This dissertation contributes to uncertainty-aware neural networks using multi-modality data, with a focus on industrial and aviation applications. Drawing from seminal works in recent years that have significantly advanced the field, this dissertation develops techniques for incorporating uncertainty estimation and leveraging multi-modality information into neural networks for tasks such as fault detection and environmental perception. The escalating complexity of data in engineering contexts demands models that predict accurately and quantify uncertainty in these predictions. The methods proposed in this document utilize various techniques, including Bayesian Deep Learning, multi-task regularization and feature fusion, and efficient use of unlabeled data. Popular methods of uncertainty quantification are analyzed empirically to derive important insights on their use in real world engineering problems. The primary objective is to develop and refine Bayesian neural network models for enhanced predictive accuracy and decision support in engineering. This involves exploring novel architectures, regularization methods, and data fusion techniques. Significant attention is given to data handling challenges in deep learning, particularly in the context of quality inspection systems. The research integrates deep learning with vision systems for engineering risk assessment and decision support tasks, and introduces two novel benchmark datasets designed for semantic segmentation and classification tasks. Additionally, the dissertation delves into RGB-Depth data fusion for pipeline defect detection and the use of semi-supervised learning algorithms for manufacturing inspection tasks with imaging data. The dissertation contributes to bridging the gap between advanced statistical methods and practical engineering applications.

Peptide-based vaccines represent a promising strategy to develop personalized treatments for cancer immunotherapy. Despite their specificity and low cost of production, these vaccines have had minimal success in clinical studies due to their lack of immunogenicity, creating a need for more effective vaccine designs. Adjuvants can be incorporated to enhance their immunogenicity by promoting dendritic cell activation and antigen cross-presentation. Due to their favorable size and ability to incorporate peptides and adjuvants, nanoparticles represent an advantageous platform for designing peptide vaccines. One prime example is RNA origami (RNA-OG) nanostructures, which are nucleic acid nanostructures programmed to assemble into uniform shapes and sizes. These stable nanostructures can rationally incorporate small molecules giving them a wide array of functions. Furthermore, RNA-OG itself can function as an adjuvant to stimulate innate immune cells. In the following study, self-adjuvanted RNA-OG was employed as a vaccine assembly platform, incorporating tumor peptides onto the nanostructure to design RNA-OG-peptide nanovaccines for cancer immunotherapy. RNA-OG-peptide was found to induce dendritic cell activation and antigen cross-presentation, which mobilized tumor-specific cytotoxic T cells to elicit protective anti-tumor immunity in tumor-bearing mice. These findings demonstrate the therapeutic potential of RNA-OG as a stable, carrier-free nanovaccine platform. In an attempt to further enhance the efficacy by optimizing the amount of peptides assembled, RNA-OG was complexed with polylysine-linked peptides, a simple strategy that allowed peptide amounts to be varied. Interestingly, increasing the peptide load led to decreased vaccine efficacy, which was correlated with an ineffective CD8+ T cell response. On the other hand, the vaccine efficacy was improved by decreasing the amount of peptide loaded onto RNA-OG, which may have attributed to greater complex stability compared to the high peptide load. These results highlight a simple strategy that can be used to optimize vaccine efficacy by altering the load of assembled peptides. These studies advance our understanding of RNA-OG as a peptide vaccine platform and provide various strategies to improve the design of peptide vaccines for translation into cancer immunotherapy.

Cell immunotherapies have revolutionized clinical oncology. While CAR T cell therapy has been very effective in clinical studies, off-target immune toxicity limits eligible patients. Thus, NK cells have been approached with the same therapy design since NK cells have a more favorable safety profile. Therefore, the purpose of this research project is to explore DNA nanotech-based NK cell engagers (NKCEs) that force an immunological synapse between the NK cell and the cancer cell, leading to cancer death. DNA tetrabody (TB) and DNA tetrahedron (TDN) are fabricated and armed with HER2 affibody for tight adhesion to HER2+ cancer cell lines like SKBR3. Overall, relationship between TB-NK treatment and cancer cell apoptosis is still unclear. TB-NK treatment induces an apoptotic profile similar to PMA/IO stimulation. Pilot cell assay needs to be replicated with additional controls and a shortened treatment window. For DNA TDN fabrication, HER2 affibody polishing with Ni-NTA affinity chromatography achieves high purity with 20% to 100% high-imidazole elution gradient. ssDNA-HER2 affibody conjugation is optimal when ssDNA is treated with 40-fold excess sulfo-SMCC for 4 hours. In conclusion, the manufacturing of DNA-based NKCEs is rapid and streamlined, which gives these NKCEs the potential to become a ready to use immunotherapy.

