Flexible foams have unique properties that make them well-suited to several engineering systems. They are often used in impact-related applications because of their superior energy absorption performance. Many multifunctional materials are also derived from flexible foams due to their high customizability, which allows them to satisfy a wide range of performance requirements. Though flexible foams have high potential in these and other classes of material applications, their success relies on the proper characterization of their complex behavior. This thesis promotes the application of flexible foams by characterizing their electromechanical response through both experimental and theoretical approaches. One study in this thesis theoretically determines material indices that minimize a foam's mass and cost while meeting particular energy absorption requirements. These novel indices are combined with a common Ashby approach to facilitate materials selection of energy-absorbing foam components. Another study uses a particular multifunctional nanocomposite foam to experimentally determine deviations in its voltage response while under a cyclic impacting regime; specifically, factors of transient effects, environmental conditions (humidity and temperature), and permanent material degradation are investigated. Results presented in this thesis promote the application of flexible foams to various forms of impact-absorbing sports equipment (specifically football helmet pads and gait-sensing shoe insoles), but are also useful in various other engineering designs.

Although most processes governing the surface mass balance on mountain glaciers are well understood, the causes and extent of spatial variability in accumulation remain poorly constrained. In the present study, the EBFM distributed mass balance model is newly coupled to terrain based modelling routines estimating mass redistribution by snowdrift, preferential deposition, and avalanching (ST-EBFM) in order to model winter balance on Storglaciären, Sweden. STEBFM improves the spatial accuracy of winter balance simulations and proves to be a versatile and computationally inexpensive model. Accumulation on Storglaciären is primarily driven by direct precipitation, which seems locally increased due to small scale orographic effects. Wind driven snow transport leads to significant deposition in the accumulation zone and slight erosion in the ablation zone. The pattern is generally consistent from year to year. Avalanching is the smallest contributor to winter balance, but cannot be neglected. The physical complexity of avalanches and high year to year variability render simulations of the process somewhat uncertain, but observations seem to confirm the large impact that the process can have on the glacier at very localised scales. The role of mass transporting processes in maintaining the current mass equilibrium on Storglaciären highlights the necessity to understand the links between climatic predictors and accumulation in order to accurately assess climate sensitivity.

The thesis empirically examined the implications of microfinance scaling up or sustainability on outreach in Nigeria. Basically, two methodologies were used namely, panel data econometric and survey methods. The panel dataset of 752 microfinance banks in Nigeria was used during the period 2011-2014, while the survey was conducted on some selected microfinance banks in Federal Capital Territory, Abuja in 2014. The findings from the thesis showed that, at the national level, yield, labour cost, orientation, efficiency, gender and size of loans are the major drivers of microfinance banks‟ sustainability in Nigeria. While at the state level, microfinance banks sustainability is driven by orientation and loan size. Findings also showed that sustainable MFBs tend to be more focused on the poor clients. The thesis showed that lending to female clients improves repayment rate of MFBs in Nigeria. Corroborating the regression result, the survey findings also suggest that lending to women had improved and enhanced repayment rate. In view of these findings, the thesis recommends that sustainability and outreach are not necessarily incompatible. However in pursuing sustainability greater attention should be on female clients, as greater lending to women would improve the repayment rate of MFBs and further engendered the industry sustainability.

Accurate and efficient localization is a fundamental requirement for autonomous operation of robots, especially in areas that deny global navigation services. Localization is even more challenging in environments that present visual and geographic difficulties. This not only includes environmental aspects like darkness, fog and dust but also geometrically monotone areas. The solution that the Center for Applied Autonomous Sensor Systems at the Örebro university decided to develop is therefore a prototype of radar-only localization and mapping (SLAM) system. The radar modality is less susceptible to the environmental factors when compared to, for example, a lidar. Our goal is to support this effort by creating an odometry module that uses radar and inertial data to provide the localization for this SLAM prototype. This radar-inertial-odometry (RIO) takes radar point clouds and inertial gyroscopic data to output an odometry message usable by other components in the robot operating system (ROS). The module has been tested on two datasets representing areas typical for deployment, one consisting of underground tunnels and the other one being an outside forest environment. The dataset has been processed by two different mappers where the lidar has been used as the basic modality. This choice allows us to evaluate the odometry module in a more practical way. The final results are promising, the underground localization closely adheres to reality. The forest dataset is more challenging although it still resembles the ground-truth position in the horizontal dimension. The module's biggest shortcoming is a noticeable drift problem in the vertical z-dimension , for which we propose a constraint that limits this drift.

