| 1. |
- Byungura, Jean Claude, 1979-
(författare)
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Improving IT Integration for Higher Education Institutional Performance : Towards a Contextualised IT-Institutional Alignment Model
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The integration of information technology (IT) into service delivery is currently seen as an innovative strategy to support the modernising of universities worldwide. However, in some institutions in developing countries, including Rwanda, IT has failed to add the intended value to university services, despite huge associated investments in IT. Consequently, IT-organisational alignment continues to be a primary concern for university managers. This alignment is viewed in terms of its strategic, socio-cultural, and technological dimensions. For effective IT-institutional alignment, several antecedents (alignment practices) for creating an appropriate fit between IT and organisations have been suggested in the literature. However, several studies exploring IT alignment focused mainly on general business companies, and similar research with an emphasis on higher education institutions is still scarce. Therefore, the aim of this research was twofold: firstly, it attempted to understand the process of IT integration into universities; and secondly, to propose a contextual model for IT-institutional alignment within a higher education context. A design science research methodology (DSRM) was applied in this research, using surveys and case studies as research strategies. Preliminary findings at the exploration phase of this research indicated a strong misalignment between IT and the university services caused by the lack of clearly defined alignment practices. Furthermore, as the research main outcome, an IT-Institutional Alignment Model (ITIAM) was proposed after reaching an understanding of the current state and challenges related to IT integration into teaching, learning, research and university administration. This model includes 44 alignment practices, related to both technical and non-technical dimensions. These alignment practices were clustered under six categories: (1) Communication, (2) Structure/Governance, (3) Technology Scope, (4) Competence/Value Measurement, (5) Skills, and (6) Partnership. Alignment practices related to institutional structure and governance, skills and communication were found to have a strong positive influence on the institutional performance, as compared to those related to competence and value measurement, partnership, and technology scope. Based on the research findings, the proposed ITIAM, which was iteratively tested and evaluated using case study institutions, was found to be a relevant tool for guiding the implementation of IT systems towards the improvement of institutional performance. Hence, this thesis makes a theoretical contribution by applying the concept of IT alignment within a higher education context and by documenting the empirically tested contextual alignment practices as conveyed in the ITIAM Model. Additionally, as a practical implication, the results can serve as a reference for an effective IT integration process in university services and for how to improve performance through effective use of IT in teaching, learning, research and educational management.
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| 2. |
- Abdalmoaty, Mohamed, 1986-
(författare)
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Learning Stochastic Nonlinear Dynamical Systems Using Non-stationary Linear Predictors
- 2017
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The estimation problem of stochastic nonlinear parametric models is recognized to be very challenging due to the intractability of the likelihood function. Recently, several methods have been developed to approximate the maximum likelihood estimator and the optimal mean-square error predictor using Monte Carlo methods. Albeit asymptotically optimal, these methods come with several computational challenges and fundamental limitations.The contributions of this thesis can be divided into two main parts. In the first part, approximate solutions to the maximum likelihood problem are explored. Both analytical and numerical approaches, based on the expectation-maximization algorithm and the quasi-Newton algorithm, are considered. While analytic approximations are difficult to analyze, asymptotic guarantees can be established for methods based on Monte Carlo approximations. Yet, Monte Carlo methods come with their own computational difficulties; sampling in high-dimensional spaces requires an efficient proposal distribution to reduce the number of required samples to a reasonable value.In the second part, relatively simple prediction error method estimators are proposed. They are based on non-stationary one-step ahead predictors which are linear in the observed outputs, but are nonlinear in the (assumed known) input. These predictors rely only on the first two moments of the model and the computation of the likelihood function is not required. Consequently, the resulting estimators are defined via analytically tractable objective functions in several relevant cases. It is shown that, under mild assumptions, the estimators are consistent and asymptotically normal. In cases where the first two moments are analytically intractable due to the complexity of the model, it is possible to resort to vanilla Monte Carlo approximations. Several numerical examples demonstrate a good performance of the suggested estimators in several cases that are usually considered challenging.
