Digital Commons Network^™

Hydraulic Connectivity And Hydrochemistry Influence Microbial Community Structure In Agriculturally Affected Alluvial Aquifers In The Midwestern United States, Hunter W. Schroer, Kendra Markland, Fangqiong Ling, Craig L. Just

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Alluvial aquifers can provide ecosystem services and drinking water but much remains unknown about human effects on aquifer microbiomes. Therefore, we used amplicon sequencing and hydro chemical characterization to pair microbial communities with environmental conditions across 37 alluvial aquifer wells. The study region spanned eastern Iowa and southern Minnesota (USA) and contained a combination of drinking water and monitoring wells. In terms of microbial ecology, dominant phyla across the wells included Proteobacteria, Bacteroidota, Patescibacteria, Planctomycetota, and Nitrospirota. Tritium, an indicator of infiltration and surface water influence, was the highest correlated variable with the Shannon index (α-diversity) by the Spearman rank …

V2vdiscs: Vehicle To Vehicle Distributed Charge Sharing In Intelligent Transportation Systems, Punyasha Chatterjee, Pratham Majumder, Sajal K. Das

Computer Science Faculty Research & Creative Works

Electric Vehicles (EVs) have become popular in the domain of Intelligent Transportation Systems for their ability to mitigate increasing environmental concerns by reducing carbon footprints and conserving fossil fuels. Due to the scarcity of static charging stations, Vehicle-to-Vehicle (V2V) charge sharing can facilitate the on-demand charging requirement of EVs. However, most of the V2V charge-sharing solutions are either centralized or semi-centralized, causing long waiting times, huge message overhead, and high infrastructural costs. For a large network, assigning a suitable donor EV for an acceptor EV as well as maximizing the matching cardinality in a distributed environment is a challenging problem. …

Forecasting State-Level Construction Labor Earnings For Enhanced Project Cost Control: An Econometric And Deep-Learning Analysis Of The Leading Economic Indicators, Ahmed Shiha, Islam H. El-Adaway

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

As a major input to several work packages, the labor element constitutes a critical component for successful performance of construction projects. Localized labor shortages, fundamental changes in prevailing wage laws, and historical shifts in the unionization rates of construction workers impair the adequate estimation of construction labor costs in diverse labor market dynamics. Meanwhile, existing studies have utilized national-level indicators to study the trends of construction labor costs, but the relationship between the multifaceted local economic factors and state-level construction labor costs remains understudied. This paper fills such a knowledge gap. A three-stage methodology is adopted: (1) data collection of …

Secure Data Relay In Federated Digital Twins Of Iot-Enabled Smart Interconnected Factories, Anusha Vangala, Jack Wyeth, Ashok Kumar Das, Sajal K. Das

Computer Science Faculty Research & Creative Works

Smart interconnected factories allow manufacturing units from physically distanced factory sites to communicate classified information needed for additive manufacturing. Each factory has interconnected digital twins of their equipment autonomously managed by a Point-of-Contact digital twin creating a hierarchical system with federated digital twins. The data sharing between the factories must be directed through an edge node responsible for managing multiple factories. We proposed a novel lightweight protocol to prevent the leakage of classified information at any nodes other than the origin and destination digital twins. It leverages elliptic curve cryptography to design a proxy re-encryption scheme with the edge node …

Cold Atmospheric Plasma Induced Degradation Of Organophosphate Pesticides On Kevlar Swatches, Ta Chun Lin, Victor Somtochukwu Mbanugo, Boluwatife Stephen Ojo, Yue-Wern Huang, Marek Locmelis, Frank Daoru Han

Biological Sciences Faculty Research & Creative Works

Cold atmospheric plasma (CAP) was evaluated for degrading organophosphate pesticide (OP) residues (acephate, malathion, and dimethoate) from Kevlar fabrics. Fourier transform-Infrared (FTIR) spectroscopy, scanning electron microscope (SEM) imaging and tensile strength testing confirmed that CAP treatment preserved Kevlar's structural integrity and mechanical properties. Results show degradation efficiency increased with higher power, shorter discharge gaps, and longer exposure duration. Hyperspectral imaging supported the detection of plasma-induced spectral changes, reinforcing the presence of reactive species. The strong oxidative potential of CAP facilitated the rapid breakdown of OPs into nontoxic byproducts. These findings demonstrate the potential of CAP as a portable, energy-efficient solution …

