Engineering Commons^™

Asphalt-Binder Mixtures Evaluated By T1 Nmr Relaxometry, Rebecca M. Herndon, Jay Balasubramanian, Magdy Abdelrahman, Klaus Woelk

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Asphalt pavements make up a majority of the essential transportation systems in the US. Asphalt mixtures age and degrade over time, reducing the pavement performance. Pavement performance critically depends on the aging of asphalt binder. the aging of asphalt binder during construction is traditionally modeled by rolling thin film oven (RTFO) testing, while aging during service life is modeled by pressure aging vessel (PAV) testing. Comparing these models to the aging of binders in actual pavements is limited because, to be used for current testing, binders must be separated from the pavement's aggregate by solvent extraction. Solvent extraction will, at …

Nuclear Magnetic Resonance (Nmr) Assessment Of Bio And Crude Oil-Based Rejuvenation, Rebecca M. Herndon, Jay Balasubramanian, Magdy Abdelrahman, Klaus Woelk

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Asphalt binders in pavements lose their stability through aging and eventually fail in the field. Using nuclear magnetic resonance (NMR) to monitor the primary longitudinal relaxation time of asphalt samples and the ratio of material that carries this primary relaxation time has been shown to indicate the impact of ultraviolet (UV) radiation on the aging of asphalt pavements. Longitudinal NMR relaxation was used to investigate two types of proposed asphalt rejuvenators, a bio-oil-based rejuvenator and a crude-oil-based rejuvenator. Two different binders with the performance grades (PG) 64-22 and 76-22 were considered for their interactions with the rejuvenators. After 72 h …

Building Services Engineering September/October 2024

Building Services Engineering

No abstract provided.

Forgotten Topological Index And Its Properties On Neutrosophic Graphs, G. Vetrivel, M. Mullai, R. Buvaneshwari

Neutrosophic Systems with Applications

Topological indices play a significant role in crisp, fuzzy graphs and their real-life application. But to avoid the vagueness in the final result, these indices should be dealt with in the neutrosophic environment since it consolidates the uncertain quantity or values of an event in the name of "indeterminacy membership". Except for the wiener index, no other indices are introduced under the neutrosophic graphical setting. In this article, we consider the forgotten topological index (ToI) and the Edge forgotten index in the 3-valued logic neutrosophic graph and came up with some important theorem results and applications.

Precision Medicine For Apical Lesions And Peri-Endo Combined Lesions Based On Transfer Learning Using Periapical Radiographs, Pei Yi Wu, Yi Cheng Mao, Yuan Jin Lin, Xin Hua Li, Li Tzu Ku, Kuo Chen Li, Chiung An Chen, Tsung Yi Chen, Shih Lun Chen, Wei Chen Tu, Patricia Angela R. Abu

Ateneo Laboratory for Intelligent Visual Environments

An apical lesion is caused by bacteria invading the tooth apex through caries. Periodontal disease is caused by plaque accumulation. Peri-endo combined lesions include both diseases and significantly affect dental prognosis. The lack of clear symptoms in the early stages of onset makes diagnosis challenging, and delayed treatment can lead to the spread of symptoms. Early infection detection is crucial for preventing complications. PAs used as the database were provided by Chang Gung Memorial Medical Center, Taoyuan, Taiwan, with permission from the Institutional Review Board (IRB): 02002030B0. The tooth apex image enhancement method is a new technology in PA detection. …

Quantum Relaxation For Solving Multiple Knapsack Problems, Monit Sharma, Jin Yan, Hoong Chuin Lau, Rudy Raymond

Research Collection School Of Computing and Information Systems

Combinatorial problems are a common challenge in business, requiring finding optimal solutions under specified constraints. While significant progress has been made with variational approaches such as QAOA, most problems addressed are unconstrained (such as Max-Cut). In this study, we investigate a hybrid quantum-classical method for constrained optimization problems, particularly those with knapsack constraints that occur frequently in financial and supply chain applications. Our proposed method relies firstly on relaxations to local quantum Hamiltonians, defined through commutative maps. Drawing inspiration from quantum random access code (QRAC) concepts, particularly Quantum Random Access Optimizer (QRAO), we explore QRAO's potential in solving large constrained …

Understanding Climate Change Adaptation And Gentrification Through The Framework Of Uneven Coastal Development In Keansburg, New Jersey, Zachary Paganini

Dissertations, Theses, and Capstone Projects

This study examines the rapid acceleration of coastal development in the United States despite projections of climate change-induced sea level rise. Like no other developed country, the U.S. has transformed its fragile coastline into sites for leisure, consumption, and retirement. These spaces now face the prospect of more frequent and severe storms, as well as land loss and inundation, due to climate change. This dilemma has sparked debates around coastal resilience, adaptation, retreat, and climate gentrification.

