Physical Sciences and Mathematics Commons^™

Solving Large Job Shop Scheduling Problems: Using Graph Classification Via Graph Neural Networks To Pre-Seed A Genetic Algorithm For Machine Dispatching Rule Optimization, Isaac Schwab

Theses and Dissertations

The job shop scheduling problem is a difficult problem to solve, and it is also difficult to implement solutions found in research into real shops. In this research, a methodology is proposed to develop schedules for real shops. The methodology utilizes a genetic algorithm to select dispatching rules for each machine cell and accesses these schedules through a simulation optimization framework. The simulation framework allows for the study of random elements including variable job processing times and random machine breakdowns. This creates a robust schedule that is easy to understand, and therefore implement, while scaling to large, real-world job shops. …

Enhancing Interactions Among Dipole Excitations Using Surface Plasmon Polaritons: Quantum Entanglement And Classical Interactions, Jay Berres

Theses and Dissertations

The interaction of light with matter at the nano-scale continues to be an important research area for the application of nano-optical devices in wide ranging areas such as biosensing, light harnessing, and optical communications, to name a few. An important aspect of this is the interaction among dipole excitations (which includes classical dipole emitters, and dipole approximations of atoms, molecules, and other quantum objects), mediated by the device medium where they are located. Since the dimensions of these devices, by design, are at the nano-scale, the size of the dipole-dipole interaction space is much less than the wavelength of light …

Exploring Healthcare Chatbot Information Presentation: Applying Hierarchical Bayesian Regression And Inductive Thematic Analysis In A Mixed Methods Study, Samuel Nelson Koscelny

All Theses

High blood pressure, also known as hypertension, significantly increases the risk of heart disease and stroke, which are leading causes of death in the United States. While contributing to over 691,000 deaths in 2021 alone in the United States (U.S.), it also imposes immense economic burden on the healthcare system, costing approximately $131 billion annually. One way to address this issue is for increased self-care behaviors and medication adherence, both of which require sufficient health literacy. Despite the importance of health literacy, 90% of U.S. adults struggle with health-related subjects. Overcoming the issues associated with health literacy requires addressing the …

Extraction Of Neutron-Gamma Irradiated Diffusion Pump Oils, Cooper L. Tillman

All Theses

This work successfully demonstrates solvent extraction methods for separation of oil from the by-products when two commercially available vacuum pump oils were exposed to an intense neutron and gamma-ray radiation environment. Nuclear fusion power at a commercial scale has accelerated the need for radiation resistant vacuum technology, such as oil-based diffusion pumps. Polyphenyl ether and aromatic silicone oils were irradiated at the Rhode Island Nuclear Science Center (RINSC) up to MGy absorbed doses with neutron and gamma-ray radiation. Solvent extractions were performed using hexane and isopropanol to characterize the oil extraction as a function of total absorbed dose. The by-products …

Surface Patterning On Zirconia Dental Implants Via Laser-Induced Shockwave Imprinting, Inomjon Majidov

Masters Theses & Specialist Projects

Zirconia is rapidly becoming a preferred alternative to titanium in dental applications, primarily due to its aesthetic resemblance to natural teeth. This material’s tooth-like color avoids the aesthetic issues associated with the grey metal tint of titanium implants. Additionally, zirconia is hypoallergenic, making it an ideal choice for patients with metal sensitivities or allergies. Despite these advantages, zirconia generally exhibits lower biocompatibility and osseointegration compared to titanium implants. This study investigates laser-assisted, controlled imprinting technique on zirconia surfaces to enhance these properties. Our research used zirconia pellets, produced from powdered monoclinic zirconia pressed in a pellet press machine. Two methods …

Training Uav Teams With Multi-Agent Reinforcement Learning Towards Fully 3d Autonomous Wildfire Response, Bryce Hopkins

All Theses

As climate-exacerbated wildfires increasingly threaten landscapes and communities, there is an urgent and pressing need for sophisticated fire management technologies. Coordinated teams of Unmanned Aerial Vehicles (UAVs) present a promising solution for detection, assessment, and even incipient-stage suppression – especially when integrated into a multi-layered approach with other recent wildfire management technologies such as geostationary/polar-orbiting satellites and CCTV detection networks. However, there remains significant challenges in developing the necessary sensing, navigation, coordination, and communication subsystems that enable intelligent UAV teams. Further, federal regulations governing UAV deployment and autonomy pose constraints on real-world aerial testing, creating a disconnect between theoretical research …

2d Temperature Map Acquisition Using Hyperspectral Imaging System (Hsis), Anthony Kim

Masters Theses

Imaging techniques are close to our lives and are used for various applications. In the engineering field, one of the dominant techniques is hyperspectral imaging. It is a necessary tool that combines spectroscopy and digital photography and provides additional information on what is imaged by the imaging system. Hyperspectral imaging has been applied to various fields including remote sensing, cultural relic conservation, food microbiology, forensic science, biomedicine, etc.

