G protein-coupled receptors (GPCRs) represent the largest membrane protein family and a significant target class for therapeutics. Receptors from GPCRs’ largest class, class A, influence virtually every aspect of human physiology. About 45% of the members of this family endogenously bind flexible peptides or peptides segments within larger protein ligands. While many of these peptides have been structurally characterized in their solution state, the few studies of peptides in their receptor-bound state suggest that these peptides interact with a shared set of residues and undergo significant conformational changes. For the purpose of understanding binding dynamics and the development of peptidomimetic drug compounds, further studies should investigate the peptide ligands that are complexed to their cognate receptor.

Snake vertebrae are common in the fossil record, whereas cranial remains are generally fragile and rare. Consequently, vertebrae are the most commonly studied fossil element of snakes. However, identification of snake vertebrae can be problematic due to extensive variation. This study utilizes 2-D geometric morphometrics and canonical variates analysis to 1) reveal variation between genera and species and 2) classify vertebrae of modern and fossil eastern North American Agkistrodon and Crotalus. The results show that vertebrae of Agkistrodon and Crotalus can reliably be classified to genus and species using these methods. Based on the statistical analyses, four of the fossil viperid vertebrae from Hickory Tree Cave were assigned to Crotalus horridus, one to C. adamanteus, and another to Agkistrodon piscivorus. The potential presence of the latter two species could indicate that the deposit is from a warm period during the Quaternary such as a Pleistocene interglacial or Holocene warm interval.

Molecular dynamics (MD) simulations find their applications in fundamental understanding of molecular level mechanisms of physical processes. This assists in tuning the key features affecting the development of the novel hybrid materials. A certain application demanding the need for a desired function can be cherished through the hybrids with a blend of new properties by a combination of pure materials. However, to run MD simulations, an accurate representation of the interatomic potentials i.e. force-fields (FF) models remain a crucial aspect. This thesis intricately explores the fusion of MD simulations, uncertainty quantification, and data-driven methodologies to accelerate the computational design of innovative materials and models across the following interconnected chapters. Beginning with the development of force fields for atomic-level systems and coarse-grained models for FCC metals, the study progresses into exploring the intricate interfacial interactions between 2D materials like graphene, MoS2, and water. Current state-of-the-art model development faces the challenge of high dimensional input parameters' model and unknown robustness of developed model. The utilization of advanced optimization techniques such as particle swarm optimization (PSO) integrated with MD enhances the accuracy and precision of FF models. Moreover, the bayesian uncertainty quantification (BUQ) assists FF model development researchers in estimating the robustness of the model. Furthermore, the complex structure and dynamics of water confined between and around sheets was unraveled using 3D Convolutional Neural Networks (3D-CNN). Specifically, through classification and regression models, water molecule ordering/disordering and atomic density profiles were accurately predicted, thereby elucidating nuanced interplays between sheet compositions and confined water molecules. To further the computational design of hybrid materials, this thesis delves into designing and investigating polymer composites with functionalized MOFs shedding light on crucial factors governing their compatibility and performance. Therefore, this report includes the study of structure and dynamics of functionalized MOF in the polymer matrix. Additionally, it investigates the biomedical potential of porous MOFs as drug delivery vehicles (DDVs). Often overlooked is the pivotal role of solvents (used in MOF synthesis or found in relevant body fluids) in the drug adsorption and release process. This report underscores the solvent's impact on drug adsorption within MOFs by comparing results in its presence and absence. Building on these findings, the study delves into the effects of MOF functionalization on tuning the drug adsorption and release process. It further explores how different physical and chemical properties influence drug adsorption within MOFs. Furthermore, the research explores the potential of functionalized MOFs for improved carbon capture, considering their application in energy-related contexts. By harnessing machine learning and deep learning, the thesis introduces innovative pathways for material property prediction and design, emphasizing the pivotal fusion of computational methodologies with data-driven approaches to advance molecular-level understanding and propel future material design endeavors. / Doctor of Philosophy / Envision a world where scientific exploration reaches the microscopic scale, powered by advanced computational tools. In this frontier of materials science, researchers employ sophisticated computer simulations to delve into the intricate properties of materials, particularly focusing on Metal-Organic Frameworks (MOFs). These MOFs, equivalent to microscopic molecular sponges, exhibit remarkable abilities to capture gases or hold medicinal drug compounds. This thesis meticulously studies MOFs alongside materials like graphene, Boron Nitride and Molybdenum disulfide, investigating their interactions with water with unprecedented precision. Through these detailed explorations and the fusion of cutting-edge technologies, we aim to unlock a future featuring enhanced drug delivery systems, improved energy storage solutions, and innovative energy applications.

