Digital Commons Network^™

When Disasters Trigger Cyber Vulnerabilities: Mapping Physical-Digital Interdependencies In Critical Infrastructure Systems, Dikshya Panta, Sicheng Wang, Aditya Sapkota, Prakash Ranganathan

Faculty Publications

Critical infrastructure (CI) systems such as power, water, communications, and emergency services are increasingly exposed to compound risks in which natural disasters and cyber incidents interact and amplify one another. Traditional risk assessments often isolate physical and digital threats, overlooking the cascading dependencies that emerge when operational stress, emergency reconfiguration, and adversarial exploitation coincide. This study conducts a 2019–2025 scoping review and introduces a Geographic Information System (GIS) driven six-stage disaster cyber compounding framework that characterizes, maps, and operationalizes compound risk across interdependent CI sectors. The framework integrates a common operating picture, analytic situational understanding, exposure mapping, threat-fingerprint encoding, detection …

Design Concept For Low-Cost Management Of Early Medial Or Lateral Osteoarthritis, Charles F. Babbs

Weldon School of Biomedical Engineering Faculty Working Papers

Background: Asymmetrical joint space narrowing occurs in many cases of knee osteoarthritis (OA). The objective of the present study is to elucidate the biomechanics of lateral wedges in shoes used to widen the medial joint space in predominantly medial OA, and the biomechanics of medial wedges in shoes used to widen the lateral joint space in predominantly lateral OA. Methods: Medial or lateral shift of the center of weight bearing of the foot is largely offset by movement of the flexible hip joint, which changes the tilt angle of the femoral head in the frontal plane. The altered tilt can …

Performance Analysis Of Fuzzy-Pid Control For Single-Phase Interior Permanent Magnet Motors, Ogholo Michael, Akinloye Benjamin, Enivweru Edewor

AUIQ Technical Engineering Science

This study presents an advanced performance analysis of a fuzzy-PID controller for Single-Phase Interior Permanent Magnet (IPM) motors using MATLAB/Simulink simulations. The fuzzy-PID controller dynamically adjusts its gains in real time via fuzzy logic, enhancing speed regulation and disturbance rejection while addressing the motor’s inherent nonlinearities and saliency effects. Performance comparisons with a conventional fixed-gain PID controller reveal that the fuzzy-PID achieves a 42% faster rise time, 68% lower overshoot, and reduced steady-state error (<0.8 rpm vs. ∼5.2 rpm for PID). Under ramp and sinusoidal inputs, the fuzzy-PID maintains lower tracking errors (∼7.5 rpm vs. ∼26 rpm for PID) and minimal phase lag (7° vs. 22° for PID). Robustness tests demonstrate superior disturbance rejection and faster recovery from load variations. The results confirm that fuzzy-PID control significantly improves transient response, accuracy, and adaptability for single-phase IPM motor drives, making it suitable for high-performance applications.

Crystal Structures, Optical Behavior, And Magnetic Properties In Hydrated Lanthanide Iron Sulfates, Chole Jones, Silu Huang, Tyler L. Spano, Eric A. Gabilondo, Mohammed Al-Fahdi, Kara Trim, Mary Douglas, Rongying Jin, Andrew Miskowiec, P. Shiv Halasyamani, Ming Hu, Jie Ling

Faculty Publications

Single crystals of LnFe(SO₄)₃(H₂O)₂ (Ln = La, Ce, Pr, Nd, Sm, Eu, Gd, Dy, Ho, Er, Tm; compounds 1–11) and LnFe(SO₄)₃(H₂O) (Ln = Tm, Yb, Lu; compounds 12–14) were synthesized under hydrothermal conditions. Single-crystal X-ray diffraction (SCXRD) analysis revealed that the dihydrated compounds (1–11) crystallize in centrosymmetric (CS) structures, with the lanthanide ions adopting eight-coordinate geometries. In contrast, the monohydrated compounds (12–14) exhibit noncentrosymmetric (NCS) structures, where the lanthanide ions are seven-coordinated. Vibrating sample magnetometry (VSM) confirmed that compounds 2, …

Biopcm-Integrated Smart Façade For Hospitals: Thermal Performance Across Floor Levels In A Hot-Arid Climate, Aia A. Qindil, Esraa Elazab, Mohamed M. Shawky Abou Liela

