Digital Commons Network^™

Conceptual Model For Protecting Personal Data By Depersonalization In Information Systems: Principles, Components, And Life Cycle, Zarina Ildarovna Azizova

Chemical Technology, Control and Management

This article presents a systematic approach to personal data protection through depersonalization in the context of regulatory pressure and growing cyber threats. It proposes a comprehensive conceptual model that formalizes the de-identification process as a manageable sequence of steps, from attribute classification and method selection to mandatory verification of the result. The article also provides a comparative analysis of existing depersonalization methods in terms of their applicability within the proposed model. The model serves as a basis for the development of specific algorithms, as demonstrated by the example of a data shuffling approach.

Cylindrical Versus Spherical Self-Similar Capillary Cavity Collapse, Karl Cardin, Christophe Josserand, Raul Bayoan Cal

Mechanical and Materials Engineering Faculty Publications and Presentations

Drop tower experiments have been performed to study the capillary collapse of large high-aspect-ratio cavities. Cavities are formed by momentarily impinging the free surface of a liquid bath with a jet of air in the microgravity environment of a drop tower. The collapse may give rise to a jet and three distinct jetting regimes are identified. Simulations are performed to further investigate the phenomena. The abrupt emergence of a thin high velocity jet is observed experimentally and numerically at a specific initial cavity aspect ratio. Different power laws are identified in different regions of the cavity during the collapse providing …

Gauss Elimination Method For Solving The System Of Neutrosophic Linear Equations, Elsayed Badr, Shokry Nada, Saeed Ali, Aya Rabie

Neutrosophic Systems with Applications

This paper proposes a unified computational framework for solving linear systems under trapezoidal Neutrosophic uncertainty. The system is formulated as A( I )x = b( I ), where both the coefficient matrix and the right-hand side vector incorporate an indeterminacy parameter I, expressed as A( I ) = A₀ + IA₁ and b( I ) = b₀ + Ib₁. A decomposition strategy is developed to separate the model into deterministic and indeterminacy components, yielding a solution of the form x( I ) = x₀ + Ix₁. The deterministic component is obtained via …

An Improved Logarithmic Ratio-Product Type Estimator For Mean Modeling And Estimation Under Neutrosophic Uncertainty, Anchal Yadav, Mukesh Kumar

Neutrosophic Systems with Applications

In classical statistics, population mean estimation generally assumes precise and determinate data along with known auxiliary information. However, in real-world situations where observations are imprecise or expressed in interval form, such as temperature variations or financial market data, classical approaches become less effective. To address this limitation, neutrosophic statistics provide a more flexible framework for handling uncertainty and indeterminacy. This study proposes a neutrosophic logarithmic ratio-product type estimator for estimating the finite population mean using auxiliary information. The bias and mean squared error (MSE) of the proposed estimator are derived using a first-order approximation. Furthermore, performance evaluation is carried out …

Multicriteria Analysis Of Vehicle Exhausts Emission Using Fuzzy Analytic Hierarchy, Sunday Ayoola Oke, Ibraheem Adedotun Abdul, Ismaila Badmus, John Rajan, Swaminathan Jose, Adekunle Adetayo Yekinni, Kabiru Alani Olaiya, Mofoluwaso Kehinde Adeniran, Pandiaraj Benrajesh

Makara Journal of Technology

This study employs the fuzzy analytic hierarchy process (FAHP) to identify the critical factors and their degree of relevance to the vehicle emission process. Its innovation lies in the potential to blend ambiguity and uncertainty with the established AHP. FAHP transforms information into a defuzzification state through signal-to-noise ratios, normalization, and pairwise comparison. The principal parameters considered are revenue, sold packing units, CAGR, packing materials, consumption, and CO2 emissions (A, B, C, D, E, and F, respectively). From the normalized defuzzified weight result, consumption (Parameter E) is the best (normalized weight, 0.8685917), while CO2 emissions (Parameter F) was the worst …

Problems With Small Drinking Water Systems In Developing And Developed Countries: How Do They Compare?, Kurt Kesteloot

