Physical Sciences and Mathematics Commons^™

The Effect Of Nanomorphology As Quantified Via The K-Index On The Drug Delivery Properties Of Isocyanate-Derived Aerogels, Stephen Y. Owusu, A. B.M.Shaheen Ud Doulah, Vaibhav A. Edlabadkar, Kamden J. George, Chariklia Sotiriou-Leventis

Chemistry Faculty Research & Creative Works

This study explores the potential to predict the drug-loading and release profiles of aerogels based on their morphologies: a milestone in drug delivery research, which can help save time and cost in formulating new aerogel drug carriers and cut-down evaluation of the drug delivery capabilities of aerogels to a few experimental runs. Polyurea (PUA) and poly(isocyanurate-urethane) (PIR-PUR) aerogels were used as model systems, while 5-fluorouracil (5-FU) and paracetamol (PM) were used as model drugs. These model systems were chosen because they can be synthesized into different morphologies, which can be quantified by the so-called K-index (water contact angle divided by …

Message From Bits 2024 Co-Chairs And Technical Program Co-Chairs; Smartcomp 2024, Sajal K. Das, Hayato Yamana, Keiichi Yasumoto, Shameek Bhattacharjee

Computer Science Faculty Research & Creative Works

No abstract provided.

Landmark-Based Localization Using Stereo Vision And Deep Learning In Gps-Denied Battlefield Environment, Ganesh Sapkota, Sanjay Madria

Computer Science Faculty Research & Creative Works

Localization in a battlefield environment is increasingly challenging as GPS connectivity is often denied or unreliable, and physical deployment of anchor nodes across wireless networks for localization can be difficult in hostile battlefield terrain. This paper proposes a novel framework for the localization of moving objects in non-GPS battlefield environments using stereo vision and a deep learning model by recognizing naturally existing or artificial landmarks as anchors. The proposed method utilizes a custom-calibrated stereo vision camera for distance estimation and the YOLOv8s model, which is trained and fine-tuned with our real-world dataset for landmark anchor recognition. The depth images are …

Posca: Path Optimization For Solar Cover Amelioration In Urban Air Mobility, Debjyoti Sengupta, Anurag Satpathy, Sajal K. Das

Computer Science Faculty Research & Creative Works

Urban Air Mobility (UAM) encompasses both piloted and autonomous aerial vehicles, spanning from small unmanned aerial vehicles (UAVs) like drones to passenger-carrying personal air vehicles (PAVs), to revolutionize smart transportation in congested urban areas. This emerging paradigm is anticipated to offer disruptive solutions to the mobility challenges in congested cities. In this context, a pivotal concern centers on the sustainability of transitioning to this mode of transportation, especially with the focus on incorporating clean technology into developing innovative solutions from the ground up. Recent studies highlight that a significant portion of the total energy consumption in UAM can be attributed …

Tasr: A Novel Trust-Aware Stackelberg Routing Algorithm To Mitigate Traffic Congestion, Doris E.M. Brown, Venkata Sriram Siddhardh Nadendla, Sajal K. Das

Computer Science Faculty Research & Creative Works

A Stackelberg routing platform (SRP) reduces congestion in one-shot traffic networks by proposing optimal route recommendations to the selfish travelers. Traditionally, Stackel-berg routing is cast as a partial control problem where a fraction of the traveler flow complies with route recommendations, while the remaining responds as selfish travelers. In this paper, we formulate a novel Stackelberg routing framework where the agents exhibit probabilistic compliance by accepting SRP's route recommendations with a trust probability. Specifically, we propose a greedy Trust-Aware Stackelberg Routing algorithm (in short, TASR) for SRP to compute unique path recommendations to each traveler flow with a unique demand. …

Minerrouter : Effective Message Routing Using Contact-Graphs And Location Prediction In Underground Mine, Abhay Goyal, Sanjay Madria, Samuel Frimpong

Computer Science Faculty Research & Creative Works

Location-based distributed communication in underground mines has been a hard problem to solve due to unreliable centralized architecture such as leaky feeder systems, high attenuation, and the unavailability of GPS signals. Delay Tolerant Networks (DTN) enable decentralized message routing using the store-carry-forward method that can help in creating situational awareness needed to handle emergency and disaster scenarios. The ability to predict where the DTN nodes (miner) might have been at/are headed to (with respect to the mine regions and pillars) at different times, combined with contact-based routing and intelligent handling of buffer, can be used for better delivery of messages. …

