Digital Commons Network^™

Supersonic Wind Tunnel Free Stream Turbulence Characterization Using 2-Point Focused Laser Differential Interferometry, Joseph Villarreal, Joshua Gary, Davide Vigano

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Non-intrusive laser-based diagnostics, such as Two-Point Focused Laser Differential Interfer-ometry (2-FLDI), play a crucial role in modern aerodynamic research by enabling simultaneous measurements of density and velocity in compressible flows. A 2-FLDI system has been developed and implemented for the Missouri S&T Supersonic Wind Tunnel to characterize free stream turbulence fluctuations and free stream convective velocity. Design choices that enabled the 2-FLDI to overcome low turbulence to measure free stream velocity are detailed. The free stream velocity measurements are validated against previous particle image velocimetry data, showing good agreement. Analysis of normalized velocities and density-based turbulence intensities found that the …

2-Point Focused Laser Differential Interferometry Measurements Of A Parallel Jet In Supersonic Flow, Joshua Gary, Joseph Villarreal, Davide Vigano

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Turbulence in compressible flows plays a central role in applications such as air-fuel mixing in supersonic combustors and is significantly more complex than incompressible turbulence due to the presence of fluctuating thermodynamic quantities. As such, models like the Strong Reynolds Analogy (SRA) are used to relate these quantities. However, SRA validity has been examined primarily in boundary-layer flows. In this work, a newly developed Two-Point Focused Laser Differential Interferometry (2-FLDI) system is implemented in a two-dimensional parallel supersonic jet. The diagnostic is described in detail, including optical alignment procedures, calibration methods, and data analysis techniques. Measurements acquired at multiple streamwise …

Special Issue: Innovative Numerical Approaches For Problems In Science And Engineering, Xiaoming He, Shuhao Cao, Qiao Zhuang

Mathematics and Statistics Faculty Research & Creative Works

No abstract provided.

Equilibrium Stability Under Nuclear Confrontation, Martin Bohner, A. A. Martynyuk

Mathematics and Statistics Faculty Research & Creative Works

This article proposes and analyzes mathematical models of confrontation between two and n countries, including countries with nuclear weapons. The proposed models are based on a generalization of Richardson's well-known mathematical model of the arms race. Namely, the factor of hostility is filled with expanded content, including public opinion and the armed forces of the opposing countries. Qualitative analysis of confrontation models is carried out by the method of Lyapunov functions and by applying nonlinear integral inequalities. As a result of the analysis, the conditions for the stability of the equilibrium state of the opposing countries are established, and the …

Threshold Asymmetric Conditional Autoregressive Range (Tacarr) Model, Isuru Ratnayake, V. A. Samaranayake

Mathematics and Statistics Faculty Research & Creative Works

This paper introduces a Threshold Asymmetric Conditional Autoregressive Range (TACARR) model for analyzing the daily price ranges of financial assets. The proposed formulation assumes that the conditional expected range switches between two regimes, representing upward and downward market states, with the disturbance distribution also allowed to vary across regimes. A self-adjusting threshold component, determined by past values of the series, is used to identify the prevailing market regime. In this way, the model is able to capture asymmetric and heteroscedastic volatility behavior in financial markets. The TACARR model is designed to address several limitations of existing price range models, including …

Reflections On Linear B, (Part 15): Sign 81 In Linear A And The Egyptian Four-Headed Ram God, Gerald Leonard Cohen

Arts, Languages and Philosophy Faculty Research & Creative Works

This article makes several tentative suggestions, including:

1. Linear A sign 81 (antecedent of Linear B81) may be an abbreviated image (two heads) of the four-headed Egyptian ram god Banebdjedet, with the horizontal line designating his horns shifted downward to the middle of the character.

2. A few other Linear A characters (e.g., sign 305) may represent slight alterations of sign A81.

3. Linear A82 includes two sets of three short horizontal lines. Those might signify brightness, and in the case of Banebdjedet the brightness would be his wisdom.

