The purpose of this study is to show how the modern society, with demands, expectations and the time constraints of adults, affects children and the everyday life at the preschool. Our intention is also to show the importance of identify stress symptoms of the children.         The study is of qualitative empirical kind and is based on a questionnaire to 19 parents of preschool children at the age of three to five years old. Interviews are made with four preschool teachers on one preschool. An observation of children in possible stressful situations is made during three days each at two preschools.      The results show that the modern society affects children and can be stressful. It also affects the everyday life at the preschool. The results show the significance of having strategies to prevent stress at the preschool in order to create a balance between demands and expectations in the everyday life and time for recreation.

KTH, Division of Neuronic Engineering, have a finite element model of the head. However, this model does not contain detailed modeling of the brain. This thesis project consists of finding a method to extract brain tissues from T1-weighted MR images of the head. The method should be automatic to be suitable for patient individual modeling. A summary of the most common segmentation methods is presented and one of the methods is implemented. The implemented method is based on the assumption that the probability density function (pdf) of an MR image can be described by parametric models. The intensity distribution of each tissue class is modeled as a Gaussian distribution. Thus, the total pdf is a sum of Gaussians. However, the voxel values are also influenced by intensity inhomogeneities, which affect the pdf. The implemented method is based on the expectation-maximization algorithm and it corrects for intensity inhomogeneities. The result from the algorithm is a classification of the voxels. The brain is extracted from the classified voxels using morphological operations.

In drug discovery, thousands of compounds are assayed to detect activity against a biological target. The goal of drug discovery is to identify compounds that are active against the target (e.g. inhibit a virus). Statistical learning in drug discovery seeks to build a model that uses descriptors characterizing molecular structure to predict biological activity. However, the characteristics of drug discovery data can make it difficult to model the relationship between molecular descriptors and biological activity. Among these characteristics are the rarity of active compounds, the large volume of compounds tested by high-throughput screening, and the complexity of molecular structure and its relationship to activity. This thesis focuses on the design of statistical learning algorithms/models and their applications to drug discovery. The two main parts of the thesis are: an algorithm-based statistical method and a more formal model-based approach. Both approaches can facilitate and accelerate the process of developing new drugs. A unifying theme is the use of unsupervised methods as components of supervised learning algorithms/models. In the first part of the thesis, we explore a sequential screening approach, Cluster Structure-Activity Relationship Analysis (CSARA). Sequential screening integrates High Throughput Screening with mathematical modeling to sequentially select the best compounds. CSARA is a cluster-based and algorithm driven method. To gain further insight into this method, we use three carefully designed experiments to compare predictive accuracy with Recursive Partitioning, a popular structureactivity relationship analysis method. The experiments show that CSARA outperforms Recursive Partitioning. Comparisons include problems with many descriptor sets and situations in which many descriptors are not important for activity. In the second part of the thesis, we propose and develop constrained mixture discriminant analysis (CMDA), a model-based method. The main idea of CMDA is to model the distribution of the observations given the class label (e.g. active or inactive class) as a constrained mixture distribution, and then use Bayes’ rule to predict the probability of being active for each observation in the testing set. Constraints are used to deal with the otherwise explosive growth of the number of parameters with increasing dimensionality. CMDA is designed to solve several challenges in modeling drug data sets, such as multiple mechanisms, the rare target problem (i.e. imbalanced classes), and the identification of relevant subspaces of descriptors (i.e. variable selection). We focus on the CMDA1 model, in which univariate densities form the building blocks of the mixture components. Due to the unboundedness of the CMDA1 log likelihood function, it is easy for the EM algorithm to converge to degenerate solutions. A special Multi-Step EM algorithm is therefore developed and explored via several experimental comparisons. Using the multi-step EM algorithm, the CMDA1 model is compared to model-based clustering discriminant analysis (MclustDA). The CMDA1 model is either superior to or competitive with the MclustDA model, depending on which model generates the data. The CMDA1 model has better performance than the MclustDA model when the data are high-dimensional and unbalanced, an essential feature of the drug discovery problem! An alternate approach to the problem of degeneracy is penalized estimation. By introducing a group of simple penalty functions, we consider penalized maximum likelihood estimation of the CMDA1 and CMDA2 models. This strategy improves the convergence of the conventional EM algorithm, and helps avoid degenerate solutions. Extending techniques from Chen et al. (2007), we prove that the PMLE’s of the two-dimensional CMDA1 model can be asymptotically consistent.

