Background and past studies: The effects of uncertain times include fluctuating markets anddemands rapid and agile means to cope with said fluctuations and occurring changes. Scenarioplanning is considered a great tool for coping with uncertainties and preparing means for futureevents. Purpose: The purpose of this study is to study how businesses implement and use scenarioplanning as a tool to minimize uncertainty in a volatile environment. Research question: How is Scenario planning utilized to minimize uncertainties in a volatileenvironment in large organizations within Sweden? Method: The study implements a qualitative approach where semi-structured interviews wereconducted with members within the top management of said seven companies. Complementarydocuments shared by the respondents have been utilized. Results and Conclusions: The results indicate that the organizations mainly conduct threescenarios ranked by either impact or probability of occurrence. The main purpose of scenarioplanning is readiness for action in case of sudden deviations with less focus on prediction offuture deviations. The process of conducting scenario planning is interactive and multi-leveledwithin the organizations with mainly a bottom-up approach.

Machine learning plays an increasingly important role in modern systems. The ability to learn from data enhances or enables many applications. Recently, quick in-stream processing of possibly a huge or even infinite amount of data gains more attention. This thesis deals with such on-line learning systems for regression that learn with every example incrementally and are reliable even in presence of uncertainties. A new learning approach, called IRMA, is introduced which directly incorporates knowledge about the model structure into its parameter update. This way it is aggressive to incorporate a new example locally as much as possible and at the same time passive in the sense that the global output is changed as little as possible. It can be applied to any model structure that is linear in its parameters and is proven to minimize the worst case prediction error in each step. Hence, IRMA is reliable in every situation and the investigations show that in every case a bad performance is prevented by inherently averting overfitting even for complex model structures and in high dimensions. An extension of such on-line learning systems monitors the learning process, regarding conflict and ignorance, and estimates the trustworthiness of the learned hypothesis by the means of trust management. This provides insight into the learning system at every step and the designer can adjust its setup if necessary. Additionally, the trust estimation allows to assign a trustworthiness to each individual prediction the learning system makes. This way the overall system can react to uncertain predictions at a higher level and increase its safety, e.g. by reverting to a fallback. Furthermore, the uncertainties are explicitly incorporated into the learning process. The uncertainty of the hypothesis is reflected by allowing less change for more certain regions of the learned system. This way, good learned knowledge is protected and a higher robustness to disturbances is achieved. The uncertainty of each example used for learning is reflected by adapting less to uncertain examples. Thereby, the learning system gets more robust to training examples that are known to be uncertain. All approaches are formally analyzed and their characteristic properties are demonstrated in empirical investigations. In addition, a real world application to forecasting electricity loads shows the benefits of the approaches.

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Covariant density functional theory (CDFT) is a modern theoretical tool for the description of nuclear structure physics. Here different physical properties of the ground and excited states in atomic nuclei have been investigated within the CDFT framework employing three major classes of the state-of-the-art covariant energy density functionals. The global performance of CEDFs for even-even nuclei are investigated and the systematic theoretical uncertainties are estimated within the set of four CEDFs in known regions of the nuclear chart and their propagation towards the neutron drip line. Large-scale axial relativistic Hartree-Bogoliubov (RHB) calculations are performed for even-even nuclei to calculate different ground state observabvles. The predictions for the two-neutron drip line are also compared in a systematic way with the non-relativistic results. CDFT has been applied for systematic study of extremely deformed, rotating N ∼ Z nuclei of the A ∼ 40 mass region. At spin zero such structures are located at high energies which prevents their experimental observation. The rotation acts as a tool to bring these exotic shapes down to the yrast line so that their observation could become possible with a future generation detectors such as GRETA or AGATA. The major physical observables of such structures, the underlying single-particle structure and the spins at which they become yrast or near yrast are defined. The search for the fingerprints of clusterization and molecular structures is performed and the configurations with such features are discussed. CDFT has been applied to study fission barriers of superheavy nuclei and related systematic theoretical uncertainties in the predictions of inner fission barrier heights in superheavy elements. Systematic uncertainties are substantial in superheavy elements and their behavior as a function of proton and neutron numbers contains a large random component. The benchmarking of the functionals to the experimental data on fission barriers in the actinides allows reduction of the systematic theoretical uncertainties for the inner fission barriers of unknown superheavy elements. However, even then they on average increase when moving away from the region where benchmarking has been performed.

