Digital Commons Network^™

Nodal Elimination For E3 Hemp Mitigation: A Physics-Informed Graph Theory Approach, Connor A. Lehman, Rush Robinett, Quinnlin R. Lehman, David G. Wilson, Wayne Weaver

Michigan Tech Publications

This paper presents a comparison of methods to determine the minimum number of placement locations for neutral blocking, global linear quadratic regulator (G-LQR), and local linear quadratic regulator (L-LQR) controllers throughout a power grid to prevent transformer saturation during the onset of an E3 high-altitude electromagnetic pulse (HEMP) disturbance. Different device placement configurations yield different efficacies in E3 HEMP mitigation. The first placement method discussed is a genetic algorithm (GA), which serves as a baseline optimizer test case. The scaling and run time of the GA depend on the complexity of the objective function and often times becomes intractable as …

Emerging Biomaterials For Next-Generation Wound Healing: From Infection Control To Tissue Regeneration, Unqa Mustafa, Aiman Kaleem, Syeda Zunaira Bukhari, Hamna Riaz, Maryam Iftikhar, Muhammad Usman Munir, Xianghao Xiao, Mingwu Shen, Xiangyang Shi, Mostafa Yazdi, Ayesha Ihsan

Chemical and Biochemical Engineering Faculty Research & Creative Works

Wound healing is a multifactorial biological process that regenerates damaged tissues through a series of molecular and cellular events in a coordinated manner. An interruption in this cascade can lead to impaired healing, especially in diabetic complications, where effective treatment is crucial for controlling infections and promoting tissue regeneration. Recent advances in bioengineering and digital health have catalyzed the development of next-generation wound therapies capable of actively modulating the wound microenvironment, promoting regeneration, and enabling real-time clinical assessment. This review summarizes the fundamentals of physiological wound healing and highlights the significant developments in biomaterial dressings and scaffolds, 3D bioprinting, as …

Effects Of Overcorrection On Mitigating Uncontrolled Tipping During Incisor Retraction Using Clear Aligners: Finite Element Analysis And In Vitro Experiment, Jianing Wang, Niko Burger, Bo Wang, Linxia Gu, Pengfei Dong

Biomedical Engineering Faculty Research and Publications

Uncontrolled tipping during incisor retraction with clear aligners continues to limit orthodontic treatment. Different countermeasures have been proposed to improve the predictability of aligners, such as power ridges and attachments, while their efficacy requires further investigation. This work investigated the biomechanics of the overcorrection in reducing the tipping angle and moment during incisor retraction with a clear aligner, through in vitro experiments and finite element analysis (FEA). Specifically, the influence of overcorrection angle (1 deg), retraction distance (0.15 mm), and the combination of both were assessed through in vitro experiments and the finite element simulation. Further computational simulations were conducted …

Stock Market Analysis And Volatility Behavior During The Covid-19 Pandemic, Manoj Venkatachalaiah, Soon Leong Yeap, Salman Ahmed Lnu, Sangwhan Cha

Harrisburg University Other Works

This report outlines the structural design, cloud implementation, and analytical findings of a scalable Big Data architecture deployed on Google Cloud Platform (GCP). The primary objective is to investigate the macroeconomic and microeconomic disruption caused by the COVID-19 pandemic on global equities, focusing on two dominant digital business models: online retail/cloud computing (Amazon, Inc. - AMZN) and subscription-based digital streaming entertainment (Netflix, Inc. - NFLX). Through a serverless orchestration pipeline leveraging GCP Cloud Run, automated workflows fetched and blended high-velocity epidemiological metrics alongside daily financial asset layers. Data transformations and parallel analytical calculations were executed utilizing Apache Beam pipelines inside …

Sainik Vol 1 Issue 3, Sastra Deemed To Be University

SAINIK

No abstract provided.

