This report evaluates methods for reconstructing the coefficients of a model that describes the relationship between environmental conditions and the performance of potato genotypes, with performance measured by the weight of storage organs. For each genotype, this relationship is modeled as a linear combination of environmental factors weighted by genotype-specific environmental coefficients. The model incorporates the soil moisture field effect, wich varies both over the growing season and spatially within the field, with the latter captured through a linear combination of basis functions weighted by field effect coefficients. Initially, synthetic environmental data is generated, and fixed field effect and genotype-specific environmental coefficients are established. The model then calculates the expected performance based on these coefficients.

Subsequently, two approaches — a two-step method and a one-step method — are tested to reconstruct the original coefficients using the environmental and performance measurements. The robustness of both methods to noise is evaluated, and the minimum required number of environmental measurement locations within the field, as well as the optimal number of intermediate harvests during the growing season, are determined.
This is important because, once refined and expanded, the model and reconstruction methods can be applied to real multi-environment data to provide insights into how environmental factors influence the growth of different genotypes. Such analyses are important for advancing plant breeding efforts.

Potato plant vitality is an important trait for good yield. Unfortunately, even with the best potato seed, vitality variation within a genotype is significant and it hurts both farmers and potato seed producers. HZPC and Averis Seeds hypothesize the existence of a link between the variation in the vitality of a plant and the field of production of the seed tuber. TU Delft, Utrecht University, HZPC and Averis Seeds joined forces to determine the existence and strength of this link in a multi-year
project funding this research. In this thesis we describe TU Delft’s contribution both in the measurement of plant vitality and the development of a model to predict plant vigor from chemical properties of the tuber...

This paper addresses the challenge of accurately monitoring power cable temperatures, which is crucial for the reliability and efficiency of electrical power systems during the energy transition. Overheating of power cables can lead to insulation failure and reduced lifespan, making precise temperature monitoring essential. Recent research by Alliander has explored the use of electromagnetic reflectometry for the recon- struction of power cable temperatures by analyzing the propagation time of pulsed electromag- netic signals within the cable. This study aims to improve upon this by directly reconstructing the electromagnetic parameters of the power cable and understanding their temperature depen- dence through a controlled experiment. Key steps include: • Cable Modeling: Using the Telegrapher’s equations to describe voltage and current distri- butions in the time and frequency domains. • Data Transformation: Converting these equations into integral equations and discretizing them to define an inverse scattering problem. • Experimental Validation: Using synthetic data and data from a temperature-controlled experiment by Alliander to test and refine the reconstruction method The study reconstructs the electrical parameters of the cable and attempts to find a relation between the parameters and the cable temperature. This method attempts a more accurate temperature monitoring system, enhancing the safety and efficiency of power grids

This thesis aims to improve the current risk classification for (company) car insurance at Achmea, focusing on WAM and ARD coverages. By using cluster analysis, specifically K-prototypes and spectral clustering, policyholders are grouped based on zip codes and license plates to enhance the current claim frequency models (and thus premium pricing models). The application of spectral clustering (with U-SPEC as the observation reduction technique) led to significant improvement of the current claim frequency GLM for the ARD coverage. This thesis highlights the potential of cluster analysis in actuarial science, offering new methods for mixed data types and high-dimensional clustering, thus providing a foundation for more accurate claim frequency models. The time stabilities and stabilities with respect to the number of observations of the clusters are also investigated.

In this thesis we implement the Lindblad equation in the matrix product state (MPS) formalism using an operator splitting method. We developed a second-order method based on a Trotter approximation and a third-order high-dimensional midpoint method and we proposed a new fourth-order method based on Duhamel’s principle and a nested RK4 method, all of which preserve positivity and Hermiticity of the density operator. We simulated spin transport through an XXZ-Hamiltonian Heisenberg chain, for which we found the magnetisation profile and measured a spin current of 0.04-0.05. The results obtained are consistent with the existing literature. The extensive error analysis shows that the time step ∆t is the main contributor to the error, if the bond dimension χ is set to at least 15. The third-order method is in general preferred to the second-order method, as only this method preserves trace. We also analysed the Hubbard model, including a spin orbit coupling, in order to propose a method for simulating the chiral induced spin selectivity (CISS) effect.

