Engineering Commons^™

Nanomagnet Based Reservoir Computing And Quantum Control, Fahim F. Chowdhury

Theses and Dissertations

Conventional CMOS scaling has driven remarkable advances in computing but faces increasing physical and energy constraints, motivating alternative computing paradigms that integrate memory and computation while improving energy efficiency. Nanoscale magnetic systems offer a promising platform for such approaches because their intrinsic nonlinear dynamics and localized magnetic fields can support both classical and quantum information processing. This thesis investigates nanomagnetic systems for physical reservoir computing and, with primary emphasis, for localized quantum control of spin qubits.

The first part explores dipole-coupled nanomagnet arrays as physical reservoirs. Micromagnetic simulations demonstrate nonlinear dynamical behavior with high short-term memory and parity-check capacity, enabling …

Early Stem Impressions, Student Engagement, And Readiness For Digitalization, Myron Sheu

Journal of International Technology and Information Management

This study examines how early impressions of science, technology, engineering, and mathematics (STEM) shape business students’ learning behaviors and, ultimately, their readiness for organizational digitalization. Focusing on gender differences, subgroup identities, and perceived obstacles, the analysis uses survey data processed through correlation matrices, regression models, and subgroup heatmaps to trace the relationship between initial attitudes toward STEM and subsequent engagement patterns. The findings reveal consistent links between positive early impressions and active participation in structured STEM activities, along with gender-based distinctions in action preferences. Subgroup analyses further uncover nuanced patterns where stereotypes or perceived barriers correspond with reduced engagement. Collectively, …

Does Digital Innovation Matter For Hospital Efficiency? Evidence From U.S. Hospitals, C. Christopher Lee, Shihui Fan, Jung Young Lee, David W. Hwang

Journal of International Technology and Information Management

Purpose – This study examines the impact of digital innovation on hospital performance, providing evidence to guide healthcare administrators and policymakers in making informed decisions regarding digital investment.

Design/Methodology/Approach – Using data from the 2020 American Hospital Association (AHA) U.S. Hospital Survey and the 2019 AHA Information Technology Survey, we empirically analyze the relationship between five dimensions of digital innovation—automation, cybersecurity, telehealth, health information exchange (HIE), and IT spending—and three efficiency indicators: occupancy rate, capacity productivity, and manpower productivity.

Findings – The results show that digital innovation has varying effects on hospital efficiency. Automation is positively associated with capacity and …

The Crowdfunding Paradox In Crisis: Rising Funder Demand Vs. Declining Entrepreneur Supply, Dan Liu, Guangzhi Shang, Cynthia Fan Yang

Journal of International Technology and Information Management

This study investigates how the crowdfunding marketplace responds to major crises, focusing on behavioral shifts among funders and entrepreneurs. Results show a dual impact on platform dynamics. On the demand side, funders become more engaged, with notable increases in the number of backers, average contributions, and total pledge amounts. This heightened activity suggests stronger altruistic motivations, as individuals view crowdfunding as a way to support others during difficult times. On the supply side, however, entrepreneurs act more cautiously, leading to a decline in new project launches. This drop likely reflects increased risk aversion and uncertainty as creators navigate volatile conditions. …

Improving Efficiency In Noma Schemes Having Inter-User Interference Using Mechanism Design, Zory Marantz

Publications and Research

Modern wireless systems utilize non-orthogonal multiple access to increase their rate capacities; however, the efficiency of the individual utility defined in bits per Joule has yet to be considered. Multiple variations of non-orthogonal multiple access have the interference of the signal-to-interference-plus-noise ratio as a function of the received power from multiple other users due to code implementations that are non-orthogonal or non-ideal cancellation in successive-interference-cancellation methods. Game theoretic concepts are used to improve user bits-per-Joule performance. Previous solutions increment transmit power and are not based on closed form systematic methods. The mechanism design presented here led to a non-cooperative Nash …

Pafex: Compiler-Based Floating-Point Exception Detection For Gpu Kernels With Papispecific Software-Defined Events, Samin Islam, Shirley V. Moore, Christoph Q. Lauter