In particular, work was done to apply hyperspectral imaging to measure the temperature and emissivity of an object. Due to its ability to measure temperature and emissivity without being in contact with …

Transfer Learning For Predictive Maintenance: A Case Study, Colter A. Swanson

Masters Theses

In light of recent strides in high-performance computing, the concept of transfer learning has emerged as a prominent paradigm within the realm of Artificial Intelligence and Machine Learning methodologies. Analogous to the human brain's capacity to assimilate information across related domains for pattern recognition, transfer learning has swiftly asserted its dominance, particularly in deep learning applications such as image classification and natural language processing. Despite its ascendancy in these domains, there exists a lack of comprehensive investigations in alternative domains, notably those encompassing tabular data formats. This thesis seeks to redress this gap by conducting an empirical examination of transfer …

Image Processing Techniques For Water Droplet Penetration Time And Contact Angle Estimation, Sai Balaji Jai Kumar

UNLV Theses, Dissertations, Professional Papers, and Capstones

Water droplet behavior on soil surfaces plays a critical role in numerous environmental processes, including soil erosion, hydrological dynamics, and ecosystem health. Accurate characterization of soil water repellency, quantified by parameters such as water droplet penetration time (WDPT) and contact angles (WDCA), is essential for informed decision-making in agricultural management, forestry practices, and land-use planning. Despite the significance of these parameters, challenges exist in reliably estimating them due to the complex and dynamic nature of soil-water interactions. This thesis address challenges in estimating WDPT and WDCA, by leveraging state-of-the-art image processing techniques and machine learning algorithms. The research focuses on …

A Heuristic Approach To Operations And Control Of Btm-Ders For Bill Management And Grid Services, Md Arshad Rashid Shaon

UNLV Theses, Dissertations, Professional Papers, and Capstones

Behind-The-Meter (BTM) distributed energy resources (DERs) have emerged as a critical and transformative force within the energy sector. These decentralized energy assets, which include solar photovoltaic (PV) systems, battery energy storage systems (BESS), and thermostatically controlled loads (TCLs), are increasingly essential for empowering customers by granting them greater control over their energy production and consumption, thereby reducing reliance on centralized power sources. Additionally, they have the potential to play a pivotal role in enhancing grid resilience by providing grid services. This study investigates the management of customer electricity bills and grid services through the integration of various BTM-DERs, particularly solar …

Magnetization And Age Of Ca. 544 Ma Syenite, Eastern Canada: Evidence For Renewal Of The Geodynamo, Tinghong Zhou, Mauricio Ibañez-Mejia, Richard K. Bono, Rory D. Cottrell, Wouter Bleeker, Kenneth P. Kodama, Wentao Huang, Eric G. Blackman, Francis Nimmo, Aleksey Smirnov, John A. Tarduno

Michigan Tech Publications, Part 2

The ca. 565 Ma Ediacaran geodynamo was highly unusual, producing an ultralow field 10 times weaker than present-day value of 8 x 1022 A m2. A ∼5 times rise in field strength is seen in time-averaged single crystal paleointensity data of ca. 532 Ma Early Cambrian anorthosites of Oklahoma (USA). The field increase could record the onset of inner core nucleation predicted by thermal evolution and numerical dynamo models. Here, we examine the renewal of the geodynamo through zircon U-Pb geochronology and single crystal paleointensity studies of plagioclase from the Chatham-Grenville syenite intrusion in the Grenville Province (Canada). U-Pb data …

Enhancing Fuel Injection System Reliability Through Weibull Family Functions Analysis, Ali Nouri Qarahasanlou, Abbas Barabadi, Meisam Saleki, Ali Rahimazar, Parisa Rezakhania, Masoume Gholamia