Reindsplit: Reinforced Dynamic Split Learning For Pest Recognition In Precision Agriculture, Vishesh Kumar Tanwar, Soumik Sarkar, Asheesh K. Singh, Sajal K. Das

Computer Science Faculty Research & Creative Works

To empower precision agriculture through distributed machine learning (DML), split learning (SL) has emerged as a promising paradigm, partitioning deep neural networks (DNNs) between edge devices and servers to reduce computational burdens and preserve data privacy. However, conventional SL frameworks' one-split-fits-all strategy is a critical limitation in agricultural ecosystems where edge insect monitoring devices exhibit vast heterogeneity in computational power, energy constraints, and connectivity. This leads to straggler bottlenecks, inefficient resource utilization, and compromised model performance. Bridging this gap, we introduce ReinDSplit, a novel reinforcement learning (RL)-driven framework that dynamically tailors DNN split points for each device, optimizing efficiency without …

Guest Editorial, Li Ai, Xiao Tan, Arslan Akbar, Guirong Yan

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

No abstract provided.

Safenav: Safe Path Navigation Using Landmark Based Localization In A Gps-Denied Environment, Ganesh Sapkota, Sanjay Madria

Computer Science Faculty Research & Creative Works

In battlefield environments, adversaries frequently disrupt GPS signals, requiring alternative localization and navigation methods. Traditional vision-based approaches like Simultaneous Localization and Mapping (SLAM) and Visual Odometry (VO) involve complex sensor fusion and high computational demand, whereas range-free methods like DV-HOP face accuracy and stability challenges in sparse, dynamic networks. This paper proposes LanBLoc-BMM, a navigation approach using landmark-based localization (LanBLoc) combined with a battlefield-specific motion model (BMM) and Extended Kalman Filter (EKF). Its performance is benchmarked against three state-of-the-art visual localization algorithms integrated with BMM and Bayesian filters, evaluated on synthetic and real-imitated trajectory datasets using metrics including Average Displacement …

Dynamic Anomaly Threshold Based Malicious Behavior Detection In Lora-Assisted Industrial Iot, Subir Halder, Amrita Ghosal, Thomas Newe, Sajal K. Das

Computer Science Faculty Research & Creative Works

Smart manufacturing, powered by Long Range (LoRa) communication-assisted Industrial Internet of Things (IIoT), offers significant benefits but also incurs security concerns due to device compromise. In addition, various application scenarios and inherent heterogeneity of IIoT devices induce significant challenges for reliable behavior detection of compromised devices. While existing work is mostly on detecting compromised devices and there exists limited work on modeling system behavior, an open question is how to model the per-device behavior in an IIoT deployment and how behavioral changes can be automatically adapted in different scenarios. This paper proposes Misbehav, a novel self-learning device behavior anomaly detection …

Iterative Recommendations Based On Monte Carlo Sampling And Trust Estimation In Multi-Stage Vehicular Traffic Routing Games, Doris E.M. Brown, Venkata Sriram Siddhardh Nadendla, Sajal K. Das

Computer Science Faculty Research & Creative Works

The shortest-time route recommendations offered by modern navigation systems fuel selfish routing in urban vehicular traffic networks and are therefore one of the main reasons for the growth of congestion. In contrast, intelligent transportation systems (ITS) prefer to steer driver-vehicle systems (DVS) toward system-optimal route recommendations, which are primarily designed to mitigate network congestion. However, due to misalignment in motives, drivers may exhibit a lack of trust in the ITS. This paper models the interaction between a DVS and an ITS as a novel, multi-stage routing game where the DVS exhibits dynamics in its trust towards the recommendations of the …

Citrus: Cost And Ischemia Time Reduction Using Urban Air Mobility Solutions For Organ Transport, Debjyoti Sengupta, Anurag Satpathy, Arindam Khanda, Sajal K. Das

