Digital Commons Network^™

The Antibiofilm Efficacy Of Copper And Zinc-Enhanced Borate Bioactive Glasses On Polymicrobial Biofilms, Sarah Fakher, David J. Westenberg

Biological Sciences Faculty Research & Creative Works

Healthcare-acquired infections (HAIs) are a significant global challenge driven by biofilm-forming pathogens. Polymicrobial biofilms, involving interactions between multiple microbial species, exacerbate treatment difficulties due to their enhanced resistance to antimicrobial therapies. Borate bioactive glasses (BBGs) are an emerging class of biomaterials that have attracted significant interest in infection control. The incorporation of copper and zinc into the BBG matrix can effectively disrupt biofilm formation and bacterial colonization. This study investigates the antibiofilm efficacy of copper and zinc-doped BBGs against polymicrobial biofilms formed by S. epidermidis, E. coli, and P. aeruginosa. Using static and dynamic biofilm models, BBGs were applied through …

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 …

Reflections On Linear B, (Part 7): Sign 75, Which Looks Like A Backwards Letter S, Might Derive From The Ancient Egyptian Uraeus Figure, Gerald Leonard Cohen

Arts, Languages and Philosophy Faculty Research & Creative Works

Linear B sign 75 is evidently of unknown origin, both with respect to its intended portrayal and a rationale for its pronunciation /we/. And yet, like several other signs of Linear B, I believe this one too derives from ancient Egyptian, viz., the mythological cobra figure poised to spit fire to protect the god/goddess or sovereign on whose crown or headdress the cobra stands guard.

Existential Resilience In The City, Shane Ray Epting

Arts, Languages and Philosophy Faculty Research & Creative Works

Severe weather continues to challenge cities worldwide. Typhoons, wildfires, floods, and extreme temperatures test urban systems, communities, and governments at all scales. To combat such effects, resilience has emerged as a term geared towards increasing their ability to address impacts, strengthen infrastructure, and deal with cleanup measures. Although this business sounds straightforward to non-philosophers, ethical and existential elements remain out of view for the sciences and require investigation. This article fleshes them out. The first step is to identify relevant stakeholder groups and competing interests. The second is discovering equitable ways to bring professionals and city dwellers together to develop …

Reflections On Linear B: An Egyptian Hieroglyphic Component, (Draft #2), Gerald Leonard Cohen

Arts, Languages and Philosophy Faculty Research & Creative Works

Most Linear B signs are presently regarded as being of unknown origin, with a general consensus that Egyptian hieroglyphs are at best of minimal help in solving the mysteries. But the present article revisits this subject and suggests that the Egyptian hieroglyphic component goes beyond a borrowing or two. Along the way I noticed that for some Linear B signs a familiarity with Egyptian religious mythology is essential in this effort.

For example, the mysterious Linear B sign 75 is pronounced /we/ and looks like a backwards letter S. I suggest it can be explained as the cobra goddess Wadjet …

From Normal To Disaster Response Mode: How Can Virtual Communities Reconfigure Themselves To Respond Effectively To A Disaster?, Mahmudul Hasan, Cecil Eng Huang Chua, Michael David Myers, Varqa Shamsi Bahar

Business and Information Technology Faculty Research & Creative Works

During a disaster, many people seek information from virtual communities. However, information overload, falsehoods and unrelated topics hinder information flow in virtual communities, putting disaster victims at risk. Given many disasters are generally of a short duration, we explore how virtual communities can quickly reconfigure themselves to respond effectively to a disaster. Drawing on risk society theory, our findings suggest reconfiguration is done via a series of cycles initially involving community members and subsequently featuring both community members and moderators working together to mitigate risks. We contribute to virtual community discourse on disaster response by showing how a virtual community …

A Tribute To The Life And Work Of Jess Chua, James J. Chrisman, Zhenyu Wu, Alfredo De Massis, Pramodita Sharma, Lloyd P. Steier, Franz W. Kellermanns, Josip Kotlar, Emanuela Rondi, Bingbing Ge, Chevy-Hanqing Fang

Business and Information Technology Faculty Research & Creative Works

Jess Chua was a leading scholar in the field of family business and a major contributor to Entrepreneurship Theory and Practice both as an author and editor. His significant contributions to the field were acknowledged by the Web of Science, which listed him among the world's most highly cited researchers in Economics and Business in 2017, 2019, and 2020. In this memorial editorial, we pay tribute to him by discussing his influence on the field through his academic work, mentoring, long-term collaborations with other scholars, and the kindness he and his wife, Eva Kan, showed to his colleagues and students.