Scanning Electron Microscopy combined with Energy Dispersive X-ray Spectroscopy (SEM-EDS) is a widely applied elemental microanalysis method. The integration of silicon drift detectors (SDDs) has notably enhanced EDS performance, enabling precise elemental identification due to its large sensitive area and low output capacitance.  Accurate simulations of SDDs can provide insights that enable the design and optimization of future models without the need for costly and time-consuming experimental iterations. Moreover, the current model-based quantification methods for EDS applications have reached their maximum predictive accuracy. As such, creating a more accurate simulation model could help achieve a higher level of precision in these quantification models, which would be immensely valuable for all EDS applications.  With this objective in mind, a simulation framework for modeling SDDs in EDS was developed based on Geant4, Allpix Squared, and COMSOL Multiphysics. The simulation encompasses the entire physics pipeline, including characteristic X-ray emission from the target sample and its absorption in the detector. The generated charge carriers within the detector are propagated through the internal electric field of the SDD, and their individual charge contribution is measured to simulate EDS spectra. The simulated model was compared to existing literature and in-house experimental measurements, showing strong agreement in the case of a well-tuned SDD. Limitations of the simulation framework are discussed, and further research to enhance accuracy and speed is explored.

Machine learning (ML) has become ubiquitous in various disciplines and applications, serving as a powerful tool for developing predictive models to analyze diverse variables of interest. With the advent of the digital era, the proliferation of data has presented numerous opportunities for growth and expansion across various domains. However, along with these opportunities, there is a unique set of challenges that arises due to the dynamic and ever-changing nature of data. These challenges include concept drift, which refers to shifting data distributions over time, and other data-related issues that can be framed as learning problems. Traditional static models are inadequate in handling these issues, underscoring the need for novel approaches to enhance the performance robustness and reliability of ML models to effectively navigate the inherent non-stationarity in the online world. The field of concept drift is characterized by several intricate aspects that challenge learning algorithms, including the analysis of model performance, which requires evaluating and understanding how the ML model's predictive capability is affected by different problem settings. Additionally, determining the magnitude of drift necessary for change detection is an indispensable task, as it involves identifying substantial shifts in data distributions. Moreover, the integration of adaptive methodologies is essential for updating ML models in response to data dynamics, enabling them to maintain their effectiveness and reliability in evolving environments. In light of the significance and complexity of the topic, this dissertation offers a fresh perspective on the performance robustness and adaptivity of ML models in non-stationary environments. The main contributions of this research include exploring and organizing the literature, analyzing the performance of ML models in the presence of different types of drift, and proposing innovative methodologies for drift detection and adaptation that solve real-world problems. By addressing these challenges, this research paves the way for the development of more robust and adaptive ML solutions capable of thriving in dynamic and evolving data landscapes. / Machine learning (ML) is widely used in various disciplines as a powerful tool for developing predictive models to analyze diverse variables. In the digital era, the abundance of data has created growth opportunities, but it also brings challenges due to the dynamic nature of data. One of these challenges is concept drift, the shifting data distributions over time. Consequently, traditional static models are inadequate for handling these challenges in the online world. Concept drift, with its intricate aspects, presents a challenge for learning algorithms. Analyzing model performance and detecting substantial shifts in data distributions are crucial for integrating adaptive methodologies to update ML models in response to data dynamics, maintaining effectiveness and reliability in evolving environments. In this dissertation, a fresh perspective is offered on the robustness and adaptivity of ML models in non-stationary environments. This research explores and organizes existing literature, analyzes ML model performance in the presence of drift, and proposes innovative methodologies for detecting and adapting to drift in real-world problems. The aim is to develop more robust and adaptive ML solutions capable of thriving in dynamic and evolving data landscapes.

This study was conducted to determine whether soybean productivity is affected by multiple, sublethal herbicide exposures. The effects of dicamba and 2,4-D on soybean (Glycine max) productivity was investigated at 17 site-years. Relative to a single exposure of dicamba at R1, an additional exposure at either V3 or R3 reduced yield up to 23%. Three or more applications did not further decrease yields relative to an R1&R3 exposure. For 2,4-D, a single application to V3, R1, R3, or R5 soybean did not affect grain yield. However, two exposures of 2,4-D occurring from V3 through R3 reduced yield 5 to 7%. Three or more applications of 2,4-D had no effect on yield relative to exposing soybean to 2,4-D twice between V3 and R3. Exposing soybean to multiple, sublethal rates of auxin herbicides can reduce yield relative to a single exposure and may be most deleterious from flowering to initial pod set.

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