Future Mining: Learning For Safety And Security, Md Sazedur Rahman, Mizanur Rahman Jewel, Sanjay Madria

Computer Science Faculty Research & Creative Works

Mining industry is rapidly transforming into an AI-driven cyber-physical ecosystem where safety and operational reliability depend on robust perception, resilient communication, trustworthy distributed intelligence and continuous monitoring of miners and equipment. Real-world mining environments impose severe constraints like poor illumination, dust, occlusion, GPS-denied conditions, irregular underground topologies, and intermittent connectivity. These factors degrade perception quality, disrupt situational awareness, impair trajectory prediction and weaken the reliability of distributed learning systems. Emerging cyber-physical threats, including backdoor triggers, sensor spoofing, label-flip attacks and poisoned model updates, further jeopardize operational safety, particularly as mines increasingly adopt autonomous vehicles, humanoid assistance, and federated learning for …

Dynamic Hub-Aware Knowledge Distillation For Efficient Traffic Flow Forecasting, Xiangjie Kong, Can Shu, Wenchao Weng, Zhenzhen Zhao, Guojiang Shen, Lei Wang, Sajal K. Das

Computer Science Faculty Research & Creative Works

Real-time traffic forecasting acts as a critical enabling service for IoT-driven Intelligent Transportation Systems (ITS). While existing Spatiotemporal Graph Neural Networks (STGNNs) achieve superior forecasting accuracy, their intensive computational complexity and high latency create a deployment bottleneck for resource-constrained IoT edge devices. To address this resource-accuracy mismatch, we propose a novel framework termed Dynamic Hub-Aware Knowledge Distillation (DHKD). Unlike traditional uniform distillation paradigms, DHKD introduces a topology-aware strategy to transfer knowledge from a complex teacher to a lightweight Spatiotemporal Multi-Layer Perceptron (STMLP) student model. Specifically, we design a dynamic hub-aware gating (DHAG) mechanism that adaptively identifies time-varying pivotal sensing nodes …

Digital Twin Freshness Maximization In Edge Computing, Jing Li, Jianping Wang, Weifa Liang, Quan Chen, Sajal K. Das, Xiaohua Jia

Computer Science Faculty Research & Creative Works

Mobile Edge Computing (MEC) shifts powerful computing resource provisioning from remote powerful data centers to the edge of core networks. Meanwhile, Digital Twin (DT) has surfaced as a promising technology to provide comprehensive and dynamic descriptions of physical objects in cyberspace with bidirectional and real-time interactions. Moreover, Internet of Things (IoT) devices have contributed abundant, heterogeneous and continuous data from interconnected devices to the explosion of DTs. With technologies evolution, there is an increasing necessity to address the freshness of both DT states and DT data, through timely synchronizations between DTs and their objects in a highly dynamic IoT environment. …

Feddot: Defending Federated Learning Against Overwhelming Targeted Attacks, Priyesh Ranjan, Ashish Gupta, Federico Coro, Sajal K. Das

Computer Science Faculty Research & Creative Works

Federated Learning (FL), which facilitates collaborative model training and protects users' privacy, has drawn great interest from the research community. With FL, participants train their models on local data and submit the corresponding updates for aggregation to a server. While concealing the identities of the participants, FL may attract adversaries in order to hamper the underlying model. In this paper, we propose an FL framework, FedDOT, to defend against adversaries performing targeted attacks. FedDOT incorporates two powerful defense algorithms, Maximum Spanning Tree based attacker detection (MSTAD) and Densest graph-based attacker detection (Density-AD), which leverage correlation between weight updates and graph …

Utility-Preserving Federated Graph Learning With Dual-Perspective Fairness, Renqiang Luo, Huafei Huang, Shuo Yu, Fengqi Yu, Feng Xia, Sajal K. Das, Chengqi Zhang