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

This dissertation compares the factors that drive the long-term functionality of small drinking water systems (serving fewer than 10,000 people) in developing and developed country contexts, with a focus on groundwater supplies. Using regression analysis in SAS and complementary fuzzy-set Qualitative Comparative Analysis (fsQCA) for developing countries, the study integrates two datasets: 23 Engineers Without Borders project surveys (90+ variables across 14 countries) and a multi-year National Park Service inspection dataset (approximately 1,561 systems; 7,460 recorded deficiencies, enriched with U.S. Census ZIP-code aggregates). In developing countries, statistically significant predictors of success include government cash contributions (odds ratio [OR] ≈ 67.6), …

Quantitative Study Of The Predictive Correlational Relationship Between Passenger Knowledge, Attitudes, And Behaviors In Aviation Ground Safety Events, Nikkolas Howard Kraay

Doctoral Dissertations and Projects

This study examined the relationships between passenger cabin safety knowledge, attitude, and behavior using the Knowledge-Attitude-Behavior (KAB) framework as the guiding theoretical model. Although prior aviation safety research has demonstrated a linear progression from knowledge to attitudes to behaviors, these studies were conducted primarily in Asian cultural contexts. The present study investigated whether this linear sequence would hold within a larger cultural passenger population. A predictive correlational design was used, and data was collected through an online survey completed by 194 adult airline passengers. The instrument measured cabin safety knowledge, attitudes, and behavioral intent, along with demographic characteristics.

Data was …

Individualized Models For Perception And Cognition, Dominic Marticorena

McKelvey School of Engineering Graduate Student Theses & Dissertations

This dissertation develops individualized models of perceptual and cognitive behavior, inferring latent capacities from limited data with sufficient efficiency, precision, and generality for person-level prediction. In perception, a nonparametric Bayesian framework for adaptive psychophysics is extended from audition to vision, producing an estimator that recovers dense individualized contrast sensitivity profiles through variational inference, a generalized kernel, and information-guided stimulus selection. A multitask extension enables simultaneous estimation of multiple psychometric fields by exploiting shared latent structure. Transferring this inferential logic to cognition, where relevant dimensions must be supplied by task design rather than physics, requires new measurement infrastructure. A Minecraft-based platform …

Inference-Time Scaling For Robust And Adaptive Computational Imaging, Yuyang Hu

McKelvey School of Engineering Graduate Student Theses & Dissertations

Computational imaging has transformed fields such as medical imaging, computational photography, and remote sensing by combining physical measurement models with advanced image reconstruction algorithms. Two major paradigms have driven significant progress: model-based deep learning (MBDL), which integrates learned priors with physics-based data consistency, and generative models, especially diffusion models, which capture complex image distributions and enable posterior sampling for uncertainty quantification. Despite their success, most existing methods still operate under static inference: they rely on fixed pretrained priors, predetermined sampling procedures, and a fixed forward model during reconstruction. This rigidity limits their robustness in the presence of distribution shift, complex …

Project Management Practices In Manufacturing: An Analytical Case Study, Margaret Randle, Robert Reynolds

Honors Capstones

Global industries are undergoing significant transitions in organizational models, technological innovation, and non-economic indicators of product quality, including sustainability and operational efficiency. The current workforce needs to not only develop competency of these technical skills, but also an adaptive, system-oriented approach to problem-solving as well. This case study, which highlights a manufacturing company’s project of increasing production efficiency, presents concepts learned through the textbook Project Management: Achieving Competitive Advantage by Jeffrey K. Pinto. The study thoroughly breaks down the full project lifecycle and the integration of systems of people, data, and initiatives in a framework that can be used to …

Molecular Catalysts For Electrochemical Nitrogen Activation Toward Sustainable Ammonia Synthesis, Jin-Xiu Han, Hao Xue, Xian-Biao Fu

Journal of Electrochemistry

Homogeneous electrocatalytic nitrogen reduction reaction (NRR) provides a powerful framework to interrogate molecular nitrogen-fixation pathways under mild conditions. By tuning the metal center, ligand architecture, and reaction medium, these systems enable capturing key intermediates and delivering mechanistic insight at the molecular-level resolution. Nevertheless, advances remain constrained by highly reduced operating potentials, intense competition from the hydrogen evolution reaction (HER), limited durability in turnover, and inadequate long-term stability.

