This thesis looks at characterising countably infinitely categorical theories. That is theories for which every countably infinite model is isomorphic to every other countably infinite model. The thesis looks at the Lindenbaum-Tarski algebra, Henkin theories, types and then ends with the Ryll-Nardzewski theorem which provides several equivalences to a theory being countably infinitely categorical.

Drug delivery systems (DDS) are essentially pharmaceutical products for human therapy, typically involving a mixture of active ingredients and excipients. Based upon quantitative characterization of structure, the thesis introduces the concept of classifying the architecture of DDS into four levels by their spatial scale and the life time period. The primary level is recognised as the static structure of the whole dosage form with a size from μm to cm with the final structure generated by formulation design. The secondary level categorises the structures of particles or sub-units to form a DDS with sizes from nm to mm as key units in processing such as mixing, grinding, granulation and packing; The tertiary level represents the dynamic structures of DDS during the drug release phase in vitro or in vivo incorporating the structure size range from nm to mm, which undergo changes during dissolution, swelling, erosion or diffusion. The spatial scale for the quaternary level is defined as the meso or micro scale architecture of active and non-active molecules within a DDS with sizes from Å to μm for the molecular structure of drug and excipients. Methods combining X-ray tomography, image processing, and 3D reconstructions have been devised and evaluated to study systematically pharmaceutical structures and correlate them with drug release kinetics of DDS. Based on the quantitative structural information of pharmaceutical intermediates and dosage forms, it is possible now to correlate structures with production processing, behaviour and function, and the static and dynamic structures of DDS with the release kinetics. Thus, a structure-guided methodology has been established for the research of DDS. / Chinese Academy of Sciences

Jeanette Winterson’s debut novel Oranges Are Not the Only Fruit is regarded as an epitome of feminist fiction. The novel centres around Jeanette, a young homosexual girl, and her mother Louie who does not accept her daughter’s sexuality. Just like most mothers in feminist fiction Louie is described, by other scholars, as a one-dimensional character who forces normative femininity upon her daughter and defends patriarchal structures. However, this thesis argues that subtle instances of characterisation create tensions in Louie’s character, especially in regard to gender norms and heterosexual norms. Although Winterson’s novel has been explored from several perspectives most scholars focus on Jeanette. Few scholars have attempted to examine Louie and how she is characterised. Thus, Louie’s character is largely unexplored. To analyse Louie, Shlomith Rimmon-Kenan’s narratological theories on characterisation are used, such as characterisation through direct definitions, actions, and speech. By showing that Louie is marked by tensions she is characterised as a mother who both upholds and challenges patriarchal structures. Since Louie cannot only be perceived as an agent of the patriarchy, she is consequently a complex character.

Today, security has become a growing concern for all internet users, where technology is developing faster than its security is implemented, which leads to insecure domains. In this thesis, we look at the reality of today’s domains and research if some categories of domains are safer than others and the reason behind it. The total amount of researched domains was 8080 divided into four categories; popular, categories, continents, and malicious. The analysis was made by looking closer at default protocols, cipher suites, certificate authorities (CAs), certificate classifications, page loading times, and vulnerabilities. Our result indicated that TLS 1.2 and TLS 1.3 are the most commonly used protocol. The largest difference between the domains could be seen among the CAs, even though no definite reason for this could be found. The most popular cipher suite for popular, categories, and malicious belonged to TLS 1.3 meanwhile, continents had a cipher suite belonging to TLS 1.2. All four categories were vulnerable to at least five out of eight different types of attacks. The least commonly used certificate classification is EV certificates, while DV is the most commonly used. Through our data collection and analysis, we could conclude that all domains are not as safe as one might think, while the underlying security infrastructure of malicious domains might be better than anyone expects.

