Anti-predator adaptations in aquatic environments

Predation is an important selective force that has led to the evolution of a variety of fascinating anti-predator adaptations, such as many types of protective coloration and prey behaviours. Because the evolution of life has begun in the aquatic environment and many anti-predator adaptations are found already in relative primitive taxa, it is likely that many of these adaptations evolved initially in the aquatic environment. Yet, there has been surprisingly little research on the mechanisms and function of antipredator adaptations in aquatic systems. To understand the function of anti-predator adaptations and natural selection imposed on prey appearance and behaviour, I have investigated how protective coloration can be used, either as such or together with behavioural adaptations, to manipulate predator behaviour and decrease predation risk. To this end I conducted a series of behaviour ecological laboratory experiments in which I manipulated the visual appearance of artificial backgrounds and prey items. In paper I of this thesis, I investigated background choice as an anti-predator strategy, by observing the habitat choice of the least killifish (Heterandria formosa) between pairs of artificial backgrounds, both in the presence and absence of predation threat. It has been suggested that prey could decrease their risk of being detected by predators either by preferring backgrounds into which they blend or by preferring visually complex backgrounds. The least killifish preferred a background that matched their patterning to a background that mismatched it, showing that they are able to respond to cues of visual similarity between their colour pattern and the surrounding environment. Interestingly however, in female least killifish visual complexity of the background was a more important cue for habitat safety and may override or act together with background matching when searching for a safe habitat. It is possible that in females, preference for visually complex backgrounds is associated with lower opportunity costs than preference for matching backgrounds would be. Generally, the least killifish showed stronger preference while under predation threat, indicating that their background choice behaviour is an antipredator adaptation. Many aquatic prey species have eyespots, which are colour patterns that consist of roughly concentric rings and have received their name because they for humans often resemble the vertebrate eye. I investigated the anti-predator function of eyespots against predation by fish in papers II, III and IV. Some eyespots have been suggested to benefit prey by diverting the strikes of predators away from vital parts of the prey body or towards a direction that facilitates prey escape. Although proposed over a century ago, the divertive effect of eyespots has proven to be difficult to show experimentally. In papers II and III, I tested for divertive effect of eyespots towards attacking fish by presenting artificial prey with eyespots to laboratory reared three-spined sticklebacks (Gasterosteus aculeatus). I found that eyespots strongly influenced the behaviour of attacking sticklebacks and effectively drew their strikes towards the eyespots. To further investigate this divertive effect and whether the specific shape of eyespots is important for it, I tested in paper III the response of fish also to other markings than eyespots. I found that eyespots were generally more effective in diverting the first strikes of attacking fish compared to other prey markings. My findings suggest that the common occurrence of eyespots in aquatic prey species can at least partly be explained by the divertive effect of the eyespot shape, possibly together with the relative simple developmental mechanisms underlying circular colour patterns. An eyebar is a stripe that runs through the eye, and this pattern has been suggested to obscure the real eyes of the prey by visually blending parts of the eyes and head of the prey and by creating false edges. In paper III, I show that an eyebar effectively disrupts an eyelike shape. This suggests that eyebars provide an effective way to conceal the eyes and consequently obstruct detection and recognition of prey. This experiment also demonstrates that through concealment of the eyes, eyebars could be used to enhance the divertive effect of eyespots, which can explain the common occurrence of eyebars in many species of fish that have eyespots. Larger eyespots have been shown to intimidate some terrestrial predators, such as passerine birds, either because they resemble the eyes of the predator’s own enemy or because highly salient features may have an intimidating effect. In papers II and IV, I investigated whether the occurrence of eyespots in some aquatic prey could be explained by their intimidating effect predatory fish. In paper IV, I also investigated the reason for the intimidating effect of eyelike prey marks. In paper II, I found no clear intimidating effect of eyespots, whereas in paper IV, using a different approach, I found that sticklebacks hesitated to attack towards eyelike but not towards non-eyelike marks. Importantly, paper IV therefore presents the first rigorous evidence for the idea that eye mimicry, and not merely conspicuousness, underlies the intimidating effect. It also showed that the hesitation shown by fish towards eyelike marks is partly an innate response that is reinforced by encounters with predators. Collectively, this thesis shows that prey colour pattern and the visual appearance of the habitat influence the behaviour of fish. The results demonstrate that protective coloration provides numerous distinctive ways for aquatic prey to escape predation. Thus, visual perception and behaviour of fish are important factors shaping the appearance and behaviours of aquatic prey.