Mansoura Engineering Journal

Hospitals are among the most energy-intensive building types because they operate around the clock and must maintain strict indoor environmental conditions, especially in hot-arid climates. Improving their performance calls for façade solutions that can store thermal energy effectively and help cut carbon emissions. This study evaluates the use of macro-encapsulated BioPCM® within the south façade of a high-rise hospital in Mansoura, Egypt, using dynamic simulations in DesignBuilder (EnergyPlus). The analysis focuses on two vertical levels, the third floor as a typical ward and the seventh floor as a rooftop ward, to investigate how elevation influences PCM performance, an aspect rarely …

Credit Card Fraud Detection Using Metaheuristic Techniques, Narges Sabry Anwer Mohammed, Mohammed Sabry Saraya, Amr M. Thabet, Labib M. Labib

Mansoura Engineering Journal

Credit Card Fraud Detection (CCFD) has become a critical challenge to financial security due to increasingly sophisticated fraudulent activities. This study investigates the effectiveness of Meta-Heuristic (MHT) optimization techniques in improving fraud detection (FD) through feature selection (FS) and model optimization. To address class imbalance, Random Under-Sampling (RUS) was applied. The selected feature subsets were evaluated using three machine learning (ML) classifiers—Decision Tree (DT), KNearest Neighbours (KNN), and XGBoost (Xgb-Tree)—across four benchmark datasets: European, Statlog (Australian Credit Approval), PaySim, and Credit Card Transactions Fraud Detection (CCTFD). Eleven binary MHT algorithms were implemented and compared. The comparative analysis shows that the …

Intrusion Detection In Cloud Computing Using Support Vector Machine And Fast Firefly Algorithm, Ayobami Taiwo Olusesi, Ignatius Kema Okakwu, Ayodeji Akinsoji Okubanjo, Ayo Isaac Oyedeji, Oluwaseyi Olawale Bello

AUIQ Technical Engineering Science

Cloud computing has become popular due to the ongoing developments in the Internet and technical advancements. Users are increasingly storing their Web resources and data in the cloud environment due to the convenience and reduced cost of cloud computing services. Data security is crucial to the development of communication systems in cloud computing. Since data in cloud storage environments needs to be protected from intruders, network security in cloud environment has grown significantly in importance in recent years. In order to address these challenges, there is a need for an intrusion detection system (IDS) that can effectively identify malicious attack …

Inverse Kinematics 3d Human Modeling Simulation Based On Multi-View Vision, Guoyu Fang, Yanze Li, Kai Chen, Xiaodong Zhao, Zizhuo Hu, Mingshi Yang, Wanqing Wu, Zichen Wang, Wenkai Guo

Journal of System Simulation

Abstract: In autonomous driving simulation and industrial virtual reality simulation, there is a high demand for accuracy and robustness in 3D human body modeling. However, current joint-based human modeling approaches suffer from issues such as continuous modeling jitter, local distortion, and poor adaptability to occlusion, which degrade model quality and limit the development of practical applications such as intelligent driving and digital factories. To address these challenges, this paper proposes a multi-view vision-based inverse kinematics 3D human modeling method using a vector quantized variational autoencoder(IK-VQ-VAE). By integrating joint training with an automatic variational gradient descent approach, the proposed method achieves …

Acknowledgement Of Reviewers

Journal of Sustainable Mining

No abstract provided.

Factors Impacting Elementary School Students’ Engineering Design And Optimization Decisions, Elaine Silva Mangiante, Ilana Haliwa

Journal of Pre-College Engineering Education Research (J-PEER)

This mixed methods study explored possible factors that could impact elementary school students’ engineering design and optimization decisions. Data were collected from six teachers and 117 students in six fourth grade classrooms that implemented the Engineering is Elementary geotechnical engineering unit, A Stick in the Mud: Evaluating a Landscape. Students were to recommend to villagers their site decision of where to build a TarPul bridge based on four properties: soil type, villager preference, amount of compaction needed, and location less prone to erosion. Data sources included a pre-and post-assessment question, students’ documentation of property choices for their first and optimized …