Computer Science Faculty Research & Creative Works

Fairness-aware federated graph neural networks (FedGNNs) necessitate consideration of both the server and the clients. However, fairness-aware methods struggle to enhance dual-perspective (i.e., server and clients) fairness without sacrificing utility due to the distributed learning framework. As a consequence, the utility sacrifices of fairness-aware graph learning methods are even exacerbated in federated frameworks. In this work we propose F3GL, a dual-perspective fairness federated graph learning method that enhances both global (for the server) and local fairness (for clients) while preserving utility. Through theoretical analysis, we delineate the similarity between original sensitive features and those after convolution under different spectra. Our …

Escher: Efficient And Scalable Hypergraph Evolution Representation With Application To Triad Counting, S. M. Shovan, Arindam Khanda, Sanjukta Bhowmick, Sajal K. Das

Computer Science Faculty Research & Creative Works

Higher-order interactions beyond pairwise relationships in large complex networks are often modeled as hypergraphs. Analyzing hypergraph properties such as triad counts is essential, as hypergraphs can reveal intricate group interaction patterns that conventional graphs fail to capture. In realworld scenarios, these networks are often large and dynamic, introducing significant computational challenges. Due to the absence of specialized software packages and data structures, the analysis of large dynamic hypergraphs remains largely unexplored. Motivated by this gap, we propose ESCHER, a GPU-centric parallel data structure for Efficient and Scalable Hypergraph Evolution Representation, designed to manage largescale hypergraph dynamics efficiently. We also design …

Infimum Dimension Nash Embeddings For 2d Projective Shape Analysis, Robert L. Paige, Vic Patrangenaru

Mathematics and Statistics Faculty Research & Creative Works

Vector embeddings make complicated data extracted from networks, words and images, more amendable to data science applications. At the present time, the Veronese-Whitney (VW) matrix embedding of the real projective space is the state of the art for making inference about digital images from an uncalibrated camera, such as a cell phone or security camera. In this work we consider vector embeddings for the projective shape data and in particular determine the minimum dimension isometric (distance-preserving or Nash) vector embedding for a projective space. We determine such an embedding for the projective plane in closed-form. From this embedding we determine …

A Fully Discrete Semi-Implicit Numerical Scheme And Its Optimal Error Estimates For Cahn-Hilliard-Mhd Model With Variable Density, Dongmei Duan, Fuzheng Gao, Xiaoming He, Yanping Lin

Mathematics and Statistics Faculty Research & Creative Works

This paper proposes and analyzes a fully discrete semi-implicit unconditionally energy stable numerical scheme to solve the Cahn-Hilliard Magnetohydrodynamics (Cahn-Hilliard-MHD) model with variable density. The unconditional energy stability and optimal L² error estimates are established for the fully discrete scheme. Major challenges in error estimation arise from the variable density, the strong nonlinearities, and the multi-physics coupling of the model. Under the mathematical induction framework, the Ritz quasi-projection and the Stokes quasi-projection, proposed in [SIAM J. Numer. Anal., 61(3):1218-1245, 2023], are utilized to avoid the gradient terms of the projection errors. The H⁻¹ superconvergence error estimates of Ritz …

Pahina: Precision-Aware Hierarchical In-Network Aggregation For Edge Distributed Training, Yingpu Nian, Bo Yi, Qiang He, Xingwei Wang, Geyong Min, Keqin Li, Sajal K. Das

Computer Science Faculty Research & Creative Works

The rise of edge intelligence is driving distributed machine learning toward a new paradigm of edge-collaborative computing. To overcome the severe communication bottleneck in this paradigm, In-Network Aggregation is a critical enabling technology. However, its effectiveness is fundamentally undermined by the profound resource heterogeneity of edge networks. Specifically, edge devices, adapting to hardware constraints, operate at varying numerical precisions, leading to significant data inflation as gradients are aggregated. Compounding this, unevenly distributed network resources and traditional, precision-oblivious routing strategies often misallocate critical, high-precision gradients to low-quality paths. This mismatch creates severe network congestion, crippling the efficiency of distributed training. To …

Smartflow: A Communication-Efficient Sdn Framework For Cross-Silo Federated Learning, Osama Abu Hamdan, Hao Che, Engin Arslan, Md Arifuzzaman