Liquid cooling solutions for rotating permanent magnet synchronous machines

In the design of electrical machines, efficiency improvements have become very important. However, there are at least two significant cases in which the compactness of electrical machines is critical and the tolerance of extremely high losses is valued: vehicle traction, where very high torque density is desired at least temporarily; and direct-drive wind turbine generators, whose mass should be acceptably low. As ever higher torque density and ever more compact electrical machines are developed for these purposes, thermal issues, i.e. avoidance of over-temperatures and damage in conditions of high heat losses, are becoming of utmost importance. The excessive temperatures of critical machine components, such as insulation and permanent magnets, easily cause failures of the whole electrical equipment. In electrical machines with excitation systems based on permanent magnets, special attention must be paid to the rotor temperature because of the temperature-sensitive properties of permanent magnets. The allowable temperature of NdFeB magnets is usually significantly less than 150 ˚C. The practical problem is that the part of the machine where the permanent magnets are located should stay cooler than the copper windings, which can easily tolerate temperatures of 155 ˚C or 180 ˚C. Therefore, new cooling solutions should be developed in order to cool permanent magnet electrical machines with high torque density and because of it with high concentrated losses in stators. In this doctoral dissertation, direct and indirect liquid cooling techniques for permanent magnet synchronous electrical machines (PMSM) with high torque density are presented and discussed. The aim of this research is to analyse thermal behaviours of the machines using the most applicable and accurate thermal analysis methods and to propose new, practical machine designs based on these analyses. The Computational Fluid Dynamics (CFD) thermal simulations of the heat transfer inside the machines and lumped parameter thermal network (LPTN) simulations both presented herein are used for the analyses. Detailed descriptions of the simulated thermal models are also presented. Most of the theoretical considerations and simulations have been verified via experimental measurements on a copper tooth-coil (motorette) and on various prototypes of electrical machines. The indirect liquid cooling systems of a 100 kW axial flux (AF) PMSM and a 110 kW radial flux (RF) PMSM are analysed here by means of simplified 3D CFD conjugate thermal models of the parts of both machines. In terms of results, a significant temperature drop of 40 ̊C in the stator winding and 28 ̊C in the rotor of the AF PMSM was achieved with the addition of highly thermally conductive materials into the machine: copper bars inserted in the teeth, and potting material around the end windings. In the RF PMSM, the potting material resulted in a temperature decrease of 6 ̊C in the stator winding, and in a decrease of 10 ̊C in the rotor embedded-permanentmagnets. Two types of unique direct liquid cooling systems for low power machines are analysed herein to demonstrate the effectiveness of the cooling systems in conditions of highly concentrated heat losses. LPTN analysis and CFD thermal analysis (the latter being particularly useful for unique design) were applied to simulate the temperature distribution within the machine models. Oil-immersion cooling provided good cooling capability for a 26.6 kW PMSM of a hybrid vehicle. A direct liquid cooling system for the copper winding with inner stainless steel tubes was designed for an 8 MW directdrive PM synchronous generator. The design principles of this cooling solution are described in detail in this thesis. The thermal analyses demonstrate that the stator winding and the rotor magnet temperatures are kept significantly below their critical temperatures with demineralized water flow. A comparison study of the coolant agents indicates that propylene glycol is more effective than ethylene glycol in arctic conditions.

Semantic and fuzzy modelling for human behaviour recognition in smart spaces : a case study on ambient assisted living.