Originally conceived as a way to scaffold molecules of interest into three-dimensional (3D) crystalline lattices for X ray crystallography, the field of deoxyribonucleic acid (DNA) nanotechnology has dramatically evolved since its inception. The unique properties of DNA nanostructures have promoted their use not only for X ray crystallography, but for a suite of biomedical applications as well. The work presented in this dissertation focuses on both of these exciting applications in the field: 1) Nucleic acid nanostructures as multifunctional drug and vaccine delivery platforms, and 2) 3D DNA crystals for structure elucidation of scaffolded guest molecules.Chapter 1 illustrates how a wide variety of DNA nanostructures have been developed for the delivery of drugs and vaccine components. However, their applications are limited under physiological conditions due to their lack of stability in low salt environments, susceptibility to enzymatic degradation, and tendency for endosomal entrapment. To address these issues, Chapter 2 describes a PEGylated peptide coating molecule was designed to electrostatically adhere to and protect DNA origami nanostructures and to facilitate their cytosolic delivery by peptide-mediated endosomal escape. The development of this molecule will aid in the use of nucleic acid nanostructures for biomedical purposes, such as the delivery of messenger ribonucleic acid (mRNA) vaccine constructs. To this end, Chapter 3 discusses the fabrication of a structured mRNA nanoparticle for more cost-efficient mRNA vaccine manufacture and proposes a multi-epitope mRNA nanostructure vaccine design for targeting human papillomavirus (HPV) type 16-induced head and neck cancers. DNA nanotechnology was originally envisioned to serve as three-dimensional scaffolds capable of positioning proteins in a rigid array for their structure elucidation by X ray crystallography. Accordingly, Chapter 4 explores design parameters, such as sequence and Holliday junction isomeric forms, for efficient crystallization of 3D DNA lattices. Furthermore, previously published DNA crystal motifs are used to site-specifically position and structurally evaluate minor groove binding molecules with defined occupancies. The results of this study provide significant advancement towards the ultimate goal of the field.

The biological lipid bilayer on cells or the cell membrane is a surface teeming with activity. Several membrane proteins decorate the lipid bilayer to carry out various functionalities that help a cell interact with the environment, gather resources and communicate with other cells. This provides a repertoire of biological structures and processes that can be mimicked and manipulated. Since its inception in the late 20th century deoxyribonucleic acid (DNA) nanotechnology has been used to create nanoscale objects that can be used for such purposes. Using DNA as the building material provides the user with a programmable and functionalizable tool box to design and demonstrate these ideas. In this dissertation, I describe various DNA nanostructures that can insert or interact with lipid bilayers for cargo transport, diagnostics and therapeutics. First, I describe a reversibly gated DNA nanopore of 20.4nm x 20.4nm cross sectional width. Controlled transport of cargoes of various sizes across a lipid bilayer through a channel formed by the DNA nanopore was demonstrated. This demonstration paves the way for a class of nanopores that can be activated by different stimuli. The membrane insertion capability of the DNA nanopore is further utilized to design a nanopore sensor that can detect oligonucleotides of a specific s equence inside a lipid vesicle. The ease with which the sensor can be modified to i dentify different diagnostic markers for disease detection was shown by designing a sensor that can identify the non small cell lung cancer marker micro ribonucleic acid -21 (miRNA21). Finally, I demonstrate the therapeutic capabilities of DNA devices with a DNA tetrabody that can recruit natural killer cells (NK cells) to target cancer cells. The DNA tetrabody functionalized with cholesterol molecules and Her2 affibody inserts into NK cell membrane leading it to Her2 positive cancer cells. This shows that inthe presence of DNA tetrabody, the NK cell activation gets accelerated.

Molecular tessellation research aims to elucidate the underlying principles that govern intricate patterns in nature and to leverage these principles to create precise and ordered structures across multiple scales, thereby facilitating the emergence of novel functionalities. DNA origami technology enables the fabrication of nearly arbitrary DNA architectures with nanoscale precision, which can serve as excellent building blocks for the construction of tessellation patterns. However, the size and complexity of DNA origami tessellation systems are currently limited by several unexplored factors relevant to the accuracy of essential design parameters, the applicability of design strategies, and the compatibility between different tiles. Here, a general design and assembly method are described for creating DNA origami tiles that grow into tessellation patterns with micrometer-scale order and nanometer-scale precision. A critical design parameter, interhelical distance (D), was identified, which determined the conformation of monomer tiles and the outcome of tessellation. Finely tuned D facilitated the accurate geometric design of monomer tiles with minimized curvature and improved tessellation capability. To demonstrate the generality of the design method, 9 tile geometries and 15 unique tile designs were generated. The designed tiles were assembled into single-crystalline lattices ranging from tens to hundreds of square micrometers with micrometer-scale, nearly defect-free areas readily visualized by atomic force microscopy. Two strategies were applied to further increase the complexity of DNA origami tessellation, including reducing the symmetry of monomer tiles and co-assembling tiles of various geometries. The designed 6 complex tilings that includes 5 Archimedean tilings and a 12-fold quasicrystal tiling yielded various tiling patterns that great in size and quality, indicating the robustness of the optimized tessellation system. The described design and assembly approach can also be employed to create square DNA origami units for algorithmic self-assembly. As the square units assembled and expanded, they executed the binary function XOR, which generated the Sierpinski triangular pattern according to the predetermined instructions. This study will promote DNA-templated, programmable molecular and material patterning and open up new opportunities for applications in metamaterial engineering, nanoelectronics, and nanolithography.

