As robustness of social science is getting more attention, analytical choices have been found to be more important than previously thought. This thesis investigates robustness of intergenerational educational mobility estimates using multiverse analysis, a technique for incorporating many analyses into one framework while varying analytical choices such as variable coding, mobility measures, and exclusion criteria. Using ESS data from 16 European countries over nine survey rounds, results show substantial variation in point estimates, which in turn creates high variation in rankings of European countries. The conclusion is that analytical choices play an important role in calculating educational mobility estimates, and that ranking of European countries according to intergenerational mobility estimates is sensitive to varying these choices. Future mobility research should take this into account.

In countries with rough weather conditions, frequent delays cause railway companies to waste time and money. Many of these delays are related to the train detection systems, as the old DC track circuits are still used in some countries, including Sweden, our case study. Since the most important factor in the railway system is safety, in some cases, the train detection system gets incorrect information and detects a non-existent train. The train slows down to avoid a problem in the track (with other trains or other faults), causing prolonged delays with cascading effects. The analysis in this licentiate contributes to the detection and reduction of TC failures; this, in turn, will save money for the railway community.A classification of the most probable causes of failures related to the train detection system was derived from the Swedish failures report database 0FELIA. After classifying failures, we focussed on the three most common worst case scenarios: low resistance between the rails, external interference such as a lightning strike, and iron-powder-bridges in the insulated joint.Electromagnetic interferences (EMI) are a problem for the railway system in general. One source of electromagnetic (EM) transients is the return current harmonic produced by the engine of the rolling stock itself. In the first stage of this licentiate, we implemented a Matlab model of the power supply system of the Swedish railway infrastructure, using the characteristics and previous measures of a real source. A model of a train as an active load validated by the manufacturer was integrated as a subsystem in different positions of the infrastructure. This method was used to study the behaviour of the low frequency system from an electrical point of view but it could also be used as input for an electromagnetic model using high frequencies. The model was validated through measurements taken in northern Sweden.In addition, a 3D model of the whole railway system was proposed. The simulation software was CST STUDIO SUITE® (Computer Simulation Technology Studio Suite), supported by real measurements on site and the lab to tune and validate the model. The results of the simulation show that the model fits with reality and is reliable for the study of track circuit sections.Some measurements followed the current standards, but we also analysed points not covered by them, allowing us to update the current standards

Tough competition and volatile global markets have pushed the manufacturing industries to develop solutions more customer-centric with optimal utilization of resources. One of the key reasons behind developing a customer-centric solution is the increased customer value that imparts a competitive edge to the manufacturing industries, eventually leading them to sustain their businesses. Over the years, this has led the transition of manufacturing industries towards offering “functions” instead of pure products. Academic literature often describes this change as the transition towards offering a Product-Service System (PSS), where the functions are typically delivered as a mix of products and services. Developing PSS is a highly challenging task as value entails a multi-dimensional viewpoint based on different stakeholders and many novel technologies integrated along uncertain lifecycles. An optimal PSS for a specific occasion becomes situational as this occasion is bound to change due to underlying future operational uncertainties. This view accentuates the need for inculcating mechanisms in the PSS to sustain value under operational uncertainties, thus attaining value robustness. Literature in systems engineering elaborates on changeability as one of the cores for developing a value-robust system. A changeable system is a system that can change internally as a response to the changes externally to maintain the value expectation over time. With this frame of reference, it is argued that the notion of changeability can be a good supplement for developing a value-robust PSS. From a design perspective, changeability needs to be quantified to strike a balance between the total change-related cost and the benefits. In this light, this thesis is directed toward the quantification of changeability for supporting early design decisions concerning value-robust PSS. To achieve this goal, this thesis first highlights the challenges concerning changeability quantification for a value-robust PSS design. Building on these challenges, it delves into established techniques of design optimization, dynamic programming, and discrete-event simulation to propose a framework that can exemplify the relationship between system configuration, system control, and contextual variables to gain insights about a suitable combination of configuration and control of the system to maintain its value in uncertain operational scenarios. To enhance the proposed framework with operational data, an outline of the state-of-the-art in the collection and utilization of operational data to support design decision-making is presented. Finally, the thesis concludes by highlighting the strength and weaknesses of the proposed framework along with some industrial implications. Broadly, two challenges are emphasized in the proposed framework, computational complexity and lack of contextual knowledge, and addressing them has been left for future studies.

