In previous studies it was determined that the fiber diameter distribution in a peripheral nerve could be estimated by a simulation technique known as group delay. These results could be further improved using a combinatorial optimization algorithm called simulated annealing. This paper explores the structure and behavior of simulated annealing for the application of optimizing the group delay estimated fiber diameter distribution. Specifically, a set of parameters known as the cooling schedule is investigated to determine its effectiveness in the optimization process. Simulated annealing is a technique for finding the global minimum (or maximum) of a cost function which may have many local minima. The set of parameters which comprise the cooling schedule dictate the rate at which simulated annealing reaches its final solution. Converging too quickly can result in sub-optimal solutions while taking too long to determine a solution can result in an unnecessarily large computational effort that would be impractical in a real-world setting. The goal of this study is to minimize the computational effort of simulated annealing without sacrificing its effectiveness at minimizing the cost function. The cost function for this application is an error value computed as the difference in the maximum compound evoked potentials between an empirically-determined template distribution of fiber diameters and an optimized set of fiber diameters. The resulting information will be useful when developing the group delay estimation and subsequent simulated annealing optimization in an experimental laboratory setting.

Peripheral neuropathy refers to diseases of or injuries to the peripheral nerves in the human body. The damage can interfere with the vital connection between the central nervous system and other parts of the body, and can significantly reduce the quality of life of those affected. In the US, approximately between 15 and 20 million people over the age of 40 have some forms of peripheral neuropathy. The diagnosis of peripheral neuropathy often requires an invasive operation such as a biopsy because different forms of peripheral neuropathy can affect different types of nerve fibers. There are non-invasive methods available to diagnose peripheral neuropathy such as the nerve conduction velocity test (NCV). Although the NCV is useful to test the viability of an entire nerve trunk, it does not provide adequate information about the individual functioning nerve fibers in the nerve trunk to differentiate between the different forms of peripheral neuropathy. A novel technique was proposed to estimate the individual nerve fiber diameters using group delay and simulated annealing optimization. However, this technique assumed that the fiber depth is always constant at 1 mm and the fiber activation due to a stimulus is depth independent. This study aims to incorporate the effect of fiber depth into the fiber diameter estimation technique and to make the simulation more realistic, as well as to move a step closer to making this technique a viable diagnostic tool. From the simulation data, this study found that changing the assumption of the fiber depth significantly impacts the accuracy of the fiber diameter estimation. The results suggest that the accuracy of the fiber diameter estimation is dependent on whether the type of activation function is depth dependent or not, and whether the template fiber diameter distribution contains mostly large fibers or both small and large fibers, but not dependent on whether the fiber depth is constant or variable.

The purpose of this study was to investigate the effect of varying optimization parameters on the proposed optimum of a tablet coating formulation requiring minimization of crack velocity and maximization of film opacity. An artificial neural network (ANN) comprising six input and two output nodes separated by a single hidden layer of five nodes was trained using 100 pseudo-randomly distributed records and optimized by guided evolutionary simulated annealing (GESA). GESA was unable to identify a formulation that satisfied both a crack velocity of 0 m s¿1 and a film opacity of 100% due to conflict centred on the response of the properties to variation in pigment particle size. Constraining film thickness exacerbated the property conflict. By adjusting property weights (i.e. the relative importance of each property), GESA was able to propose formulations that were either crack resistant or that were fully opaque. Reducing the stringency of the performance criteria (crack velocity >0 m s¿1, film opacity <100%) enabled GESA to propose optima that met or exceeded the looser targets. Under these conditions, starting GESA from different locations within model space resulted in the proposal of different optima. Therefore, application of loose targets resulted in the identification of an optimal zone within which all formulations satisfied these less stringent performance criteria. It is concluded that application of the most stringent performance criteria and selection of appropriate property weights is necessary for unequivocal identification of the true optimum. A strategy for optimization experiments is proposed.

With the recent upsurge in mental health concerns and ongoing isolation regulations brought about by the COVID-19 pandemic, it is important to understand how an individual's daily travel behavior can affect their mental health. Before finding any correlations to mental health, researchers must first have individual travel behavior information: an accurate number of activities and locations of those activities. One way to obtain daily travel behavior information is through the interpretation of cellular Global Positioning System (GPS) data. Previous methods that interpret GPS data into travel behavior information have limitations. Specifically, rule-based algorithms are structured around subjective rule-based tests, clustering algorithms include only spatial parameters that are chosen sequentially or require further exploration, and imputation algorithms are sensitive to provided context (input parameters) and/or require lots of training data to validate the results of the algorithm. Due to the lack of provided training data that would be required for an imputation algorithm, this thesis uses a previously adopted clustering method. The objective of this thesis is to determine which spatial, entropy, and time parameters cause the clustering algorithm to give the most accurate travel behavior results. This optimal set of parameters was determined using a comparison of two non-linear optimization methods: simulated annealing and a limited-memory Broyden-Fletcher-Goldfarb-Shanno Bound (L-BFGS-B) optimizer. Ultimately, simulated annealing optimization found the best set of clustering parameters leading to 91% clustering algorithm accuracy whereas L-BFGS-B optimization found parameters that were only able to produce a maximum of 79% accuracy. Using the most optimal set of parameters in the clustering algorithm, an entire set of GPS data can be interpreted to determine an individual's daily travel behavior. This resulting individual travel behavior sets the groundwork to answer the question of how individual travel behavior can affect mental health.