In this review, we take electron delivery to the molecular active site as the guiding principle for organizing homogeneous electrochemical N₂ activation and transformation. We classify reported systems into direct electron transfer …

Water-Driven Solid Electrolyte Interphase Governs Continuous-Flow Ammonia Electrosynthesis, Peng-Bo Liu, Sheng-Liang Zhai, Ji Huang, Zhong-Shuo Zhang, Jie Zeng, Shao-Feng Li

Journal of Electrochemistry

Flow-cell architectures have emerged as a powerful platform for continuous and stable lithium-mediated nitrogen reduction (Li-NRR), enabling ambient-condition electrochemical ammonia synthesis and offering a promising alternative to Haber-Bosch processes. However, Li-NRR is exceptionally sensitive to trace water, and even minor variations in water content can profoundly alter interfacial chemistry. Here, we systematically investigate how initial water concentration affects Li-NRR performance in a continuous-flow cell. Excess water drives the formation of a thick solid electrolyte interphase (SEI) layer, which may impede nitrogen access to metallic lithium and hinder lithium-ion transport. As a result, the ammonia Faradaic efficiency collapses from ~61% to …

Insertion Of Noble Metal Free Cathodic Catalyst Layer With Fe-N-C Catalyst For Boosted Performance Of Pemfc, Shi Zhou, Muhammad Tariq, Asif Nadeem Tabish, Muhammad Salman, Fan-Di Ning, Muhammad Rayyan Tayyab, Ran-Ran Peng, Meng-Geng Hao, Wen-Mu Li, Xiao-Chun Zhou

Journal of Electrochemistry

Economical Fe-N-C catalysts are considered as promising alternatives to platinum group metal catalysts for proton exchange membrane fuel cells (PEMFCs). Despite exhibiting robust activity on rotating disk electrodes, their performance within membrane electrode assemblies often experiences limitations, such as decreased O₂ diffusion, high H₂O₂ formation, low proton conduction, and a lower electron transfer number. In this study, key factors, including proton transport, electron conduction, and gas diffusion within air-breathing PEMFCs, have been investigated by adjusting cathode catalyst layer (CCL) compositions. From the experimental results, the optimal peak power density was obtained when the loading of Fe-N-C …

Key Issues For Speed Safety Cameras: Results From A Semi-Structured Interview Of Agencies In The United States, Sophia Semensky, Miguel Figliozzi

Civil and Environmental Engineering Faculty Publications and Presentations

Speed safety cameras (SSCs) are a recognized countermeasure for targeting speeding-related crashes, injuries, and fatalities. SSCs have been introduced in various cities and are steadily expanding in use both in the U.S. and internationally. The extensive literature review of domestic and international documents and research papers reveals that the U.S. has been comparatively slow to adopt SSCs, with some other countries implementing these systems more extensively and earlier. Many local agencies are eager to learn SSC best practices and implementation lessons within the U.S. context. In this research, key issues related to SSC implementation are explored through semi-structured interviews with …

Computer Vision And Deep Learning-Based Decision Support System Using Eye Motion Tracking For Nystagmus Detection, Kowshik Balasubramanian

Electronic Theses and Dissertations 2020 - Present

This thesis presents the design, implementation, and experimental validation of an artificial intelligence (AI)-driven system for detecting and quantifying nystagmus an involuntary, rhythmic oscillation of the eyes intended as a portable, low-cost complement to conventional Videonystagmography (VNG). The complete pipeline integrates six algorithmic stages: face landmark detection, contrast enhancement, background-aware pixel thresholding, grid-based vertical column filtering, connected-component cluster analysis, and centroid computation, operating in real time on standard smartphone video to extract a sub-pixel normalized iris position time-series without any specialized eye-tracking hardware or infrared illumination. The system supports diagnostic decision-making, highlighting its promise for incorporation into telemedicine settings. The …

