Digital Commons Network^™

Serum Biomarker Trajectory Clusters Predict Functional Outcome And Quality Of Life For Traumatic Brain Injury, Thanh Son Do, Chantal Carnes, Zhihui Yang, Firas Kobeissy, Hamad Yadikar, Gayla R. Olbricht, Olli Tenovuo, Jussi P. Posti, Ewout W. Steyerberg, Lindsay Wilson, Nicole Von Steinbüchel, Endre Czeiter, Andras Buki, David K. Menon

Mathematics and Statistics Faculty Research & Creative Works

Serum brain-enriched biomarkers are increasingly employed in the clinical evaluation of traumatic brain injury (TBI) to assist with triage, neuroimaging decisions, and prognostication. However, the potential of temporal biomarker trajectories to inform disease monitoring and long-term outcomes remains underexplored. We aim to identify distinct biomarker trajectory (TRAJ) profiles in traumatic brain injury patients and to examine their associations with long-term clinical outcomes. The study included 373, CT-positive Intensive Care Unit (ICU) traumatic brain injury patients (256 with initial Glasgow Coma Scale 3–12) from the Collaborative European Neurotrauma Effectiveness Research in TBI (CENTER-TBI) core study who had at least two serum …

A Large Thermal Vacuum (Tvac) Facility To Simulate Cryogenic Space Environments, Emmanuel Kofi Asuako Wie-Addo, Lucas Alexander Scott, Frank Daoru Han

Mechanical and Aerospace Engineering Faculty Research & Creative Works

This work reports the upgrade of a 10-ft (3.0 m) long x 6-ft (1.8 m) diameter vacuum facility as part of ongoing efforts to address some of the technology gaps in NASA's Moon to Mars mission architecture, which include systems to survive and operate through extended periods in extreme environments. Consequently, a removable thermal shroud has been fabricated and installed to facilitate the simulation of extreme cryogenic conditions. The cooling rates of the shroud and a surrogate test article, using liquid nitrogen as the coolant are analyzed and documented under varying vacuum environments during cryogenic testing. The attainable vacuum level …

Consumer Responses To Ethical And Privacy Concerns In Ai Chatbots: Evidence From Offensive Language, Gender Bias, And Digital Privacy, Jikhan Jeong

Economics Faculty Research & Creative Works

Due to the rapid innovation of artificial intelligence (AI) and big data, AI chatbots have become increasingly influential computational social systems (CSS) in everyday life. However, the expansion of interactions between AI chatbots and consumers has raised significant ethical and privacy concerns. Using data from an online survey, this study empirically examines how consumer concerns about AI ethics and privacy influence their intention to use AI chatbots. These concerns are categorized into three types: 1) offensive language (generated by AI chatbots or users); 2) gender-biased language (from either source); and 3) digital privacy violations. The findings suggest that prior experience …

Long Short-Term Memory (Lstm) -Based Neural Network Model For Optimizing Composite Manufacturing Process Using Autoclave, Sourav Bolar, Steven Corns, Nayan Pundhir, Kumbla Chandrashekhara

Engineering Management and Systems Engineering Faculty Research & Creative Works

Producing high-quality fiber-reinforced composites requires precise temperature control during autoclave curing, as even small variations can lead to defects that compromise strength and reliability. At the same time, manufacturers aim to reduce energy use and shorten curing cycles without sacrificing material performance. To address these challenges, this study develops a data-driven Long Short-Term Memory (LSTM) neural network model capable of forecasting temperature evolution inside the autoclave throughout the curing cycle. The model is trained on time-series temperature data collected from multiple sensing locations, enabling it to learn the spatial and temporal trends that govern heat flow during curing. Data augmentation …

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.

Orchestrating Bone Healing: Biomaterial-Driven Immunoengineering Of The Bone Microenvironment For Advanced Regenerative Therapies, Ah Joung Rachel Yu, Mohammad Reza Zare, Edikan Ogunnaike, Mostafa Yazdi

Chemical and Biochemical Engineering Faculty Research & Creative Works

Bone regeneration is a multistage and tightly regulated process driven by coordinated interactions between the immune and skeletal systems. While inflammation is essential for initiating repair, its dysregulation contributes to delayed union, nonunion, and impaired healing commonly seen in trauma, chronic disease, and aging. Traditional interventions, including autografts and inert biomaterials, provide structural support but fail to engage or modulate the immune microenvironment that governs successful regeneration. Emerging research in osteoimmunology has revealed the centrality of immune cell populations such as macrophages, T regulatory cells, dendritic cells, and myeloid-derived suppressor cells in directing osteogenesis, angiogenesis, and the resolution of inflammation. …

One Size Doesn’T Fit All: Navigating Gendered Ageism In Older Women’S Entry Into Entrepreneurship, Yanying Chen, Zijie Song, Chevy-Hanqing Fang

Business and Information Technology Faculty Research & Creative Works

Though older women may choose entrepreneurship as a way to escape discrimination in the labor market, they may face other forms of discrimination in the entrepreneurial domain. Drawing on intersectionality theory, we posit that older women encounter amplified discrimination due to the stereotype-consistent implications from gender and age norms, namely gendered ageism. Using 815,428 individual observations pooled across country–year surveys from 56 countries between 2009 and 2018, we found that older women were least likely to enter entrepreneurship. And this intersectional disadvantage could be mitigated by weakening the normative exclusion and possessing resource endowments that signal capability. However, strategies focused …

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 …

Carbonated Blast-Furnace Slag As Supplementary Cementitious Material: Phase Transition And Effect On Cement Hydration, Gao Deng, Nannan Zhang, Wenyu Liao, Yongjia He, Linnu Lu, Lingyu Chi, Hongyan Ma