A Machine Learning Framework For Residential District Cooling: Forecasting Consumption, Explaining Drivers, And Evaluating Decarbonization Pathways, Kadhim Hayawi, Husna Maliakkal, Neethu Venugopal, Thanveer Musthafa Hussain, Gomathi Bhavani Rajagopalan

All Works

District Cooling Systems (DCS) in the Middle East, while energy-efficient, are significant contributors to carbon emissions. This study introduces a novel framework to decarbonize DCS operations by integrating predictive machine learning, explainable AI (XAI), and renewable energy planning, all grounded in extensive real-world data. Leveraging a unique dataset from 59 residential buildings in the UAE—including energy consumption, climate variables, and building features—we developed a high-fidelity cooling load forecasting model. Following a rigorous chronological validation methodology, the Random Forest model was identified as the most robust, achieving a strong performance (R² = 0.8256, RMSE = 11,668.31). Outdoor temperature was confirmed …

Toward Data-Centric Ai: A Comprehensive Survey Of Traditional, Reinforcement, And Generative Approaches For Tabular Data Transformation, Dongjie Wang, Yanyong Huang, Wangyang Ying, Haoyue Bai, Nanxu Gong, Xinyuan Wang, Sixun Dong, Tao Zhe, Kunpeng Liu, Meng Xiao, Multiple Additional Authors

Computer Science Faculty Publications and Presentations

Tabular data is one of the most widely used formats across industries, driving critical applications in areas such as finance, healthcare, and marketing. In the era of data-centric AI, improving data quality and representation has become essential for enhancing model performance, particularly in applications centered around tabular data. This survey examines the key aspects of tabular data-centric AI, emphasizing feature selection and feature generation as essential techniques for data space refinement. We provide a systematic review of feature selection methods, which identify and retain the most relevant data attributes, and feature generation approaches, which create new features to simplify the …

Long-Term Mine Planning: A Survey Of Classical, Hybrid And Artificial Intelligence-Based Methods, Nurul Asyikeen Azhar, Aldy Gunawan, Shih-Fen Cheng, Erwin Leonardi

Research Collection School Of Computing and Information Systems

The aim of long-term mine planning (LTMP) is two-fold: to maximize the net present value of profits (NPV) and determine how ores are sequentially processed over the lifetime. This scheduling task is computationally complex as it is rife with variables, constraints, periods, uncertainties, and unique operations. In this paper, we present trends in the literature in the recent decade. One trend is the shift from deterministic toward stochastic problems as they reflect real-world complexities. A complexity of growing concern is also in sustainable mine planning. Another trend is the shift from traditional operational research solutions — relying on exact or …

To Wait Or To Transfer? A Three-Level Optimization Framework For Intermodal Transfer Coordination In First Train Timetabling And Bus Bridging Services Management, Hao Li, Liujiang Kang, Norman Weik, Huijun Sun, Qingying Lai, Zhiguang Cao

Research Collection School Of Computing and Information Systems

This study addresses the integrated optimization of the first train timetabling and bus bridging service design (FTT-BBSD) for morning transfer challenges, two critical but interdependent passenger services in the public transit system. In contrast to most existing studies and conventional approaches, this study explicitly models the influence of passenger path choices and transfer mode selections on FTT-BBSD. Through a novel dual-level network representation that integrates subway and bus systems, we formulate the FTT-BBSD problem as a mixed-integer nonlinear programming model. The model simultaneously determines subway and bus timetables and bridging line deployment to minimize total travel time for all first …

Deficient Executive Control In Transformer Attention, Suketu Chandrakant Patel, Hongbin Wang, Jin Fan

Publications and Research

Although transformers in large language models (LLMs) effectively implement a self-attention mechanism that has revolutionized natural language processing, they lack an explicit architecture for the executive control of attention found in humans, which is essential for resolving conflicts and selecting relevant information in the presence of competing computations and is critical for adaptive behavior. To investigate the impact of this limitation in LLMs, we employed the classic color Stroop task, widely regarded as the gold standard, to test the executive control of attention in these models. Our results revealed a typical conflict effect of underperformance in terms of accuracy in …

Repurposing Electrical Waste Materials For Developing Corrosion-Resistant Medium Entropy Alloys, Iman El-Mahallawi, Shaza Raji Eng, Shimaa El-Hadad Prof., Mahmoud Tash Prof., Lamiaa Zaki Dr.