Background: Grocery stores in the retail industry, which are forms of Small and medium-sized enterprises, are particularly vulnerable to changes in the economic environment. This thesis examines how grocery store managers handle their financial management decision-making process in response to uncertainty. 2022 has been a year of unprecedented uncertainty with unforeseen events that affected the Swedish economy. Therefore, these uncertain events in 2022 present an exceptional opportunity to delve into this subject of study.  Purpose: The purpose of this thesis is to analyze how managers of grocery stores handle the financial management decision-making process regarding financing, investing, and profit distribution when faced with uncertainty.  Method: This thesis builds upon qualitative data and aims to formulate a theory concerning the decision-making process under uncertainty within grocery stores by employing an inductive reasoning approach. The study utilizes primary descriptive data gathered from interviews and information from academic articles, books, and authorized websites. To ensure objectivity in the analysis, the role of the researchers was carefully considered. Semi-structured interviews served as the primary method for data collection, and a theoretical framework was established at the outset of the research to guide the analysis and structural organization of the data. During the data analysis phase, a theory-driven thematic analysis methodology was employed.  Conclusion: This thesis concludes that managers of grocery stores handled their financial management decision-making process regarding financing, investing, and profit distribution when faced with uncertainty in different ways. The thesis demonstrated caution in financing by cutting costs and amortizing loans. Additionally, managers employed strategies of reprioritizing and choosing long-term investments to ensure financial stability. At the same time, profit distribution was limited due to increased expenses, leading managers to redirect profits for the survival of the grocery store. Overall, this research highlights the dynamic nature of financial management’s decision-making process and the need for adaptive strategies in the face of uncertainty.

The external environment, currently considered as VUCA environment, in which orgnaizations and individuals operate can be characterized by numerous uncertainties. As a result of these uncertainties, one significant aspect that is affected is the individual KSAOs as part of the human capital concept. A thorough understanding of how uncertainties affect KSAOs allows individuals and organizations to adapt, be resilient, and respond effectively to the changing environment. One research field that encompasses the external environment and its uncertainties as well as individual KSAOs is strategic human resource management (SHRM). However, the relationship between these key concepts has not yet been addressed. Also, much of SHRM research has been devoted to the company's perspective, largely ignoring the employees' point of view. Thus, this research aims to fill a gap in prior research by exploring the relationship between the external environment with its uncertainties and individual KSAOs from the employee's perspective. To further implement this knowledge in practice, two scenarios are created to demonstrate the factors companies should focus on in order to increase their human capital's resilience. To identify the relationship between external uncertainties and individual KSAOs, a conceptual framework was developed. The framework outlines the foundation for a qualitative case study with a Swedish consultancy company as a single case. A comprehensive data foundation was constructed through data triangulation, which involves combining both primary and secondary sources of data. In this study, organizational documents provided by the case company were used as secondary data, while primary data was gathered through semi-structured interviews with six employees of the case company. The findings indicate differences between prior literature and the collected empirical data. For example, employees' views on prioritizing different KSAOs (Knowledge, skills, abilities, and other characteristics) showed that soft skills were seen as far more important than hard skills when coping with uncertainties. This challenges the prior literature on human capital that has mostly focused on hard skills and the importance of their development through education and training. By using COVID-19 as a recent and accurate example of uncertainty, the data revealed changes in ways of working and work-life balance. It also revealed that environmental and technological uncertainties are currently considered the most effective. While they were seen to have unpredictable, and possibly even negative long-term effects, they were also considered as possibilities for the future. With subjective research philosophy, we could find contradictory opinions within the sample which further extended prior research by highlighting the need for considering individuality rather than the company perspective when making decisions that relate to the external environment , its uncertainties, and the individual KSAOs.