Journal of Sustainable Mining

The efficient operation of heavy machinery is crucial to the success of mining and civil construction operations. To guarantee this performance, equipment performance is assessed using the Reliability Index, which analyzes failures to study the ability of a system to carry out its intended functions under predetermined conditions. On the other hand, the failure rate and operational environmental condition (such as management decisions, maintenance performance, etc., that are defined as “risk factors”) over the life cycle of industrial systems pose a significant challenge to reliability analysis. This paper proposes an approach to address these challenges by extending Weibull family functions …

The Role Of Sensing Modalities In Shaping Collective Motion And Group Behavior, Poorendra P. Ramlall

Doctoral Dissertations and Master's Theses

Collective behavior refers to the coordinated movements that emerge from simple interactions between individuals within a group. Traditionally, researchers have modeled these interactions assuming individuals can sense their surroundings in all directions, like having eyes all around their heads. While this is a useful simplification, it does not capture the diverse ways animals actually sense the world. In this thesis, we take inspiration from the natural world, particularly from animals like bats and dolphins that use a combination of hearing and sight to navigate their environments. We explore how combining these sensory cues in a three-dimensional space affects the way …

Smart Airports: Artificial Intelligence–Enabled Internet Of Things Networks Using Blockchain Technology, Edwin Ongola

Journal of Aviation Technology and Engineering

This article provides a perspective on how an internet of heterogeneous self-service airport terminal systems can be used for data collection, which is stored on a private or consortium blockchain depending on the ownership or operations of an airport or both. Such a setup would help to increase efficiency, reduce costs, and improve traveler experience at airport terminals. Moreover, it would allow airports to gather data directly from passengers as opposed to waiting to receive the same data from airlines. Subsequently, this data, now on a blockchain system, becomes a data source for other applications such as machine learning. In …

Personalized Driving Using Inverse Reinforcement Learning, Rodrigo J. Gonzalez Salinas

Theses and Dissertations

This thesis introduces an autonomous driving controller designed to replicate individual driving behaviors based on a provided demonstration. The controller employs Inverse Reinforcement Learning (IRL) to formulate the reward function associated with the provided demonstration. IRL is implemented through a dual-feedback loop system. The inner loop utilizes Q-learning, a model-free reinforcement learning technique, to optimize the Hamilton-Jacobi-Bellman (HJB) equation and derive an appropriate control solution. The outer loop leverages this derived control solution to generate parameters for the reward function, which are subsequently integrated into the HJB equation. The ultimate control policy is deduced from the final reward function obtained …

Space Radiation Assessment And Mitigation: Meeting The Growing Demand For Shielding Solutions By Enhancing Models And Exploring Novel Shielding Opportunities, M. Laura Sorgi Johann

Doctoral Dissertations and Master's Theses

The rapid growth of satellite technology and the increasing presence of vulnerable technology and human life in space have highlighted the need for improved shielding materials. Industry standard protocols for shielding are being pushed to their limits as we gain a deeper understanding of the harsh space environment and as chip sizes approach the scale of radiation wavelengths. Furthermore, the commercialization of space is attracting interest from industries such as pharmaceuticals and semiconductor crystal growing, as they explore the potential benefits of zero-gravity production environments. However, the eagerness to develop lighter shielding and the lack of consideration for existing tools …

Empirical Algorithms For Estimating Downwelling Light Attenuation At Λ=490 Nm From Sentinel-2 In A Narrow Optically Complex Bay, Plymouth Sound, United Kingdom, Louis J. Thornton

The Plymouth Student Scientist

This study investigates the potential of reflectance band ratios, modelled on Sentinel-2’s MultiSpectral Instrument (MSI) bands 10m resolution to estimate the downwelling attenuation coefficient for planar irradiance, Kd(λ), at 490nm (Kd(490)) in Plymouth Sound. Kd(λ) is vital for characterising subsurface light climate, strongly influencing physical and biogeochemical processes in coastal and oceanic waters. In temperate shelf seas, human activities have led to loss in vitality and spatial extent of structurally complex seabed, emphasizing the need for enhanced monitoring. Satellite-based remote sensing (RS) offers spatial and temporal advantages to conventional in situ methods. There are, however two main inadequacies that persist …

More Resilience, More Green Berets: A Quantitative Analysis Of The Effects Of Functional Imagery Training During British Army Commando Training, Emma S. J. Yates