Electrospun Pan-Derived Carbon Nanofibers Embedded With Nicofew-Based High-Entropy Oxides For Electrocatalytic Oxygen Evolution Reaction, Alireza Noroozi, Mahdavi, M. Baris Yagci, Mohammad Mahdi A. Shirazi, Saeed Bazgir, Hadi Jahangiri, Uğur Ünal

Research outputs 2022 to 2026

High-entropy oxides composed of Ni, Co, Fe, W, and a fifth transition element (Mo, Mn, or Cu) were integrated into electrospun polyacrylonitrile nanofibers to form porous nitrogen-doped carbon membranes for the oxygen evolution reaction. Gas-assisted electrospinning enabled uniform dispersion of high-entropy oxide nanoparticles within the polyacrylonitrile matrix, while calcination at 800°C produced conductive carbon frameworks embedding crystalline high-entropy oxides with homogeneous elemental distribution. Structural and surface analyses confirmed the preservation of high-entropy oxide phases, catalytic graphitization of the carbon matrix, and strong interfacial bonding between metal oxides and carbon. Among all compositions, Mo-HEO@NC exhibited the lowest overpotential (≈240 mV at …

Evaluation Of Quantification Methods Applied To Overlapping Ftir Features, Benjamin P. Diehl

Reviews, Analyses, and Instructional Studies in Electrochemistry (RAISE)

Quantifying overlapping FTIR spectral features is critical for electrochemical analysis but often requires complex deconvolution. This work systematically evaluates several area ratio and proxy-based quantification methods using a controlled synthetic spectral model with varying signal-to-noise ratio, peak spacing, width, background, and data resolution. Constrained peak fitting and fixed-window integration consistently exhibit near-perfect monotonicity (Spearman ≈ 1.000) and linearity (Pearson ≈ 0.999–1.000) across all conditions. In contrast, derivative and moment-based metrics show sensitivity to peak overlap, width, and background. These results demonstrate that physically grounded area-based methods provide the most robust and reliable quantification of overlapping FTIR features.

Open Multi-Agent Systems: The Free-Range-Zoo Framework And Moasei Competition, Ceferino J. Patino Iv

School of Computing: Dissertations, Theses, and Student Research

The field of multi-agent reinforcement learning (MARL) has made significant strides in addressing sequential decision-making problems under uncertainty. However, traditional MARL frameworks assume closed-world settings with fixed agent sets, static task distributions, and unchanging environment dynamics. This thesis presents two complementary contributions that advance the state of open-world multi-agent systems research: (1) the free-range-zoo framework, an open-source environment suite for MARL in open environments featuring dynamic agent populations, evolving task sets, and changing operational frames; and (2) the MOASEI Competition, an international benchmarking event that leverages free-range-zoo to evaluate how artificial agents handle openness in complex, partially observable domains. The …

Modified Anodic Stripping Voltammetry For In Situ Analysis Of Liquid Metal Composition In Molten Salt Electrorefining Systems, James H. Manner

Reviews, Analyses, and Instructional Studies in Electrochemistry (RAISE)

The development of in-situ analytical techniques for high-temperature molten salt systems is critical for advancing electrorefining in nuclear fuel recycling and metallurgical purification. This study proposes Modified Anodic Stripping Voltammetry (MASV), using a “dip-and-strip” approach where a tungsten electrode captures molten Sn–Ni alloy before stripping in LiCl–KCl–CaCl₂ at 415 °C. OCP results showed rapid loss of the metal layer due to poor wetting, and stripping peaks gave compositions inconsistent with the alloy, likely reflecting Sn/Sn²⁺ and Sn²⁺/Sn⁴⁺ redox processes. These results highlight interfacial limitations and the need for improved electrode materials and surface modification.