Managing Digital Transformation With Blockchain: Distributed Ledger Innovation Platforms, Nobuyuki Fukawa, Gregory J. Fisher

Business and Information Technology Faculty Research & Creative Works

Realizing its overarching potential in achieving digital transformation, leading organizations, such as Air New Zealand, have recently employed blockchain technology to manage 3D printing innovation. Despite the potential benefits, including the key role of blockchain as a supporting technology in Industry 5.0, these applications of blockchain technology in 3D printing are still at a nascent stage in practice and mostly limited to producer innovation. Additionally, 3D printing and blockchain technologies are often independently revolutionizing value capture and value creation, whereas the theoretical implications of their combined impacts on innovation management remain an underexplored domain. Limited digital transformation efforts have been …

Modeling Ion-Specific Effects In Polyelectrolyte Brushes: A Modified Poisson-Nernst-Planck Model, William J. Ceely, Marina Chugunova, Ali Nadim, James D. Sterling

Business and Information Technology Faculty Research & Creative Works

Polyelectrolyte brushes consist of a set of charged linear macromolecules, each tethered at one end to a surface. An example is the glycocalyx which refers to hair-like negatively charged sugar molecules that coat the outside membrane of all cells. We consider the transport and equilibrium distribution of ions and the resulting electrical potential when such a brush is immersed in a salt buffer containing monovalent cations (sodium and/or potassium). The Gouy-Chapman model for ion screening at a charged surface captures the effects of the Coulombic force that drives ion electrophoresis and diffusion but neglects non-Coulombic forces and ion pairing. By …

Keep Family Business Innovating: Revisit The Relationship Between Succession And Innovation From Ownership Succession, Yuan Chen, Weize Huang, Chevy-Hanqing Fang, Ling Chen

Business and Information Technology Faculty Research & Creative Works

This study examines how family owner-manager successors' ownership influences R&D investment in family businesses. Drawing on the socioemotional wealth (SEW) perspective, we theorize that successors with higher family ownership prioritize extended SEW, leading to stronger R&D investment, while those with lower family ownership prioritize restricted SEW, resulting in weaker R&D investment. Additionally, we explore how family dynamics—specifically, the number of family directors and asymmetric salary favoring family members—moderate this relationship. Our findings enhance the understanding of successor heterogeneity and its impact on innovation, offering insights for succession planning that support the long-term sustainability of family businesses.

Mindful Or Mindless? How And Why Virtual Communities Fail To Contain Information Pollution Across Different Disasters, Mahmudul Hasan, Varqa Shamsi Bahar, Cecil Eng Huang Chua, Michael David Myers

Business and Information Technology Faculty Research & Creative Works

People visit virtual communities to find trusted information (i.e. verifiable, accurate and reliable information) during disasters. Virtual communities are open spaces where people interact and share information. However, virtual communities often suffer from information pollution (including too much information and false or contradictory information) which decreases peoples' ability to find trusted information. Oddly, some virtual communities will develop useful practices for combatting information pollution during one disaster but subsequently fail to effectively employ the same practices or adapt them in subsequent disasters. We conducted a longitudinal, exploratory cross-case study to better understand how and why this occurs. Using the lenses …

Sosta: Skill-Oriented Stable Task Assignment With Bidirectional Preferences In Crowdsourcing, Riya Samanta, Soumya K. Ghosh, Sajal K. Das

Computer Science Faculty Research & Creative Works

Traditional task assignment approaches in crowdsourcing platforms have focused on optimizing utility for workers or tasks, often neglecting the general utility of the platform and the influence of mutual preference considering skill availability and budget restrictions. This oversight can destabilize task allocation outcomes, diminishing user experience, and, ultimately, the platform's long-term utility and gives rise to the Worker Task Stable Matching (WTSM) problem. To solve WTSM, we propose the Skill-oriented Stable Task Assignment with a Bi-directional Preference (SoSTA) method based on deferred acceptance strategy. SoSTA aims to generate stable allocations between tasks and workers considering mutually their preferences, optimizing overall …