Mechanical Engineering

Reusing electrical waste as a sustainable metal source is a promising approach for producing next-generation alloys. In this work, vacuum arc melting (VAM) was used to produce three aluminum-based medium-entropy alloys (AlCuFeCrSi MEAs) from recycled electrical waste. The alloys contained Al in the range 36-46%; Cu in the range 28-35%; Fe in the range 11-18%; Cr in the range 5-6%; and Si in the range 3-6%, with variations in elemental composition resulting from the use of different recovered electrical waste components. Their corrosion performance was evaluated at room temperature, in a 3.5% NaCl solution with and without 1 g/L Mn …

A Data Analysis Study Of Temperature Based On California Cooperative Oceanic Fisheries Investigations (Calcofi) Data., Afsana Mimi

Dissertations, Theses, and Capstone Projects

This research presents an integrated analytical framework for predicting ocean temperatures using long-term observations from the California Cooperative Oceanic Fisheries Investigations (CalCOFI) data files. The study applies advanced data engineering and statistical modeling to assess how a variety of indicators, such as salinity, dissolved oxygen, key nutrients, and ocean depth shape oceanic temperature patterns along the California coastline. Using Microsoft SQL Server software for large-scale data integration and the R for statistical packages (R Core Team, 2025) for statistical computation, I developed and compared multiple regression models, including the ridge regression model, to evaluate the relative influence of physical and …

Computational Insights Into Nucleosome Dynamics In Epigenetics Using Molecular Dynamics Simulations, Rutika Patel

Dissertations, Theses, and Capstone Projects

Nucleosome core particles (NCP) are the building blocks that form a highly organized and compact chromatin structure. Nucleosomes package DNA in the nucleus of eukaryotic cells. The NCP consists of about 147 base pairs of DNA wrapped around the histone octamer, with 1.65 superhelical turns in a left-handed manner. The histone octamer is composed of two copies of H3, H4, H2A, and H2B. Together with histone H1 and linker DNA, they further assemble into a higher-order chromatin structure. The nucleosome complex is stabilized by electrostatic interactions between positively charged histone residues and the negatively charged DNA backbone. To effectively access …

Gas Adsorption Behavior In Organic-Rich Carbonate Source Rocks: Insights Into Hydrogen Storage, Methane Recovery, And Co2 Sequestration In Oil Shales, Muhammad Usman, Muhammad Ali, Tawanda Matamba, Alireza Keshavarz, Stefan Iglauer, Hussein Hoteit, Volker Vahrenkamp

Research outputs 2022 to 2026

Gas adsorption in unconventional petroleum source rocks, e.g. organic-rich mudrocks and shales, is influenced by significant heterogeneities in organic and inorganic composition, structure, and pore networks. An understanding of the interplay between these properties is essential for optimizing hydrocarbon extraction, CO2 sequestration, and hydrogen storage in such reservoirs. Herein, the factors controlling hydrogen, methane, and CO2 adsorption in unconventional organic-rich Jordan source rock samples are investigated, focusing on the roles of organic matter, mineral composition, and textural properties. The samples are characterized via multiple techniques, followed by adsorption experiments. The results reveal that H2 has the lowest adsorption capacity, being …

Enhancing Urban Refueling Efficiency: Strategies For Sustainable Mobility And Economic Impact, Navid Hashemi Taba, Iftekhar Ahmad, Ahdieh Sadat Khatavakhotan

Research outputs 2022 to 2026

In response to increasing concerns about operational inefficiencies and environmental impacts at petrol stations, this operational research investigates strategies to enhance refueling efficiency across 103 stations in Perth. A pilot study employing observational methods and time-motion analysis established ideal benchmarks for core refueling activities. Empirical analysis of driver behavior during high-demand periods—often driven by fuel price discounts—and regular off-peak hours revealed significant performance gaps, highlighting substantial opportunities for efficiency improvements. Eliminating non-essential tasks could yield time savings of up to 67% during peak periods and 45% during off-peak periods. Economic analysis demonstrated mutual benefits when refueling processes align with these …