Extreme Ultraviolet lithography is a vital step in the production of cutting edge computer chips that drive emergent technologies like AI, VR and the internet of things [1]. Careful control of Ultraviolet light beams by EUV machine modules such as the Unicom, Uniformity correction module, enable extremely precise printing of nano-scale structures on these chips. This thesis focuses on the construction of a model that predicts the impact of the Unicom on EUV illumination in ASML's lithography machines. Such a model could be used in a predictive maintenance scheme to prevent a fraction of unscheduled machine downtime, which can be estimated to cost ASML's customers around 38 million dollars per machine per year [2-4]. Similar problems have been tackled before [5, 6], but both existing models disregard multiple properties of the EUV machine and are incompatible with the type of measurements obtained by ASML. Therefore, the question remains how an accurate Unicom model can be constructed.
In this thesis, a physics based Unicom model was developed that can be fine-tuned to machine specific measurements. Significant reductions of up to 90.3% of prediction errors were obtained by using the model. Overall, making use of the model provided better or equivalent predictions when compared to not using the model for all but one of the investigated indicators of prediction quality. For the latter indicator errors remained within the desired bound, but further investigation is needed to discover why the Unicom model adversely affected this indicator. With an average execution time of 31.4 s, the created Unicom model in general enables swift and substantial accuracy gains.

In this study, a model is developed in COMSOL to investigate the nature of energy losses in ion exchange membranes used in electrochemical energy conversion. By use of electrochemical impedance spectroscopy, the nature of resistance and reactance, and therefore the nature of Ohmic resistances and charge transfer resistances, are evaluated. This is done by first obtaining a model of the setup and the resulting impedance spectrum and then making adjustments in the model to observe the impact on the impedance spectrum as well as the resulting ion concentration profiles. In the used system, the diffusion effects were dominant compared to the double layer effects. Therefore, the impedance spectrum is mainly shaped by the diffusion boundary layer, especially at low frequencies. The ion concentration, diffusion coefficients, and membrane properties were found to have a significant influence on the impedance spectrum. Insights from this study can be used in the optimization of ion exchange membranes and the efficiency of energy conversion systems.

In agricultural studies it is often important to predict the performance of genetically different plants. To make sure predictions are done well, it is necessary to make sure they are not influenced by effects of the field on which they are planted. These field effects or spatial effects are in practice often quite complicated and can be due to a wide variety of reasons. To get a better view of these field effects a good mathematical model is desired. In this paper a model is presented which helps to find these field effects. This model tries to estimate the field effect by comparing data of the same plant on different positions of the field. Data is obtained in a finite amount of positions, which means that the model finds the field effect in a finite amount of positions as well. This field effect is found using a cross-validation technique obtained from Tikhonov regularization. The field effect in a finite amount of positions is extended to a field effect in every position of the field. To do this in a good way a kernel method is used, the advantage of which is that it does not depend on a mesh. This kernel method is here applied with a kernel function that is based on Gaussian distribution. This model is applied to several fields of crops to get a view of the performance of the model on real data.

In this paper, we want to create a prediction model for target attainment and a scoring system based on these models. The Random Forest model, Logistic Regression model, and Naive Bayes model are employed to achieve this. Classification trees and predictors from the random forest model are used to create the scoring system. The theory behind each model and scoring system is described in detail. The properties of proper scoring rules were checked when making our scoring systems. We have made three models which can reasonably predict target attainment. The logistic regression model performed the best for internal and external validation, with an AUC of 0.84 and 0.80, respectively, whereas the RF model performed slightly worse. The AUC of the random forest differed by 0.01 in both validation sets. The scoring system performed reasonably with an AUC of 0.74. These models and the scoring system perform reasonably well and will help the medical practitioner assess if an ICU patient will attain their beta-lactam antibiotic target.