Human activity recognition in everyday environments is a critical, but challenging task in Ambient Intelligence applications to achieve proper Ambient Assisted Living, and key challenges still remain to be dealt with to realize robust methods. One of the major limitations of the Ambient Intelligence systems today is the lack of semantic models of those activities on the environment, so that the system can recognize the speci c activity being performed by the user(s) and act accordingly. In this context, this thesis addresses the general problem of knowledge representation in Smart Spaces. The main objective is to develop knowledge-based models, equipped with semantics to learn, infer and monitor human behaviours in Smart Spaces. Moreover, it is easy to recognize that some aspects of this problem have a high degree of uncertainty, and therefore, the developed models must be equipped with mechanisms to manage this type of information. A fuzzy ontology and a semantic hybrid system are presented to allow modelling and recognition of a set of complex real-life scenarios where vagueness and uncertainty are inherent to the human nature of the users that perform it. The handling of uncertain, incomplete and vague data (i.e., missing sensor readings and activity execution variations, since human behaviour is non-deterministic) is approached for the rst time through a fuzzy ontology validated on real-time settings within a hybrid data-driven and knowledgebased architecture. The semantics of activities, sub-activities and real-time object interaction are taken into consideration. The proposed framework consists of two main modules: the low-level sub-activity recognizer and the high-level activity recognizer. The rst module detects sub-activities (i.e., actions or basic activities) that take input data directly from a depth sensor (Kinect). The main contribution of this thesis tackles the second component of the hybrid system, which lays on top of the previous one, in a superior level of abstraction, and acquires the input data from the rst module's output, and executes ontological inference to provide users, activities and their in uence in the environment, with semantics. This component is thus knowledge-based, and a fuzzy ontology was designed to model the high-level activities. Since activity recognition requires context-awareness and the ability to discriminate among activities in di erent environments, the semantic framework allows for modelling common-sense knowledge in the form of a rule-based system that supports expressions close to natural language in the form of fuzzy linguistic labels. The framework advantages have been evaluated with a challenging and new public dataset, CAD-120, achieving an accuracy of 90.1% and 91.1% respectively for low and high-level activities. This entails an improvement over both, entirely data-driven approaches, and merely ontology-based approaches. As an added value, for the system to be su ciently simple and exible to be managed by non-expert users, and thus, facilitate the transfer of research to industry, a development framework composed by a programming toolbox, a hybrid crisp and fuzzy architecture, and graphical models to represent and con gure human behaviour in Smart Spaces, were developed in order to provide the framework with more usability in the nal application. As a result, human behaviour recognition can help assisting people with special needs such as in healthcare, independent elderly living, in remote rehabilitation monitoring, industrial process guideline control, and many other cases. This thesis shows use cases in these areas.

Analyzing and Computing the Sustainability Gains of Building Automation

Smart home implementation in residential buildings promises to optimize energy usage and save significant amount of energy simply due to a better understanding of user's energy usage profile. Apart from the energy optimisation prospects of this technology, it also aims to guarantee occupants significant amount of comfort and remote control over home appliances both at home locations and at remote places. However, smart home investment just like any other kind of investment requires an adequate measurement and justification of the economic gains it could proffer before its realization. These economic gains could differ for different occupants due to their inherent behaviours and tendencies. Thus it is pertinent to investigate the various behaviours and tendencies of occupants in different domain of interests and to measure the value of the energy savings accrued by smart home implementations in these domains of interest in order to justify such economic gains. This thesis investigates two domains of interests (the rented apartment and owned apartment) for primarily two behavioural tendencies (Finland and Germany) obtained from observation and corroborated by conducted interviews to measure the payback time and Return on Investment (ROI) of their smart home implementations. Also, similar measures are obtained for identified Australian use case. The research finding reveals that building automation for the Finnish behavioural tendencies seems to proffers a better ROI and payback time for smart home implementations.