Noninvasive Condition Monitoring For Eccentricity Fault Detection In Large Hydro Generators, Atena Tazikeh Lemeski, Di̇dem Tekgün, Ozan Keysan, Kemal Leblebi̇ci̇oğlu, Murat Göl

Turkish Journal of Electrical Engineering and Computer Sciences

Eccentricity faults in electric machines remain a critical concern, as they generate uneven magnetic forces that increase vibration and noise, ultimately raising the risk of premature motor failure. This study proposes a method for the early detection of dynamic eccentricity (DE) faults in hydropower plants through an advanced optimization-based parameter identification technique integrated with finite element analysis (FEA). Finite element modeling (FEM) is first used to analyze an existing salient-pole synchronous generator (SPSG) from a hydroelectric power plant in Türkiye. The effects of DE faults on the SPSG’s magnetic equivalent circuit parameters are then examined under various fault severities. A …

A Deep Learning Model For Accurate Tomato Leaf Disease Identification, Maheen Shahzad, Muhammad Abdullah Javed, Erum Ashraf, Hafiz Ishfaq Ahmad, Sabeen Masood

Turkish Journal of Electrical Engineering and Computer Sciences

Recent advances in machine learning and deep learning have greatly improved how we detect plant diseases, making diagnoses more accurate, faster, and easier to scale. However, many existing solutions depend on large, pretrained models that need powerful hardware, which limits their use in the field, especially in areas with limited resources. To tackle this, we designed a custom lightweight convolutional neural network (CNN) built from scratch using 20,000 carefully selected images from the PlantVillage tomato dataset. Our model uses Squeeze-and-Excitation (SE) blocks and Swish activation functions to boost performance, reaching an accuracy of 97.7% while using far fewer computing resources …

Improving Rail System Signaling Efficiency Through Ai-Based Driving Profile Generation: A Comparative Performance Analysis, Mehmet Taci̇ddi̇n Akçay, Abdurrahi̇m Akgündoğdu

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, a dataset comprising 3600 discrete operational snapshots (rather than continuous time-series data) derived from real-field operations is used to obtain a high-accuracy driving profile equation using a second-degree Polynomial Regression method. This equation demonstrates the model’s interpretability. The performance metrics obtained with the second-degree polynomial regression model’s equation are as follows: a coefficient of determination (R2) of 0.84, a Pearson Correlation Coefficient of 0.91, and an RMSE of 11.13. These results indicate the effectiveness of artificial intelligence-based approaches in improving the efficiency of the railway signaling system. The same dataset is also utilized with other machine learning …

Distribution System Reliability Evaluation Considering Protection Coordination Using Petri Nets, Rani Kumari, Bhukya Krishna Naick

Turkish Journal of Electrical Engineering and Computer Sciences

Maintaining reliable and high-quality power delivery becomes increasingly complex with expanding power grids. The lack of protection coordination poses a significant threat, compromising overall system reliability. This research addresses this challenge by proposing a method for coordinating protective devices within the distribution system, specifically during network faults. The proposed approach utilizes a stochastic timed Petri net (STPN) based methodology to model protective device coordination across various fault scenarios. This technique effectively captures the dynamic behavior and interactions of protective equipment, allowing for the anticipation of potential disturbances. This proactive insight facilitates preventative measures to address prewarning situations, thereby preventing cascading …

Proposed Methodology For Correcting Fourier-Transform Infrared Spectroscopy Field-Of-View Scene-Change Artifacts, Kody A. Wilson, Michael L. Dexter, Benjamin F. Akers, Anthony L. Franz

Faculty Publications

Fourier-transform spectrometers are widely used for spectral measurements. Changes in the field of view during measurement introduce oscillations into the measured spectra known as scene-change artifacts. Field-of-view changes also introduce uncertainty about which target the measured spectrum represents. Though scene-change artifacts are often present in dynamic data, their significance is disputed in the current literature. This work presents a theoretical framework and experimental validation for scene-change artifacts. Field-of-view changes introduce variable interferogram offsets, which standard processing techniques assume are constant. The error between the interferogram offset and its estimate is Fourier-transformed, yielding scene-change artifacts, often confused with noise, in the …