<p>The rapid growth of fields such as metamaterials, composites, architected materials, porous solids, and micro/nano materials, along with the continuing advancements in design and fabrication procedures have led to the synthesis of complex structures having intricate material distributions and non-trivial geometries. These materials find important applications including biomedical implants and devices, aerospace and naval structures, and micro/nano-electromechanical devices. Theoretical and experimental evidences have shown that these structures exhibit size-dependent (or, nonlocal) effects. This implies that the response of a point within the solid is affected by a collection of points; ultimately a manifestation of the multiscale deformation process. Broadly speaking, at a continuum level, the mathematical description of these multiscale phenomena leads to integral constitutive models, that account for the long-range interactions via nonlocal kernels. </p> <p><br></p> <p>Despite receiving considerable attention, the existing class of approaches to nonlocal elasticity are predominantly phenomenological in nature, following from their definition of the material parameters of the nonlocal kernel based on 'representative volume element' (RVE)-based statistical homogenization of the heterogeneous microstructure. The size of the RVE required for practical simulation, does not achieve a full-resolution of the intricate heterogeneous microstructure, and also implicitly enforces the use of symmetric nonlocal kernels to achieve thermodynamic consistency and mathematically well-posedness. The latter restriction directly limits the application of existing approaches only to the linear deformation analysis of either periodic or isotropic nonlocal structures. Additionally, the lack of a consistent characterization of the nonlocal effects, often results in inconsistent (also labeled as 'paradoxical') predictions depending on the nature of the external loading. In order to address these fundamental theoretical gaps, this dissertation develops a fractional-order kinematic approach to nonlocal elasticity by leveraging cutting-edge mathematical operators derived from the field of fractional calculus.</p> <p><br></p> <p>In contrast to the class of existing class of approaches that adopt an integral stress-strain constitutive relation derived from the equilibrium of the RVE, the fractional-order approach is predicated on a differ-integral (fractional-order) strain-displacement relation. The latter relation is derived from a fractional-order deformation-gradient mapping between deformed and undeformed configurations, and this approach naturally localizes and captures the effect of nonlocality at the root of the deformation phenomena. The most remarkable consequence of this reformulation consists in its ability to achieve thermodynamic and mathematical consistency, irrespective of the nature of the nonlocal kernel. The convex and positive-definite nature of the formulation enabled the use of variational principles to formulate well-posed governing equations, the incorporation of nonlinear effects, and enabled the development of accurate finite element simulation methods. The aforementioned features, when combined with a variable-order extension of the fractional-order continuum theory, enabled the physically consistent application of the nonlocal formulation to general continua exhibiting asymmetric interactions; ultimately a manifestation of material heterogeneity. Indeed, a rigorous theoretical analysis was conducted to demonstrate the natural ability of the variable-order in capturing the role of microstructure in the deformation of heterogeneous porous solids. These advantages allowed the application of the fractional-order kinematic approach to accurately and efficiently model the response of porous beams and plates, with random microstructural descriptions. Results derived from multiphysical loading conditions, as well as nonlinear deformation regimes, are used to demonstrate the causal relation between the kinematics-based fractional-order characterization of nonlocal effects and the natural role of microstructure in determining the macroscopic response of heterogeneous solids. The potential implications of the developed formalism on scientific discovery of material laws are examined in-depth, and different areas for further research are identified.</p>

<p>With the rising prices of energy and the harmful environmental effects many of conventional energy generation techniques the world is pushing for new, cleaner, more efficient and more environmental renewable energy sources. Thermoelectric generators are one of the potential solutions to these problems of unclean and expensive energy. Thermoelectric generators are solid state devices that convert thermal energy into useful electrical energy.</p> <p>Over the last ten years the progress in materials science have led to advancements in thermoelectrics. However as of yet no standardised method of testing thermoelectric generators has been established and as such data provided for thermoelectric generators is regarded as questionable. This thesis deals with two commercial thermoelectric generator models, TEG1 12610-5.1 AND TEG1B 12610-5.1, and quantifies the deviation of the manufacturer’s specifications to what is experimentally achieved by the generators as 147% and 22% respectively. The variance of the outputs between thermoelectric generators was measured by comparing the maximum power output for the models in question over a sample size of four, it was found to be as much as 20% and 8% respectively.</p> <p>A full characterisation of the thermoelectric generators is performed on the two generator models to obtain the data as to their power output and thermal conductivity for the purpose of design of a waste energy harvesting device. The full characterisation was also used to validate the testing apparatus as a device capable for the use as a standardised method of characterising the performance of thermoelectric generation modules.</p> <p>A mechanistic model is created using the experimental characterisation data. This mechanistic model has the ability to accurately predict the voltage and current output of the thermoelectric generator models under any given temperatures and electrical loading condition with a minimum R-squared value of 0.94. The thermal conductivity is also found to be predictable using an established equation modified with an empirical constant.</p> / Master of Applied Science (MASc)