Green-Synthesised Nh₂-Mil-101(Cr) Mof–Reinforced Starch Bioplastic Films For Sustainable Food Packaging, Shima Jafarzadeh, Yasaman Esmaeili, Moon Paul, Seyedeh Zahra Haeri, Colin J. Barrow, Mina Dokouhaki, Masoumeh Zargar, Minoo Naebe

Research outputs 2022 to 2026

This study introduces a green, aqueous synthesis approach for NH₂-MIL-101(Cr), avoiding toxic organic solvents while preserving its high crystallinity, porosity (>1000 m2/g), and amino functionality. To develop sustainable packaging materials the synthesized MOF was incorporated into sago starch-based bioplastic films across control 0%, 1%, 3%, and 5% treatments. Nanoparticle characterizations via SEM, TEM, FTIR, XRD, and nitrogen adsorption-desorption isotherms confirmed a porous, crystalline structure (100–300 nm) with a surface area exceeding 1000 m2/g. Film characterizations using SEM, FTIR, color analysis, tensile testing, TGA, and UV-Vis spectroscopy revealed that the 3% treatment offered the best balance, with tensile strength increasing …

Multiple-Nozzle Sprays: Comparison To Single-Nozzle Sprays And Role Of Directional Conditional Filtering, Farzad Hossain, Matthew J. Cleary, Yasir M. Al-Abdeli, Mehdi Khiadani

Research outputs 2022 to 2026

Spray-based droplet evaporation systems used in desalination and cooling applications depend on both nozzle arrangement and the interpretation of measured droplet behaviour. This study examines (i) how multi-nozzle configurations differ from single-nozzle characteristics and (ii) how ensemble-averaged measurements change when droplets are separated according to their motion direction (upward or downward). Atmospheric water sprays generated by full-cone spray nozzles were investigated using Particle Image Velocimetry (PIV) for droplet axial velocity measurements and Shadowgraph imaging to determine droplet size and count. Directional filtering was performed using Large Eddy Simulation (LES), validated against experiments. Single- and dual-nozzle configurations were tested with horizontal …

From Thin Films To Energy Systems: A Perspective-Oriented Translational Framework For Deployable Functional Materials, Mohammad Nur-E-Alam

Research outputs 2022 to 2026

The translation of high-performance thin-film materials from lab-scale development to field-deployable energy systems remains a significant holdup in sustainable technology development. The real-world impact of functional thin films, nanocomposites, and emerging photovoltaic architectures is often limited by issues with scalability, interfacial reliability, environmental stability, and system-level integration, despite their excellent optical and electronic performance at the device level. A translational framework that is deployment-oriented and systematically connects interface-centric device integration, application-context validation, system-level performance feedback, and process-aware thin-film engineering has been presented in this article. Common structure-property-deployment trade-offs are identified through a comparative analysis of representative case domains, such as …

Nanofluid Flooding As A Sufficient Alternative To Waterflooding For Incremental Oil Recovery From Carbonate Reservoirs, Sarmad Al-Anssari, Dhifaf Sadeq, Hassanain A. Hassan, Ahmed Hamid Al-Taie, Hasan Ali Abood, Mohammed Mahdi, Zain Ul Abedin Arain

Research outputs 2022 to 2026

Oil recovery from carbonate reservoirs is one of the critical challenges in the oil industry due to the strongly oil-wet nature, natural fractures, and the heterogeneity of carbonate rocks. Subsequently, waterflooding can only displace oil from large fractures, leaving the majority of oil trapped in the rock matrix. This work suggests that nanofluid flooding, as a predesigned flooding method, is an alternative to conventional waterflooding. Various concentrations of silica nanofluid at different nanoparticle concentrations were formulated and systematically investigated for their characteristics, stability at reservoir conditions, and their influence on wettability and oil recovery. Silica nanoparticles were sustainably synthesized from …

A Review On Credit Card Electronic Fraud Detection Methodologies, Titilayo Mary Sayikanmi, Ibrahim Adepoju Adeyanju, Bolaji Abigail Omodunbi