Spectroscopic Investigations On Polyvinylidene Fluoride Nanofibers, Parinaz Amaniabdolmalaki, Jui Vitthal Kharade, Alexandro Trevino, Lydia Morales, Karen Lozano, Victoria Padilla, Karen S. Martirosyan, Horacio Vasquez, Mircea Chipara

Physics & Astronomy Faculty Publications

The production of polyvinylidene fluoride nanofibers by force-spinning from polymer solutions was confirmed by electron microscopy. The structural and phase characteristics of the resulting nanofiber mats were examined using Fourier Transform Infrared Spectroscopy in Attenuated Total Reflectance mode, Raman spectroscopy, and X-ray Diffraction. Results from all these techniques consistently indicated that both the powder and the mats of polyvinylidene fluoride predominantly contain the α phase, with a small admixture of the β phase. Within experimental errors, no other phases were noticed both in the powder and in the as-obtained mats. The ratios of the areas of the Raman lines at …

Single-Valued Neutrosophic Pessimistic Multi-Granulation Rough Set Model Based On (A,B,C)-Cut Relations And Its Applications, Xu-Xi Wu, Hu Zhao, Qiao-Ling Song, Xiong-Wei Zhang

Neutrosophic Systems with Applications

To address uncertainty in multi-source data, this paper proposes a single-valued neutrosophic pessimistic multi-granulation rough set (P-SVN-MGRS) model based on (a,b,c)-cut relations. In this framework, each neutrosophic relation is characterized by three membership-degree functions: T(x,y), I(x,y), and F(x,y). These functions correspond to truth-membership, indeterminacy, and falsity, respectively. The (α,β,γ)-cut relation employs parameters α,β,γ∈(0,1] as thresholds for the three functions. A pair (x,y) belongs to …

Methods For Radiation-Sparing Navigation In Endovascular And Spine Surgery, William Ross Warner

Dartmouth College Ph.D Dissertations

Modern surgical navigation systems improve procedural accuracy, limit complications, and can reduce reliance on intraoperative ionizing radiation, yet their adoption remains uneven across surgical domains. Informed by clinical immersion, this thesis identifies and addresses two unmet needs revealed through contrasting image-guidance practices in endovascular and spine surgery, where existing guidance techniques are costly, intermittent, or fundamentally limited. Specifically, these needs include (1) the absence of radiation-sparing navigation techniques for endovascular procedures, which rely heavily on 2D x-ray angiography, and (2) the need for dynamic registration methods in open spine surgery, where navigation accuracy can degrade after initial registration and intraoperative …

Volumetric Wear Analysis Of Polyethylene Orthopedic Liners Using Point Clouds, Gabriel Landi

Dartmouth College Master’s Theses

In vivo wear of ultra-high molecular weight polyethylene (UHMWPE) orthopedic bearings represents a major problem in shoulder and knee arthroplasty, with significant amounts of devices failing due to the complication itself, and even more due to associated reasons for retrieval, including aseptic loosening. However, quantification of UHMWPE wear has largely been limited to linear and in vitro volumetric analyses, obscuring the understanding of the amount of clinically relevant material loss. This study proposes new methods to measure articular, rim, and backside surfaces of retrieved shoulder and knee UHMWPE liners, centered around the coordinate measuring machine (CMM)-based collection of high density …

Trust In Automation Causing Automation Complacency In Relation To General Aviation: A Quantitative Study, Bart Augustin Marcules

Doctoral Dissertations and Projects

This quantitative causal-comparative study examined whether a relationship exists between Trust in Automation (TIA) and the potential for automation complacency in general aviation between pilots flying automated and non-automated aircraft as single pilots, while controlling for pilot generation. Autonomation can lead to automation complacency through overreliance and trust in the automation. Automation complacency may cause pilots to lose situational awareness and be surprised by automation surprises, leading to detrimental outcomes. Although automation complacency has long challenged commercial pilots, resulting in significant research on this issue, little research has focused on this issue in general aviation. The limited research is due …

Exploring Runtime Sparsification Of Yolo Model Weights During Inference, Tanzeel-Ur-Rehman Khan, Sanghamitra Roy, Koushik Chakraborty