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 …

Smartsla: Enabling Quality Of Service In Blockchain-Enabled Iot Networks, Kyle M. Whitlatch, Asad Waqar Malik, Sanjay Madria

Computer Science Faculty Research & Creative Works

The significant advancement in Internet of Things (IoT) adoption has enabled Multi-access Edge Computing (MEC) to mitigate IoT sensors limited computational, transmission power constraints, and data distribution overhead. However, integrating MEC with the IoT ecosystem poses several challenges, resulting in integrity issues with the MECs, impacting their capacity to effectively serve users seeking data generated by IoT sensors. To address this, we propose SmartSLA, a blockchain based solution to ensure Quality of Service (QoS) from third party IoT devices. SmartSLA leverages the decentralized and immutable nature of blockchain to combat the shortcomings of MECs. Using smart contracts, we develop a …

Feature Fusion Transferability Aware Transformer For Unsupervised Domain Adaptation, Xiaowei Yu, Zhe Huang, Zao Zhang

Computer Science Faculty Research & Creative Works

Unsupervised domain adaptation (UDA) aims to leverage the knowledge learned from labeled source domains to improve performance on the unlabeled target domains. While Convolutional Neural Networks (CNNs) have been dominant in previous UDA methods, recent research has shown promise in applying Vision Transformers (ViTs) to this task. In this study, we propose a novel Feature Fusion Transferability Aware Transformer (FFTAT) to enhance ViT performance in UDA tasks. Our method introduces two key innovations: First, we introduce a patch discriminator to evaluate the transferability of patches, generating a transferability matrix. We integrate this matrix into self-attention, directing the model to focus …

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 …

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 …

Echopulse: Ecg Controlled Echocardiograms Video Generation, Yiwei Li, Sekeun Kim, Zihao Wu, Hanqi Jiang, Yi Pan, Pengfei Jin, Sifan Song, Yucheng Shi, Xiaowei Yu, Tianze Yang, Tianming Liu, Quanzheng Li, Xiang Li

Computer Science Faculty Research & Creative Works

Echocardiography (ECHO) is essential for cardiac assessments, but its video quality and interpretation heavily rely on manual expertise, leading to inconsistent results from clinical and portable devices. ECHO video generation offers a solution by improving automated monitoring through synthetic data and generating high-quality videos from routine health data. However, existing models often face high computational costs, slow inference, and rely on complex conditional prompts that require experts' annotations. To address these challenges, we propose ECHOPulse, an ECG-conditioned ECHO video generation model. ECHOPulse introduces two key advancements: (1) it accelerates ECHO video generation by leveraging VQ-VAE tokenization and masked visual token …

Effective Approaches To The Dynamical Properties Of Two Distinguishable Bose Polarons, F. Theel, S. I. Mistakidis, P. Schmelcher

Physics Faculty Research & Creative Works

We explore effective approaches for describing the dynamics of induced interactions among two noninteracting distinguishable impurities (Bose polarons) when their couplings to the host species are switched on. First, we evaluate the time-dependent characteristics of each polaron through reduced single-particle models. Their validity is ultimately judged by comparing them to observables obtained within a many-body variational approach. We argue that utilizing a time-dependent optimization process with the effective mass and frequency being fitting parameters leads to an accurate matching on the level of one-body observables with the prediction of the many-body method. Next, we construct two-body effective models, which, in …

Balance Assistance Without Mechanical Support Using A Virtual Cane With Haptic Feedback, Sindhu Reddy Alluri, Sambad Regmi, Fazlur Rashid, Devin Michael Burns, Yun Seong Song

Psychological Science Faculty Research & Creative Works

Light Touch (LT) has been known to improve standing balance without mechanical support by providing sensory information about the movement of the body. Inspired by this, this work developed a Virtual Cane (VC) which gives no physical support but provides other sensory information that a physical cane would. The VC is developed with a distance sensor and vibration actuators to provide cane tip-to-ground distance information to the user. The extent to which this haptic feedback improves standing balance was assessed in a human experiment. 10 healthy young participants underwent a standing balance experiment with tandem stance and eyes closed, using …