Mansoura Engineering Journal

Credit card fraud remains a critical and escalating challenge within the global financial ecosystem, driving substantial annual losses and necessitating the continuous evolution of detection methodologies. This paper presents a systematic literature review, conducted via the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework, which comprehensively analyzes the state-of-the-art in electronic credit card fraud detection. Through a rigorous examination of 49 high-quality studies, this review maps the methodological evolution from traditional rulebased systems and statistical models to advanced artificial intelligence techniques, including machine learning, deep learning, and graph-based approaches. The analysis reveals that while individual methods possess distinct advantages …

Deep Learning Approaches For Ctenophore Identification And Tracking, Anagha Bharadwaj

Theses

Ctenophores are translucent marine organisms with nearly invisible tentacles and pose significant challenges due to their transparent morphology and ambiguous structural features. This research addresses the classification and tracking of these organisms and evaluates the performance of current computer vision models under sparse-data environments.

A dataset from the NJIT Life History Lab consisting of microscopic laboratory videos and photographs of different growth stages is used to train and assess a number of convolutional neural network designs, including VGG16, ResNet, BioCLIP2, YOLO, and DeepLabCut. Additionally, a web-based interface is developed to evaluate expert-labeled ground truth with the model's performance.

The findings …

Adaptive Multimodal Smart Home Control On A Raspberry Pi 5 Using Hand Gestures, Voice Cues, And User Feedback, Vaibhav Bora

Theses

A real time multimodal smart home control system deployed on a Raspberry Pi 5 is presented. The system combines hand gestures, short voice cues, and proximity aware interaction to execute household commands such as light brightness control, fan speed adjustment, and stop or kill switch actions. Lightweight gesture and keyword spotting voice classifiers were trained offline and exported to TensorFlow Lite for efficient on device inference. For more natural spoken phrases, the system additionally integrates a locally deployed pretrained offline ASR component rather than a speech recognizer trained from scratch. Using a USB camera and microphone, the system operates fully …

Evaluation Of Medical Instrument Reliability For The Heath And Maddox Components Of The Accommodative-Vergence System, Jacob Chabuel

Theses

Binocular vision relies on the coordination of near response triad composed in part by the vergence and accommodative systems. Binocular dysfunctions linked to these systems impact the quality of life of those affected by complicating day-to-day tasks and leading to further health concerns. Clinical evaluations that diagnose dysfunctions and tailor therapies rely on subjective methods, creating a need for a reliable, quantitative, measurement tool. This is achieved using a haploscope, a tool that quantitatively measures and observes binocular vision to identify deficiencies within the eye. Modernization of a haploscope system, measurement of the Heath and Maddox components of the visual …

Autonomous Exploration Of An Environment With Static Obstacles Using A Ppo Agent, Brandon Knight

Theses

Research in autonomous exploration has created many effective algorithms that have been tested and proven to work in many different virtual and physical environments. Many optimizations have also been developed to reduce computational effort and increase exploration speed.

However, despite optimizations, these algorithms can still require considerable computational effort and time to explore even small environments. To obtain further improvements in computation and exploration speed, a reinforcement learning agent using actor-critic style proximal policy optimization (PPO) is trained to explore various environments efficiently, then compared to an algorithm using contemporary exploration methods.

Testing is performed in virtual environments with ideal …

Retinomorphic Mid-Wave Infrared In-Sensor Processing Engine: Device-To-Architecture Co-Design, Hemalatha Nagaraju

Theses

Conventional frame-based CMOS image sensors acquire full-frame pixel data at discrete time intervals, resulting in substantial spatial redundancy and loss of temporal information between frames. The repeated conversion and transfer of redundant pixel data increases bandwidth and power consumption in machine vision systems. Retinomorphic sensing architectures address these limitations by enabling programmable, analog-domain processing directly at the sensor interface. A compact behavioral model of the PbSe device is developed in HSPICE based on calibrated TCAD simulation data to capture gate-controlled photocurrent modulation under varying illumination and gate bias conditions. Error analysis is performed to quantify the deviation between TCAD-generated photocurrent …

Robustness Of Ai-Driven Histopathology Under Real-World Adversarial Examples, Ruchik N. Yajnik

Theses

This robustness of histopathology classification models under adversarial and real-world perturbations resembling clinical artifacts is being investigated.