While many studies seek to measure perceptions of climate change risk and vulnerability, this dissertation reverses the question by positioning risk perceptions as an …

Resting-State Eeg Microstate Features For Major Depressive Disorder Classification, George M. V. Quinn

Dissertations, Theses, and Capstone Projects

Neuroimaging studies have revealed consistent abnormalities in functional connectivity within specific neural networks that may serve as biomarkers for major depressive disorder (MDD). It is important to find inexpensive, non-invasive techniques that target these biomarkers to make diagnosis easier and more objective. EEG microstates are quasi-stable potential topographies that are thought to reflect the quasi-stable network activity of the underlying neural generators. MDD has been shown to alter features of the four canonical EEG microstates (A, B, C, D) with some conflicting results. The most consistent network abnormalities in MDD are found in the anterior default mode network, and this …

Nanofluid-Assisted Enhanced Sealing Security For Efficient Geological Hydrogen Storage In Saudi Arabian Basalt, Muhammad Ali, Nurudeen Yekeen, Sarmad Al-Anssari, Aliakbar Hassanpouryouzband, Alireza Keshavarz, Hussein Hoteit

Research outputs 2022 to 2026

The modification of hydrophobic rock surfaces to the water-wet state via nanofluid treatment has shown promise in enhancing their geological storage capabilities and the efficiency of carbon dioxide (CO2) and hydrogen (H2) containment. Despite this, the specific influence of silica (SiO2) nanoparticles on the interactions between H2, brine, and rock within basaltic formations remains underexplored. The present study focuses on the effect of SiO2 nanoparticles on the wettability of Saudi Arabian basalt (SAB) under downhole conditions (323 K and pressures ranging from 1 to 20 MPa) by using the tilted plate technique to measure the contact angles between H2/brine and …

Aligning Human And Computational Coherence Evaluations, Jia Peng Lim, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Automated coherence metrics constitute an efficient and popular way to evaluate topic models. Previous work presents a mixed picture of their presumed correlation with human judgment. This work proposes a novel sampling approach to mining topic representations at a large scale while seeking to mitigate bias from sampling, enabling the investigation of widely used automated coherence metrics via large corpora. Additionally, this article proposes a novel user study design, an amalgamation of different proxy tasks, to derive a finer insight into the human decision-making processes. This design subsumes the purpose of simple rating and outlier-detection user studies. Similar to the …

Enhancing Multi-Agent System Testing With Diversity-Guided Exploration And Adaptive Critical State Exploitation, Xuyan Ma, Yawen Wang, Junjie Wang, Xiaofei Xie

Research Collection School Of Computing and Information Systems

Multi-agent systems (MASs) have achieved remarkable success in multi-robot control, intelligent transportation, and multiplayer games, etc. Thorough testing for MAS is urgently needed to ensure its robustness in the face of constantly changing and unexpected scenarios. Existing methods mainly focus on single-agent system testing and cannot be directly applied to MAS testing due to the complexity of MAS. To our best knowledge, there are fewer studies on MAS testing. While several studies have focused on adversarial attacks on MASs, they primarily target failure detection from an attack perspective, i.e., discovering failure scenarios, while ignoring the diversity of scenarios. In this …

Bugs In Pods: Understanding Bugs In Container Runtime Systems, Jiongchi Yu, Xiaofei Xie, Ceng Zhang, Sen Chen

Research Collection School Of Computing and Information Systems

Container Runtime Systems (CRSs), which form the foundational infrastructure of container clouds, are critically important due to their impact on the quality of container cloud implementations. However, a comprehensive understanding of the quality issues present in CRS implementations remains lacking. To bridge this gap, we conduct the first comprehensive empirical study of CRS bugs. Specifically, we gather 429 bugs from 8,271 commits across dominant CRS projects, including runc, gvisor, containerd, and cri-o. Through manual analysis, we develop taxonomies of CRS bug symptoms and root causes, comprising 16 and 13 categories, respectively. Furthermore, we evaluate the capability of popular testing approaches, …