Electrical and Computer Engineering Student Research

In the pursuit of real-time object detection with constrained computational resources, the optimization of neural network architectures is paramount. We introduce novel sparsity induction methods within the YOLOv4-Tiny framework to significantly improve computational efficiency while maintaining high accuracy in pedestrian detection. We present three sparsification approaches: Homogeneous, Progressive, and Layer-Adaptive, each methodically reducing the model’s complexity without compromising its detection capability. Additionally, we refine the model’s output with a memory-efficient sliding window approach and a Bounding Box Sorting Algorithm, ensuring precise Intersection over Union (IoU) calculations. Our results demonstrate a substantial reduction in computational load by zeroing out over 50% …

Tuning Oxygen Reduction Kinetics In Lasrcoo4 With Strained Epitaxial Thin Films And Wrinkled Freestanding Membranes, Habib Rostaghi Chalaki, Ebenezer Sessi, Mohammad El Loubani, Dongkyu Lee

Faculty Publications

Sluggish oxygen reduction reaction (ORR) remains a critical barrier to advancing intermediate-temperature electrochemical energy devices. Here, we demonstrate that strain engineering in two platforms, epitaxial thin films and freestanding membranes, systematically tunes ORR kinetics in Ruddlesden-Popper LaSrCoO4. In epitaxial films, film thickness is varied to control in-plane tensile strain, whereas in freestanding membranes strain relaxation during the release step using water-soluble sacrificial layers produces flat or wrinkled architectures. Electrochemical impedance spectroscopy analysis reveals more than an order of magnitude increase in the oxygen surface exchange coefficient for tensile-strained films relative to relaxed films, together with a larger oxygen vacancy concentration. …

A Review Of Routing Attacks In Routing Protocol Over Low-Power And Lossy-Based Iot Networks, Lanka Chris Sejaphala Mr., Vusimuzi Malele Prof, Francis Lugayizi Prof.

Journal of Cybersecurity Education, Research and Practice

Low-power and Lossy IoT Networks (LLNs) comprise physical sensors, processing capability, power, and other technologies to exchange information between systems and devices over the internet. However, these networks are susceptible to routing attacks affecting resources, traffic flow, and topology formation. In the related work, it has been discovered that many previous studies do not consider algorithms and implementation approaches for routing attacks. This research study provides a comprehensive in-depth synthesis insight into the description, effects, and algorithms & implementation of four routing attacks in LLNs i.e., rank, sinkhole, DIS-flooding, and worst parent attacks. The findings of this research study highlight …

Reformulation Of The Protein Databank For Real-Time Search Of Geometrical Attributes Of Protein Structures, Musa Azeem, Christopher Lee, Aaron Hein, Christopher Ott, Homayoun Valafar

Faculty Publications

Introduction:

In this study, we introduce the design and implementation of PDBMine, a large-scale, queryable platform for mining sequence-structure statistics from the Protein Data Bank (PDB). PDBMine enables rapid analysis of local conformational trends across proteins by extracting dihedral angles and sequence patterns at scale. In addition to the design and implementation of PDBMine, we also present results validating its ability to return structurally meaningful information.

Methods:

We first assess the accuracy of its dihedral angle distributions by comparing them to established Ramachandran space and verifying expected behaviors of residues such as glycine and proline. We then use PDBMine to …

An Optimized Hybrid Xgboost-Lstm-Gru Model For Energy Consumption Forecasting In Smart Homes Using Weather Data, Marwah J. Salman, Eman M. El-Gendy, Amira Y. Haikal, Mahmoud M. Saafan

Mansoura Engineering Journal

Energy consumption is increasing every day due to the proliferation of smart homes and smart household appliances, which deplete existing energy resources. Therefore, efficient energy management by accurately predicting energy consumption is critical. In this paper, a hybrid prediction model is proposed to forecast energy consumption in smart homes using weather data. The proposed system follows five steps: data acquisition, data preprocessing, feature selection, prediction, and hyperparameter optimization. In the data acquisition step, all attributes except the weather attributes are removed. The min-max algorithm is then applied to normalize the dataset to a specific scale, preventing the prediction model from …

General And Special Relativity May Explain The Hubble Tension And Galactic Rotation, Charles F. Babbs

Weldon School of Biomedical Engineering Faculty Working Papers

Using two equations from general and special relativity, one can explore the hypothesis that gravitational time dilation and the relativistic Doppler effect cause the Hubble tension. Predicted results show good agreement between theory and observation, as long as mass is restricted to ordinary baryonic matter. This unexpected finding suggested a surprising continuation, namely that observed galactic rotation also be explained by special relativity. This idea survived initial tests using both Vera Rubin’s original data and more recent observations.

