectiveness within entrepreneurial teams and team venture performance that often relates to the team-level issues in particular. In addition, as the collective-level of entrepreneurial teams has been approached in various ways in the existing literatures, the phenomenon has been difficult to understand in research and practice. Hence, there is a need to understand entrepreneurial teams at the collective-level through a systematic and comprehensive perspective. This study takes part in the discussions on entrepreneurial teams. The overall objective of this study is to offer a description and understanding of collectiveness within entrepreneurial teams beyond individual(s). The research questions of the study are: 1) what collectiveness within entrepreneurial teams stands for, what constitutes the basic elements of it, and who are included in it, 2) why, how, and when collectiveness emerges or reinforces within entrepreneurial teams, and 3) why collectiveness within entrepreneurial teams matters and how it could be developed or supported. In order to answer the above questions, this study bases on three approaches, two set of empirical data, two analysis techniques, and conceptual study. The first data set consists of 12 qualitative semi-structured interviews with business school students who are seen as prospective entrepreneurs. The data is approached through a social constructionist perspective and analyzed through discourse analysis. The second data set bases on a qualitative multiplecase study approach that aims at theory elaboration. The main data consists of 14 individual and four group semi-structured thematic interviews with members of core entrepreneurial teams of four team startups in high-technology industries. The secondary data includes publicly available documents. This data set is approached through a critical realist perspective and analyzed through systematic thematic analysis. The study is completed through a conceptual study that aims at building a theoretical model of collective-level entrepreneurship drawing from existing literatures on organizational theory and social-psychology. The theoretical work applies a positivist perspective. This study consists of two parts. The first part includes an overview that introduces the research background, knowledge gaps and objectives, research strategy, and key concepts. It also outlines the existing knowledge of entrepreneurial team literature, presents and justifies the choices of paradigms and methods, summarizes the publications, and synthesizes the findings through answering the above mentioned research questions. The second part consists of five publications that address independent research questions but all enable to answer the research questions set for this study as a whole. The findings of this study suggest a map of relevant concepts and their relationships that help grasp collectiveness within entrepreneurial teams. The analyses conducted in the publications suggest that collectiveness within entrepreneurial teams stands for cognitive and affective structures in-between team members including elements of collective entity, collective idea of business, collective effort, collective attitudes and motivations, and collective feelings. Collectiveness within entrepreneurial teams also stands for specific joint entrepreneurial action components in which the structures are constructed. The action components reflect equality and democracy, and open and direct communication in particular. Collectiveness emerges because it is a powerful tool for overcoming individualized barriers to entrepreneurship and due to collectively oriented desire for, collective value orientation to, demand for, and encouragement to team entrepreneurship. Collectiveness emerges and reinforces in processes of joint creation and realization of entrepreneurial opportunities including joint analysis and planning of the opportunities and strategies, decision-making and realization of the opportunities, and evaluation, feedback, and sanctions of entrepreneurial action. Collectiveness matters because it is relevant for potential future entrepreneurs and because it affects the ways collective ventures are initiated and managed. Collectiveness also matters because it is a versatile, dynamic, and malleable phenomenon and the ideas of it can be applied across organizational contexts that require team work in discovering or creating and realizing new opportunities. This study further discusses how the findings add to the existing knowledge of entrepreneurial team literature and how the ideas can be applied in educational, managerial, and policy contexts.

Tissue Proteomes: Quantitative Mass Spectrometry of Murine Liver and Ovarian Endometrioma

A human genome contains more than 20 000 protein-encoding genes. A human proteome, instead, has been estimated to be much more complex and dynamic. The most powerful tool to study proteins today is mass spectrometry (MS). MS based proteomics is based on the measurement of the masses of charged peptide ions in a gas-phase. The peptide amino acid sequence can be deduced, and matching proteins can be found, using software to correlate MS-data with sequence database information. Quantitative proteomics allow the estimation of the absolute or relative abundance of a certain protein in a sample. The label-free quantification methods use the intrinsic MS-peptide signals in the calculation of the quantitative values enabling the comparison of peptide signals from numerous patient samples. In this work, a quantitative MS methodology was established to study aromatase overexpressing (AROM+) male mouse liver and ovarian endometriosis tissue samples. The workflow of label-free quantitative proteomics was optimized in terms of sensitivity and robustness, allowing the quantification of 1500 proteins with a low coefficient of variance in both sample types. Additionally, five statistical methods were evaluated for the use with label-free quantitative proteomics data. The proteome data was integrated with other omics datasets, such as mRNA microarray and metabolite data sets. As a result, an altered lipid metabolism in liver was discovered in male AROM+ mice. The results suggest a reduced beta oxidation of long chain phospholipids in the liver and increased levels of pro-inflammatory fatty acids in the circulation in these mice. Conversely, in the endometriosis tissues, a set of proteins highly specific for ovarian endometrioma were discovered, many of which were under the regulation of the growth factor TGF-β1. This finding supports subsequent biomarker verification in a larger number of endometriosis patient samples.