Comparison Of Evolutionary Algorithms: A Case Study On The Multi-Objective Carbon-Aware Mine Planning, Nurul Asyikeen Binte Azhar, Aldy Gunawan, Shih-Fen Cheng, Erwin Leonardi

Research Collection School Of Computing and Information Systems

The NP-hard precedence-constrained production scheduling problem (PCPSP) for mine planning chooses the ordered removal of materials from the mine pit and the next processing steps based on resource, geological, and geometrical constraints. Traditionally, it prioritizes the net present value (NPV) of profits across the lifespan of the mine. Yet, the growing shift in environmental concerns also requires shifts to more carbon-aware practices. In this paper, we use the enhanced multi-objective version of the generic PCPSP formulation by adding the NPV of carbon costs as another objective. We then compare how the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Pareto …

Review On Assessment And Performance Mechanism Evaluation Of Non-Structural Concrete Incorporating Waste Materials, Nuha S. Mashaan, Appuwa De Silva

Research outputs 2022 to 2026

This research seeks to solve the multi-faceted problem of waste disposal by analysing the application of waste plastic and tyre material within non-structural concrete to ensure more sustainability and less environmental degradation. The study focusses on material properties, including specific gravity, water absorption, and bulk density and characteristics of the concrete that is produced by the utilization of the above waste aggregates, including workability, compressive strength, flexural strength, and tensile strength. This paper employs results from published past research from the literature and MATLAB (R2021b) in the analysis of the findings, pointing to the fact that the mechanical properties reduce …

Nano-Enhanced Phase Change Materials: Fundamentals And Applications, Zafar Said, A. K. Pandey, Arun Kumar Tiwari, B. Kalidasan, Furqan Jamil, Amrit Kumar Thakur, V. V. Tyagi, Ahmet Sarı, Hafiz Muhammad Ali

Research outputs 2022 to 2026

Phase Change Materials (PCMs) enable thermal energy storage in the form of latent heat during phase transition. PCMs significantly improve the efficiency of solar power systems by storing excess energy, which can be used during peak demand. Likewise, they also contribute to reduced overall energy demand through passive thermal regulation. Nonetheless, thermal energy charging and discharging are restricted due to the low conducting nature of the energy storage medium. Various research investigations are being carried out to improve the thermal characteristics of PCMs through techniques such as a) dispersion of nanoparticles, b) inserting fins, and c) cascading PCMs. Among the …

Verifying An Improved Intermittent Water Distribution Network Analysis Method Based On Epa-Swmm, Dondu Sarisen, Raziyeh Farmani, David W. Watkins, Brian Barkdoll, Brian Rivers, Chao Zhang, Fayyaz Ali Memon

Michigan Tech Publications

Modelling Intermittent Water Supply (IWS) presents challenges, as traditional hydraulic methods based on EPANET are often inadequate due to their inability to simulate the network filling process. While EPA-SWMM (EPA’s Storm Water Management Model)-based methods enhance IWS analysis, they remain network-specific and lack universal applicability. This study aims to calibrate and verify an improved EPA-SWMM-based model on a 6 m × 5 m laboratory-scale IWS. Experiments were conducted to capture flow rate data from demand nodes under various conditions. The EPA-SWMM model, based on uncontrolled outlets with flow rate varying by pressure, was calibrated using, an automated procedure that integrated …

Investigating The Neurovascular Correlates Of Cognitive Function In Individuals With Spinal Cord Injury Using Functional Near-Infrared Spectroscopy, Donna Yali Chen

Dissertations

Individuals with spinal cord injury (SCI) have a 13 times greater risk of cognitive impairment than able-bodied individuals and commonly report symptoms of depression and anxiety. The reports of cognitive impairment in individuals with SCI are independent of concomitant brain injury. Although the direct cause of cognitive impairment in individuals with SCI is unknown, key contributors may include comorbid depression, chronic pain, anxiety, fatigue, brain injury, or cardiovascular dysfunction. Previous studies have focused on SCI at the level of the spinal cord but have under-investigated the supraspinal levels. Due to the lack of neuroimaging studies on SCI rehabilitation, it is …

A Micromagnetic Study Of Skyrmions In Thin-Film Multilayered Ferromagnetic Materials, Nicholas J. Dubicki

Dissertations

Magnetic skyrmions are topologically protected, localized, nanoscale spin textures in non-centrosymmetric thin ferromagnetic materials and heterostructures. At present they are of great interest to physicists for potential applications in information technology due to their particle-like properties and stability. In a system of multiple thin ferromagnetic layers, the stray field interaction was typically treated with various simplifications and approximations. It is shown that extensive analysis of the micromagnetic equations leads to an exact representation of the stray field interaction energy in the form of layer interaction kernels, a so-called 'finite thickness' representation. This formulation reveals the competition between perpendicular magnetic anisotropy …

Federated Learning Systems For Mobile Sensing Data, Xiaopeng Jiang

Dissertations

Federated Learning (FL) has emerged as a new distributed Deep Learning (DL) paradigm that enables privacy-aware training and inference on mobile devices with help from the cloud. This dissertation presents a comprehensive exploration of FL with mobile sensing data, covering systems, applications, and optimizations.