Unsafe Events Detection In Smart Water Meter Infrastructure Via Noise-Resilient Learning, Ayanfeoluwa Oluyomi, Sahar Abedzadeh, Shameek Bhattacharjee, Sajal K. Das

Computer Science Faculty Research & Creative Works

Residential smart water meters (SWMs) collect real-time water consumption data, enabling automated billing and peak period forecasting. The presence of unsafe events is typically detected via deviations from the benign profile of water usage. However, profiling the benign behavior is non-trivial for large-scale SWM networks because once deployed, the collected data already contain those events, biasing the benign profile. To address this challenge, we propose a real-time data-driven unsafe event detection framework for city-scale SWM networks that automatically learns the profile of benign behavior of water usage. Specifically, we first propose an optimal clustering of SWMs based on the recognition …

Traffic Prediction-Based Vnf Auto-Scaling And Deployment Mechanism For Flexible And Elastic Service Provision, Bo Yi, Jiacheng Wang, Qiang He, Xingwei Wang, Min Huang, Sajal K. Das, Keqin Li

Computer Science Faculty Research & Creative Works

Network Function Virtualization (NFV) provides a flexible way to provision new services by decoupling network functions from hardware and implementing them as Virtual Network Functions (VNFs). However, the rapid development of technologies greatly promotes the explosion of diverse services, which directly results in the exponential increase of heterogeneous traffic. In addition, such a tremendous amount of heterogeneous traffic will generate bursts in a more dynamic and unexpected manner, so it becomes extremely hard to satisfy the customer demands. Aiming at addressing these challenges, this work proposes a positive and elastic VNF deployment mechanism for service provisioning, which introduces three novelties: …

Federated Graph Anomaly Detection Via Contrastive Self-Supervised Learning, Xiangjie Kong, Wenyi Zhang, Hui Wang, Mingliang Hou, Xin Chen, Xiaoran Yan, Sajal K. Das

Computer Science Faculty Research & Creative Works

Attribute graph anomaly detection aims to identify nodes that significantly deviate from the majority of normal nodes and has received increasing attention due to the ubiquity and complexity of graph-structured data in various real-world scenarios. However, current mainstream anomaly detection methods are primarily designed for centralized settings, which may pose privacy leakage risks in certain sensitive situations. Although federated graph learning offers a promising solution by enabling collaborative model training in distributed systems while preserving data privacy, a practical challenge arises as each client typically possesses a limited amount of graph data. Consequently, naively applying federated graph learning directly to …

Log Sequence Anomaly Detection Based On Template And Parameter Parsing Via Bert, Xiaolin Chai, Hang Zhang, Jue Zhang, Yan Sun, Sajal K. Das

Computer Science Faculty Research & Creative Works

Logs record various operations and events during system running in text format, which is an essential basis for detecting and identifying potential security threats or system failures and is widely used in system management to ensure security and reliability. Existing log sequence anomaly detection is limited by log parsing and does not consider all key features of logs, which may cause false or missed detection. In this paper, we propose a fast and accurate log parsing method and feed the entire log content into the deep learning network for analysis. To avoid semantic loss during parsing, we replace some variables …

L3geocast: Enabling P4-Based Customizable Network-Layer Geocast At The Network Edge, Xindi Hou, Shuai Gao, Ningchun Liu, Fangtao Yao, Hongke Zhang, Sajal K. Das

Computer Science Faculty Research & Creative Works

Geocast is a one-to-many communication paradigm that enables the transmission of data packets to a designated area rather than an IP address. The most common geocast solutions rely on the application-layer Geolocation-to-IP database. But these IP-based approaches cannot cope with the challenges of flexibility and mobility in a granularity-customizable geocast scenario. While some non-IP network-layer (L3) attempts have resulted in low addressing accuracy and poor routing scalability. Besides, the clean-slate design is incompatible with the existing network. To address these issues, this article proposes an innovative network-layer geographic addressing scheme that leverages P4-based Software Defined Networks (SDN) to enable flexible …

Mobilytics: Mobility Analytics Framework For Transferring Semantic Knowledge, Shreya Ghosh, Soumya K. Ghosh, Sajal K. Das, Prasenjit Mitra