The aim of this thesis is to simulate quantum spin chains at a finite temperature. This has been achieved by using purification to write the incoherent thermal states of the spin chain as pure states, after which the matrix product state (MPS) formalism to simulate the spin chain. The influence of temperature on the spin chain has been tested by comparing chain magnetization to the strength of the external magnetic field at different temperatures. Real time evolution has also been applied under the assumption that the time evolution is adiabatic. The results have been compared to the analytical solution found by directly calculating the density matrix. The model is very accurate for imaginary time evolution, which is required to evolve the system to the desired temperature. During real time evolution the results oscillate slightly around the analytical solution, which remains constant. This oscillation is a consequence of model truncation and the Suzuki-Trotter approximation. The effect of allocating more numerical resources to limit this phenomenon is explored, and a method to increase reachable timescales by decreasing entanglement growth in the chain over time is tested and verified.

To regulate pollution emission of $\text{NO}_2$ and greenhouse gasses like carbon dioxide and methane, satellite images of the TROPOspheric Monitoring Instrument (TROPOMI) are used to map the spread of air pollution. This instrument delivers near-daily 'snapshots' of the $\text{NO}_2$ concentration over the whole Earth. In this report, first, the spread of $\text{NO}_2$ pollution is modelled using a so called convection-diffusion equation. This equation is constructed from a convection term, which contributes to how much the wind transports matter, and a diffusion term which described the natural mixing of gases. Next, $\text{NO}_2$ pollution sources were reconstructed using the inverse of the time propagation of the model. Thus, known $\text{NO}_2$ concentrations are used to reconstruct temporal information. By finding out what is missing between data snapshots, sources can be reconstructed. The verification of the algorithm was first performed on artificial data, after which it was applied to experimental data.

The convection-diffusion equation that was used is a two dimensional equation, using longitude and latitude as Cartesian coordinates. The equation was solved by the method of lines, i.e. first a spatial discretisation was made using the Finite Volume Method, after which time integration methods were used to propagate the problem in time. In this report two time integration methods formed the basis for the time propagation and two source reconstruction algorithms. The algorithms take two $\text{NO}_2$ snapshots $\textbf{y}_n$ and $\textbf{y}_{n+m}$, with $m$ time steps in between. First the inverse form of Forward Euler is used, which performed well on artificial data on a small time scale. Afterwards the Backward Euler method is used to define the inverse problem of finding the source function. This algorithm performed well on artificial data with 24 hours between snapshots. The algorithms were found to be sensitive to noise and to be influenced greatly by the second $\text{NO}_2$ snapshot. After applying the algorithm to experimental data it was found that the algorithm is able to localise extended sources of emissions, but is not capable of pin-pointing individual point sources. In addition, sources with low emission rates were left undetected due to information being lost in the noise. Emission rates were not reconstructed accurately either, although it was able to find the main sources of emissions. The performance of the algorithm will take advantage from more frequent data. With this improvement, this method is a viable option for finding not only $\text{NO}_2$ sources, but can also be applied to other tropospheric gases.