Socially shared metacognitive regulation during collaborative learning processes in student dyads and small groups

Traditionally metacognition has been theorised, methodologically studied and empirically tested from the standpoint mainly of individuals and their learning contexts. In this dissertation the emergence of metacognition is analysed more broadly. The aim of the dissertation was to explore socially shared metacognitive regulation (SSMR) as part of collaborative learning processes taking place in student dyads and small learning groups. The specific aims were to extend the concept of individual metacognition to SSMR, to develop methods to capture and analyse SSMR and to validate the usefulness of the concept of SSMR in two different learning contexts; in face-to-face student dyads solving mathematical word problems and also in small groups taking part in inquiry-based science learning in an asynchronous computer-supported collaborative learning (CSCL) environment. This dissertation is comprised of four studies. In Study I, the main aim was to explore if and how metacognition emerges during problem solving in student dyads and then to develop a method for analysing the social level of awareness, monitoring, and regulatory processes emerging during the problem solving. Two dyads comprised of 10-year-old students who were high-achieving especially in mathematical word problem solving and reading comprehension were involved in the study. An in-depth case analysis was conducted. Data consisted of over 16 (30–45 minutes) videotaped and transcribed face-to-face sessions. The dyads solved altogether 151 mathematical word problems of different difficulty levels in a game-format learning environment. The interaction flowchart was used in the analysis to uncover socially shared metacognition. Interviews (also stimulated recall interviews) were conducted in order to obtain further information about socially shared metacognition. The findings showed the emergence of metacognition in a collaborative learning context in a way that cannot solely be explained by individual conception. The concept of socially-shared metacognition (SSMR) was proposed. The results highlighted the emergence of socially shared metacognition specifically in problems where dyads encountered challenges. Small verbal and nonverbal signals between students also triggered the emergence of socially shared metacognition. Additionally, one dyad implemented a system whereby they shared metacognitive regulation based on their strengths in learning. Overall, the findings suggested that in order to discover patterns of socially shared metacognition, it is important to investigate metacognition over time. However, it was concluded that more research on socially shared metacognition, from larger data sets, is needed. These findings formed the basis of the second study. In Study II, the specific aim was to investigate whether socially shared metacognition can be reliably identified from a large dataset of collaborative face-to-face mathematical word problem solving sessions by student dyads. We specifically examined different difficulty levels of tasks as well as the function and focus of socially shared metacognition. Furthermore, the presence of observable metacognitive experiences at the beginning of socially shared metacognition was explored. Four dyads participated in the study. Each dyad was comprised of high-achieving 10-year-old students, ranked in the top 11% of their fourth grade peers (n=393). Dyads were from the same data set as in Study I. The dyads worked face-to-face in a computer-supported, game-format learning environment. Problem-solving processes for 251 tasks at three difficulty levels taking place during 56 (30–45 minutes) lessons were video-taped and analysed. Baseline data for this study were 14 675 turns of transcribed verbal and nonverbal behaviours observed in four study dyads. The micro-level analysis illustrated how participants moved between different channels of communication (individual and interpersonal). The unit of analysis was a set of turns, referred to as an ‘episode’. The results indicated that socially shared metacognition and its function and focus, as well as the appearance of metacognitive experiences can be defined in a reliable way from a larger data set by independent coders. A comparison of the different difficulty levels of the problems suggested that in order to trigger socially shared metacognition in small groups, the problems should be more difficult, as opposed to moderately difficult or easy. Although socially shared metacognition was found in collaborative face-to-face problem solving among high-achieving student dyads, more research is needed in different contexts. This consideration created the basis of the research on socially shared metacognition in Studies III and IV. In Study III, the aim was to expand the research on SSMR from face-to-face mathematical problem solving in student dyads to inquiry-based science learning among small groups in an asynchronous computer-supported collaborative learning (CSCL) environment. The specific aims were to investigate SSMR’s evolvement and functions in a CSCL environment and to explore how SSMR emerges at different phases of the inquiry process. Finally, individual student participation in SSMR during the process was studied. An in-depth explanatory case study of one small group of four girls aged 12 years was carried out. The girls attended a class that has an entrance examination and conducts a language-enriched curriculum. The small group solved complex