Computer Science Faculty Research & Creative Works

The proliferation of sensor-equipped smartphones has led to the generation of vast amounts of GPS data, such as timestamped location points, enabling a range of location-based services. However, deciphering the spatio-temporal dynamics of mobility to understand the underlying motivations behind travel patterns presents a significant challenge. his paper focuses on how individuals' GPS traces (latitude, longitude, timestamp) interpret the connection and correlations among different entities such as people, locations or point-of-interests (POIs), and semantic contexts (trip-purpose). We introduce a mobility analytics framework, named Mobilytics designed to identify trip purposes from individual GPS traces by leveraging a “mobility knowledge graph” (MKG) …

Lifelong Learning-Based Optimal Trajectory Tracking Control Of Constrained Nonlinear Affine Systems Using Deep Neural Networks, Irfan Ganie, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This article presents a novel lifelong integral reinforcement learning (LIRL)-based optimal trajectory tracking scheme using the multilayer (MNN) or deep neural network (Deep NN) for the uncertain nonlinear continuous-time (CT) affine systems subject to state constraints. A critic MNN, which approximates the value function, and a second NN identifier are together used to generate the optimal control policies. The weights of the critic MNN are tuned online using a novel singular value decomposition (SVD)-based method, which can be extended to MNN with the N-hidden layers. Moreover, an online lifelong learning (LL) scheme is incorporated with the critic MNN to mitigate …

Deep Learning For Uav Detection And Classification Via Radio Frequency Signal Analysis, Prajoy Podder, Maciej Zawodniok, Sanjay Madria

Electrical and Computer Engineering Faculty Research & Creative Works

Unmanned Aerial Vehicles (UAVs) are advertised as great tool that benefits society and humanity. However, UAVs also pose significant security threats ranging from privacy invasions, to interfering with commercial aircraft landing and takeoff, to accidently crashing into vehicles or people, to military or terrorist attacks. Consequently, there is a pressing need to detect and identify UAVs to mitigate such potential risks. While image-based methods are crucial for UAV detection, radio frequency (RF) emissions offer additional valuable insights. Analyzing RF signals, such as those used in UAV-ground station communications, can provide information about UAV types based on distinct frequency usage or …

Re-Evaluating Missouri’S Strategic Element Potential: A Geochemical Study Of The Mesoproterozoic Fe-Cu-Co-Ree Deposits In Southeast Missouri, Usa, Brandon James Sullivan

Doctoral Dissertations

"Iron-oxide-copper-gold (IOCG) deposits are poorly understood mineral systems. For example, we do not know why Cu- and Co-rich IOCG deposits typically occur proximal to Fe ore deposits that are notably Cu and Co-poor, such as Iron Oxide Apatite (IOA) deposits. To better understand the formation of IOA and IOCG deposits in Missouri, USA, this PhD thesis examines the genesis of the Kratz Spring IOA and the Boss Central Dome IOCG deposits. This study presents the first constraints on formation conditions and fluid sources in the studied deposits using integrated petrographic, mineral composition, and Fe isotope analyses of oxide minerals. Observations …

Understanding Catalyst Design Principles In Transition Metal Mixed Anionic Chalcogenides For Electrocatalytic Energy Conversion, Ibrahim Abdullahi

Doctoral Dissertations

"This research focused on the synthetic design of transition metal mixed anionic chalcogenide catalysts containing various ligand types around the central metal atom (chalcogen anion and chalcogen-based organic ligand) generating diverse crystal structure types applied for water splitting and carbon dioxide reduction reactions (CO₂RR).

A series of catalysts were synthesized starting with isolated metal complexes (ME_n) with a central metal core (M = Co, Ni, Cu, and Cr) through molecular clusters, to bulk nanostructured solids of similar M-E coordination. Bis(dichalcogenoimidodiphosphinato) were employed as ligands in the metal complexes, and anionic chalcogen (E = S, Se, and …

Multiple Imputation For Robust Cluster Analysis To Address Missingness In Medical Data, Arnold Harder, Gayla R. Olbricht, Godwin Ekuma, Daniel B. Hier, Tayo Obafemi-Ajayi

Mathematics and Statistics Faculty Research & Creative Works

Cluster Analysis Has Been Applied To A Wide Range Of Problems As An Exploratory Tool To Enhance Knowledge Discovery. Clustering Aids Disease Subtyping, I.e. Identifying Homogeneous Patient Subgroups, In Medical Data. Missing Data Is A Common Problem In Medical Research And Could Bias Clustering Results If Not Properly Handled. Yet, Multiple Imputation Has Been Under-Utilized To Address Missingness, When Clustering Medical Data. Its Limited Integration In Clustering Of Medical Data, Despite The Known Advantages And Benefits Of Multiple Imputation, Could Be Attributed To Many Factors. This Includes Methodological Complexity, Difficulties In Pooling Results To Obtain A Consensus Clustering, Uncertainty Regarding …

