NTNU Open (Norwegian University of Science and Technology)
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The Role of Extended Nuclear Deterrence in the Russo-Ukrainian War.
Full text linkDenne masteroppgaven undersøker hvordan USAs bruk av utvidet atomavskrekking, strategisk tvetydighet og konvensjonell militærmakt har påvirket Russlands beslutningstaking om atomvåpen og konfliktdynamikk. Studien anvender en Small-N forskningsdesign med Structured, Focused Comparison (George & Bennett, 2005), hvor fem konflikter analyseres: Koreakrigen, Taiwanstredet-krisene, Yom Kippur-krigen, Vietnamkrigen og Russisk-Ukrainske krigen. Metoden sikrer systematisk sammenligning av hvordan USAs strategi har påvirket eskalering. Funnene viser at i fire av fem tilfeller har kombinasjonen av atomavskrekking, strategisk tvetydighet og militærmakt gjort det mulig for USA å støtte allierte uten å utløse bredere krig, mens Vietnamkrigen skiller seg ut ved fraværet av atomavskrekking og tvetydighet. Studien konkluderer med at en lagdelt avskrekkingsstrategi forklarer hvorfor USA har kunnet balansere støtte til allierte uten eskalering, noe som har viktige implikasjoner for atomavskrekkingens rolle i moderne konflikter, særlig i lys av den pågående Russisk-Ukrainske krigen.This thesis examines how the United States’ use of extended nuclear deterrence, strategic ambiguity, and conventional military strength has influenced Russia’s nuclear decision-making and conflict dynamics. Using a Small-N research design with Structured, Focused Comparison (George & Bennett, 2005), it analyzes five conflicts: the Korean War, Taiwan Strait Crises, Yom Kippur War, Vietnam War, and Russo-Ukrainian War. This method ensures systematic comparison of U.S. strategy and escalation dynamics. The findings indicate that in four of five cases, the combination of nuclear deterrence, ambiguity, and military strength enabled the U.S. to support allies without provoking wider war, while the Vietnam War lacked these deterrent elements. The study concludes that a layered deterrence strategy explains the U.S.’s ability to balance ally support and escalation risk, with key implications for nuclear deterrence in modern conflicts, particularly in the ongoing Russo-Ukrainian War
Pickering Emulsions Stabilized by Hybrid TiO2‑pNIPAm Composites for the Photocatalytic Degradation of 4‑Propylbenzoic Acid
Full text linkPickering emulsions (PEs) have demonstrated significant potential in various fields, including catalysis, biomedical applications, and food science, with notable advancements in wastewater treatment through photocatalysis. This study explores the development and application of TiO2-poly(N-isopropylacrylamide) (pNIPAm) composite gels as a novel framework for photocatalytic wastewater remediation. The research focuses on overcoming challenges associated with conventional nanoparticle-based photocatalytic systems, such as agglomeration and inefficient recovery of particles. By integrating TiO2 nanoparticles into pNIPAm gels, we aimed to achieve high emulsion stability and photocatalytic efficiency while suppressing the effects of pNIPAm’s volume phase transition temperature (VPTT) to facilitate effective emulsion recovery. The study involves the synthesis of TiO2-pNIPAm composites with varying monomer-to-particle ratios, characterizing their VPTT behavior, morphology, and thermal stability. These composites were then evaluated for their emulsification properties, phase transition behavior, and photocatalytic activity in degrading 4-propylbenzoic acid, a model pollutant. The results highlight the effectiveness of the TiO2-pNIPAm Pickering emulsions in wastewater treatment, offering improved stability and reusability compared to traditional dispersion-based systems. This work provides new insights into the design of composite materials for enhanced photocatalytic applications and demonstrates the potential of Pickering emulsions in sustainable environmental remediation.publishedVersio
Implementation and Validation of Virtual Clones of Coloured Building-Integrated Photovoltaic Facades
Full text linkA newly introduced colour correction transmittance factor (CCTF) and an innovative probabilistic-to-deterministic approach were applied to create virtual clones of coloured building-integrated photovoltaic (BIPV) systems. These virtual clones calculate the current at maximum power point () by adjusting the plane-of-array irradiance according to the transmittance properties of the coloured layer, which are governed by the CCTF. An ensemble of 200 randomly combined physical photovoltaic model chains was implemented (probabilistic approach), and the median of the diverse outputs was calculated to provide a deterministic estimations. The virtual clones were validated against observations from two BIPV facades located in Zwolle (The Netherlands), where black (CCTF=1.00), light-grey (CCTF=0.89), and terracotta (CCTF=0.70) photovoltaic modules were mounted. Hourly data were collected from June 2023 to May 2024. The performance of different regression techniques was evaluated for the calibration of the virtual clones. The non-calibrated virtual clones showed similar accuracy throughout the year, with the determination coefficient () that ranged from 0.594 (light-grey) to 0.613 (terracotta). Although the models generally overestimated , the results demonstrated that such a tendency was accentuated during overcast days. Consistent biases were also observed for solar elevations greater than 30°. Finally, the façade orientation influenced the simulation performance. Indeed, the non-calibrated models overestimated by circa 150 the annual from the south-facing façade, and by more than 700 the annual from the façade oriented south-west, regardless of the colour. However, calibration, particularly with Random Forest and Gradient Boosting, consistently reduced cumulative error across all scenarios.publishedVersio
Improved decision support for bridge safety assessment and maintenance by probabilistic methods