<p> The recrystallization behavior of three Al-Mg-Si-Cu alloys with varying iron and manganese additions was studied by differential power scanning calorimetry under nonisothermal annealing conditions. The influence of cold deformation on the precipitation sequence and its interaction with recrystallization was also investigated. The DSC experiments were complemented by hardness measurements and microstructural studies by optical and electron microscopy. The DSC signals, after optimization of the baseline, were used for the calculation of the kinetic parameters of the recrystallization process. Two different modeling approaches based on global JMAK kinetics were implemented. The first approach utilizes the classical isothermal JMAK expression directly, while the second approach introduces a path variable related to the thermal history of the material in the JMAK description. Model-independent estimates of the activation energy were also evaluated using the Flynn-Wall-Ozawa integral isoconversion method. </p> <p> The results show that the initial stages of recrystallization are not affected by the preceding precipitation processes and recrystallization always follows the precipitation of the Q' phase. However, during recrystallization enhanced coarsening of the Q' phase takes place leading to its transformation to the more stable Q phase. The Q phase exerts a Zener pinning pressures on the migrating boundaries preventing the formation of an equilibrium grain structure. Moreover, for high Fe and Mn additions, discontinuous precipitation of Mg2Si overlaps with the end of recrystallization and exerts an additional pinning pressure on the boundaries. Varying the Fe and Mn content significantly affects the recrystallization kinetics. PSN is promoted in alloys with the higher Fe and Mn content and the recrystallization temperature shifts to lower values. The modeling results show that the recrystallization process conforms to the classical JMAK type behavior. The course of the reaction was reproduced successfully by the path variable approach and the evaluated activation energies were in good agreement with the isoconversional model-independent estimates. However, when the classical JMAK expression was applied directly to non-isothermal measurements, a dependency of the recrystallization process on thermal history was observed. </p> / Thesis / Doctor of Philosophy (PhD)

One of two project reports. Part A can be found at: http://hdl.handle.net/11375/18522 / Recent channeling-backscattering measurements of the disorder induced by heavy ion irradiation of semiconductors has indicated radiation damage far in excess of that predicted by linear transport theory. The present work extends the investigation to TEM and compares the two techniques in an annealing study of ion irradiated silicon (~ 80-200 a.m.u. ions of 15-100 keV) for low fluence (typically 3×10¹¹/cm² for TEM and 10¹²-10¹³/cm² for channeling) bombardment. In addition to showing a good correlation between the techniques, the results indicate that neither does there exist a unique relationship between lattice disordering and cascade energy density, nor that a well defined amorphous structure seems to exist. / Thesis / Master of Engineering (ME)

In this thesis, we tackle a significant optimization challenge within Vehicular Ad Hoc Networks (VANETs) by employing a simulated annealing approach. We focus on developing an efficient Vehicle Routing Problem (VRP) algorithm to sift through numerous potential solutions and identify the best one. Our VANET scenario revolves around four distinct vehicles traversing four unique routes. The primary objective is to minimize the total distance covered by these vehicles while ensuring that they visit all designated waypoints. We implement this problem using MATLAB to establish initial routes for each simulation uniquely. Simulated annealing proves to be a valuable tool in optimizing VANETs. The gradual cooling process reduces the likelihood of accepting suboptimal solutions over time, allowing the algorithm to escape local optima and converge towards nearly optimal solutions. Regarding routing protocol parameter configuration, simulated annealing is the technique of choice for identifying the most influential parameters. It evaluates the cost and creates new routes based on neighboring nodes, calculating the cost function for these new routes. Starting from an initial configuration, the algorithm iteratively refines it by introducing random changes, retaining only those that enhance the objective function. Our objective function defines the Quality of Service (QoS) and communication efficiency of the routing protocol. The gradual reduction in the acceptance of less favorable configurations over time is called the annealing schedule, enabling the algorithm to escape local optima and approach nearly optimal designs.