With the growing use and complexity of proton therapy, the safety and accuracy of the machines becomes increasing important. This, to be able to deliver the prescribed dose to the target while minimizing the dose to healthy tissue. In this project, machine quality assurance data are analyzed to quantify the existing positional machine uncertainties in the form of deviations from expected value and their effect on the dose accuracy in order to improve precision. The method consisted of two main parts. In the first part, two systems to monitor the measured deviations variations from the machine quality assurance tests were implemented. In the second part, two ways to measure the impact of the positional machine uncertainties were developed. The monitoring systems showed that the uncertainties had shrunken over time or were stable, and that the tolerance limits currently used for the machine quality assurance can be lowered. The measured impact of the positional machine uncertainties showed that a margin of 0.61 mm for treatment room 1 and a margin of 1.02 mm for treatment room 2 was required to compensated for the machine uncertainties. When the uncertainties we reincorporated into a clinical approved robust optimized plan, the result showed no significant change in dose to the different treatment volumes. The result gives the Scandion clinic insight and tools to minimize the impact of machine uncertainties and to be able to improve the precision of future treatments.

Site characterization and the subsequent determination of soil strengthparameters constitute a significant part of slope stability assessment.Geotechnical engineering is always governed by great uncertainties sincethe soil properties are challenging to estimate for a large soil volume. Nostandardized method exists for evaluation of soil parameters, since eachcountry uses its own methods for evaluation and interpretation of soils.Uncertainties are common in geotechnical engineering and thereforequalitative and quantitative risk management is required for most projects.The evaluation of the shear strength is essential in slope stability, since ithas a great influence on the stability. This report investigates threedifferent methods regarding the estimation of the characteristic value ofthe undrained shear strength in clay for heterogeneous soils. The followingmethods are used: de 7 with the 5 % fractile, the Swedish applicationdocuments with 𝜂-factor regarding uncertainties and the subjectiveassessment of geotechnical engineers by a questionnaire. In the case studyfor this thesis work, three quayside slopes at Slussen in central Stockholmcontaining highly heterogeneous organic clay located beneath gravel filland esker material were analysed to estimate the stability of the slopes. Theresults show that the 𝜂-factor returned the lowest values, the questionnaireresulted in the second highest and 5 % fractile basted on the samplingdistribution on the mean gave the highest values. No correlation could befound connecting the subjective estimation of the shear strength and otherinfluencing parameter. A method with low subjective judgement, that takethe spatial variability and some degree of interpretation uncertainties witha factor would be to prefer. / Utvärdering av jordar med bedömning av dess egenskaper ochklassificering är en viktig del i släntstabilitetsutredningar. Utmaningenligger i att geoteknik styrs av stora osäkerheter eftersom markensegenskaper är omständliga att uppskatta för en hel jordvolym. Problemetär att det inte existerar någon standardiserad metod för bedömningen avjordparametrar då varje land har sina egna metoder för utvärderingen.Stora osäkerheter ett vanligt problem inom geoteknik och därför krävsbåda kvalitativa och kvantitativa riskhanteringar inom de flesta projekt.Utvärderingen av skjuvhållfasthet är en viktig del i släntstabilitetsutredningareftersom denna parameter har stort inflytande på stabiliteten,därmed även säkerheten. I denna rapport har tre olika metoder undersöktsmed avseende på bedömningen av det karakteristiska värdet förskjuvhållfasthet i lera för heterogena jordar. Dessa är: Eurokod 7 med 5 %fraktil, den svenska metoden med 𝜂-faktor för att beakta osäkerheter ochslutligen subjektiva bedömningen från geotekniker med hjälp av ettfrågeformulär. I denna fallstudie har tre slänter belägna vid kajer i Slusseni Stockholm analyserats. Jordprofilen innehåller heterogen organisk lerasom täcks av en grusig fyllning och överlagrar av årsmaterial. Resultat visaratt metoden med hänsyn till både osäkerheter med 𝜂-faktor samtsubjektiva bedömning gav lägst skjuvhållfasthet, frågeformuläretresulterade i det näst hösta värdena och metoden som gav högts resultatvar 5 % fraktilen baserad på medelvärden. Ingen korrelation kunde hittasangående den subjektiva bedömningen av odränerad skjuvhållfasthet ochinfluensen från andra faktorer. En metod med subjektiv bedömning, somtar rumslig variation samt inkluderar osäkerheter i en faktor skulle vara attföredra eftersom detta skulle medföra ett mer tillförlitligt resultat