In this thesis, we study the degree of the canonical map of surfaces of general type. In particular, we give the first examples known in the literature of surfaces having degree d=10,11, 13, 14, 15, and 18 of the canonical map. They are presented in a self-contained and independent way from the rest of the thesis. We show also how we have discovered them. These surfaces are product-quotient surfaces. In this thesis, we study the theory of product-quotient surfaces giving also some new results and improvements. As a consequence of this, we have written and run a MAGMA script to produce a list of families of product-quotient surfaces having geometric genus three and a self-intersection of the canonical divisor large. After that, we study the canonical map of product-quotient surfaces and we apply the obtained results to the list of product-quotient surfaces just mentioned. In this way, we have discovered the examples of surfaces having degree d=10,11,14, and 18 of the canonical map. The remaining ones with degrees 13 and 15 do not satisfy the assumptions to compute the degree of the canonical map directly. Hence we have had to compute the canonical degree of these two families of product-quotient surfaces in a very explicit way through the equations of the pair of curves defining them. Another work of this thesis is the classification of all smooth surfaces of general type with geometric genus three which admits an action of a group G isomorphic to \mathbb Z_2^k and such that the quotient is a projective plane. This classification is attained through the theory of abelian covers. We obtained in total eleven families of surfaces. We compute the canonical map of all of them, finding in particular a family of surfaces with a canonical map of degree 16 not in the literature. We discuss the quotients by all subgroups of G finding several K3 surfaces with symplectic involutions. In particular, we show that six families are families of triple K3 burgers in the sense of Laterveer. Finally, in another work we study also the possible accumulation points for the slopes K^2/ \chi of unbounded sequences of minimal surfaces of general type having a degree d of the canonical map. As a new result, we construct unbounded families of minimal (product-quotient) surfaces of general type whose degree of the canonical map is 4 and such that the limits of the slopes K^2/ \chi assume countably many different values in the closed interval [6+2/3, 8].

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The run-time reconfiguration of Field Programmable Gate Arrays (FPGAs) opens new avenues to hardware reuse. Through the use of process migration between hardware and software, an FPGA provides a parallel execution cache. Busy processes can be migrated into hardware-based, parallel processors, and idle processes can be migrated out increasing the utilization of the hardware. The application of hardware/software process migration to the acceleration of Register Transfer Level (RTL) circuit simulation is developed and analyzed. RTL code can exhibit a form of locality of reference such that executing processes tend to be executed again. This property is termed executive temporal locality, and it can be exploited by migration systems to accelerate RTL simulation. In this dissertation, process migration is first formally modeled using Finite State Machines (FSMs). Upon FSMs are built programs, processes, migration realms, and the migration of process state within a realm. From this model, a taxonomy of migration realms is developed. Second, process migration is applied to the RTL simulation of digital circuits. The canonical form of an RTL process is defined, and transformations of HDL code are justified and demonstrated. These transformations allow a simulator to identify basic active units within the simulation and combine them to balance the load across a set of processors. Through the use of input monitors, executive locality of reference is identified and demonstrated on a set of six RTL designs. Finally, the implementation of a migration system is described which utilizes Virtual Machines (VMs) and Real Machines (RMs) in existing FPGAs. Empirical and algorithmic models are developed from the data collected from the implementation to evaluate the effect of optimizations and migration algorithms. / Ph. D.