In recent years, non-fullerene acceptors (NFAs) have attracted enormous interest in the field of organic solar cells (OSCs), they improve power conversion efficiency (PCE) compared to the classical fullerene acceptor. In this work, OSCs based on PBDB-T as the donor material and the very well-known NFA ITIC, along with its fluorinated and chlorinated derivatives (IT-2F, IT-4F, IT-2Cl, IT-4Cl) were fabricated to investigate the effect of the halogenation end group on the photovoltaic parameters. Optical characterization reveals that both chlorination and fluorination are effective in downshifting the molecular energy levels and redshifting the absorption spectra, which results in higher Jsc but lower Voc compared to pristine ITIC. In addition, the halogenated ITIC device exhibited enhanced FF and PCE. Various optoelectronic techniques were also used to investigate the charge recombination dynamics and charge extraction process. It has been found that (IT-2F, IT-2Cl) show suppressed monomolecular recombination compared to di-substituted NFA (IT-4F, IT-4Cl). Furthermore, fluorinated ITIC has a longer charge carrier recombination lifetime but a lower carrier extraction rate. Lastly, the best-performing device from the preceding component mixtures PBDB-T:IT-2F was exposed to thermal annealing at different stages of the fabrication process to investigate how annealing affects the photovoltaic parameters. According to our findings, both post and 2-stage annealing improve FF and PCE, but the latter is even more beneficial. In further studies, the annealing effect on the HTL layer (MoOx) has also been investigated. Annealing improved the MoOx’s work function, resulting in higher internal electric field that thereby facilitated hole extraction, as demonstrated by TPC where 2-stage annealed devices exhibited a faster carrier extraction rate.

Trading contracts with future commitments requires posting a collateral, called initial margin requirement, to cover associated risks. Differences in estimating those risks and varying risk appetites can however lead to identical contracts having different initial margin requirements at different market places. This creates a potential for minimizing those requirements by reallocating contracts. The task of minimizing the requirement is identified as a black-box optimization problem with constraints. The aim of this project was to investigate that optimization problem, how it can best be tackled, and comparing different techniques for doing so. Based on the results and obstacles encountered along the way, some guidelines are then outlined to provide assistance for whomever is interested in solving this or similar problems. The project consisted both of a literature study to examine existing knowledge within the subject of optimization, and an implementation phase to empirically test how well that knowledge can be put to use in this case. During the latter various algorithms were tested in a number of different scenarios. Focus was put on practical aspects that could be important in a real situation, such as how much they could decrease the initial margin requirement, execution time, and ease of implementation. As part of the literature study, three algorithms were found which were evaluated further: simulated annealing, differential evolution, and particle swarm optimization. They all work without prior knowledge of the function to be optimized, and are thus suitable for black-box optimization. Results from the implementation part showed largely similar performance between all three algorithms, indicating that other aspects such as ease of implementation or parallelization potential can be more important to consider when choosing which one to use. They were all well able to optimize different portfolios in a number of different cases. However, in more complex situations they required much more time to do so, showing a potential need to speed up the process.

Superhydrophobic fabrics have gained a huge interest in the industries recently. New legislation pushes the industries to eliminate the use of fluorinated materials in the production of these type of fabrics. Hydrophobic and self-cleaning garments textiles can deliver stable water repellent properties without the need for fluorinated chemicals and reduce the consumption of detergents. New methods that could be implemented in current textile industry processes without major changes in instruments or materials is essential to move this industry to the next level. Filament development with the hydrophobic structure without coating could be strategic on one side and tricky on the other side. It has been proved that a stable hydrophobic self cleaning surface needs a hierarchical micro-nano structure to present sustainable properties. In this thesis, we used common materials in the textile industry for filament production which are polyethylene terephthalate (PET) and high molecular weight polystyrene (PS) and low molecular weight polystyrene (LMPS) to shape the microstructures on the surface of filaments. By adding the common compatibilizer polystyrene-co-maleic anhydride (PSMA) to the blend, PS in the matrix of PET could migrate to the surface. Even 1% wt. of PSMA boosted the migration of PS polymer droplets to the surface. The blend including compatibilizer was compounded, melt-spun into the monofilament, drawn, and annealed for various time durations in the furnace. Next, the filaments were immersed in tetrahydrofuran(THF) to remove the PS component obtaining the rough surface. We investigated the effect of mixture components content and different process parameters such as draw ratio and annealing time on hydrophobicity by the aid of statistical design. Applying the Wilhelmy method for contact angle measurement, we could achieve an advancing contact angle (ACA)of 114º and the average ACA of 96º by making micro-size structure on raw PET with an average ACA of 80º and the intrinsic contact angle of around 70º.