The potato is a plant that can grow from another potato tuber. Farmers and producers have to contend with the declining quality of soil and are producing less vital potato tubers. The differences in emergence speed between batches of the same variety of potato tubers cannot be explained. This is why Project ‘Flight to Vitality’ (FtV) is developing a diagnostic test to make predictions about the vitality of different varieties of potato tubers, with various measurements. One of these measurements consists of hyperspectral images taken from potato tubers, prior to planting. Another measurement is the actual growth of the potato plants, which was recorded after emerging. This thesis attempts to relate both measurements by means of regression. After grouping both data sets on each batch of tubers, we are left with an explanatory matrix that has more columns than rows. This causes an ordinary least squares model to overfit new data. Therefore an alternative regression model is proposed, which is Partial Least Squares (PLS). The goal of PLS is to find latent variables, that maximizes the covariance between both sets, to explain the variance in the explanatory space as good as possible, and to provide good predictions on the response space. The predictive performance of PLS, which is for the most part measured with the Mean Squared Error, relies on the number of latent variables used. Cross-validation is performed for finding this number. By splitting the data into training and testing sets, we evaluate PLS and find that some of the variances are explained well with PLS. This model is extended by including variable selection on the explanatory variables, which are the frequency bands taken from hyperspectral imaging. With this extension, the same experiment was rerun and we saw that the predictive performance can increase. However, the number of variables omitted seem to be inconsistent across different experiments. We have also found that the performance can slightly decrease, but the number of omitted variables remain much more consistent (and were overlapping across different experiments). With this established baseline model, we have also looked at the predictive information on different tuber parts. By conducting a similar experiment, it appears that the model is capable of explaining almost all variance and performs outstanding. We have also looked at leaving one tuber variety out before training the model. This variety is then used for evaluation. From this experiment, it seems that there is a strong variety-dependent component found in the FtV data, which makes it impossible to predict the relative vitality of the tubers. Finally, we compare PLS with another model that belongs to the same Krylov Subspace method, which is LSQR. The same experiment is conducted on both models and it seems like PLS and LSQR are not identical in terms of finding the number of latent variables used. Regardless, as an independent regression estimator, LSQR is much faster than PLS.

In this paper, a method is proposed to automatically correct misalignment of orthophotos in time-series caused by an inaccurate geotransform. The proposed method relies on common literature concepts such as keypoint identification, keypoint matching, and model fitting using random sample consensus (RANSAC). Traditional keypoint identification methods such as the scale invariant feature transform (SIFT) are not suited for this problem as no real scale- or rotation-invariance is required, instead, time-invariance is required. To achieve this, crops are suggested as keypoints, and two different keypoint descriptors are put forth. The first descriptor is based on the shape and size of the crops, while the alternative descriptor is based on the planting pattern of crops. The method, and both descriptors, generate promising results for certain scenario's. However, in later growth stages performance drops significantly as the identification of crops -required beforehand- becomes troublesome due to dense growth.

In this thesis the matrix product state(MPS) formalism is applied to simulate a device created for remote detection in atomic spin chains. Although simulation of the full device quickly becomes infeasible for direct algorithms, MPS allowed for highly accurate simulations while requiring only modest computational resources. It has been verified that the simulation conserves energy and maintains normalization. Furthermore, the simulation is shown to successfully perform both a coherent real-time evolution and iteration towards the ground state. Thus MPS is a very promising tool that has potential to serve as a guide for further experiment as well as a tool to understand experimental results.

The problem of quantifying the growth dynamics of plants from time-lapse images is important for determining variety-specific characteristics and subsequent breeding. The goal of this paper is to address this problem and reconstruct three-dimensional images such that growth becomes visible. To this end, data of growing plants were collected in the form of multi-angle time-lapse images. In each image separately, the plants' outline was detected with a GVF-Snakes algorithm. The success of this algorithm is ensured by pre-processing steps and placing the initial contour based on color specifics of the image.
The two-dimensional contours are then combined to reconstruct a three-dimensional contour in the camera reference frame. To obtain this reconstruction, points along the contour are matched using a RANSAC-based algorithm to estimate the essential matrix. The three-dimensional reconstructions show information about size, height, leaf structure, and growth of the plant, but the stability of the method has to be improved. Finally, an attempt is made to combine the GVF-Snakes with the point-matching algorithm, however, the convexity of the coplanarity constraint should be explored further.