The objective of this research is to increase the robustness of discrete-event simulation (DES) when input uncertainties associated models and parameters are present. Input uncertainties in simulation have different sources, including lack of data, conflicting information and beliefs, lack of introspection, measurement errors, and lack of information about dependency. A reliable solution is obtained from a simulation mechanism that accounts for these uncertainty components in simulation. An interval-based simulation (IBS) mechanism based on imprecise probabilities is proposed, where the statistical distribution parameters in simulation are intervals instead of precise real numbers. This approach incorporates variability and uncertainty in systems. In this research, a standard procedure to estimate interval parameters of probability distributions is developed based on the measurement of simulation robustness. New mechanisms based on the inverse transform to generate interval random variates are proposed. A generic approach to specify the required replication length to achieve a desired level of robustness is derived. Furthermore, three simulation clock advancement approaches in the interval-based simulation are investigated. A library of Java-based IBS toolkits that simulates queueing systems is developed to demonstrate the new proposed reliable simulation. New interval statistics for interval data analysis are proposed to support decision making. To assess the performance of the IBS, we developed an interval-based metamodel for automated material handling systems, which generates interval performance measures that are more reliable and computationally more efficient than traditional DES simulation results.

This work presents the experimental study of the flow generated in the wakes of three three-dimensional bumps in the Virginia Polytechnic Institute and State University Boundary Layer Wind Tunnel. The three bumps examined are named bump 1, small bump 3, and large bump 3, and are the same test cases studied by Byun et al. (2004) and Ma and Simpson (2004) with a LDV system and a quad-wire hot-wire probe, respectively. Various experimental methods are used in this work: For measuring the mean velocity component in the planes examined, a seven-hole pressure probe is used with the data reduction algorithm developed by Johansen et al. (2001). A sixteen-hole pressure rake is used for boundary layer data on the sidewalls and ceiling of the test section and a Pitot-static probe is used to obtain mean velocity magnitude in the centerline of the test section. Specific techniques are developed to minimize the uncertainties due to the apparatus used, and an uncertainty analysis is used to confirm the efficiency of these techniques. Measurements in the wake of bump 1 reveal a strong streamwise vorticity creating large amounts of high moment fluid entrained close to the wall. In the wake of small bump 3, the amount of high momentum fluid entrained close to the wall is small as well as the streamwise vorticity. The flow in the wake of large bump 3 incorporate the characteristics of the two previous bumps by having a relatively large entrainment of high momentum fluid close to the wall and a low generation of streamwise vorticity. In the wakes of the three bumps, a pair of counter rotating vortices is created. The influence of large bump 3 on the incoming flow-field is found to be significant and induces an increase of the boundary layer thickness. By comparing LDV data and quad-wire hot-wire data with seven-hole probe data in the wakes of the bumps at the same locations, it is shown that uncertainties defined for a quasi-steady, non-turbulent flow-field without velocity gradient are bad indicators of the magnitude of the uncertainties in a more complex flow-field. A theoretical framework is discussed to understand the effects of the velocity gradient and of turbulence on the pressures measured by the seven-hole probe. In this fashion, a model is proposed and validated to explain these effects. It is observed that the main contribution to the uncertainties in seven-hole probe measurements due to the velocity gradient and to the turbulence comes from the velocity gradient. To correct for the effects of the velocity gradient on seven-hole probe measurements in an unknown flow-field, a technique is proposed. Using an estimation of the velocity gradient calculated from the seven-hole probe, the proposed model could be used to re-evaluate non-dimensional pressure coefficients used in the data reduction algorithm therefore correcting for the effects of the velocity gradient on seven-hole probe measurements. / Master of Science