Aevitas is an industrial wastewater treatment plant that receives about 300 m3/day of mixture of wastewater from different industries. The chemical oxygen demand of higher 600 ppm and the variety of the chemical constitution of industrial wastewater are two significant problems on Aevitas. Therefore, there is a strong need for developing advanced analytical techniques that can identify the specific compounds that are the source of COD. During 10 months, about 75 industrial samples were characterized using a battery of tests including GC/MS, COD, TOC, and pH to identify the chemicals that are main source of COD in the industrial wastewaters. Results showed that the COD of 87% of 75 provided samples from Aevitas plant was higher than 600. At the first step of process design, activated carbon was used to eliminate the identified organic chemicals from the wastewaters. The maximum and minimum of COD removal (depends on the chemical composition) of the wastewaters were obtained as 94 and 24%, respectively. Moreover, the amount of COD and TOC that can be adsorbed on the surface of 1 gram of the activated carbon were 25 and 7 mg, respectively. Although activated carbon is capable to reduce the COD, its capacity of adsorption is limited. To overcome this problem an alternative process, membrane filtration was applied for COD removal. Two types of crossflow NF (NF270, NF90, NFX, NFW, NFS, TS80, XN45, and SXN2_L) and RO (BW60 and TW30) membranes in two modules of the spiral wound and flat sheet were used. The filtration results of 11 different industrial wastewaters showed that NF90, TS80, NFX, and NFS were effective in COD removal. However, in terms of output flux NFX and NFS flat sheet were better than others were. Similar to the activated carbon process, the COD removal in filtration process was between 30 and 90%. The obtained results can be used to scale up the membrane filtration process at Aevitas. / Thesis / Master of Chemical Engineering (MChE) / Aevitas is an industrial wastewater treatment plant, which is situated at the City of Brantford. Every day, this plant receives about 15 trucks of the mixture of wastewaters from many different industries. The input wastewater into the plant should be treated and meet the environmental standard so that it can be discharged into a municipal wastewater plant. Currently, the maximum allowable chemical oxygen demand (COD) for discharging the treated wastewater from Aevitas to the municipal wastewater treatment plant is 600 ppm. Despite the fact, the current system in Aevitas is not efficient to meet this criterion. Thus, we strive to design efficient processes to overcome the problem. To this end, 75 samples were collected from Aevitas to observe the kind of chemicals that are the source of COD and then, two processes including activated carbon adsorption and membrane filtration were used for further reduction of COD. Although activated carbon can reduce the COD, the limited adsorption capacity was a major concern for its long-term application, especially if the COD of influent wastewater is higher than 2000 ppm. Membrane filtration was used as an alternative for activated carbon and the results showed that membrane could reduce the COD below 600 in 48% of the cases.

<p>The design and discovery of novel materials are difficult not only due to expensive and time- consuming calculation and measurements of their properties, but also thanks to the infinite search spaces. With the increasingly abundant data from experiments and simulations, learning from data has the potential of bypassing complex physics-based simulations and experiments and providing fast approximations of the solution. Deep learning models are helpful in the design process that requires prohibitively expensive iterative computations. In addition, as efficient and accurate sur- rogate models, trained deep networks can incorporate techniques, such as sensitivity analysis and active learning, to provide guidance in searching promising candidates. Moreover, deep learning models need to account for the material structural information, such as molecule and atom align- ments, chemical bonds, and grain-level interactions, as it plays an important role in determining the macroscopic properties. In this thesis, we start with developing two standard deep learning model- based materials design frameworks for lithium-ion batteries and thermoelectric materials, and we then investigate the feasibility of standard deep learning models on data with graph-structured in- formation and identify the challenges. Finally, we propose a deep graph operator network that effectively capture the spatial dependency encoded in the graph structure to solve networked dy- namical systems.</p> <p><br></p> <p>In the first half of the thesis, we propose a hybrid convolutional neural network to infer lithium- ion battery microstructure properties, Bruggeman’s exponent and shape factor, given its voltage vs. capacity curves. The trained model accurately predicts the microstructural properties on both experimental and simulation data, and it can readily accelerate the processing-properties- performance and degradation characteristics of the existing and emerging chemistries of lithium- ion batteries. Also, we develop a AI-guided framework to discover and design thermoelectric materials, where we train classifiers based on the materials chemical and structural information embeddings and combine with variance-based sensitivity analysis to suggest candidates and con- duct fast screening.</p> <p><br></p> <p>In the second half of the thesis, we build a data-centric framework with a recurrent neural network-based classifier to achieve traffic incident detection on highway networks. We incorporate weak supervised learning and design labeling functions to create large amount of training data with probabilistic labels. The trained deep ensemble accurately detects incidents with predictive uncertainty. To capture the structural information in the network, we then propose a deep graph operator network that maps the input graph state function to the output graph state function. The proposed model enables resolution-independence and zero-shot transfer, where we do not require a set of fixed sensors to encode the graph trajectory and can use the trained model directly on larger graphs with high accuracy. We utilize the proposed model to solve power grid transient stability prediction and traffic forecasting problems.</p>