A Machine Learning Framework For Ddos Attack Detection In Sdn-Enabled Mobile Wireless Networks, Ishita Sharma, Satyam Agarwai, Shashi Shekhar Jha, Sumit Chakravarty

Faculty Articles

Attacks on network components and devices pose a significant threat to service continuity, necessitating robust detection mechanisms. This paper presents a Distributed Denial of Service (DDoS) attack detection framework tailored for heterogeneous mobile wireless networks within a Software-Defined Networking architecture. A two-tier model is proposed: localized attack detection at access points (APs) using a Multi-Layer Perceptron (MLP) classifier, and centralized detection under mobility at the controller using a Long Short-Term Memory (LSTM) model. The system incorporates novel traffic features such as flow count, speed of source IP, source and destination IP address entropy, proportion of bidirectional flows, and handover frequency, …

Extraction And Interpretation Of Eeg Features For Diagnosis And Severity Prediction Of Ad And Ftd Using Deep Learning, Tuan Vo

Electronic Theses and Dissertations 2020 - Present

Alzheimer’s disease (AD) is the most common form of dementia and is characterized by progressive cognitive decline and memory impairment. Frontotemporal dementia (FTD), the second most prevalent form, primarily affects the frontal and temporal lobes and often leads to changes in personality, behavior, and language. Due to overlapping clinical symptoms, FTD is frequently misdiagnosed as AD. Electroencephalography (EEG) offers a portable, non-invasive, and cost-effective method for studying brain activity; however, its diagnostic utility for differentiating dementia subtypes is limited by signal complexity and noise. In this dissertation, I propose an EEG-based feature extraction framework that leverages deep learning to identify …

Ai-Driven Detection Of Failure Modes In Thermoplastic Composites Using Acoustic Emission Techniques, M. M. Shahzamanian, Li Ai, Paul Ziehl

Civil Engineering Faculty Publications

Composite materials in aircraft structures can suffer impact damage that leaves barely visible yet structurally significant defects, which degrade mechanical performance, especially under compressive loads. Existing methods for identifying failure modes in compression after impact (CAI) tests using acoustic emission (AE) data are limited in accuracy and scope. This study introduces an approach combining AE sensing with a heterogeneous ensemble convolutional neural network (CNN) to detect and classify failure mechanisms in impacted composite specimens. The novelty of this work lies in employing Red, Green, and Blue (RGB) wavelet images, produced through continuous wavelet transform (CWT) of AE signals, as inputs …

Sdn Controller For Distributed Quantum Computing, Firas Selmi

Masters Theses

Quantum networks promise transformative capabilities for computation [1], but current hardware remains limited; state-of-the-art quantum processors still operate with only a few hundred qubits [2], far below the scale required for practical applications. This limitation motivates the use of Distributed Quantum Computing (DQC), where computation is performed across multiple interconnected nodes. However, efficient DQC requires global network awareness and orchestration, a role analogous to Software-Defined Networking (SDN) in classical systems. In this work, we investigate the impact of SDN-inspired control logic on quantum networks by executing a scaled distributed implementation of Shor’s algorithm to factor N = 15 over a …

Dynamic Prediction On Goaf Surface Subsidence For The Last Two Periods Based On The Erf Time Function, Ren Lianwei, Li Shihui, Wang Wenchang, Li Zhenhua, Chen Shaojie, Dun Zhilin

Coal Geology & Exploration

Background The surface subsidence induced by coal seam mining represents a continuous dynamic evolution process. Constructing a full-time perdition model for surface subsidence in goafs holds significant theoretical and practical values for the rational utilization of land resources, the safety guarantee of engineering construction, and the transformation and development of resource-based cities. Methods To accurately predict the surface subsidence in goafs during its decline and residual periods, this study proposed a prediction model based on the error function (Erf). First, the evolution characteristics of Erf were analyzed, verifying that the subsidence, as well as its velocity and acceleration, predicted using …

Optical Mechanisms Behind Vitrinite Reflectance Suppression Caused By Strong Anisotropy: A Case Study Of Contact Metamorphic Coals, An Yanfei, He Shuyang, Chen Kaixin, Gao Junhan