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| 3. |
- Engren, Jimmy, 1971-
(författare)
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Railroading and Labor Migration : Class and Ethnicity in Expanding Capitalism in Northern Minnesote, the 1880s to the mid 1920s
- 2007
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In the 1880s, capitalism as a social and economic system integrated new geographic areas of the American continent. The construction of the Duluth & Iron Range Railroad (D&IR), financed by a group of Philadelphia investors led by Charlemagne Tower and later owned by the US Steel was part of this emerging political economy based on the exploitation of human and material resources. Migrant labor was in demand as it came cheap and, generally, floated between various construction-sites on the “frontier” of capitalism. The Swedish immigrants were one part of this group of “floaters” during the late 1800s and made up a significant part of the force that constructed and worked on the D&IR between the 1880s and the 1920s. This book deals with power relations between groups based on class and ethnic differences by analyzing the relationship between the Anglo-American bourgeois establishment and the Swedish and other immigrant workers and their children on the D&IR and in the railroad town of Two Harbors, Minnesota. The Anglo-American bourgeois hegemony in Minnesota, to a large extent, dictated the conditions under which Swedish immigrants and others toiled and were allowed access to American society. I have therefore analyzed the structural subordination and gradual integration of workers and, in particular, immigrant workers, in an emerging class society. The book also deals with the political and the cultural opposition to Anglo-American bourgeois hegemony that emerged in Two Harbors and that constructed a radical public sphere during the 1910s. In this process, new group identities based on class and ethnicity emerged in the working class neighborhoods in the wake of the capitalist expansion and exploitation, and as a result of worker agency. Building on traditions of political insurgency an alliance of immigrant workers, particularly Swedes, Anglo skilled workers and parts of the local petty bourgeoisie rose to a position of political and cultural power in the local community. This coalition was held together by the language of class that became the basis of a local multi-ethnic working class identity laying claim to its own version of Americanism. The period of preparedness leading up to the Great War, the war itself, and its aftermath, produced a reaction from the Anglo American bourgeoisie which resulted in a profound change in the public sphere as a coalition between “meliorist middle class reformers”, represented primarily by the YMCA and local church leaders and the D&IR and its program of welfare capitalism launched a broad program to counter socialism locally, and to forge new social bonds that would cut across class lines and ethnic boundaries. By this process, the ethnic working class in Two Harbors was offered entry into American society by acquiring citizenship and by their inclusion in a broader civic community undifferentiated by class. But this could only be realized by the workers’ adoption of an Anglo-American national identity based on identification with corporate interests, a new local solidarity that cut across class lines and a white racial identity that diminished the significance of ethnic boundaries. By these means the Swedish immigrants, or at least a portion of them, became Americans on terms established by the D&IR and its class allies.
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| 4. |
- Zhang, Tianfang
(författare)
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Probabilistic machine learning methods for automated radiation therapy treatment planning
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In this thesis, different parts of an automated process for radiation therapy treatment planning are investigated from a mathematical and computational perspective. Whereas traditional inverse planning is labor-intensive, often comprising several reiterations between treatment planner and physician before a plan can be approved, much of recent research have been aimed at using a data-driven approach by learning from historically delivered plans. Such an automated planning pipeline is commonly divided into a first part of predicting achievable values of dose-related quantities, and a second part of finding instructions to the treatment machine mimicking as best as possible the predicted values. Challenges associated with this type of prediction–mimicking workflow exist, however—for example, in typical applications, patient data is high-dimensional, scarce and has relatively low signal-to-noise ratio due to inter-planner variations, and significant information may be lost in the transition between prediction and mimicking.We propose to address these challenges through better probabilistic modeling of the predictive inferences of dose-related quantities and increased accuracy of the optimization functions used for dose mimicking. In particular, starting with the disconnect between conventional planning objectives and evaluation metrics, in the first paper, we establish a framework for handling dose statistics as optimization function constituents. Subsequently, in the second and fourth papers, we present ways of predicting spatial dose and dose statistics, respectively, in a probabilistically rigorous fashion, the latter application relying on the similarity-based mixture-of-experts model developed in the third paper. As a nonparametric Bayesian regression model, equipped with a mean-field and stochastic variational inference algorithm, this mixture-of-experts model is suitable for managing complex input–output relationships and skewed or multimodal distributions. The second and fourth papers also introduce dose mimicking objectives able to leverage predictive distributions of spatial dose and dose statistics. In the fifth paper, we further build upon the probabilistic paradigm, merging the fields of multicriteria optimization and automated planning to create a semiautomatic alternative workflow in which certain manual intervention is possible. Lastly, in the sixth paper, we present a means of incorporating robustness against geometric uncertainties into an automated planning pipeline.