Graduate Student Papers (CS)

As high-performance computing becomes progressively heterogeneous, the dependence upon vendor specific tools for numerical correctness has become an impediment to portability. Although modern GPUs comply with the IEEE 754 standard, the lack of practical native hardware support to raise and handle exceptions (special values like ±∞ or NaN) is a well-known architectural limitation. To embed portable numerical correctness across heterogeneous systems, we propose an architecture agnostic prototype based on LLVM-compiler infrastructure. This framework detects floating-point exceptions in GPU kernels at the Intermediate Representation (IR) level, instrumenting both device code and host code, strictly complying with the 2019 IEEE 754 standard. …

Toward Neurosymbolic Reinforcement Learning Via Editable Specifications, Vedant Khandelwal, Hong Yung Yip, Amit Sheth

Publications

Reinforcement learning systems are commonly adapted to new settings by retraining or fine-tuning policies. This default is costly, difficult to audit, and poorly aligned with structured requirement changes such as revised safety rules, new operational constraints, or updated user preferences. We argue for an alternative abstraction: adaptation via edits to an external, human-readable specification that the agent consults at execution time. We propose conditioning decision-making on an editable knowledge graph encoding (i) rules capturing action applicability and high-level effects, (ii) hard constraints defining feasibility, and (iii) soft preferences shaping tradeoffs among feasible behaviors. Requirement changes become graph edits, not policy …

Implications Of Quantum Computing For Enterprise Cybersecurity And Data Integrity, Manikantha Varaprasad Inakollu

Computer Science and Engineering Faculty Publications

Quantum computing represents a paradigm shift in computational capabilities that poses both unprecedented threats and opportunities for enterprise cybersecurity. This research examines the implications of quantum computing advancement on current cryptographic systems, data protection mechanisms, and organizational security frameworks. Through analysis of quantum computing developments from 2019-2024 and surveys of 280 cybersecurity professionals across various industries, this study identifies critical vulnerabilities in existing encryption standards and explores emerging quantum-resistant solutions. The findings reveal that approximately 78% of enterprises remain unprepared for quantum threats, with current RSA and ECC encryption systems facing potential compromise within the next 10-15 years. The research …

Edge Guided Channel Attention In Fsrcnn: A Novel Approach For Depth Super Resolution, Yagneshkumar Jayantilal Parmar, Paresh M. Dholakia

Mansoura Engineering Journal

Depth images from low-cost sensors often suffer from blurred edges and structural distortions when processed with standard super-resolution models. While FSRCNN is efficient for RGB images, it struggles to handle the unique geometric requirements of depth maps. To solve this, we propose the Edge Guided Channel Attention FSRCNN (EGCA FSRCNN). This method incorporates an edge-guided modulation mechanism to preserve object boundaries and a Squeeze and Excitation (SE) block to focus on critical structural features. A major benefit of this framework is the use of frozen, pretrained FSRCNN weights, which bypasses the requirement for retraining. Our evaluation on the UTKinect, Middlebury, …

Designing Narrative-Based Ai Assistance For Sensemaking In Collaborative Environments: Case Studies In Education And Dementia Care, Dylan Edward Moore

Dartmouth College Ph.D Dissertations

This thesis addresses a gap in the human-computer interaction literature regarding the design, development, and evaluation of narrative-based AI assistance for collaborative, complex problem solving. I explore this design space through three case studies across the domains of education and dementia care. This work encompasses multi-year industry partnerships and longitudinal fieldwork, user-centered design, dataset curation, model training, and system evaluation.