Computer Science Faculty Research & Creative Works

Urban Air Mobility (UAM) involves the use of both piloted and autonomous aerial vehicles, ranging from small unmanned aerial vehicles (UAVs), such as drones, to larger passenger-carrying personal air vehicles (PAVs). This ground-breaking approach holds the potential to transform healthcare logistics by facilitating the fast and efficient transportation of organs between hospitals, addressing critical mobility challenges in healthcare delivery. However, scheduling organ transport is fraught with challenges, including (1) the limited availability of UAM vehicles at specific hospital branches, (2) the critical Cold Ischemia Time (CIT) for various organs, and (3) the high flying costs associated with moving organs from …

Ca-Vqvae: Cortical Folding Aware Numerical Representation Of White-Matter Structure, Yanjun Lyu, Jing Zhang, Lu Zhang, Tong Chen, Xiaowei Yu, Minheng Chen, Yan Zhuang, Chao Cao, Tianming Liu, Dajiang Zhu

Computer Science Faculty Research & Creative Works

White matter (WM) serves as a fundamental component of the brain providing essential structural support and facilitating the brain cognitive processes. Thus, an accurate and efficient description of the brain's white matter structure is essential for understanding brain function connectivity and development. In this work we used the deep model to combine the information of the WM fiber bundle shape and its related cortical folding patterns together representing the WM fiber bundle from diffusion MRI tractography into a pre-defined low-dimensional space and generate the numerical representation vector. This cortical-aware vector-quantized variational encoder (CA-VQVAE) framework leverages cortical locations and folding patterns …

Classiffication Of Mild Cognitive Impairment Based On Dynamic Functional Connectivity Using Spatio-Temporal Transformer, Jing Zhang, Yanjun Lyu, Xiaowei Yu, Lu Zhang, Chao Cao, Tong Chen, Minheng Chen, Yan Zhuang, Tianming Liu, Dajiang Zhu

Computer Science Faculty Research & Creative Works

Dynamic functional connectivity (dFC) using resting-state functional magnetic resonance imaging (rs-fMRI) is an advanced technique for capturing the dynamic changes of neural activities and can be very useful in the studies of brain diseases such as Alzheimer's disease (AD). Yet, existing studies have not fully leveraged the sequential information embedded within dFC that can potentially provide valuable information when identifying brain conditions. In this paper, we propose a novel framework that jointly learns the embedding of both spatial and temporal information within dFC based on the transformer architecture. Specifically, we first construct dFC networks from rs-fMRI data through a sliding …

Polarimetry Based Radar Estimation Of Extreme Rainfall: Case Studies, Bong Chul Seo, Witold F. Krajewski, James A. Smith

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

The study evaluated radar-derived polarimetric rainfall estimates for extreme rain events that occurred in the Kansas City Metropolitan area in the United States. To derive quantitative precipitation estimates (QPE), we implemented two polarimetric algorithms based on specific attenuation (A) and specific differential phase (KDP), along with the reflectivity (Z) based one using data from two radars in the study area. The analysis to assess radar-rainfall estimates (R) utilizes ground observations from a dense network of about 170 rain gauges. Based on our analysis results, the two polarimetric estimates from R(A) and R(KDP) outperform the conventional estimation R(Z). R(A) appeared to …

Real-Time Testbed For Studying Cyberattacks And Defense In Der-Integrated Smart Inverter Systems, M. Maliha, A. Oluyomi, M. Booge, S. Bhattacharjee, N. Braasch, P. Gomez, Sajal K. Das

Computer Science Faculty Research & Creative Works

In this paper, we propose a Hardware-in-the-Loop (HIL) simulation testbed suitable for the implementation and testing of realistic cyberattacks on grid-tied smart inverter systems integrated with Distributed Energy Resources (DER) that use the Distributed Network Protocol-3 (DNP3) protocol for communications between grid components. Specifically, our testbed combines a Real-Time Digital Simulator (RTDS) NovaCor device, outfitted with GNETx2 network interface cards, a grid-tied DER topology implemented via the RTDS software package RSCAD, and a custom virtual network that emulates a man-in-the-middle (MITM) attacker. The MITM attacker captures DNP3 traffic and falsifies telemetry data in DNP3 packets to trigger unwarranted commands from …