Full text linkWhen assessing the safety of existing bridges, uncertainties, information, and consequences are different compared to the design stage. Higher-level verification formats, such as probabilistic and risk-based methods, may help overcome these challenges but are rarely used in practice. With this thesis, the implementation of these methods into practice is accelerated by i) demonstration of information integration by probabilistic methods on realistic assessment situations, ii) application of Bayesian decision-making to bridge maintenance to find optimal intervention strategies and iii) improvement of the design value method by tailored alpha values. This PhD project thereby improves the basis for safely keeping existing bridges in service to a greater extent, instead of building new ones
Modeling and analysis of offshore wind farm wake effects on wind turbine components and power production
Full text linkWith the increasing size of wind farms and wind turbines, and their deployment further offshore, addressing power losses and operational challenges becomes critical for minimizing the levelized cost of energy (LCOE). This is particularly important as reduced spacing between wind farms leads to increased wake losses due to their aerodynamic interactions. These cluster wakes have a substantial impact on energy yield, requiring accurate calibration of analytical models to improve power production estimates. Simultaneously, the remote locations of offshore wind farms lead to longer downtime when components fail, necessitating remaining useful life (RUL) models that predict component failure and further reduce the LCOE.
SCADA (Supervisory Control and Data Acquisition) measurements, which capture real-time turbine performance metrics such as wind speed, power output, and wind direction, along with condition monitoring systems (CMS), provide important information to address these challenges. Accurate analysis of SCADA measurements from large offshore wind farms yields valuable insights into wake effects within and across wind farms. In addition, SCADA measurements combined with CMS data enable the development of physicsbased RUL models that can function with SCADA data as a reference.
This thesis uses SCADA and CMS data to develop, calibrate, and validate models across wind farm, turbine, drivetrain, and component scales. A calibration framework for analytical wake models that incorporates SCADA data in time-series form is developed, addressing the limitations of traditional binned methods. The framework demonstrates scalability by enabling calibration for both individual and multiple wind farms, facilitating analysis of large-scale cluster wakes, and achieving a strong match between model predictions and observed data. The convergence of wake losses across different models is observed after calibration, with varying performance identified through quantitative metrics. Using a calibrated analytical wake model and a leading-edge erosion (LEE) model, it is highlighted that wake effects lead to variability in the LEE of wind turbine blades, revealing differences of up to 35% within the studied concession zone. Axial induction and wake steering control strategies are analyzed, showing that while wake steering achieves higher power gains, axial induction control reduces drivetrain loads more effectively. SCADA and CMS data are evaluated to assess the feasibility of identifying drivetrain dynamic properties, leading to the development of a drivetrain model for RUL assessment. The model focuses on high-speed shaft bearings and leverages SCADA data, demonstrating strong alignment with high-fidelity simulations and strain gauge measurements
Dynamic linkages and spillover effects of biodiversity risk in socially responsible investment and commodity markets
Full text linkThis study employs a novel biodiversity risk measure, developed through textual analysis, to examine how biodiversity risk affects socially responsible investment (SRI) and commodity markets. Biodiversity-related financial risks, arising from ecosystem degradation, represent an emerging and underexplored dimension of market risk, particularly for investors seeking sustainability-aligned portfolios. Our analysis reveals that both SRI equity and commodity indices consistently exhibit negative time-varying correlations with biodiversity risk, with correlations as low as −0.62 for the FTSE4Good US 100 and -0.53 for the FTSE4Good Global 100. Similarly, commodities like silver, gold, crude oil, and wheat also show negative correlations with biodiversity risk. These findings indicate that neither asset class serves as a reliable hedge against biodiversity-related shocks. Furthermore, biodiversity risk has a significant long-term spillover effect on SRI equity and commodity market returns. As biodiversity risk increases, it strengthens the connectedness between these markets, thereby amplifying the transmission of risk across them. These findings highlight the need for new risk management strategies and regulatory frameworks that account for biodiversity risk, opening new research pathways in finance and environmental sustainability.publishedVersio
When petroleum revenue transparency policy meets citizen engagement reality: Survey evidence from Indonesia
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New Public Management in the Norwegian Hospital Sector: Budgeting, efficiency, and economies of scope
No full textIn the 1990s, the Norwegian hospital sector struggled with long waiting lists and a lack of cost control. In response to this, several reforms labelled under the umbrella term “New Public Management” (NPM) were introduced in the Norwegian hospital sector in the late 1990s and early 2000s. In brief terms, NPM consists of introducing management practices inspired from the private sector into the public sector, with the goal of making the public sector more efficient. The reforms introduced radical changes in how the hospitals were organized, managed, and financed. The most extensive reform was the 2002 Hospital Ownership Reform. This reform moved the ownership of the hospitals from the counties to the state, while reorganizing the hospitals as health trusts. These health trusts were organized as self-governing entities with control of their own personnel and capital.