Specifically, the first case study considers a story-based web platform for teaching AI literacy through peer-generated, personalized narrative scaffolding. Learners on the platform showed significant knowledge gains and other learning-related outcomes. To describe the novel design of this system, I …

Cognitive Load Classification Using Functional Near-Infrared Spectroscopy, Pratham Shah

Theses and Dissertations (Comprehensive)

This thesis investigates the classification of cognitive load using functional near-infrared spectroscopy (fNIRS) signals recorded during an N-back working memory task. The study introduces a novel short-channel correction layer designed to suppress superficial physiological noise adaptively, addressing limitations of traditional General Linear Model (GLM) based regression. A single participant dataset comprising 69 validated sessions was analyzed using both conventional machine learning and deep learning approaches. Traditional classifiers: Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), Random Forests, and Gradient Boosting were first evaluated using statistical features (mean, variance, peak, and slope). Among these, Gradient Boosting achieved the highest accuracy (55.6%), …

A Novel Federated Llm Framework For Distributed Traffic Modelling In Intelligent Transportation Systems, Seerat Kaur

Theses and Dissertations (Comprehensive)

Intelligent transportation systems (ITS) depend on accurate traffic prediction to support congestion management, infrastructure planning, and real-time operational decisions. Despite substantial progress in data-driven forecasting, several challenges continue to limit practical deployment: traffic data is distributed across independent regional authorities, making centralized aggregation infeasible, standard federated aggregation strategies ignore traffic-specific characteristics that meaningfully affect model quality, and existing models produce only numerical outputs without interpretable reasoning that urban planners can act upon. This thesis addresses these challenges through four contributions that collectively advance privacy-preserving, explainable, and scalable traffic forecasting.

The first contribution provides a systematic review of 129 peer-reviewed publications, …

Minimizing Performance Overheads For Crash-Consistency In Disaggregated Persistent Memory, Khan Shaikhul Hadi

Graduate Studies Theses and Dissertations 2026

Compute express link (CXL) enables persistent memory disaggregation with memory pooling and hardware managed multi-host memory sharing capability, resulting in better resource utilization, increased scalability. Persistency-aware applications need to manage crash consistency across the system which results in significant performance overhead. This dissertation systematically investigates performance overhead to achieve crash consistency in disaggregated persistent memory and proposes solutions to enable persistency-aware application scaling for distributed system. First, we study persistent parallel programming to scale computation capability beyond single processor and determine the underlying hardware limitation to adopt lock-free data structure. We propose hardware support to design durable atomic instruction (DAI) …

Memory-Efficient Acceleration For Emerging Applications Via Hardware/Software Co-Design, Shilin Tian

Graduate Studies Theses and Dissertations 2026

Emerging artificial-intelligence and data-intensive scientific workloads increasingly face a memory wall: irregular access patterns and large intermediate data volumes make data movement, rather than arithmetic, the primary constraint on performance and energy efficiency. This dissertation develops a memory-centric hardware/software co-design methodology that jointly reshapes algorithms, architectures, and dataflows to retain frequently reused data on chip. The methodology is demonstrated through three accelerators and an RTL design tool. VITA replaces multi-head attention in vision-transformer-based 3D human mesh recovery with hardware-friendly average pooling and maps the resulting operators to a reconfigurable datapath, achieving 5.05-fold and 69.12-fold speedups over a state-of-the-art GPU and …

Investigation Of Automated 3d Scanning Strategies For Large-Scale Polishing Applications, Casey Egan

Open Access Master's Theses

In automated scanning for industrial refinishing applications, large workpieces often exceed the reach of a single robot base position, and may require the environment to be scanned from multiple locations and stitched to a common reference frame. This thesis compares two robotic scanning workflows for such an object: a full scan that reconstructs the object from a single robot base position, and a segmented scan occurring across multiple simulated robot base positions that are stitched together through fiducial-based localization. The results from both workflows are compared for measurement accuracy of the object's bounding box against known dimensions, position of the …

Computational Methods For Identification Of Molecular Signatures, Weijun Yi

Graduate Theses, Dissertations, and Problem Reports (ETD)

This work develops computational methods for identifying molecular signatures from high-throughput genomic data and for modeling long non-coding RNA (lncRNA) sub-cellular localization. The response of multiple myeloma to CB-6644, a selective RUVBL1/2 complex inhibitor with potential anti-tumor activity, is analyzed to identify drug-responsive pathways and molecular signatures. Conventional gene set enrichment analysis (GSEA) often excludes low-expression genes. Here, phenotype comparison is reformulated as a supervised machine learning problem: genes most informative for discrimination are first selected using a machine learning approach, and GSEA is then applied to these machine-learning derived gene sets. This framework improves detection of CB-6644-associated pathways. For …