science problems in an asynchronous CSCL environment, participating in research-like processes of inquiry during 22 lessons (á 45–minute). Students’ network discussion were recorded in written notes (N=640) which were used as study data. A set of notes, referred to here as a ‘thread’, was used as the unit of analysis. The inter-coder agreement was regarded as substantial. The results indicated that SSMR emerges in a small group’s asynchronous CSCL inquiry process in the science domain. Hence, the results of Study III were in line with the previous Study I and Study II and revealed that metacognition cannot be reduced to the individual level alone. The findings also confirm that SSMR should be examined as a process, since SSMR can evolve during different phases and that different SSMR threads overlapped and intertwined. Although the classification of SSMR’s functions was applicable in the context of CSCL in a small group, the dominant function was different in the asynchronous CSCL inquiry in the small group in a science activity than in mathematical word problem solving among student dyads (Study II). Further, the use of different analytical methods provided complementary findings about students’ participation in SSMR. The findings suggest that it is not enough to code just a single written note or simply to examine who has the largest number of notes in the SSMR thread but also to examine the connections between the notes. As the findings of the present study are based on an in-depth analysis of a single small group, further cases were examined in Study IV, as well as looking at the SSMR’s focus, which was also studied in a face-to-face context. In Study IV, the general aim was to investigate the emergence of SSMR with a larger data set from an asynchronous CSCL inquiry process in small student groups carrying out science activities. The specific aims were to study the emergence of SSMR in the different phases of the process, students’ participation in SSMR, and the relation of SSMR’s focus to the quality of outcomes, which was not explored in previous studies. The participants were 12-year-old students from the same class as in Study III. Five small groups consisting of four students and one of five students (N=25) were involved in the study. The small groups solved ill-defined science problems in an asynchronous CSCL environment, participating in research-like processes of inquiry over a total period of 22 hours. Written notes (N=4088) detailed the network discussions of the small groups and these constituted the study data. With these notes, SSMR threads were explored. As in Study III, the thread was used as the unit of analysis. In total, 332 notes were classified as forming 41 SSMR threads. Inter-coder agreement was assessed by three coders in the different phases of the analysis and found to be reliable. Multiple methods of analysis were used. Results showed that SSMR emerged in all the asynchronous CSCL inquiry processes in the small groups. However, the findings did not reveal any significantly changing trend in the emergence of SSMR during the process. As a main trend, the number of notes included in SSMR threads differed significantly in different phases of the process and small groups differed from each other. Although student participation was seen as highly dispersed between the students, there were differences between students and small groups. Furthermore, the findings indicated that the amount of SSMR during the process or participation structure did not explain the differences in the quality of outcomes for the groups. Rather, when SSMRs were focused on understanding and procedural matters, it was associated with achieving high quality learning outcomes. In turn, when SSMRs were focused on incidental and procedural matters, it was associated with low level learning outcomes. Hence, the findings imply that the focus of any emerging SSMR is crucial to the quality of the learning outcomes. Moreover, the findings encourage the use of multiple research methods for studying SSMR. In total, the four studies convincingly indicate that a phenomenon of socially shared metacognitive regulation also exists. This means that it was possible to define the concept of SSMR theoretically, to investigate it methodologically and to validate it empirically in two different learning contexts across dyads and small groups. In-depth micro-level case analysis in Studies I and III showed the possibility to capture and analyse in detail SSMR during the collaborative process, while in Studies II and IV, the analysis validated the emergence of SSMR in larger data sets. Hence, validation was tested both between two environments and within the same environments with further cases. As a part of this dissertation, SSMR’s detailed functions and foci were revealed. Moreover, the findings showed the important role of observable metacognitive experiences as the starting point of SSMRs. It was apparent that problems dealt with by the groups should be rather difficult if SSMR is to be made clearly visible. Further, individual students’ participation was found to differ between students and groups. The multiple research methods employed revealed supplementary findings regarding SSMR. Finally, when SSMR was focused on understanding and procedural matters, this was seen to lead to higher quality learning outcomes. Socially shared metacognition regulation should therefore be taken into consideration in students’ collaborative learning at school similarly to how an individual’s metacognition is taken into account in individual learning.