This thesis covers a study period between 2011-2019, when the Norwegian hospital sector was relatively stable, in terms of both financing and organization. The thesis consists of three papers, as empirical studies, investigating three different aspects of the 2002 Hospital Ownership Reform one decade after its initial implementation.
Paper 1 investigates how the health trusts adapted to a model whereby they are responsible for financing both the day-to-day operations of the health trust, as well as investments. Specifically, we look at both at the degree to which the health trusts have planned for budget surpluses, and the accuracy of this planning. We furthermore investigate whether there have been any associations between structural/organizational characteristics and the accuracy of budgeted surpluses. We find that the health trust for the most part budgets for a positive result of between 0-3 per cent of total operating costs. When comparing the budgeted results with the actual results, we find indications pointing towards the health trusts being too optimistic when planning future surpluses, but we also find examples of pessimism. Larger health trusts seem to have a greater accuracy in their budgeted results than smaller health trusts, while health trusts with more a more complex pool of patients have lower accuracy in their surplus budgeting.
Paper 2 investigates one of the main objectives of the NPM reforms, namely efficiency. In the study, we first measure the efficiency of the whole hospital sector through a non-parametric method. Secondly, we investigate how NPM-related tools are related to the efficiency. We find that from 2011 to 2019, the average efficiency level of Norwegian health trusts increased somewhat. We find that a variable capturing the NPM component of incentivization is associated with the efficiency score, while a variable capturing the NPM component of competition is not associated with the efficiency of the health trusts.
Paper 3 investigates the potential presence of economies of scope in the Norwegian hospital sector. Following the 2002 Hospital Ownership Reform, the Regional Health Authorities had the freedom to decide on the separation of functions within the health region. The individual health trusts were also given greater management autonomy. Economies of scope refer to situations where cost savings occur from the joint production of services in the same unit, rather than from separate production in specialized units. For the 2013-2019 period, the study investigates whether there were any differences in average efficiency between relatively specialized and differentiated hospitals, and whether the Norwegian hospital sector was characterized by economies or diseconomies of scope While the findings concerning the first question are somewhat ambiguous, the findings concerning the second question indicate that the sector is characterized by economies of scope
Regime switching forecasting for cryptocurrencies
No full textThere are many ways to model complex time series. The simplest approach is to increase the complexity, and thus, the flexibility of the model, for the entire time series. As an example, one could use a neural network. Another solution would be to change the parameters of a model dependent on the “state” or “regime” of the time series. A typical example here would be the Hidden Markov model (HMM). This paper combines the two concepts to create a Reinforcement Learning (RL) model that adds variables that depend on the state of the time series. To test the concept, the RL model is used with cryptocurrency data to determine the share to invest into the cryptocurrency index CRIX in order to maximize wealth. The results have shown that cryptocurrency metadata is useful as supplementary data for analysis of the respective prices. The Reinforcement learning model with regimes shows potential for investment management, but comes with some caveats.publishedVersio
School-Based Teacher Educators’ Experiences of Collaboration in Field Practice
Full text linkThis study explores Norwegian school-based teacher educators´ (SBTEs) experiences of tripartite collaboration in teacher education. Using a mixed-method approach, the study combines quantitative survey data (n = 242) with qualitative insights from reflective journals (n = 21). Despite governmental directives on facilitating third-space activity in teacher education, the findings reveal a considerable discrepancy between trends and actual practices. Most SBTEs work alone and lack arenas to collaborate, both within their partner schools and with the university. The study underscores the importance of mentoring competence, and structured third-space activities to enhance the quality of teacher education. Closer attention to the role of school leadership and mentor education programmes can be key factors in fostering enhanced collaboration and coherence among different parties.publishedVersio