Hysplit In Simulating The Atmospheric Dispersion Of Hazardous Aerosols: A Case Study In St. Louis, Missouri, Ahmet Tolga Odabasi

Masters Theses

"Atmospheric dispersion and transmission play an important role in the behavior and effects of air pollution. Human health can be adversely affected by air pollution in a variety of ways, both immediately and over time. The Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) modeling program, a computer model and software package, tracks the transport trajectories and distributions of air pollution and various pollutants, including radioactive pollutants, in the atmosphere. It also facilitates research on pollution sources. This study simulated the transport of hazardous aerosols in St. Louis region for the years 2020, 2021, and 2022 using the HYSPLIT modeling …

Branching Fractional Brownian Motion, Reece Beattie-Hauser

Masters Theses

"Fractional Brownian Motion (FBM) is a Gaussian process whose increments are correlated over long times. FBM is an example of anomalous diffusion, and recently it has been used to model the distribution of serotonergic fibers in the brain [1, 2]. To better represent these fibers, branching FBM (bFBM), where FBM trajectories may randomly split into two, is introduced. One-dimensional bFBM is studied in both sub diffusive and super diffusive regimes, examining three potential behaviors of the correlations (memory) in a branching event: both trajectories retain the memory of previous steps, only one keeps the memory, and neither keeps the memory. …

Comparative Study Of Crypto Volatility And Price Forecasting Using A Mixture Of Time Series And Machine Learning Models, Abhishek Kafle

Masters Theses

"Forecasting financial product volatility and price is crucial for informed decision-making in investment and risk management. The models considered include GARCH, LSTM, GRU, BiLSTM, and hybrid models that incorporate various combinations of these models. We present a comparative analysis of forecasting volatility and price using the aforementioned models.

We also introduce a user-friendly dashboard for model training and evaluation, enabling users to upload datasets and customize model parameters. The dashboard allows users to select the type of model, specify the dataset range for training, determine the number of epochs, adjust the number of layers for deep …

A New Proper Orthogonal Decomposition Method With Second Difference Quotients For The Wave Equation, Andrew Calvin Janes

Masters Theses

"Recently, researchers have investigated the relationship between proper orthogonal decomposition (POD), difference quotients (DQs), and pointwise in time error bounds for POD reduced order models of partial differential equations. In \cite {Sarahs}, a new approach to POD with DQs was developed that is more computationally efficient than the standard DQ POD approach and it also retains the guaranteed pointwise in time error bounds of the standard method. In this thesis, we extend the new DQ POD approach from \cite {Sarahs} to the case of second difference quotients (DDQs). Specifically, a new POD method utilizing DDQs and only one snapshot and …

Adversarial Transferability And Generalization In Robust Deep Learning, Tao Wu

Doctoral Dissertations

Despite its remarkable achievements across a multitude of benchmark tasks, deep learning (DL) models exhibit significant fragility to adversarial examples, i.e., subtle modifications applied to inputs during testing yet effective in misleading DL models. These meticulously crafted perturbations possess the remarkable property of transferability: an adversarial example that effectively fools one model often retains its effectiveness against another model, even if the two models were trained independently. This research delves into the characteristics influencing the transferability of adversarial examples from three distinct and complementary perspectives: data, model, and optimization. Firstly, from the data perspective, we propose a new method of …

Critical Behavior And Dynamics Of The Superfluid-Mott Glass Transition, Jack Russell Crewse

Doctoral Dissertations

This work studies the effects of disorder on the thermodynamic critical behavior and dynamical properties of the superfluid-Mott glass quantum phase transition. After a brief introduction covering relevant fundamentals, we present the dissertation in the form of four separate but related publications. In the first two publications, we calculate the thermodynamic critical exponents of the superfluid-Mott glass quantum phase transition in both two and three spatial dimensions. The undiluted transition exhibits critical exponents that violate the Harris criterion, and thus the critical behavior is expected to change upon introducing disorder. We confirm this behavior via Monte Carlo simulation of a …

Highly Dipole-Parallel Aligned Nonlinear Optical Organic Molecular Crystalline Materials: Rational Design, Experimental And Theoretical Studies Of Supramolecular Structures And Non-Covalent Interactions, Harmeet Singh Bhoday