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Blast-furnace slag, which contains high levels of CaO (and MgO), holds potential as a feedstock for CO₂ capture and storage. This study investigates the phase transitions occurring during the wet carbonation of ground granulated blast-furnace slag (GGBFS), characterizes the physical and chemical properties of carbonated GGBFS (CS), evaluates its pozzolanic reactivity, and examines the microstructure and performance of cement pastes blended with GGBFS or CS. The findings reveal that aragonite and calcite, the primary carbonation products, precipitate on the surface of GGBFS, hindering its dissolution and thereby reducing both the pozzolanic reactivity and the early strength of cement pastes. …

Biaxial Bending Response Of Prestressed Concrete Girders Subjected To Accidental Asymmetric Prestressing Strand Loss, Haitham Abdelmalek, Mohanad Abdulazeez, Ahmed Ibrahim, Mohamed Elgawady

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Bridge prestressed concrete girders are vulnerable to sudden rupture of prestressing strands caused by accidental collisions with over-height trucks. These incidents often result in the asymmetric loss of prestressing strands and concrete sections, generating a biaxial bending moment caused by the combined effects of lateral bending and existing service loads. The induced lateral bending moment reduces the flexural resistance of prestressed concrete (PC) girders. However, the current AASHTO LRFD (load and resistance factor design) provisions for flexural resistance in PC members do not explicitly address the complexities introduced by biaxial bending, leaving the extent of strength reduction uncertain. This paper …

Multi-User Collaboration In Augmented Reality For Beyond-Visual-Line-Of-Sight Bridge Inspection, Joel Murithi Runji, Genda Chen

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Bridge inspection traditionally relies on scaffolds, snooper trucks, and aerial robots for hard-to-reach areas beyond the visual line of sight of inspectors. This study automates the collaborative inspection workflow for multiple users when inspecting hard-to-reach bridge sections by interfacing low-cost multimodal sensors mounted on a magnetically wheeled climbing robot with augmented reality devices in a cyber-physical system. Non-destructive and visual sensors provide near real-time information of automated steel thickness measurements and visual observations of corrosive regions, respectively. The visual sensors also support robot teleoperation. The cyber-physical system is evaluated for its user-friendliness and performance. The system performance is quantified by …

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 …

Metagenomic Polymorphic Toxin Effector And Immunity Profiling Predicts Microbiome Development And Disease-Related Dysbiosis, Hunter W. Schroer, Francesco Beghini, Juan Antonio Raygoza Garay, Nicholas A. Christakis, Dustin E. Bosch

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Bacteria use antagonistic interbacterial weapons, such as polymorphic toxin secretion systems (TSS), to compete for niches in the human gut microbiome. We hypothesized that TSS influence gut microbiome development and disease-related dysbiosis. We developed a bioinformatic marker gene approach (PolyProf) to quantify TSS including ~200 effector and immunity genes and applied it to ~15,000 publicly available human metagenomes. PolyProf alpha and beta diversity readily distinguished 12 different human disease states and enabled the construction of highly accurate linear regression classifier machine learning models. Elastic net machine learning models integrating bacterial taxonomy with PolyProf had strong predictive value for 12 disease …

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 …

Dermatology Skin Lesion Image Analysis, Victoria Wegley, Joshua Hoog, Tristan Crawford, Keith Miller, William Fons, K. Pugh, J. Taylor, S. Swinfard, A. Fernandes, G. Patel, J. Hagerty, W. V. Stoecker, Ronald Joe Stanley

Research Data

Dermatology skin lesion image analysis research has been ongoing at Missouri S&T (previously UMR) since the 1980s. Our group has been successful in finding over 20 key dermoscopic structures in melanoma, melanoma mimics, and nonmelanoma skin cancers using iterative structure-based analysis. Structures are chosen to reduce system errors which are concentrated in a few classes: amelanotic/featureless, regressed, and small in situ melanomas, and the most difficult benign lesions to identify: Clark nevi, lentigines, and seborrheic keratoses [1]. Preliminary research detecting and using annotated lesion structures [2-7] and lesion artifacts [8,9] guided by clinical experience [10] with image processing and deep …

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 …

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 …

Soil Freezing Characteristic Surface For Partially Frozen Soils, Antai Dong, Xiong Zhang, Ming Xiao

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

The soil freezing characteristic curve (SFCC), which relates unfrozen water content to temperature, is a fundamental constitutive relationship in frost heave simulations. However, the influence of soil suction, another critical state variable, is often neglected. This study highlights how the SFCC is significantly affected by suction conditions, with unfrozen water content varying markedly between saturated (suction = 0) and completely dried (suction = ∞) conditions at the same subfreezing temperature. Recognizing this limitation, the concept of the soil freezing characteristic surface (SFCS) is proposed to incorporate the impacts of both temperature and suction on unfrozen water content. After that a …

First Remi Experiments At A Cryogenic Ion Storage Ring, M. Schulz, F. Herrmann, W. Zhang, A. Dorn, M. Grieser, F. Grussie, H. Kreckel, O. Novotny, A. Wolf, T. Pfeifer, C. D. Schröter, R. Moshammer

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

We have recorded double, triple, and quadruple coincidences between neutralized projectiles, recoiling target ions, and two electrons created in collisions of slow anions with neutral atoms. The experiments were performed at the cryogenic storage ring in Heidelberg. The recoil ions and ejected electrons were momentum-analyzed using a Reaction Microscope (ReMi) spectrometer. Various processes involving electron detachment from the projectile accompanied by various transitions in the target were investigated. Detachment without any transition in the target is qualitatively well described by a quasi-free electron model in the case of an Ar target. In detachment with single target ionization, no signatures of …