First, a mobile-cloud FL system, FLSys, is designed to balance model performance with resource consumption, tolerate communication failures, and achieve scalability. In FLSys, different DL models with different FL aggregation methods can be trained and accessed concurrently by different apps. In addition, FLSys provides advanced privacy-preserving mechanisms and a common API for third-party app developers to …

Data Driven Decision Making For Sustainable Planning And Operations Of Large Scale Networks, Bahareh Kargar

Dissertations

This dissertation explores data-driven decision-making networks, focusing on sustainable planning and operations for large-scale systems such as healthcare supply chains and power systems. One significant application in healthcare is the optimization of vaccine supply chains. An agent-based simulation-optimization modeling framework is developed to enhance the efficiency and sustainability of vaccine distribution. First, an agent-based epidemiological model of COVID-19 is extended to capture disease transmission dynamics and forecast the number of susceptible individuals and infections. Then, a sustainable vaccine supply chain considering the impacts of greenhouse gases is developed and integrated with the simulation model to minimize total costs and environmental …

Electrified Membrane For Water Treatment And Resource Recovery: Advancing Multiscale Strategies In Gas-Involving Reactions, Jianan Gao

Dissertations

Multifunctional, modular, and scalable water treatment technologies that operate without chemical additives are crucial for addressing global water pollution challenges. While traditional membrane-based separation processes are crucial for ensuring clean water supplies, they primarily focus on pollutant removal rather than on the upcycling of pollutants into valuable resources. Electrified membrane filtration presents a robust alternative, utilizing minimal or no chemical additives and harnessing electrical power to directly reduce contaminants or produce reactive oxidants or reductants on-site.

This dissertation is dedicated to the development of innovative electrochemically reactive membranes designed to address three major challenges: (1) the engineering of electrocatalysts with …

Microwave Catalysis-Enabled Membrane Processes For Microbial Inactivation, Fangzhou Liu

Dissertations

The COVID-19 pandemic has highlighted the urgent need for advanced technologies to combat virus transmission. Traditional membrane filtration technologies primarily serve as physical barriers, capturing pollutants but failing to inactivate microbial agents such as bacteria and viruses. These membranes typically target larger particles but are ineffective against sub-micrometer viral particles. Thus, there's a critical demand for membrane filtration processes that combine robust filtration with antifouling and reaction -enabled functions for efficient pathogen disinfection. This research explores a novel microwave-assisted membrane filtration process incorporating catalytic reactions to enhance microbial disinfection and prevent membrane fouling, offering a groundbreaking solution to global challenges …

Estimation Of Spo2 Levels And Heart Rate From Ppg Signals In The Presence Of Motion Artifacts, Chizhong Wang

Dissertations

In the rapidly advancing world of health technology, wearable devices are at the forefront, fundamentally changing how individuals monitor their health. The key to this transformation is photoplethysmography (PPG), a non-invasive optical method that measures blood flow variations through the skin by detecting changes in light absorption with each heartbeat. This capability makes PPG essential for monitoring vital signs such as heart rate and blood oxygen saturation(SpO₂). Furthermore, the utility of PPG signals has been extended beyond traditional health metrics to include human activity classification, offering a holistic perspective on an individual's physical health and activity levels, in …

Order-Picking Strategies And Efficiency Models For The Fulfillment Of Multi-Line E-Commerce Grocery Orders, Zijia Wang

Dissertations

The fulfillment process for Buy Online Pickup from Store grocery orders (BOPS-Grocery) is particularly challenging, given the large item count per order and the low-profit margins. This research investigates the BOPS-Grocery model and pursues the following objectives: (i) Defining and specifying the BOPS-Grocery fulfillment process. Differentiating the process from classical warehouse order picking., identifying performance objectives, and characterizing design options and facility layout. (ii) Modelling and developing order picking algorithms specific to the straight aisle section. Formulated as an order hatching problem with picker movement minimization. No order splitting, and (iii) modeling and developing order picking algorithms specific to the …