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This thesis presents a comparison of M-estimator, BLAVE, and RANSAC method in point clouds registration. The comparison is performed empirically by applying all the estimators on a simulated data added with noise plus gross errors, ToF data and RGB-D data. The RANSAC method is the fastest and most robust estimator from the comparison. The 2D feature extracting methods Harris corner detector, SIFT and SURF and 3D extracting method ISS are compared in the real-world scene data as well. SIFT algorithm is proven to have extracted the most feature points with accurate features among all the extracting methods in different data. In the end, ICP algorithm is used to refine the registration result based on the estimation of initial transform. / Denna avhandling presenterar en jämförelse av tre metoder för registrering av punktmoln: M-estimator, BLAVE och RANSAC. Jämförelsen utfördes empiriskt genom att använda alla metoder på simulerad data med brus och grova fel samt på ToF - och RGB-D -data. Tester visade att RANSAC-metoden är den snabbaste och mest robusta metoden.  Vi har även jämfört tre metoder för extrahering av features från 2D-bilder: Harris hörndetektor, SIFT och SURF och en 3D extraheringsmetod ISS. Denna jämförelse utfördes md hjälp av verkliga data. SIFT -algoritmen har visat sig fungera bäst bland alla extraheringsmetoder: den har extraherat flesta features med högst precision. I slutändan användes ICP-algoritmen för att förfina registreringsresultatet baserat på uppskattningen av initial transformering.

Today, 3D modelling and structural analysis of buildings are performed in various software. Collaboration between various software is common today but breaks the flow in the construction design phase. To achieve an uninterrupted flow in the construction design phase, a constellation of modelling and structural analysis is needed in a single software. To enable a constellation, there are today many developed digital methods for this.Parametric design is a digital method that is mostly used to handle complex shapes. In recent years, the parametric design has evolved even more and the algorithmic thinking in parametric design provides opportunities for performing structural analyses. The development includes various plug-in programs that have structural analysis capabilities. However, this degree project emphasizes that this can be achieved without a plug-in program that has structural analysis capabilities. With only one visualization program and a plug-in that handles visual programming, the ability to produce what is to be visualized with a script arises.The structural analysis in this thesis includes robustness analysis that is important in the context of progressive collapse, and only the alternative load path method is considered. Progressive collapse is an important analysis for buildings that arise due to known or unknown accident loads. To increase the redundancy of the bearing structure, the alternative load path method can be used, which is a branch under unknown accident loads.Robustness analysis is a time-consuming process and automation can make this more efficient. With parameter-driven modelling and robustness analysis, the constructor can indicate at an early stage possible structure failure before the building is completed. Early action also leads to a reduction in waste of material resources.The alternative load path method provides the possibility to analyze whether the building receives alternate load path in the event of loss of load-bearing elements. This research report analyses column loss. Automated visualization of alternate load path enables to be able to analyze the load redistribution after the loss of column.Today some buildings are at risk against the progressive collapse, people's lives and health are therefore at risk when all or part of the building collapses. That is why efficiency is needed. The research report showed that the script automated the modelling and robustness analysis of buildings. Two different loss scenarios were analyzed and the authors found different updated loading areas and load redistribution.