Computer Science Faculty Research & Creative Works

Cross-silo Federated Learning (FL) enables multiple institutions to collaboratively train machine learning models while preserving data privacy. In such settings, clients repeatedly exchange model weights with a central server, making the overall training time highly sensitive to network performance. However, conventional routing methods often fail to prevent congestion, leading to increased communication latency and prolonged training. Software-Defined Networking (SDN), which provides centralized and programmable control over network resources, offers a promising way to address this limitation. To this end, we propose SmartFLow, an SDN-based framework designed to enhance communication efficiency in cross-silo FL. SmartFLow dynamically adjusts routing paths in response …

You Only Need One Stage: Novel-View Synthesis From A Single Blind Face Image, Taoyue Wang, Xiang Zhang, Xiaotian Li, Huiyuan Yang, Lijun Yin

Computer Science Faculty Research & Creative Works

We propose a novel one-stage method, NVB-Face, for generating consistent Novel-View images directly from a single Blind Face image. Existing approaches to novel-view synthesis for objects or faces typically require a high-resolution RGB image as input. When dealing with degraded images, the conventional pipeline follows a two-stage process: first restoring the image to high resolution, then synthesizing novel views from the restored result. However, this approach is highly dependent on the quality of the restored image, often leading to inaccuracies and inconsistencies in the final output. To address this limitation, we extract single-view features directly from the blind face image …

Aim5b: Ai Integrated Semantic Framework For 5g And Beyond Network Management, Thanveer Sulthana, Ava Sharif Jourabchi, Venkat Rao Manavarthi, Jayadithya Nalajala, Ankitha Srirama Reddy, Baek Young Choi, Sejun Song

Computer Science Faculty Research & Creative Works

Scalable, interpretable, and intelligent network monitoring and management are critical for 5 G and future networks. This paper introduces Aim5B, an AI-integrated semantic framework for 5 G and beyond network management to address these challenges. Aim5B processes unstructured logs from key 5G core network functions, and transforms them into a knowledge graph aligned with the semantic structure of control-plane events. Leveraging a large language model (LLM), Aim5B enables natural language queries to be translated into Cypher graph queries, facilitating precise log retrieval, event analysis, temporal correlation, and statistical summarization-without relying on static parsing rules or predefined dashboards. Integrated on a …

Harnessing Coherent-Wave Control For Sensing Applications In Scattering Media, Pablo Xavier Jara Palacios

Doctoral Dissertations

"Diffuse electromagnetic waves are widely used for non-invasive sensing and imaging in complex scattering media such as biological tissues. A fundamental limitation of such techniques is the scarcity of detected photons: as the source-detector separation increases to access deeper regions of the medium, the signal strength decays rapidly, leading to poor signal-to-noise ratio and limited sensitivity. This dissertation addresses this challenge through a combination of theory and computation.

We show that coherent control of the incident optical wavefront can compensate for the scarcity of detected photons that limits conventional diffuse optical imaging, typically performed in the near-infrared spectral region. By …

Scalable Transfer Of Dna Origami–Directed Nanoparticle Arrays Onto Functional Surfaces Using Thermal Release Tape, Yuwei Zhang, Chikadibia Genevieve Edward, Ethan Keuhn, Shelley D. Minteer, Risheng Wang

Chemistry Faculty Research & Creative Works

Precise integration of DNA origami nanostructures onto functional substrates is essential for advancing electronics, photonics, and biosensing. However, most surface-assembly methods are highly sensitive to substrate type, limiting their broader applications. Here, we introduce a thermal release tape (TRT)-assisted transfer method that enables efficient, large-area printing of DNA origami-guided gold nanoparticle (AuNP-DNA origami) arrays from mica onto diverse surfaces. The method relies on differential adhesion: AuNP-DNA origami arrays form strong electrostatic and hydrogen-bonding interactions with 3-aminopropyltriethoxysilane (APTES)-functionalized receiver substrates, while the TRT adhesive weakens upon heating to 120°C, allowing release from the tape. Using this approach, well-ordered AuNP-DNA origami arrays …