The Plymouth Student Scientist

This study investigates the low completion rates of the British Army commando training course in 2018-2019, recorded at 20%, following concerns raised by course Directing Staff. It examines the relationship between resilience and performance in military contexts and evaluates whether resilience-building interventions can improve completion rates. To establish how completion rates could be improved, sixty-two candidates from three commando training cohorts were recruited and assigned to either a Functional Imagery Training (FIT) intervention or a control group. Interventions were delivered in week one of training with a booster in week five. FIT, a goal-centred imagery intervention, has previously improved resilience-related …

The Wellbeing Benefits Of Exposure To Nature: The Difference Between Environment Types On Different Wellbeing Types, Lily-Mae Petherick

The Plymouth Student Scientist

The world is facing a mental health crisis, thus implementing measures to improve wellbeing is a global priority. One way of combating this worldwide issue is through the well-known wellbeing benefits of nature. However, natural environments include more than just the well-known green spaces, and wellbeing encompasses multiple components. Consequently, this study compares the wellbeing outcomes of virtual exposure to a green space, blue space, and urban environment on both hedonic (pleasure) and eudaimonic wellbeing (meaningfulness). A factorial design was employed in which 96 University of Plymouth students watched one stressor video and then one of three environment videos (green, …

Professor Yong Yang, The Editorial Board Member Of Journal Of Electrochemistry, Is Elected As A Fellow Of The Electrochemical Society, Editorial Office Of J.Electrochem.

Journal of Electrochemistry

No abstract provided.

Kai Wu, The Editorial Board Member Of Journal Of Electrochemistry And Chief Scientist Of Catl, Receives The National Science And Technology Progress Award, Editorial Office Of J.Electrochem.

Journal of Electrochemistry

No abstract provided.

Series Reports (1/4): Advancements In Electrochemical Energy Conversions At Prof Wei’S Group Of Chongqing University: High-Performance Oxygen Reduction Catalysts For Fuel Cells, Fa-Dong Chen, Zhuo-Yang Xie, Meng-Ting Li, Si-Guo Chen, Wei Ding, Li Li, Jing Li, Zi-Dong Wei

Journal of Electrochemistry

Two major challenges, high cost and short lifespan, have been hindering the commercialization process of low-temperature fuel cells. Professor Wei’s group has been focusing on decreasing cathode Pt loadings without losses of activity and durability, and their research advances in this area over the past three decades are briefly reviewed herein. Regarding the Pt-based catalysts and the low Pt usage, they have firstly tried to clarify the degradation mechanism of Pt/C catalysts, and then demonstrated that the activity and stability could be improved by three strategies: regulating the nanostructures of the active sites, enhancing the effects of support materials, and …

First Announcement Of 76th Annual Meeting Of The International Society Of Electrochemistry, Editorial Office Of J.Electrochem.

Journal of Electrochemistry

No abstract provided.

Sorbitol-Electrolyte-Additive Based Reversible Zinc Electrochemistry, Qiong Sun, Hai-Hui Du, Tian-Jiang Sun, Dian-Tao Li, Min Cheng, Jing Liang, Hai-Xia Li, Zhan-Liang Tao

Journal of Electrochemistry

The unstable zinc (Zn)/electrolyte interfaces formed by undesired dendrites and parasitic side reactions greatly hinder the development of aqueous zinc ion batteries. Herein, the hydroxy-rich sorbitol was used as an additive to reshape the solvation structure and modulate the interface chemistry. The strong interactions among sorbitol and both water molecules and Zn electrode can reduce the free water activity, optimize the solvation shell of water and Zn²⁺ ions, and regulate the formation of local water (H₂O)-poor environment on the surface of Zn electrode, which effectively inhibit the decomposition of water molecules, and thus, achieve the thermodynamically stable …

Data-Driven Viewpoint For Developing Next-Generation Mg-Ion Solid-State Electrolytes, Fang-Ling Yang, Ryuhei Sato, Eric Jian-Feng Cheng, Kazuaki Kisu, Qian Wang, Xue Jia, Shin-Ichi Orimo, Hao Li

Journal of Electrochemistry

Magnesium (Mg) is a promising alternative to lithium (Li) in solid-state batteries due to its abundance and high theoretical volumetric capacity. However, the sluggish Mg-ion conduction in the lattice of solid-state electrolytes (SSEs) is one of the key challenges that hamper the development of Mg-ion solid-state batteries. Though various Mg-ion SSEs have been reported in recent years, key insights are hard to be derived from a single literature report. Besides, the structure-performance relationships of Mg-ion SSEs need to be further unraveled to provide a more precise design guideline for SSEs. In this Viewpoints article, we analyze the structural characteristics of …