Yes / A study has been carried out to investigate controlled release performance of caplet shaped injection moulded (IM) amorphous solid dispersion (ASD) tablets based on the model drug AZD0837 and polyethylene oxide (PEO). The physical/chemical storage stability and release robustness of the IM tablets were characterized and compared to that of conventional extended release (ER) hydrophilic matrix tablets of the same raw materials and compositions manufactured via direct compression (DC). To gain an improved understanding of the release mechanisms, the dissolution of both the polymer and the drug were studied. Under conditions where the amount of dissolution media was limited, the controlled release ASD IM tablets demonstrated complete and synchronized release of both PEO and AZD0837 whereas the release of AZD0837 was found to be slower and incomplete from conventional direct compressed ER hydrophilic matrix tablets. Results clearly indicated that AZD0837 remained amorphous throughout the dissolution process and was maintained in a supersaturated state and hence kept stable with the aid of the polymeric carrier when released in a synchronized manner. In addition, it was found that the IM tablets were robust to variation in hydrodynamics of the environment and PEO molecular weight. / The research was funded by AstraZeneca, Sweden.

Heterogeneous data with different characteristics are ubiquitous in the modern digital world. For example, the observations collected from a process may change on its mean or variance. In numerous applications, data are often of mixed types including both discrete and continuous variables. Heterogeneity also commonly arises in data when underlying models vary across different segments. Besides, the underlying pattern of data may change in different dimensions, such as in time and space. The diversity of heterogeneous data structures makes statistical modeling and analysis challenging. Detection of change-points in heterogeneous data has attracted great attention from a variety of application areas, such as quality control in manufacturing, protest event detection in social science, purchase likelihood prediction in business analytics, and organ state change in the biomedical engineering. However, due to the extraordinary diversity of the heterogeneous data structures and complexity of the underlying dynamic patterns, the change-detection and analysis of such data is quite challenging. This dissertation aims to develop novel statistical modeling methodologies to analyze four types of heterogeneous data and to find change-points efficiently. The proposed approaches have been applied to solve real-world problems and can be potentially applied to a broad range of areas. / Ph. D. / Heterogeneous data with different characteristics are ubiquitous in the modern digital world. Detection of change-points in heterogeneous data has attracted great attention from a variety of application areas, such as quality control in manufacturing, protest event detection in social science, purchase likelihood prediction in business analytics, and organ state change in the biomedical engineering. However, due to the extraordinary diversity of the heterogeneous data structures and complexity of the underlying dynamic patterns, the change-detection and analysis of such data is quite challenging. This dissertation focuses on modeling and analysis of data with heterogeneous structures. Particularly, four types of heterogeneous data are analyzed and different techniques are proposed in order to nd change-points efficiently. The proposed approaches have been applied to solve real-world problems and can be potentially applied to a broad range of areas.

This master thesis investigates the integration of sustainability and resilience principles into the design of a distribution supply chain network for copper mining, using a case study at Copperstone Resources AB. The aim is to demonstrate how these considerations can be effectively implemented in the copper mining industry, an area where such integration is largely unexplored. Using a case study approach, an optimization model and sensitivity analysis were developed, resulting in four alternative supply chain network designs: two proposed by the company and two generated by the optimization model. Through multi-criteria decision analysis methods, including the analytical hierarchy process and weighted sum method, the alternatives were assessed with input from Copperstone Resources AB. The findings indicate potential cost savings ranging from 200 to 550 MSEK across the four alternatives over 10 years. The preferred option identified by the analysis involves intermodal transport utilizing electric trucks, railways, and ships to transport copper concentrate to Skelleftehamn and iron concentrate to Amsterdam, Rotterdam, and Antwerp. This alternative is deemed the most efficient, environmentally friendly, and resilient, making it the recommended distribution supply chain network design. It is projected to emit between 25 872 and 81 545 tonnes of CO2 over 10 years, with an estimated investment and operating expense of approximately 2.57 BSEK over the same period. This research contributes to the scientific understanding of integrating sustainability and resilience in supply chain network design, particularly in the copper mining industry. The thesis provides insights for practitioners seeking to optimize their distribution networks while considering environmental and resilience factors.