Effect Of Local Steel Slag Incorporation On The Mechanical Properties Of Concrete, Ahmed Safaa Tariq Karmah, Wissam Khadum Alsaraj, Luma A. Zghair

Al-Esraa University College Journal for Engineering Sciences

The increasing interest in the production of concrete through the utilization of inexpensive materials has caused a rise in the number of studies concerning the use of materials with cementitious properties, such as Silica Fume, Slag, Fly Ash and other pozzolanic materials. Moreover, the increasing amounts of Steel Slag being produced from local factories that are being deposited without an available method of disposal provides an opportunity to harness the Slag in the production of an inexpensive, and possibly better concrete. Slag has been used as both an addition and substitution of the cement and fine aggregate. The mixtures included …

Decoding Climate Change: A Comprehensive Statistical Insight Into Temperature Anomalies In Iraq And Its Neighboring Countries, Jameel T. Al-Naffakha, Mohammed R. Al-Qassabanda, Israa Jafar

Al-Esraa University College Journal for Engineering Sciences

This study applies advanced statistical techniques, including the Mann-Kendall Trend Test and ARIMA forecasting, to validate and predict temperature anomalies across Iraq and its neighboring countries (2014–2024). Findings confirm a statistically significant warming trend (p < 0.00000005) across all nations, with Syria (1.02°C) and Turkey (0.97°C) experiencing the highest anomalies. GIS-based spatial analysis highlights regional disparities, identifying climate-vulnerable zones. The persistent rise in temperature anomalies correlates with worsening water scarcity, desertification, and extreme weather events, including prolonged droughts and heatwaves. Iraq’s peak anomaly (1.48°C) has exacerbated heat stress, agricultural decline, and reduced river inflows, while Kuwait and Saudi Arabia struggle to maintain critical infrastructure under record-breaking temperatures exceeding 50°C. These climate shifts pose severe risks to water availability, food security, and energy demand, necessitating urgent policy interventions. Key recommendations include enhanced water resource management, climate-adaptive agriculture, and renewable energy expansion. If left unaddressed, rising temperatures could destabilize the region, increasing socio-economic vulnerabilities and escalating resource conflicts. This study underscores the need for cross-border collaboration and sustainable adaptation policies to mitigate the long-term impacts of climate change in the Middle East.

Optimal Power Flow Control In The Iraqi Power Grid Using Artificial Intelligence Algorithms For Carbon Emission Reduction, Mohammed Shakir Juber

Al-Esraa University College Journal for Engineering Sciences

Iraq’s power sector remains in a protracted and severe crisis characterized by a significant mismatch between supply and demand, high levels of losses during transmission and distribution, as well as an increasing challenge to the resources base being largely unfavourably. These inefficiencies impose heavy costs on the national economy in excess of 40 billion annually and they also reinforce greenhouse gas emissions, and exacerbate environmental issues. To deal with these problems, we proposed in this paper that a new hybrid AI tool should be developed for the solution of multi-objective optimization problem based on purpose Genetic Algorithm (GA) merger with …

Applying Machine Learning Techniques For Early Detection Of Cyber Attacks On Iot Devices, Noor Adnan Allamy

Al-Esraa University College Journal for Engineering Sciences

This research designs, implements, and evaluates a machine learning-based framework for the early detection of cyber attacks targeting Internet of Things (IoT) devices, with a specific focus on the context and challenges present in Iraq. The study conducts a comparative analysis of three supervised learning algorithms—Support Vector Machine (SVM), Random Forest (RF), and Deep Neural Networks (DNN)—using a combination of benchmark datasets (NSL-KDD, CIC-IDS-2017, Bot-IoT) and a synthesized dataset adapted to simulate the Iraqi threat landscape. Key performance metrics, including accuracy, precision, recall, and F1-score, were used for evaluation. The proposed Random Forest model demonstrated superior performance, achieving an accuracy …

Ai-Enhanced Heat Transfer Optimization In Magnetic Bio-Nanofluids, Hasan Attyah Shaboot