Mgco: Mobility-Aware Generative Computation Offloading In Edge-Cloud Systems., Aswini Ghosh, Nelson Sharma, Shivendu Mishra, Rajiv Misra, Sajal K. Das

Computer Science Faculty Research & Creative Works

Mobility introduces significant challenges for optimal computation offloading, latency minimization, and efficient re source utilization in multi-access edge computing (MEC) systems. A key difficulty lies in leveraging real user trajectories to jointly optimize horizontal (inter-edge) and vertical (edge-to-cloud) task offloading decisions. This paper proposes a two-dimensional offloading scheme for a multi-layer edge–cloud architecture that enables collaborative task execution among resource-constrained edge nodes under mobility conditions. We present MGCO (Mobility-Aware Generative Computation Offloading), a generative AI–driven Transformer-based sequence-to-sequence Deep Q-Network (s2s-DQN) framework that learns from real-time trajectory data to anticipate user movement and optimize task placement dynamically. The Transformer architecture is …

Content Subversion Against 1 Information-Based Systems, Junjie Xiong, Ian Markwood, Dakun Shen, Yao Liu, Zhuo Lu

Computer Science Faculty Research & Creative Works

We present a novel class of content subversion attacks against information-based services, causing documents to appear to humans dissimilar to the underlying content extracted by information-based services. We demonstrate the significant impact of these attacks on real-world systems through five distinct variants. Our first attack allows academic paper writers and reviewers to collude via subverting the automatic reviewer assignment systems in current use by academic conferences including INFOCOM, which we reproduced. Our second attack renders ineffective plagiarism detection software, particularly Turnitin, targeting specific small plagiarism similarity scores to appear natural and evade detection. In our third attack, we place masked …

Fuzzy-Based Deep Reinforcement Learning For Suicidal Ideation Detection In Online Social Networks, Greeshma Lingam, Sajal K. Das

Computer Science Faculty Research & Creative Works

Suicidal ideation is a major psychological problem, and preventing this social risk is recognized as an important research topic. In reality, there can be several reasons why a person experiences suicidal ideation. Each individual can express views, emotions, and several types of symptoms related to suicidal ideation on the most popular social media platforms. In online social networks (OSNs), identification of suicidal ideation is one of the major challenging tasks. Existing studies have shown that the delay in understanding and identifying various risk factors can cause the suicidal event to occur. Due to the scarcity of data and understanding, the …

Circa: A Framework For Collaborative Identification Of Root Cause Analysis In Iot Microservices, Xingguo Jiang, Hong Luo, Yan Sun, Sajal K. Das

Computer Science Faculty Research & Creative Works

With continuous growth of IoT applications, service failures are quite inevitable. Due to the complexity and dynamics of IoT services, the root cause analysis (RCA) following an alert can assist in quickly resolving the possible faults. However, the time scales of metrics (e.g., CPU utilization, memory usage) generated by microservices and the dynamic topologies generated by calls between the Application Program Interfaces (APIs) are different. Moreover, the status of devices is an important aspect of RCA in IoT. All these make it extremely challenging to learn failure features of microservice metrics and API calls. Therefore, we propose a novel framework …

When Federated Learning Meets Quantum Computing: Survey And Research Opportunities, Aakar Mathur, Ashish Gupta, Sajal K. Das

Computer Science Faculty Research & Creative Works

Quantum Federated Learning (QFL) is an emerging field that harnesses advances in Quantum Computing (QC) to improve the scalability and efficiency of decentralized Federated Learning (FL) models. This paper provides a systematic and comprehensive survey of the emerging problems and solutions when FL meets QC, from research protocol to a novel taxonomy, particularly focusing on both quantum and federated limitations, such as their architectures, Noisy Intermediate Scale Quantum (NISQ) devices, and privacy preservation, so on. With the introduction of two novel metrics, qubit utilization efficiency and quantum model training strategy, we present a thorough analysis of the current status of …