Coal Geology & Exploration

Objective For vitrinite with weak anisotropy, which proves uniaxial and optically negative, replacing random vitrinite reflectance (R_ran) with the maximum vitrinite reflectance (R_max) allows for the accurate correction of vitrinite reflectance suppression induced by anisotropy. However, as its anisotropy intensifies, vitrinite exhibits increasingly complex optical properties, rendering it challenging to reflect the thermal metamorphic temperature of vitrinite using R_max. Methods This study investigated the strongly anisotropic vitrinite in coals and natural coke subjected to magma intrusion in the Shitai coal mine, Huaibei City, Anhui Province. Using a reflected-polarized light microscope (R-PLM), an …

A Cutting Trajectory Planning Technology Driven By Coal Seam Data In A Comprehensive Mining Face, Guo Yifeng, Xue Xusheng, Ma Hongwei, Zhang Guangming, Su Hao, Qi Ailing, Nie Zhen, Ma Kexiang, Wu Xuefei

Coal Geology & Exploration

Objective Currently, the insufficient full-element digitization and the prevalence of “data silos” in fully mechanized mining faces (FMMF) result in poor cutting precision and operational instability of shearers under complex undulating and abrupt geological conditions. These factors pose significant challenges to high-precision proactive trajectory planning. Methods Guided by the academic philosophy that “mining is data acquisition”, this paper proposes a coal seam data-driven cutting trajectory planning method for FMMFs. It elaborates on three core technologies: high-precision digital coal seam modeling, generative model updating and digital twin construction, and data-driven trajectory planning.To address low modeling precision, an intelligent interpolation-based digital coal …

Cost Analysis Formulation For Pv–Evcs–Dstatcom Integrated Distribution Systems Using Enhanced Hunter–Prey Optimization, Pappu Soundarya Lahari, Varaprasad Janamala

Mansoura Engineering Journal

With the rapid and significant growth of Electric Vehicles (EVs) and Distributed Generation (DGs), such as Photovoltaic (PV) deployment, in the distribution system, it is essential to address the challenges of rising power losses, voltage fluctuations, and reactive power imbalances. Optimal DSTATCOM placement in PV-EV integrated systems to mitigate power quality issues is an effective solution. It is thus necessary to evaluate the economic considerations of such heavily compensated networks. This study proposes Enhanced Hunter-Prey Optimization (EHPO) to assess the net savings of the PV-EVCS-DSTATCOM integrated IEEE-33,69, and 118 bus distribution networks in both single and multi-objective modes. The multi-objective …

Nocap: Article Fact-Checking With Ai, Thomas Chamberlain, Anthony Ciero, Varun Doddapaneni, Joshua Pechan

Electrical Engineering and Computer Science Student Publications

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Aggregate scores of articles by the same publisher in a database allowing users to view their scores and article authenticity reports

Analysis Toolkit For Potential Lunar Landing Zones, Jacobo Matallana

Morehead State Theses and Dissertations

A thesis presented to the faculty of the College of Science and Engineering Morehead State University in partial fulfillment of the requirements for the Degree Master of Science by Jacobo Matallana on April 24, 2026.

Automatic Determination Of Mechanical Properties From Molecular Dynamic Stress-Strain Curves Using Regression Fringe Response, Josh Kemppainen, Tristan Muzzy, Travis Wavrunek, Gregory Odegard

Michigan Tech Publications

Molecular dynamics (MD) simulations have become a powerful tool for studying polymers, designing new materials, and composites. Mechanical behavior in MD simulations are typically evaluated through stress-strain curves. However, MD-generated stress-strain curves are often obscured by thermal noise from atomic vibrations. This noise complicates the reliable derivation of mechanical properties and hinders objective and reproducible analysis. The noise in the stress-strain curves has been removed by using a Butterworth low-pass filter, enabling clearer interpretation of stress-strain data. Building on this, a novel analysis framework the Regression Fringe Response (RFR) is introduced to systematically determine the Young's modulus, yield strength, and …