Al-Esraa University College Journal for Engineering Sciences

This study presents a hybrid Artificial Intelligence–Computational Fluid Dynamics (AI-CFD) framework for optimizing heat transfer in magnetic bio-nanofluids subjected to external magnetic fields. Magnetic bio-nanofluids, composed of biocompatible base fluids containing superparamagnetic nanoparticles, exhibit tunable thermal and flow behavior, making them promising for biomedical and micro-cooling applications. Conventional optimization methods based on experiments or brute-force CFD are computationally expensive and limited in exploring the full design space. To overcome these challenges, an Artificial Neural Network (ANN) surrogate model was developed to predict two key performance indicators, the Nusselt number and the friction factor, with high accuracy (R² > 0.997). The surrogate …

Hybrid Experimental–Computational Study On The Energy Absorption Of Graphene Nanoplatelet-Reinforced Sandwich Structures, Hamdan Yousif Hamdan

Al-Esraa University College Journal for Engineering Sciences

Background: Sandwich composites are widely used in aerospace, automotive, and marine applications because of their lightweight, stiffness, and strength, but they remain prone to out-of-plane impacts and barely visible impact damage (BVID). Graphene Nanoplatelets (GNPs) have shown strong potential to improve fracture toughness and impact resistance. However, hybrid experimental–computational studies applying such reinforcement are still lacking in developing contexts like Iraq, where practical and accessible solutions are essential. Aims: The study investigates the effect of GNP reinforcement on the low-velocity impact behavior and energy absorption of glass fiber/epoxy sandwich panels with PVC foam and balsa wood cores. Objectives include fabricating …

Effect Of Thick Closed Walls Of West And South Elevations Of Iraqi Middle And South Territory Housing Building On Reducing Energy Use, Abeer Qasim Jbur, Shaimaa M. Hamza, Mayyadah Lutfi Abdulwahhab

Al-Esraa University College Journal for Engineering Sciences

Thermal insulation is one of the most important factors in achieving sustainability, increasing efficiency, and reducing energy consumption, but modern insulation materials are expensive, in addition to their availability. Therefore, through our research, we resorted to investigating the issue of reducing thermal load in buildings through the use of thick-walled facades in the southern and western facades, where we studied a building with two windows with windows on the southern and western facades and another without these windows and with thick walls, for two locations, the first in central Iraq, which is the city of Baghdad, and the second in …

The Impact Of Ethyl Levulinate Additive On Diesel Engine Performance And Emissions: An Experimental Study, Maha Ali Ghadbaan

Al-Esraa University College Journal for Engineering Sciences

This study explores the environmental and operational impact of adding Ethyl Levulinate—a biodegradable ester derived from biomass—to diesel fuel. A fuel blend known as B5El6 (comprising 5% biodiesel and 6% Ethyl Levulinate) was prepared and tested experimentally using a single-cylinder diesel engine operating at a constant speed of 1500 rpm under full load conditions. Performance metrics and emission data were recorded, and a Life Cycle Assessment (LCA) was conducted using the IMPACT2002+ methodology.

The results revealed that B5El6 significantly reduced greenhouse gas emissions (100 kg CO2 eq) and improved ecosystem quality. However, it presented challenges in terms of human health …

The Role Of The Scenario In Formulating The Contemporary Architectural Text, Rounaq Arif Mohsin, Abbas Ali Hamza, Bashar Shamil Alkhafaji

Al-Esraa University College Journal for Engineering Sciences

In order to create an integrated architectural text, which is a complicated art that blends beauty and practical elements, a rigorous design procedure is necessary. In this situation, the scenario’s role becomes crucial in developing the current architectural text and directing architectural approaches. In order to better understand how the scenario influences the design process and the interactions between various architectural aspects, this project intends to explore and analyze the impact of the scenario in developing and refining architectural text formulation approaches.