L'Electrogravimetria AC empra una microbalança de quars electroquímica (EQCM) en règim dinàmic. En l'EQCM un dels elèctrodes d'or depositats sobre el cristall és recobert amb una fina pelolícula d'un polímer electroactiv i és emprat com a elèctrode de treball (WE) dins d'una celola electroquímica. Les variacions de la freqüència de ressonància de la microbalança de quars (QCM) permeten obtindre la resposta massa associada amb la transferència de càrrega que es dóna en la interfície polímer-electròlit. L'Electrogravimetria AC va ser proposta a fi de caracteritzar i separadament identificar el moviment dels ions i el solvent en la interfície polímer-electròlit. En esta tècnica s'analitza en el domine de la freqüència la resposta de massa davant de xicotetes pertorbacions de voltatge gràcies a l'ocupació de la microbalança de quars en règim dinàmic. Per a este propòsit s'aplica una xicoteta pertorbació sinusoidal superposada a una tensió contínua, entre l'elèctrode de referència i l'elèctrode de treball de la celola. Posteriorment, es pot dibuixar la funció de transferència electrogravimètrica (EGTF), definida esta com la raó (?m/?E) entre l'amplitud dels canvis de massa induïts (?m) i l'amplitud de la pertorbació sinusoïdal aplicada (?E). Esta funció de transferència se dibuixa en un pla complex per a cada una de les freqüències de la senyal de pertorbació. Les distintes espècies iònicas involucrades són identificades en el pla complex per mitjà de bucles característics sempre que els bucles no se superposen. Per mitjà d'esta tesi doctoral es proposa un nou sistema de conversió de freqüència-tensió basat en un doble ajust de freqüència implementat amb un PLL mesclant elements analògics i digitals (AD PLL). Els resultats trobats tant en la caracterització electrònica del dispositiu com en la fase experimental proven la fiabilitat del sistema per als mesuraments realitzats en la tècnica d'Electrogravimetria AC. / Torres Villa, RA. (2007). Instrumental techniques for improving the measurements based on Quartz Crystal Microbalances (Técnicas instrumentales para mejorar las mediciones con microbalanzas de cuarzo) [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/17323

<p dir="ltr">Energetic materials are substances with considerable amounts of energy that can detonate under shock, pressure, or high temperature conditions, making them acceptable candidates for applications such as explosives, propellants, and fuels. One example of an energetic material is the explosive known as cyclotetramethylene tetranitramine (HMX). When subjected to impact, HMX can undergo thermo-mechanical responses that may lead to deflagration or, in the most severe cases, detonation. Due to the multiscale nature of these phenomena and the varying impact velocity magnitudes, replicating such responses can be challenging or even unattainable in an experimental setting. Consequently, computational models capable of predicting real-world conditions beyond experimental reach are highly valuable to the explosives research community.</p><p dir="ltr">This study continues the work from previous analyses (Duarte 2021) by developing a finite element model of HMX combined with an aluminum rod, predicting damage evolution and dynamic response under shock compression. The impact velocities applied in the model ranged from 0.1 km/s to 0.6 km/s using three different crystal orientations to investigate their corresponding effects. The results indicate that impacting in the direction normal to the HMX plane [110], which exhibited high levels of plastic energy, had the most resistant to cracking near the HMX-aluminum interface. Furthermore, these findings show that elastic energy accumulation is the primary driver in this analysis of crack propagation and bulk damage in HMX crystals.</p><p dir="ltr">Additionally, the HMX and aluminum results were compared against two additional models: a homogeneous HMX sample without discontinuities and an HMX sample with a void in place of the aluminum rod. Comparisons of the models show that the most severe damage field occurs in the void model, while the shock wave accelerated through the aluminum rod but also decelerated significantly in the presence of a void due to wave refraction at traction free boundaries. These results provide another level of understanding into the role of material interfaces and voids in the dynamic response of HMX under shock loading. Experimental validation of these findings is recommended for future studies, assuming the conditions are feasible for testing.</p>