Closing The Nitrogen Loop In Groundwater With Biohybrid Technologies, Linjie Zhou, Biao Li, Jianhua Guo, Shelley D. Minteer, Yifeng Zhang

Chemistry Faculty Research & Creative Works

Nitrate in groundwater should be treated as a nitrogen source rather than a contaminant. Biohybrid technologies coupling microbial selectivity with renewable electro(photo)chemical energy offer opportunities to convert nitrate to value-added ammonium, although challenges remain in scalability, microbial stability, material–microbe integration, process engineering, regulatory compliance, and economic feasibility.

Assessing The Criticality Of Construction Trades: Skilled Labor Shortages And Their Cost And Schedule Impacts, Tamima Elbashbishy, Islam H. El-Adaway

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Skilled labor shortages are a pressing issue in the construction industry. Existing research has primarily focused on the effects of labor shortages at the project or industry level, but there is limited exploration of how these shortages vary across specific trades and their distinct impacts on project outcomes. Evidence indicates that labor shortages vary significantly among trades. This paper addresses this knowledge gap through quantitatively assessing the criticality of key construction trades based on the (1) extent of skilled labor shortages currently witnessed in each trade, (2) impact of these shortages on cost and schedule performance, and (3) degree of …

Environmental Engineers Develop Solutions To Problems Of Planetary Health: The Legacy Of Professor P. Aarne Vesilind, Daniel B. Oerther

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

The principal ethical obligation of the engineering profession, to "hold paramount the health, safety, and welfare of the public," has been challenged for decades. Professor P. Aarne Vesilind identified two primary deficiencies: an anthropocentric bias that values nature only instrumentally and the use of non-mandatory, aspirational language for environmental protection, which resulted in the exclusion of enforceable environmental canons. This paper argues that two recent developments provide a comprehensive response to Vesilind's recommendations for engineering ethics. First, the United States Bureau of Labor Statistics has redefined environmental engineering as "developing solutions to problems of planetary health." Second, Oerther proposed a …

Distributed Vibration Sensing For Identification Of Loose Connectors In Coaxial Data Transmission Lines, Saidanvar Esanjonovich Valiev, Anthony C. Okafor, Jeremiah J. Rittenhouse, Jie Huang, Daniel S. Stutts

Mechanical and Aerospace Engineering Faculty Research & Creative Works

This study investigates the effect of vibration-induced loose connections on signal reflection (S11) for loose connection identification in aerospace coaxial cables using distributed sensing approach, which is effective in filtering the noise and identifying minor discontinuities. In this approach, a sliding gated window is applied to S11 signal, a fast Fourier transform is performed over the gated windows, cross-correlation is computed between the baseline and vibration-affected signals, and the standard deviation is mapped along the cable length. Sinewave signals from 9 kHz to 5 GHz were swept through cables with vibrating connectors under three conditions: fully tightened, loosened by 180°, …

Evaluation Of Chromium-Crosslinked Amps-Hpam Copolymer Gels: Effects Of Key Parameters On Gelation Time And Strength, Maryam Sharifi Paroushi, Baojun Bai, Thomas P. Schuman, Yin Zhang, Mingzhen Wei

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Controlling CO₂ channeling in heterogeneous reservoirs remains a major challenge for both enhanced oil recovery (EOR) and secure geological storage. AMPS-HPAM copolymers exhibit high-temperature resistance and brine tolerance compared with conventional HPAM gels, making them well suited for the harsh environments associated with CO₂ injection. Chromium-based crosslinkers (CrAc and CrCl₃) were investigated because sulfonic acid groups in AMPS can coordinate with trivalent chromium ions, enabling dual ionic crosslinking and the formation of a robust gel network. While organic crosslinked AMPS-HPAM gels have been widely studied, the behavior of chromium-crosslinked AMPS-containing systems, particularly their gelation kinetics under …