Efficient Deep Neural Network Compression For Environmental Sound Classification On Microcontroller Units, Shan Chen, Na Meng, Haoyuan Li, Weiwei Fang

Turkish Journal of Electrical Engineering and Computer Sciences

Environmental sound classification (ESC) is one of the important research topics within the non-speech audio classification field. While deep neural networks (DNNs) have achieved significant advances in ESC recently, their high computational and memory demands render them highly unsuitable for direct deployment on resource-constrained Internet of Things (IoT) devices based on microcontroller units (MCUs). To address this challenge, we propose a novel DNN compression framework specifically designed for such devices. On the one hand, we leverage pruning techniques to significantly compress the large number of model parameters in DNNs. To reduce the accuracy loss that follows pruning, we propose a …

Network Intrusion Detection Based On Machine Learning Strategies: Performance Comparisons On Imbalanced Wired, Wireless, And Software-Defined Networking (Sdn) Network Traffics, Hi̇lal Hacilar, Zafer Aydin, Vehbi̇ Çağri Güngör

Turkish Journal of Electrical Engineering and Computer Sciences

The rapid growth of computer networks emphasizes the urgency of addressing security issues. Organizations rely on network intrusion detection systems (NIDSs) to protect sensitive data from unauthorized access and theft. These systems analyze network traffic to detect suspicious activities, such as attempted breaches or cyberattacks. However, existing studies lack a thorough assessment of class imbalances and classification performance for different types of network intrusions: wired, wireless, and software-defined networking (SDN). This research aims to fill this gap by examining these networks’ imbalances, feature selection, and binary classification to enhance intrusion detection system efficiency. Various techniques such as SMOTE, ROS, ADASYN, …

Enrichment Of Turkish Question Answering Systems Using Knowledge Graphs, Okan Çi̇ftçi̇, Fati̇h Soygazi̇, Selma Teki̇r

Turkish Journal of Electrical Engineering and Computer Sciences

Recent capabilities of large language models (LLMs) have transformed many tasks in Natural Language Processing (NLP), including question answering. The state-of-the-art systems do an excellent job of responding in a relevant, persuasive way but cannot guarantee factuality. Knowledge graphs, representing facts as triplets, can be valuable for avoiding errors and inconsistencies with real-world facts. This work introduces a knowledge graph-based approach to Turkish question answering. The proposed approach aims to develop a methodology capable of drawing inferences from a knowledge graph to answer complex multihop questions. We construct the Beyazperde Movie Knowledge Graph (BPMovieKG) and the Turkish Movie Question Answering …

A New Approach: Ordinal Predictive Maintenance With Ensemble Binary Decomposition (Opmeb), Ozlem Ece Yurek, Derya Birant

Turkish Journal of Electrical Engineering and Computer Sciences

Predictive maintenance (PdM), a fundamental element of modern industrial systems, employs machine learning to monitor equipment conditions, estimate failure probabilities, and optimize maintenance schedules. Its core objective is to enhance equipment reliability, extend lifespan, and minimize costs through data-driven insights by enabling efficient maintenance scheduling, reducing downtime, and optimizing resource allocation. In this paper, we propose a novel ordinal predictive maintenance with ensemble binary decomposition (OPMEB) method for the PdM domain, considering the hierarchical nature of class labels reflecting the machine's health status, including categories like healthy, low risk, moderate risk, and high risk. The proposed OPMEB method was validated …

A Real-Time Embedded System Designed For Nilm Studies With A Novel Competitive Decision Process Algorithm, Sai̇d Mahmut Çinar, Rasi̇m Doğan, Emre Akarslan

Turkish Journal of Electrical Engineering and Computer Sciences

This paper explores the determination of any load or load combination in a power system at any moment. This process requires measurements at the main electric utility service entry of a house, known as nonintrusive measurement. To accurately identify loads, total harmonic distortion, RMS, third harmonic currents, and power consumption are considered their fingerprints. Based on these fingerprints, an algorithm called the competitive decision process is developed and integrated into an embedded system. This algorithm has a two-level decision mechanism. In the first stage, the winner loads with the highest similarity scores from each feature are determined, and the loads …