Uppsatsens syfte är identifiera och analysera de förväntningar som medarbetare och chef har på varandra inom den avreglerade apoteksmarknaden och se hur väl dessa förväntningar överensstämmer med varandra. Detta görs med kvalitativa intervjuer.  Studien bygger på tre intervjuer, med två medarbetare och en chef, på ett mindre apotek i stockholmsområdet. Resultatet visar att förväntningarna, på rollerna medarbetare och chef, överlag överrensstämmer väl och även viljan att nå en ömsesidig förståelse mellan parterna är god. Potentiella problem kan uppstå i de områden där båda parterna visserligen har förståelse för varandras förväntningar, men inte äger möjligeheten att uppfylla dem. Studien har identifierat ett antal områden där det finns potential för att brott mot det psykologiska kontraktet kan uppstå. Exempel på detta är den balansgång där chefen dels skall vara en i gruppen och samtidigt skall kunna fatta svåra beslut. Teorin har utgått från Morrison och Robinsons modell om hur brott mot det psykologiska kontraktet utvecklas. Studien berör de delarna i denna modell som hanterar de faktorer som påverkar uppkomsten av kontraktsbrott. / The purpuse of this studie is to identify and analyze the expectations between employee and manager in the deregulated farmaci market and to see how well these expectaions correlate with eachother. This by using  three qualitative interwives with two employees and one manager at a small farmaci store in the Stockholm region. The result shows that the expectation over all correlates well and so does the will to reach a mutual understanding for the other parts needs. The identified difficulties found in this studie lies in the areas where both part are awere of the other parts expectations, but find it difficulties fullfill them. We have identified  a number of areas in witch the potential for developmet of psychological contract breach can occur. An example is the anticipal role of the manager who shall be a part of, as well as being able to make hard decsions over the group. The theory is based on a modell, founded by Morrison and Robinson, about how psychological violations develops. The studie mainly focus on the first parts of the model which concerns the development of the contract breach.

Live performing arts are one of the most important heritages, defining the cultural identity of a society. Since live performing arts are cultural services, the process of experiencing them is pivotal in forming satisfaction on the customer side. Achieving customer satisfaction is crucial for non-profit cultural institutions operating under the Swedish cultural model. An investigation of the achieved level of customer satisfaction and a comparison to customer expectations is therefore necessary for offering cultural services, which meet customer expectations. Since service quality and hence customer satisfaction can be controlled via the elements of the services marketing mix, the authors focused on investigating the marketing mix of Programbolaget i Jönköping (PJ). The authors wish to investigate to what extent PJ’s current audiences are satisfied with the quality of the services marketing mix provided by PJ, what customers expect from it and what managerial implications can be advised to improve the service. The authors use a mixed methods approach. Quantitative data about customer satisfaction is collected via a questionnaire, which applies a five point Likert scale. Descriptive and non-parametric statistics are used in the data processing. Semi-structured interviews with PJ and customers are conducted to obtain information about the nature of PJ and customer expectations. The conclusions drawn from this study are that the overall level of satisfaction with PJs services marketing mix among PJ’s customers is satisfying, while deviations between the different elements exist, where the elements price, promotion and product score lowest. It is also found that some parameters of the marketing mix have significant levels of importance for customer groups. Customer expectations regarding the services, which scored low, were investigated through interviews. Based on the conducted statistical tests and the expectations of the customers, recommendations regarding PJ’s services are made in order to increase customer satisfaction.

Förväntningar inför föräldrarollen och i vilken utsträckning som dessa infrias har betydelse för anpassningen till föräldraskapet och den nyblivna förälders hälsa. Studiens fokus var oväntade upplevelser i föräldrarollen och hur dessa står i relation till tankar och förväntningar inför rollen. Fyra heterosexuella par som fått sitt första barn för mellan fem månader och två år sedan intervjuades i studien. Det semi-strukturerade intervjuerna transkriberades för att sedan menings-koncentreras och tematiseras. Meningskoncentreringen resulterade i fyra huvudteman: Tid, Prestation, Relationen till barnet och Relationen till andra. Upplevelsen av föräldrarollen kan utifrån studien anses vara kopplad till hur väl deltagarna upplever att de kunde tillgodose barnets behov. Resultatet är trovärdigt utifrån att deltagarna med egna ord kunnat beskriva sin upplevelse av fenomenet, dock har inga generaliseringar gjorts. Ett förslag till fortsatt forskning är att studera likheter och skillnader i förväntningar inför första och andra barnet.