Jiving With Llms: Assessing First Year Students’ Computer Programming Self Efficacy After Reading Code With Llms, Michelle Jarvie-Eggart, Joseph Roy Teahen, Daniel T. Masker, Jose Padilla, Leo C. Ureel Ii, Laura E. Brown, Scott Pomerville, Jon Sticken

Michigan Tech Publications

This study investigated the impact of leveraging generative artificial intelligence (GenAI) to assist 1st-year engineering and computer science (CS) students in reading code in a new (to them) language. Students were asked to comment code in FORTRAN. They were then asked to run the code through ChatGPT-4.0 for its comments and reflect on what they learned from the experience. Participants completed survey items from Ramalingam and Wiedenbeck’s Computer Programming Self-Efficacy Scale (CPSES) prior to and after the intervention. Additional open-ended reflective (qualitative) questions were added to the quantitative questions in the postintervention questionnaire. This study documents increases in self-efficacy for …

Capturing Sclera Anisotropy Using Direct Collagen Fiber Models: Linking Microstructure To Macroscopic Mechanical Properties, Fengting Ji, Xuehuan He, Frederick Sebastian, Mohammad R. Islam, Hannah Schilpp, Bingrui Wang, Yi Hua, Rouzbeh Amini, Ian A. Sigal

Mechanical Engineering Faculty Publications

Collagen fibers are essential to the mechanical behavior of soft tissues, including sclera. Conventional models often represent these fibers statistically, potentially missing crucial aspects of their role in tissue behavior. In this study, we expand on a direct fiber modeling approach that we recently presented based on explicitly representing the sclera long, interwoven fiber bundles. Specifically, our goal was to capture specimen-specific 3D fiber architecture and anisotropic mechanics of four ovine sclera samples (superior from Eye-1, temporal and superior from Eye-2, and temporal from Eye-3), each tested under five conditions: equi-biaxial (1:1) and four non-equi-biaxial (1:0.75, 0.75:1, 1:0.5, and 0.5:1). …

Simulation Results Of Spaceborne Ssa Using A Comprehensive Passive Radar Model, Chinmay Gaikwad, Filipe Senra, Thomas Alan Lovell, Hao Peng, Berker Pekoz, Tianyu Yang

Publications

In this paper, a high-fidelity simulation framework is developed to assess the feasibility of tracking space debris using a large low-Earth orbit (LEO) satellite constellation equipped with onboard passive radar sensors. By exploiting illumination from a distributed network of ground-based transmitters, the constellation provides consistent line-of-sight access to debris objects at higher altitudes, enabling angles-only detection and tracking. This approach yields a scalable, automated, and cost-effective architecture for next-generation space surveillance and contributes to more resilient space traffic management. A complete angles-only passive-radar orbit-determination pipeline is introduced and demonstrated. Initial orbital states are generated using a rate-aware constrained admissible-region multiple-hypothesis …

Novel Repurposing Of Empagliflozin-Loaded Buccal Composite (Chitosan/Silk Fibroin/Poly(Lactic Acid)) Nanofibers For Alzheimer’S Disease Management Via Modulation Of Aβ–Ager–P-Tau Pathway, Samar A. Salim

Nanotechnology Research Centre

Background/Objectives: Empagliflozin (EMPA) was repurposed for Alzheimer’s disease (AD) treatment via buccal delivery, exploiting novel nanofibers (NFs) integrating chitosan (Cs), silk fibroin (Fb), and poly(lactic acid) (PLA). Methods: EMPA-loaded Cs/Fb/PLA NFs were electrospun in different formulations to optimize the formulation parameters. The optimized formulation was then investigated for its enhanced in vivo effect. Results: Optimized nanofiber diameters ranged from 459 ± 173 to 668 ± 148 nm, possessing bead-free morphology confirmed by SEM and satisfactory mechanical properties. EMPA was successfully well-dispersed in the polymer matrix as evidenced by FTIR, XRD, and drug content. The optimized NFs displayed a hydrophilic surface …