Collision Dynamics Of Compound Droplets In Microchannels: A Combined Numerical And Data-Driven Study, S M Abdullah Al Mamun

Dissertations

Understanding and predicting the hydrodynamic interactions of micron-scale droplets is crucial in a wide range of industrial and real-life applications, including microfluidics, pharmaceutics, drug delivery, food science, and enhanced oil recovery. These multi-phase and multi-scale phenomena are further complicated by the presence of core droplets of an immiscible fluid within shell droplets, known as compound droplets. The collisions and interactions of droplets in emulsions are influenced by various physical and geometric parameters, leading to distinct rheological and dynamic responses. This research employs numerical methods for a systematic parametric study of both simple and compound droplet pair collisions under confined shear …

Modular Reconfigurable Rotary Style Soft Pneumatic Actuators, Nicholas Pagliocca, Aatish Gupta, Joshua Knospler, Nand K. Singh, Mitja Trkov

Henry M. Rowan College of Engineering Departmental Research

Modular soft robots have the advantage in their ability to be reconfigured and change their morphology on-demand according to a specified task. The actuator design and particularly the inter-unit connectivity strength commonly govern the usability and functionality of these modular robots and their applications. In this paper, we present development of reconfigurable modular rotary soft actuators that allow the construction of highly versatile soft robotic actuators and systems. These pneumatically driven modular actuators are composed of an inner balloon and a flexible arched reinforcement structure to produce a rotary motion. They serve as building blocks which may be combined in …

Investigations On Structure-Related Properties Of Organic Inorganic Hybrid Materials For Multifunction, Anu Ranjani E Ms

Theses and Dissertations

Two-dimensional (2D) hybrid halide perovskites have become a versatile material in diverse applications like optoelectronics, lighting and display, magnetism, etc. These materials are often termed as layered hybrid halide perovskites (LHHPs) due to their unique structure with alternate organic and inorganic layers. The presence of insulating organic layers separates the inorganic perovskite slabs, creating a quantum-confined architectural configuration. This unique configuration endows these materials with a spectrum of distinctive functionalities, encompassing photovoltaics, lighting and displays, second harmonic generation, optical limiting, ferroelectricity, and ferromagnetism. The exceptional attribute is that a single LHHP can respond autonomously to external stimuli, such as changes …

Enhancing Security And Privacy For Smarter Environment Through A Robust Cyber-Physical System Framework, Ramya S

Theses and Dissertations

As digital computing paradigm and practices have emerged in disciplines, devices with processors and sensors were rudimentary, performing independent tasks with limited power. The first computer processors were slow, bulky and consuming high energy, as sensors in thermometers and pressure gauges provide original, independent measurements without effective communication, 1999. It often required powerful, energy-efficient processors and advanced sensors to enable seamless communication and sophisticated data processing.

These devices, since smart home systems to industrial automation tools, which continuously collect, analyse and share data via the internet, facilitating if real-time management, predictive maintenance, and improved seamless experience are used, transforming everyday …

Deep Learning Technique For The Classification Of Stress Among The Students Using Physiological Biomarkers With A Hybrid Feature Approach, Rajendran Vg

Theses and Dissertations

Adolescence is a crucial part in life, and the presence of stress, anxiety, depression, and health issues during this stage is a great concern. This research aims to analyze and predict the cognitive stress in students during the examination period using EEG biomarkers. In this study, raw EEG data is acquired under two different experimental conditions, before and after examination, from 14 subjects with an eight-channel Enobio device. After preprocessing of the EEG signal, the brain rhythms such as theta, alpha, and beta sub-band energies and EEG band ratios such as neural activity, heart rate, arousal index, vigilance index and …

Ai In Healthcare: Early Diagnosis Of Skin Cancer Using Medical Image Processing And Deep Neural Networks, Nirmala V

Theses and Dissertations

Several cancer types are commonly prevalent, and skin cancer is one among them, becoming even more widespread worldwide in the last few decades. To diagnose skin cancer at an early stage and obtain appropriate therapy to treat it, there is a demand to know more about the disease’s characteristics or severity. Skin cancer is caused mainly by various reasons, including damage of the sun or tanning beds by ultraviolet light exposure.

Failing to treat skin cancer might substantially impair an individual’s quality of life as the victim. They likely to experience physical issues linked with the deformities caused by psychological …