<p>In this dissertation, I discuss two important factors in individuals' decision-making processes: subjective expectation bias and time-inconsistent preferences. In Chapter I, I look at how individuals' own subjective expectations about certain future events are different from what actually happens in the future, even after controlling for individuals' private information. This difference, which is defined as the expectation bias in this paper, is found to have important influence on individuals' choices. Specifically, I look into the relationship between US elderly's subjective longevity expectation biases and their smoking choices. I find that US elderly tend to over-emphasize the importance of their genetic makeup but underestimate the influence of their health-related choices, such as smoking, on their longevity. This finding can partially explain why even though US elderly are found to be more concerned with their health and more forward-looking than we would have concluded using a model which does not allow for subjective expectation bias, we still observe many smokers. The policy simulation further confirms that if certain public policies can be designed to correct individuals' expectation biases about the effects of their genes and health-related choices on their longevity, then the average smoking rate for the age group analyzed in this paper will go down by about 4%.</p><p>In Chapter II, my co-author, Hanming Fang, and I look at one possible explanation to the under-utilization of preventive health care in the United States: procrastination. Procrastination, the phenomenon that individuals postpone certain decisions which incur instantaneous costs but bring long-term benefits, is captured in economics by hyperbolic discount factors and the corresponding time-inconsistent preferences. This chapter extends the semi-parametric identification and estimation method for dynamic discrete choice models using Hotz and Miller's (1993) conditional choice probability approach to the setting where individuals may have hyperbolic discounting time preferences and may be naive about their time inconsistency. We implement the proposed estimation method to US adult women's decisions of undertaking mammography tests to evaluate the importance of present bias and naivety in the under-utilization of mammography, controlling for other potentially important explanatory factors such as age, race, household income, and marital status. Preliminary results show evidence for both present bias and naivety in adult women's decisions of undertaking mammography tests. Using the parameters estimated, we further conduct some policy simulations to quantify the effects of the present bias and naivety on the utilization of preventive health care in the US.</p> / Dissertation

The current study examined two means of reducing uncertainty in visual search: 1) visual relatedness of a prime to the target (a data-driven, bottom-up processing) and 2) expectancy (a top-down process based on the proportion of validly primed trials). The two processes were decoupled using a short and a long inter-stimulus interval (ISI) to examine their time course in visual search. Competing hypotheses were contrasted in order to determine whether relatedness is associated with iconic memory (Neely, 1977) or a longer lasting visual-structural implicit memory (Schacter and Cooper, 1995) and what role participant expectancy plays in visual search performance. Twelve participants engaged in a discrimination task and a visual search task. The obtained results suggest that visual relatedness is a bottom-up process, probably mediated by a short-term iconic store that affects search performance early, but whose effects rapidly decay. They also suggest that expectancy is a top-down process that requires time to build up before it can affect visual search performance, but whose effects are more long lasting than visual relatedness.

With the emergence of computer networks as one of the primary modes of communication, and with their adoption for an increasingly wide range of applications, there is a growing need to understand and characterize the traffic they carry. The rise of large scale network attacks adds urgency to this need. However, the large size, high speed and increasing complexity of these networks imply that tracking and characterizing the traffic they carry is an increasingly difficult problem. Dealing with higher level aggregates, such as flows instead of packets, does not solve the problem because these aggregates tend to be quite numerous and exhibit dynamics of their own. In this thesis, we investigate a novel approach to deal with the immense amounts of data associated with problems in network measurement and monitoring. Building upon the paradigm of Data Streaming, which processes a large stream of data using a small working memory to answer a class of queries, we develop an architecture for Network Data Streaming that can accommodate additional constraints imposed in the context of network monitoring. Using this architecture, we design algorithms for monitoring properties of network traffic that have traditionally been considered too difficult to monitor at high speed network links and routers. Our first algorithm provides the ability to accurately estimate the size of individual flows. A second algorithm to estimate the distribution of flow sizes enables network operators to monitor anomalies in the traffic. Incorporating the use of packet sampling, we can extend the latter algorithm to estimate the flow size distribution of arbitrary subpopulations. Finally, we apply the tools of Network Data Streaming to the operation of packet sampling itself. Using the ability to efficiently estimate flow-statistics such as approximate per-flow size, we design a family of mechanisms where the sampling decision is guided by this knowledge. The individual solutions developed in this thesis share a common architectural theme, supporting the monitoring of highly dynamic populations. Integrating this with the traditional sampling based framework for network monitoring will enable a broad range of applications for accurate and comprehensive monitoring of network traffic.

Social network site (SNS) has been very popular with global Internet users since 2008, the amount of social network users grew very much. Based on some reasons, users enjoyed using social network site. However, whether the current users or new users will continue to use social network site or not is an issue today. To assure social network site¡¦s company could develop and run well in the future, we must realize the factors that can increase and retain the user to use. This study based on expectation confirmation theory (ECT) and through literature review to explore the factors that can influence the user¡¦s satisfaction. Successfully, we integrated perceived utilitarian benefit and perceived hedonic benefit into original ECT model. In this study, an empirical survey methodology with eight hypotheses was applied to verify this model. Covariance-based structural equation model (SEM) was used to analyze data and evaluate the model. The results pointed out that past ECT model with only one aspect can be extended from utilitarian and hedonic dimensions. It indicated that confirmation of utilitarian benefit, perceived utilitarian and hedonic benefits have significant influence on user¡¦s satisfaction. Lastly, user¡¦s satisfaction will positively influence the continuance intention. In sum, this study integrated utilitarian and hedonic dimensions into original ECT model, and proposed a more comprehensive framework to explain the continuance usage of social network site. This study also provided a reference model for future continuance intention research and some suggestions for social network site practitioners.