How Do Human And Ai Gender Bias Interact In Hiring Decisions?, Eyuel Getahun, Daniel Burton Shank, Casey I. Canfield, Jessica L. Cundiff, Jenny L. Davis, Celia Freed

Psychological Science Faculty Research & Creative Works

The hiring process is crucial for organizational success but has long been troubled by human biases. Many organizations now include AI in their hiring protocols to mitigate these biases and increase efficiency. However, AI itself can have biases baked-in. Human biases and AI biases are distinct but related; here, we examine how human and AI biases interact to affect hiring outcomes. Through an online experiment, we examine this question in the context of gendered hiring for a male-dominated leadership position in electrical engineering. The study tests how elevated and depressed AI recommendations for male and female job candidates affect participant …

Comparison Of Cnn And Lstm Networks On Human Intention Prediction In Physical Human-Robot Interactions, Khosro Ghorbani Zadeh, Niloofar Zendehdel, George L. Holmes, Keyri Moreno Bonnett, Amy Costa, Devin Michael Burns, Ming-Chuan Leu, Yun Seong Song

Psychological Science Faculty Research & Creative Works

Advancements in robotics and AI have increased the demand for interactive robots in healthcare and assistive applications. However, ensuring safe and effective physical human-robot interactions (pHRIs) remains challenging due to the sophistication of human motor communication and intent recognition. Traditional physics-based models struggle to capture the dynamic nature of human force interactions, limiting robot adaptability. To address these limitations, neural networks (NNs) have been explored for force-movement intention prediction. While multi-layer perceptron (MLP) networks show potential, they struggle with temporal dependencies and generalization. Long Short-Term Memory (LSTM) networks effectively model sequential dependencies, while Convolutional Neural Networks (CNNs) enhance spatial feature …

The Organization Of Serotonergic Fibers In The Pacific Angelshark Brain: Neuroanatomical And Supercomputing Analyses, Skirmantas Janušonis, Ralf Metzler, Thomas Vojta

Physics Faculty Research & Creative Works

Serotonergic axons (fibers) are a universal feature of all vertebrate brains. They form meshworks, typically quantified with regional density measurements, and appear to support neuroplasticity. The self-organization of this system remains poorly understood, partly because of the strong stochasticity of individual fiber trajectories. In an extension to our previous analyses of the mouse brain, serotonergic fibers were investigated in the brain of the Pacific angel shark (Squatina californica), a representative of a unique (ray-like) lineage of the squalomorph sharks. First, the fundamental cytoarchitecture of the angel shark brain was examined, including the expression of ionized calcium-binding adapter molecule 1 (Iba1, …

High-Frequency Gold Price Forecasting: Optimizing Multi-Layer Perceptron With Genetic Algorithm, Vrtagic Sabahudin, Fatih Dogan

Materials Science and Engineering Faculty Research & Creative Works

Accurately forecasting gold price actions is critical in financial markets due to gold's role as a safe-haven asset. This paper addresses the challenge of forecasting gold prices by applying a Genetic Algorithm (GA) with a Multi-Layer Perceptron (MLP) model. The research exploits historical financial data from various instruments, including gold futures, Bitcoin, and key currency pairs, to improve prediction accuracy. By optimizing the MLP's hyper parameters through GA, the model efficiently captures complex relationships in high-frequency data, achieving a remarkable R² score of 0.9993. This level of precision demonstrates the model's potential for providing actionable insights for traders and investors. …

2024 Scholarly Productivity Report, Missouri University Of Science And Technology

Civil, Architectural and Environmental Engineering Scholarly Productivity Reports

No abstract provided.