The ongoing worldwide energy transition has prompted significant scientific interest in energy storage. Rechargeable lithium-ion batteries have become a standard for energy storage in mobile devices and electric vehicles for their mass-energy density. While industry standard lithium-ion cells are currently based on liquid electrolytes, solid electrolytes promise to bring the next step towards safety, energy density and sustainability. However, there are critical challenges, notably improving the lithium conduction of solid electrolytes. Furthermore, as a relatively new research field it is important to target high recyclability and sustainability for materials early on. Battery modelling facilitates performance comparisons of for example battery materials and geometric properties, and subsequent optimisation. The report treats battery modelling with a specific focus on solid electrolytes. Two solid electrolyte models, based on weak electrolyte theory and a lattice gas model by Landstorfer et al., were implemented in the Multiphase Porous Electrode Theory software (Smith,2017). The former is relevant for glass type electrolytes, while the latter models crystalline materials. The models showed comparable performance, with a constant voltage offset but good mutual agreement in term of behaviour. It is important to verify the results experimentally. The numerical methods used in porous electrode theory were treated with the creation of a standalone porous electrode model, with separate domains for the electrolyte and active cathode particles. This model is stable and functional with the two domains in isolation, while a fault remains in the coupling between the domains. Lastly, a proposal is made for the addition of interface regions in the MPET software. With these domains situated between the bulk electrolyte and active cathode particles, porous electrode models could include a variety of phenomena that are currently impossible to implement. For solid electrolytes in particular, modelling dynamic stress effects and lithium conduction across particle boundaries would be valuable additions.

Magnetic Resonance Imaging is a painless procedure to produce high-resolution diagnostic images. Today, it is one of the essential clinical imaging modalities. One of the major challenges involved with this imaging modality is its long scanning time. Parallel imaging in combination with compressed sensing has overcome this challenge to a great extent. As a quid pro quo for this reduced scan time is the increase in image reconstruction time. An extensive research is focused to develop algorithms to make the image reconstruction faster. Fast Iterative Shrinkage Threshold algorithm is one of these algorithms (which is clinically viable) that speeds up the image reconstruction process. The present project focuses to speed up the particular algorithm, fast iterative shrinkage threshold algorithm, by preconditioning the convex optimization problem. This work proposes two new preconditioners, specifically in the context of the given algorithm, but otherwise can be used with different frameworks solving similar problems. The first preconditioner is a degree one polynomial of the system matrix and the second preconditioner is a block diagonal matrix where each block is a circulant matrix. The preconditioners are evaluated using two stopping criteria: residual error and relative error. The computation complexity of both the preconditioners are evaluated by measuring the floating point operations and total time consumption. Additionally, the simulations are performed by undersampling the data at two factors r=2 and r=4. The results indicate that the polynomial preconditioner reduces the overall time by a factor of 0.25 however is computationally expensive to construct. On the other side, block diagonal circulant preconditioner is extremely cheap to construct and evaluate on a vector but does not provide the desired results within the current framework. The study concludes that a suitable preconditioner for FISTA is the one that without affecting the largest eigenvalue of the system matrix improves the condition number and simultaneously is cheap to construct and evaluate.

Danieli Corus supplies tailor-made solutions for the global steel industry. Cameras are used in tuyeres of blast furnaces to detect physical aspects and irregularities. No operator could monitor those videos all day, which is why video analyses are needed. A program is written to analyse a test video and give information about the temperature, the injection of pulverized coal and the movement of rocks. The mathematical background of these calculations is presented, which depend on the transport of light from the blast furnace to the camera through the tuyere. Also the implementation of the calculations can be found. The results are interpreted and discussed and further improvements are suggested.

Experimental data have been extracted from wound-healing time-lapse videos. These data include the detection of the area of the wound and the detection of individual cells. Extracting the area of the wound is done by using a function in Python OpenCV called cv2.findcontours. An improved method to extract the area of the wound is introduced as well using the Sobel filter. Detecting the individual cells is done by localizing the local maxima in an image. Histogram equalization is applied to enhance the global contrast to increase the performance of cell detection. The continuum model is applied in one of the videos, where we used different parameters to model the data. A method is described to nd the optimal parameter of the continuum model by using the method of least-squares. Finally, two methods using the kernel density estimation are described which can be useful in future studies.