Enhanced Kneser-Type Oscillation Criteria For Second-Order Functional Quasilinear Dynamic Equations On Time Scales, Taher S. Hassan, Elvan Akın, Bassant M. El-Matary, Ioan Lucian Popa, Mouataz Billah Mesmouli, Ismoil Odinaev, Akbar Ali

Mathematics and Statistics Faculty Research & Creative Works

This work presents new Kneser-type oscillation criteria for second-order quasilinear functional dynamic equations defined on arbitrary unbounded above time scales. Our approach employs the Riccati transformation technique in conjunction with the integral averaging method. The results show a significant improvement over recent Kneser-type oscillation criteria. We provided several illustrative examples to highlight the importance of our findings.

Aqueous Carbonation Of Steel Slags: A Comparative Study On Mechanisms, Nannan Zhang, Gao Deng, Wenyu Liao, Hongyan Ma, Chuanlin Hu

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

This study investigated the aqueous carbonation mechanisms of three typical steel slags: ladle metallurgy furnace (LMF) slag containing high Al content, electric arc furnace (EAF) slag featuring high Si content and relatively low Al content, and ladle-arc fusion (LAF) slag with medium-Al content. It was found that the carbonation kinetics of the three slags were similar and followed the surface coverage model within the first 6 h of carbonation. Initially, the carbonation process was primarily governed by the reaction product precipitation. After 3 h of carbonation, the process was dominated by mineral dissolution, controlled by the uncovered reactive sites. The …

Reevaluating The Van’T Hoff And Arrhenius Equations: How Temperature And Pressure Affect Chemical Reaction Thermodynamics, Equilibrium, And Kinetics, Jianmin Wang

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

The van’t Hoff equation, which is used to calculate the effect of temperature on equilibrium constants, was derived from the Gibbs – Helmholtz equation that relates the molar Gibbs free energy to the molar enthalpy of a chemical reaction. However, the Gibbs – Helmholtz equation was developed for systems that do not consider contributions of Gibbs free energy from chemical species. Consequently, the van’t Hoff equation and the Arrhenius equation derived from it are technically incomplete.

By employing Hess’s law and the appropriate Gibbs free energy equation for chemical reaction systems, this investigation develops theoretical equations to calculate the effects …

An Improved Table Method For Coded Target Identification With Application To Photogrammetric Analysis Of Soil Specimen During Triaxial Testing, Xiaolong Xia, Xiong Zhang, Sara Fayek, Zhaozheng Yin

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Accurate and efficient recognition and identification of coded targets are of great importance in coded target-based photogrammetry. Recently, a deep learning-based method has been utilized to recognize the coded targets. Then, a table method has been developed to decode the coded targets, identify falsely identified coded targets, and recover missing coded targets. This method takes advantage of the geometric arrangement of the coded targets. In this paper, an improved table method has been developed to improve the coded targets recognition and identification results. Blob analysis, instead of deep learning, is utilized to recognize coded targets. Then, the RANSAC algorithm was …

Grace-Fl: Green Resource-Aware Communication-Efficient Federated Learning, Dipanwita Thakur, Antonella Guzzo, Giancarlo Fortino, Sajal K. Das

Computer Science Faculty Research & Creative Works

Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy, but its deployment on resource-constrained devices is hindered by high communication overhead, inefficient energy usage, and poor convergence under non-IID data distributions. To address these challenges, we propose GRACE-FL: a Green Resource-Aware Communication-Efficient Federated Learning framework that explicitly incorporates device energy capacity into training. Each client adapts its learning rate, number of local epochs, and gradient quantization bit-width based on its available energy, allowing high-capacity devices to sustain more intensive training while low-capacity devices operate with lighter configurations. A novel energy-weighted aggregation strategy ensures that clients …

Evaluation Of The Impact Of Secondhand E-Cigarette Aerosols On Ros-Mediated Alterations Of Epigenetic Events: Implications For Copd Pathogenesis And Assessment Of The Efficacy Of Cold Atmospheric Plasma In Degrading Organophosphate Pesticides On Kevlar Materials, Ta-Chun Lin