Harnessing Extrinsic Dissipation To Enhance The Toughness Of Composites And Composite Joints: A State-Of-The-Art Review Of Recent Advances, Gilles Lubineau, Marco Alfano, Ran Tao, Ahmed Wagih, Arief Yudhanto, Xiaole Li, Khaled Almuhammadi, Mjed Hashem, Ping Hu, Hassan A. Mahmoud, Fatih Oz

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Interfaces play a critical role in modern structures, where integrating multiple materials and components is essential to achieve specific functions. Enhancing the mechanical performance of these interfaces, particularly their resistance to delamination, is essential to enable extremely lightweight designs and improve energy efficiency. Improving toughness (or increasing energy dissipation during delamination) has traditionally involved modifying materials to navigate the well-known strength-toughness trade-off. However, a more effective strategy involves promoting non-local or extrinsic energy dissipation. This approach encompasses complex degradation phenomena that extend beyond the crack tip, such as long-range bridging, crack fragmentation, and ligament formation. This work explores this innovative …

Utility Of Immobilized Metal Salens As Electrocatalysts: Fuel Cells And Organic Electrosynthesis, Zachary A. Nguyen, Shelley D. Minteer

Chemistry Faculty Research & Creative Works

There have been significant advancements in the electrosynthesis of fuels and organic molecules, making it an increasingly sustainable and cost-effective alternative to traditional chemical redox reagents. Early versions of these systems faced challenges in chemo selectivity due to high applied overpotentials, which have been mitigated with the introduction of molecular electrocatalysts, like metal salens (MSalens). These MSalens reduce the required overpotentials, increase turnover numbers (TON), and have simple modularity within their ligand structure allowing for tunable selectivity. While these MSalen electrocatalysts are typically used homogeneously for engineering simplicity, downstream separations are often costly and time-consuming. Immobilization of MSalens addresses these …

Zeolite 13x Particles With Porous Tio2 Coating And Ag2o Nanoparticles As Multi-Functional Filler Materials For Face Masks, Wei Su, Kaiying Wang, Han Yu, Fateme Fayyazbakhsh, Jeremy Watts, Yue-Wern Huang, Jee-Ching Wang, Xinhua Liang

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Adsorbent components in face masks are crucial for protecting individuals exposed to dangerous situations. In this study, one porous TiO₂ layer was deposited on zeolite 13X particles by coating hybrid organic/inorganic titanium alkoxide film via molecular layer deposition (MLD), followed by heat treatment to remove organic components in the MLD film. Various concentrations of Ag₂O nanoparticles were impregnated on the TiO₂-coated 13X particles, offering a wide range of antibacterial, antifungal, and antiviral characteristics. The obtained composite particles were characterized using X-ray photoelectron spectroscopy and thermal gravimetric analysis to study the composition and mass loading of …

Optimized Fabrication Of Dendritic Mesoporous Silica Nanoparticles As Efficient Delivery System For Cancer Immunotherapy, Varsha Godakhindi, Mostafa Yazdimamaghani, Sudip Kumar Dam, Farzana Ferdous, Andrew Z. Wang, Mubin Tarannum, Jonathan Serody, Juan L. Vivero-Escoto

Chemical and Biochemical Engineering Faculty Research & Creative Works

In the past decade, cancer immunotherapy has revolutionized the field of oncology. Major immunotherapy approaches such as immune checkpoint inhibitors, cancer vaccines, adoptive cell therapy, cytokines, and immunomodulators have shown great promise in preclinical and clinical settings. Among them, immunomodulatory agents including cancer vaccines are particularly appealing; however, they face limitations, notably the absence of efficient and precise targeted delivery of immune-modulatory agents to specific immune cells and the potential for off-target toxicity. Nanomaterials can play a pivotal role in addressing targeting and other challenges in cancer immunotherapy. Dendritic mesoporous silica nanoparticles (DMSNs) can enhance the efficacy of cancer vaccines …

Conditional Cooperation With Longer Memory, Nikoleta E. Glynatsi, Ethan Akin, Martin A. Nowak, Christian Hilbe

Mathematics and Statistics Faculty Research & Creative Works

Direct reciprocity is a wide-spread mechanism for the evolution of cooperation. In repeated interactions, players can condition their behavior on previous outcomes. A well-known approach is given by reactive strategies, which respond to the coplayer's previous move. Here, we extend reactive strategies to longer memories. A reactive-n strategy takes into account the sequence of the last n moves of the coplayer. A reactive-n counting strategy responds to how often the coplayer cooperated during the last n rounds. We derive an algorithm to identify the partner strategies within these strategy sets. Partner strategies are those that ensure mutual cooperation without exploitation. …