Effect Of Ionic Strength On Gelation Time And Strength Of Amps-Based Polymer Gels, Maryam Sharifi Paroushi, Xuyang Tian, Baojun Bai, Thomas P. Schuman, Yin Zhang, Mingzhen Wei

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Polymer gel treatment has been widely applied for improving sweep efficiency and controlling excessive water and gas production. Their performance depends on gelation time and final gel strength. In most studies, brine salinity is used to describe the effect of formation water on gel behavior. However, changing salinity also changes ionic strength and ion composition at the same time. Because of this coupling, it is difficult to identify the mechanisms controlling gelation, which has led to inconsistent trends in the literature. Increasing salinity has been reported to either slow or accelerate gelation and to weaken or strengthen gels depending on …

Empirical-Based Model Of Spatio-Temporal Errors, Godwin Naaba Ndaa

Masters Theses

This research develops an empirical model to characterize spatial-temporal InSAR errors and improve the accuracy of deformation time-series analysis. Using standardized Sentinel-1 HyP3 products and MintPy, the study quantifies how correlated noise affects velocity precision and validates the results against continuous GNSS velocities.

Residual velocities are near-Gaussian, with σ ~0.92–2.04 cm/yr and ~1 cm/yr on average. Variograms show power-law spatial structure with a non-zero nugget implicating troposphere and decorrelation while errors drop exponentially with more acquisitions; spatial uncertainty is strongly affected by unwrapping errors, coherence, and tropospheric noise, not simply troposphere.

A comparative assessment of on-demand cloud processing with other …

Wattage- And Composition-Dependent Ocular Surface Toxicity: An In Vitro Study On E-Cigarette Aerosol Exposure In Human Corneal Epithelial Cells, Tzu-Ling Lin

Masters Theses

As electronic cigarette consumption continues to escalate globally, its impact on non-respiratory mucosal surfaces, particularly the ocular surface, remains largely understudied. This study investigated the toxicological effects of e-cigarette aerosols using a human corneal epithelial cell (HCE-2) model. Our findings reveal that aerosol exposure induces a significant, dose-dependent reduction in cell viability, exacerbated by higher device wattage and specific flavor agents such as watermelon. Exposure was found to trigger intracellular oxidative stress and the activation of apoptotic pathways, leading to programmed cell death. Furthermore, the upregulation of proinflammatory cytokines suggests that vaping may predispose users to chronic inflammatory conditions of …

Aura-Visionpose: Creating Datasets For Vision-Based Pose Estimation, Mohanad Alameer

Masters Theses

"Spacecraft rendezvous is an integral part of many orbital missions, including active space debris removal (ADR) and in-orbit servicing, assembly, and manufacturing (ISAM). Traditionally, rendezvous missions rely mainly on manual ground-based control to perform rendezvous and docking. While this can be effective on an individual basis, it becomes less practical at scale. Autonomous vision-based methods can reduce reliance on human control, and may improve scalability, response time, and reduce the possibility of human error for both ADR and ISAM. This work lays the necessary groundwork for developing a machine learning (ML) model for autonomous rendezvous by creating a labeled image …

Evaluation Of A Novel Re-Crosslinkable Preformed Particle Gel For Conformance Control In Ultra-High Temperature Reservoirs, Yanbo Liu, Tao Song, Caleb Kwasi Darko, Thomas P. Schuman, Mingzhen Wei, Baojun Bai

Chemistry Faculty Research & Creative Works

Preferential fluid flow remains a major challenge in subsurface energy production and gas storage operations, resulting in excessive water production in mature oil fields, reduced heat extraction in geothermal reservoirs, and low sweep and storage efficiency in CO₂-EOR projects. Polymer gels are widely used to mitigate high-permeability channels; however, conventional systems exhibit limited plugging efficiency and short lifetimes in ultra-high-temperature reservoirs due to poor thermal stability. This study presents a novel ultra-high-temperature-resistant preformed particle gel (UHT-PPG) developed for conformance control in reservoirs with temperatures of 150–275 °C and severe super-K or channeling problems. The material was evaluated in …