Masters Theses

"We explored 1) the toxicological impacts of e-cigarette aerosols and 2) the decontamination efficacy of Cold Atmospheric Plasma (CAP) technology over organophosphates. In examining the effects of firsthand and secondhand e-cigarette aerosol exposure on A549 and BEAS-2B lung cells, we observed significant cytotoxicity, increased ROS levels, and epigenetic modifications, including heightened DNA methylation and downregulation of histone deacetylases (HDACs). These molecular alterations may contribute to chronic respiratory conditions, such as COPD, underscoring the health risks of e-cigarette exposure.

We investigated CAP’s effectiveness in degrading organophosphate pesticide (OP) residues on fabric surfaces, revealing that CAP treatment effectively decomposes OPs such as …

Individual Design Task Final Report Security Design, Ciarrah Bell

Honors Academy

"The Environmental Services Campus project is a comprehensive development designed to support the City of Springfield’s Environmental Services operations, with a focus on functionality, sustainability, and safety. The campus includes administrative office space, staff parking, a maintenance vehicle garage, and educational facilities aimed at community outreach. A component of the project is establishing site-wide security that not only protects personnel and assets but also integrates seamlessly with the architectural and environmental goals of the facility.

The primary focus of the Security IDT is the development and implementation of a comprehensive security strategy for the entire site. This includes designing and …

Leveraging Intrinsic Properties For Classification Of Coal Seams Towards Spontaneous Combustion Proclivity And Predicting Susceptibility Using Machine Learning: Smart And Sustainable Mining Approach, Siddhartha Agarwal, Pradeep K. Gautam, Yuhao Zou, Rishabh Dwivedi, Durga C. Panigrahi, Cihan H. Dagli, A. Singh

Engineering Management and Systems Engineering Faculty Research & Creative Works

Mine fires and other hazards caused by spontaneous coal combustion are a pervasive and longstanding issue in Jharia coalfields, India. This study proposes a novel approach to classify coal seams based on their propensity to spontaneous combustion using the intrinsic properties of 30 coal samples from different seams. This method eliminates the need for expensive and time-consuming experimental determinations of susceptibility indices (SI) such as crossing point temperature (CPT), critical air blast (CAB), and differential thermal analysis (DTA). All clustering models, viz. hierarchical, k-means, and multidimensional scaling, aptly classify coal seams into three categories: highly risky, medium risky, and low …

Using Telemetry To Assess Operator Effects On Hydraulic Shovel Energy Efficiency. Part Ii: Assessing Operator Effects, Noah Adekunle Aluko, Kwame Awuah-Offei

Mining Engineering Faculty Research & Creative Works

In this second part of a two-part series, this study investigates whether operator practices significantly affect the energy efficiency of hydraulic shovels, using detailed telemetry data. Rather than assuming this relationship, the research systematically tests it through a combination of statistical and regression analyses. First, Welch's and Kruskal–Wallis ANOVA confirm that operators differ significantly in their energy efficiency (p = 0.0000). Next, correlation analysis links parameters identified in Part I to energy per unit loading rate, and difference regression analysis determines which parameters most strongly influence efficiency variations. The results show that differences in payloads are the primary driver of …

Understanding The Usefulness Of Self-Escape Technologies In Underground Mining: Perspectives Of Metal/Nonmetal Miners, Eugene A. Gyawu, Kwame Awuah-Offei, D. A. Baker

Mining Engineering Faculty Research & Creative Works

Research on mine self-escape often focuses on coal mining, while perspectives from underground metal/nonmetal miners remain understudied despite their distinct emergency response challenges and unique operating environments. Using a scenario-based survey approach, this study evaluated underground metal/nonmetal miners' perceptions of the usefulness of 18 hypothetical self-escape interventions and how these perceptions are influenced by worker characteristics. Employment type was the strongest predictor of usefulness ratings, with hourly employees rating several self-escape interventions significantly higher than salaried employees, including those related to improving self-contained self-rescuers (SCSRs) and tethered guidance systems. The data suggested potential trends where perceived usefulness increased with more …