National Airspace Systems (NAS) are complex cyber-physical systems that require swift air traffic management (ATM) to ensure flight safety and efficiency. With the surging demand for air travel and the increasing intricacy of aviation systems, the need for advanced technologies to support air traffic management and air traffic control (ATC) service has become more crucial than ever. Data-driven models or artificial intelligence (AI) have been conceptually investigated by various parties and shown immense potential, especially when provided with a vast volume of real-world data. These data include traffic information, weather contours, operational reports, terrain information, flight procedures, and aviation regulations. Data-driven models learn from historical experiences and observations and provide expeditious recommendations and decision support for various operation tasks, directly contributing to the digital transformation in aviation. This dissertation reports several research studies covering different aspects of air traffic management and ATC service utilizing data-driven modeling, which are validated using real-world big data (flight tracks, flight events, convective weather, workload probes). These studies encompass a range of topics, including trajectory recommendations, weather studies, landing operations, and aviation human factors. Specifically, the topics explored are (i) trajectory recommendations under weather conditions, which examine the impact of convective weather on last on-file flight plans and provide calibrated trajectories based on convective weather; (ii) multi-aircraft trajectory predictions, which study the intention of multiple mid-air aircraft in the near-terminal airspace and provide trajectory predictions; (iii) flight scheduling operations, which involve probabilistic machine learning-enhanced optimization algorithms for robust and efficient aircraft landing sequencing; (iv) aviation human factors, which predict air traffic controller workload level from flight traffic data with conformalized graph neural network. The uncertainties associated with these studies are given special attention and addressed through Bayesian/probabilistic machine learning. Finally, discussions on high-level AI-enabled ATM research directions are provided, hoping to extend the proposed studies in the future. This dissertation demonstrates that data-driven modeling has great potential for aviation digital twins, revolutionizing the aviation decision-making process and enhancing the safety and efficiency of ATM. Moreover, these research directions are not merely add-ons to existing aviation practices but also contribute to the future of transportation, particularly in the development of autonomous systems.

Assembly lines are low-cost production systems that manufacture similar finished units in large quantities. Manufacturers utilize mixed-model assembly lines to produce customized items that are not identical but share some general features in response to consumer needs. To maintain efficiency, the aim is to find the best feasible option to balance the lines efficiently; allocating each task to a workstation to satisfy all restrictions and fulfill all operational requirements in such a way that the line has the highest performance and maximum throughput. The work to be done at each workstation and line depends on the precise product configuration and is not constant across all models. This research seeks to enhance the subject of assembly line balancing by establishing a model for creating the most efficient assembly system. Several realistic characteristics are included into efficient optimization techniques and mathematical models to provide a more comprehensive model for building assembly systems. This involves analyzing the learning growth by task, employing parallel line designs, and configuring mixed models structure under particular constraints and criteria. This dissertation covers a gap in the literature by utilizing some exact and approximation modeling approaches. These methods are based on mathematical programming techniques, including integer and mixed integer models and heuristics. In this dissertation, heuristic approximations are employed to address problem-solving challenges caused by the problem's combinatorial complexity. This study proposes a model that considers learning curve effects and dynamic demand. This is exemplified in instances of a new assembly line, new employees, introducing new products or simply implementing engineering change orders. To achieve a cost-based optimal solution, an integer mathematical formulation is proposed to minimize the production line's total cost under the impact of learning and demand fulfillment. The research further creates approaches to obtain a comprehensive model in the case of single and mixed models for parallel lines systems. Optimization models and heuristics are developed under various aspects, such as cycle times by line and tooling considerations. Numerous extensions are explored effectively to analyze the cost impact under certain constraints and implications. The implementation results demonstrate that the proposed models and heuristics provide valuable insights.