The study looks at the idea of a scenario and how it has been used in architecture, as …

Investigation Of Some Physical Properties Of Slurry Infiltrated Fibrous Concrete (Sifcon) Under Different Curing Times, Rand Kh. Mahmoud, Mohmmed Juad Khadhim, Fayq Hasan Jabbar

Al-Esraa University College Journal for Engineering Sciences

This research studied the effects of different types fibers (Basalt, Carbon and Basalt-Carbon hybrid mix) and curing ages (7, 14, 28 and 56 days) on some physical properties of - Slurry infiltrated fibrous concrete (SIFCON). Density, ultrasonic pulse velocity UPV, Poisson’s ratio and water absorption were measured for the experimental study with 30% cement replacement by Class F fly ash to increase sustainability. It was found that properties were significantly affected by the type and dosage of fiber admixture. The UPV was enhanced by basalt fibers and the absorption reduced up to an optimal range of 3–5% after which agglomeration …

Real-Time Deep Learning Detection Of Toraja Carving Motifs Using Yolo11m For Cultural Heritage Preservation, Herman Herman, Farid Wajdi Mufti, Abdul Rachman Manga, Haidawati Nasir

Knowledge Engineering and Data Science

Toraja carvings are an important part of Indonesia’s cultural heritage, rich in symbolic, aesthetic, and philosophical meaning. However, the identification and preservation of carving motifs still rely on subjective, time-consuming manual processes, limiting scalability and inconsistent knowledge transmission. From a Knowledge Engineering and Cognitive Data Science perspective, this challenge highlights the need for mechanisms that can transform visual cultural artifacts into structured, machine-interpretable knowledge. This study investigates the use of the YOLO11m model as a data-driven approach for modeling cultural knowledge through automated detection of three Toraja carving motifs: pa_tedong, pa_kapu_baka, and pa_manu_londongan using original images collected directly from traditional …

Mechanical And Durability Properties Of Cement Panels Reinforced With Hybrid And Pva Fibers, Shukran H. Faraj, Mohammed J. Kadhim

Al-Esraa University College Journal for Engineering Sciences

This study examines the synergistic effects of hybrid fibers (HF) and polyvinyl alcohol (PVA) fibers on the structural and thermal properties of silica-fume cement panels. Scanning electron microscopy (SEM) was employed to examine mortar mixtures with varying fiber content to assess their enhancement of microstructure. We conducted several experiments on compressive, flexural, and splitting tensile strength, in addition to water absorption and thermal conductivity. The results indicate that 1% HF exhibits superior mechanical qualities, with a compressive strength of 46.89 MPa, a flexural strength of 8.56 MPa, and a reduced thermal conductivity of 0.83 W/m•K. PVA fibers at 2% enhance …

Heathammer: Effects Of Thermal Stress On Dram Technology Reliability Using Rowpress And Rowhammer, Filip Roth Tronnes-Christensen

Theses

Modern DRAM scaling has reduced cell capacitance and increased thermal sensitivity, making disturbance-based faults such as RowHammer and RowPress increasingly significant reliability and security concerns. RowHammer induces bit flips through repeated row activations, while RowPress does so by holding a wordline open for an extended duration; both exploit inherent capacitive coupling and leakage mechanisms in dense DRAM arrays. This thesis introduces HeatHammer, a thermally assisted disturbance exploit that interleaves RowPress and RowHammer operations to amplify charge leakage and trigger row-traversing bit flips. Using the FPGA-based DRAM-Bender test platform, HeatHammer is evaluated on four commercially available DDR4 modules from different manufacturers …

Experimental Analysis Of Droplet Deformation Dynamics In Combined Dc Electric And Shear Flow Fields Using Dpiv, Ayad Ibrahim Khlewee

Al-Esraa University College Journal for Engineering Sciences

This paper presented a systematic study of such droplet deformation at the intersection of uniform DC electric field and hydrodynamic shear flow, in which we focused on conductivity ratio (R) and permittivity ratio (S), describing their influence. The results demonstrate that droplet dynamics is extremely sensitive to the regime: under DC only, either elongation or compression depending on whether R > S or R S, similarly to classical EHD predictions. Under shear-only conditions, deformation was dictated by the balance of elongational stresses (EC) and rotational stresses (RC), with increasing capillary number (Ca) leading to progressive elongation and oscillatory orientation dynamics. When …