Using whole-slide images from the CAMELYON17 cohort, four representative architectures—ResNet-18, ResNet-50, HIPT-2MLP, and ViT-B/16 —are benchmarked across controlled pixel-level distortions and artifact-like transformations. Adversarial methods include iterative Fast Gradient Sign, Projected Gradient Descent, Salt-and-Pepper noise, and the Adversarial Watermark—Stain Shift (AWSS). Three defense strategies—Randomized Smoothing, Adversarial Training, and an Artifact Detector—are evaluated for their ability to preserve diagnostic accuracy and model reliability. Structured perturbations consistently degrade performance, with transformer-based models showing the greatest sensitivity. The benchmark developed here offers a reproducible framework for …

Machine Learning-Driven Prediction And Mechanistic Insight Into Co2 Adsorption On Biomass-Derived Activated Carbons Using Explainable Ai (Xai), Dalia A. Ali Dr.

Chemical Engineering

To improve CO2 uptake in Biomass-Derived Activated Carbon (BDAC), this study develops a multiscale hybrid digital twin framework. By integrating microscopic descriptors from Density Functional Theory and Molecular Dynamics (DFT/MD) with experimental data from 63 chemically diverse biomass precursors, a Gaussian Process Regression (GPR) model was developed using the Materń 5/2 Automatic Relevance Determination (ARD) kernel. The framework achieved high internal training accuracy (R2 = 0.968) and Root Mean Square Error (RMSE = 0.2552), while providing a realistic generalization baseline across heterogeneous precursors with a 5-fold Cross Validated (CV) R2 of 0.1567 and CV RMSE of 0.283. Explainable Artificial Intelligence …

Omama-Db: The Oregon-Massachusetts Mammography Database, Avanih Kanamarlapudi

Graduate Masters Theses

Public datasets for training AI models in breast cancer screening are limited in size and quality, making it difficult to develop reliable systems. We introduce OMAMA-DB, an extensive publicly available collection of 2D mammograms and 3D tomosynthesis volumes. Starting from 967,991 images, we created a curated set of 231,080 images us ing a multi-stage filtering process that removes missing labels, uncommon dimensions, rare scanner types, duplicate studies, and invalid DICOM files. All 2D images then undergo additional outlier detection using histogram filtering and a variational autoen coder to remove low-quality outliers. OMAMA-DB includes pathology-based cancer labels and automated lesion annotations …

Reliable Offline Evaluation Under Exposure Bias: A Cross-Scale Study Of Counterfactual Estimators In Recommender Systems, Lakshmi Pranathi Vutla

Graduate Masters Theses

Offline evaluation underpins model selection in recommender systems, yet historical interaction logs are shaped by prior recommendation policies. Because users only provide feedback on exposed items, logged data entangles user preferences with exposure mechanisms, leading to exposure bias and potentially misleading model comparisons. Counterfactual estimators such as IPS, SNIPS, CRM, and DR offer principled corrections, but their empirical reliability across datasets and exposure regimes remains insufficiently under- stood. We present a systematic, cross-scale study of counterfactual evaluation in recommender systems. Comparing IPS, SNIPS, CRM, and DR on datasets with randomized exposure (Yahoo! R3, Coat, and KuaiRec), we analyze estimator behavior …

Polarimetric Terahertz Imaging For The Measurement Of Birefringence In Plastic, Rachel Cohen

Theses

Birefringence offers a promising way to observe stress concentration in materials such as glass and plastic, and thereby to identify weaknesses. Polarimetric imaging can be used to measure the birefringence of material, so long as the material is transparent to the light being used for the imaging. In this research, 2D Terahertz imaging was investigated as a means of measuring the birefringence of plastics that are opaque to visible light but transparent to THz radiation, for the eventual purpose of analyzing the residual stress present. In order to do so, two separate terahertz cameras were characterized for potential use in …