Hierarchy and Expansiveness in Two-Dimensional Subshifts of Finite Type

Subshifts are sets of configurations over an infinite grid defined by a set of forbidden patterns. In this thesis, we study two-dimensional subshifts offinite type (2D SFTs), where the underlying grid is Z2 and the set of for-bidden patterns is finite. We are mainly interested in the interplay between the computational power of 2D SFTs and their geometry, examined through the concept of expansive subdynamics. 2D SFTs with expansive directions form an interesting and natural class of subshifts that lie between dimensions 1 and 2. An SFT that has only one non-expansive direction is called extremely expansive. We prove that in many aspects, extremely expansive 2D SFTs display the totality of behaviours of general 2D SFTs. For example, we construct an aperiodic extremely expansive 2D SFT and we prove that the emptiness problem is undecidable even when restricted to the class of extremely expansive 2D SFTs. We also prove that every Medvedev class contains an extremely expansive 2D SFT and we provide a characterization of the sets of directions that can be the set of non-expansive directions of a 2D SFT. Finally, we prove that for every computable sequence of 2D SFTs with an expansive direction, there exists a universal object that simulates all of the elements of the sequence. We use the so called hierarchical, self-simulating or fixed-point method for constructing 2D SFTs which has been previously used by Ga´cs, Durand, Romashchenko and Shen.

Successful new venturing process implemented by the founding entrepreneur: A case of Finnish sawmill industry

The fundamental purpose of this research is to emphasise a founding entrepreneur’s own role in the construction of a successful business story, with the focus being on the analysis of the entrepreneur’s activities. The theoretical section sheds light on the heterogeneous nature of existing performance research and, thereby, opens the way for the behavioural approach research of entrepreneurs in the field of new venture performance research. This research can be seen to be in line with the latest trends in entrepreneurship research, which question the applicability of different organisational theories in entrepreneurship research. For this reason, the founding entrepreneur has been chosen, instead of the company, to be the unit of analysis in this research in order to lighten the link in question while developing and refining new knowledge in the field of entrepreneurship. The empirical section of this research focuses on the entrepreneur’s own actions or behaviours that can be seen to be associated with the company’s success. Although some of these actions may resemble the strategic actions of a company as defined in strategic management literature, these actions taken by the entrepreneur themselves must be distinguished from the different organisational actions. Usually, an entrepreneur makes decisions rather independently, mainly on basis of their own intuition and prevailing market conditions, whereas organisational actions are very systematic, and each decision involves many different people. For this reason, an entrepreneur’s actions must be distinguished from organisational actions. In additional to different action paths, the empirical data collected for this research also offers almost unambiguous proof that the actions taken by an entrepreneur at the different stages of a company’s development do play a crucial role in the success of the companies studied in this research. In this way, it is possible to identify a significant link between the behavioural approach research of entrepreneurs and new venture performance research. Due to a lack of behavioural research into founding entrepreneurs, this research has utilised a qualitative (hermeneutic) research approach. The researcher strove to establish a particularly close connection with the entrepreneurs that were studied here and, thus, understand the actions taken at the different stages of their companies’ development as well as the motives and fundamental purposes of these actions. It would not have been possible to manage such profound data that focuses on causalities by using quantitative methods. In addition to interviews, this research used corporate histories of the companies for collecting some of the research data. These corporate histories can be considered excellent tools for the researcher to obtain a preliminary understanding and can, thereby, be seen to have laid the ground for more in-depth and diverse analyses.