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| 5. |
- Westerberg, Marcus, 1990-
(författare)
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Prostate cancer incidence, treatment and mortality : Empirical longitudinal register-based studies and methods for handling missing data
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The diagnostic activity for prostate cancer has increased substantially in Sweden, primarily due to increased use of prostate-specific antigen (PSA) testing in asymptomatic men, and this has led to a large increase in diagnoses. There have also been changes in the diagnostic workup, guidelines, treatment strategies, and more effective treatments have been introduced in different phases of the disease. This thesis aims to increase the understanding of consequences of changes in diagnostic activity and treatment, with a focus on empirical studies, methodological development, and handling of missing data.In paper I, the survival of men with metastatic prostate cancer was investigated across calendar time periods by use of Kaplan-Meier analyses and Cox regression. The median survival from diagnosis increased with six months comparing men diagnosed 1998-2001 with men diagnosed 2010-2015, while median PSA decreased.In paper II, a discrete time multivariate longitudinal model was combined with a proxy for the unobserved level of diagnostic activity to produce prognoses of incidence and mortality. Simulations indicated that a higher diagnostic activity was associated with fewer men diagnosed with metastatic disease and fewer prostate cancer deaths.In paper III, we looked for clinical variables predictive of the survival of men with castration-resistant prostate cancer (CRPC). A new data base was created including longitudinal data on prescriptions of hormonal treatment, PSA, and cause of death. We found that PSA doubling time and PSA at time of CRCP were highly predictive and could be used for treatment decision.In paper IV, we estimated annual incidence of metastatic prostate cancer using different methods for handling missing data in metastatic status (M stage). Missing data in M stage was high and varied over calendar time and risk groups, yet each method indicated a downward trend in incidence. Although men with unknown metastatic status cannot be assumed to have nonmetastatic disease in general, this may be reasonable among those with tumour characteristics that indicate a low risk of metastases.In paper V, the estimation of multivariate longitudinal models was considered in a context where some events are observed on a coarser level (e.g. grouped) at some time points, causing gaps in the data. The likelihood function, score and observed information were derived under an independent coarsening mechanism. A simulation study was conducted comparing properties of several estimators including direct maximum likelihood and Monte Carlo Expectation Maximisation.
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| 6. |
- Westerborn, Johan
(författare)
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On particle-based online smoothing and parameter inference in general hidden Markov models
- 2015
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis consists of two papers studying online inference in general hidden Markov models using sequential Monte Carlo methods.The first paper present an novel algorithm, the particle-based, rapid incremental smoother (PaRIS), aimed at efficiently perform online approximation of smoothed expectations of additive state functionals in general hidden Markov models. The algorithm has, under weak assumptions, linear computational complexity and very limited memory requirements. The algorithm is also furnished with a number of convergence results, including a central limit theorem.The second paper focuses on the problem of online estimation of parameters in a general hidden Markov model. The algorithm is based on a forward implementation of the classical expectation-maximization algorithm. The algorithm uses the PaRIS algorithm to achieve an efficient algorithm.
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