Doctoral Dissertations

Highly dipole-parallel aligned donor-acceptor substituted organic molecules are attractive for a wide array of applications, including nonlinear optics. However, only a limited number of crystals are known to adopt polar non-centrosymmetric space groups. The concepts guiding the fabrication of these polar materials have been described. The study focused on unsymmetrical donor-acceptor substituted azines and butadiene's. An improved design over (MeO, Y)-azines led to the realization of three crystals perfectly aligned in dipole parallel orientation for (PhO, Y)-azines with Y = Cl, Br, I, and one nearly perfectly aligned crystal for Y = F (Paper I). The unique bonding properties associated …

Crystal Structure Prediction Of Metal Chalcogenides, Qi Zhang

Doctoral Dissertations

A novel crystal structure prediction (CSP) method has been developed to predict energetically favorable (stable) structures based on targeted chemical compositions. It leverages the structural characteristics of recurring motifs featured in many crystals and symmetry restrictions from space groups to effectively lower the degrees of freedom of a system when conducting CSP simulations. The proposed method is applied to predicting low-energy structures of two metal chalcogenide systems: Li₃PS₄ and Na₆Ge₂Se₆. Both systems feature rigid bodies in their structures as building blocks, making them particularly suited to the proposed method. The validity …

The Deep Bsde Method, Daniel Kovach

Masters Theses

"The curse of dimensionality is the non-linear growth in computing time as the dimension of a problem increases. Using the Deep Backwards Stochastic Differential Equation (Deep BSDE) method developed in [HJE18], I approximate the solution at an initial time to a one-dimensional diffusion equation. Although we only approximate a one-dimensional equation, this method extends well to higher dimensions because it overcomes the curse of dimensionality by evaluating the given partial differential equation along "random characteristics''. In addition to the implementation, I also present most of the mathematical theory needed to understand this method"-- Abstract, p. iii

Radiofrequency Interference Detection Using Lstmand Statistical Analysis Discriminator, Luke Smith

Masters Theses

"Wireless devices are becoming increasingly pervasive across all aspects of society. Examples of such devices include radios, routers, mobile phones, tablets, and more. As the number of radio frequency (RF) devices continues to rise, so does the amount of interference and noise increase. This is why an efficient approach to interference detection is explored. Most research within this area has been done strictly within the frequency domain as viewing a signal within this domain provides many insights into what makes the signal. This has, however, led to the time domain being underutilized for this area of research.

To explore the …

Cryptographic Algorithms, Cryptocurrencies, And A Predictive Model Of Bitcoin Value By Pls Regression, Paul Kenneth O'Connor

Masters Theses

"With the invention of Bitcoin in 2009, as a seemingly timed response to the ongoing financial crisis, the popularity of the cryptocurrency has since continued to grow. Just this year, the Security Exchange Commission approved Bitcoin for exchange traded funds, allowing major investment firms to begin product trading. With this approval, and during this very moment of writing, Bitcoin has entered a bull market and reached a record value of over 72,000 USD. In addition, the Bitcoin halving event in April of 2024 is expected to increase demand even further. It has been anticipated that Bitcoin and other cryptocurrencies will …

The Geodetic Strain Rates From Gnss In The North American-Carribean-Cocos Triple Junction In Guatemala And Implications For Seismic Hazard, Tanaya Kashyap

Masters Theses

"Geodetic velocity data are pivotal for deciphering seismic risks, providing initial constraints for crustal surface strain rates. Gaining an understanding of the 2D surface strain tensor across an entire area necessitates knowledge of the surface velocity field, yet geodetic data such as measurements from the Global Navigation Satellite System (GNSS) are dispersed across the Earth’s surface. In the current scope of study, we have employed various methodologies to estimate strain rates in Guatemala, utilizing GNSS velocity data and leveraging the open-source Python tool Strain_2D. Strain_2D facilitates the computation of strain rates through diverse methods applied to the same dataset and …

The Exponential Function In Discrete Fractional Calculus Under The Delta Operator, Brayton James Link

Masters Theses

"Previously the exponential problem in discrete fractional calculus under the nabla operator was solved with the discrete Mittag--Leffler function. We now show the solution to the exponential problem in discrete fractional calculus under the delta operator, providing multiple derivations of the solution with recursion and Laplace transforms. We also share some computational and numerical results of experiments with different orders of difference to display the nature of the solution" -- Abstract, p. iii