Engineering Commons^™

Multi-Source Remote Sensing–Based Soil Moisture Prediction Using Machine Learning, Niraj Neupane

Electronic Theses and Dissertations

Soil moisture (SM) plays a central role in climatic and environmental processes, influencing shear strength of soil, agricultural productivity, land–atmosphere interactions, and hydrologic functioning. However, accurately estimating SM across diverse climatic regions remains challenging due to spatial heterogeneity, limited in situ measurements, and inconsistencies in sensor resolution. Machine learning (ML) and remote sensing offer promising avenues for improving SM prediction, yet many existing approaches struggle with generalization across climatic gradients and often fail to capture temporal variability. This study integrates multi-source satellite and climate datasets, including SMAP L4_SM, MODIS land surface temperature, Daymet meteorological variables, and in situ observations from …

Enhancing Channel Data Savings And Information Transfer Efficiency In Ultrasound Imaging, Sai Konda, Hicham Chaoui

Electrical & Computer Engineering Faculty Publications

Ultrasound is a popular imaging technique mainly due to its non-invasive nature. And so, it is being used in a variety of applications. Due to plane wave imaging technique in ultrasound, frame rate of ultrasound imaging has the potential for being very high. Due to which, many channel data frames are being generated within a few seconds. As a result, tasks such as storing data frames and transferring them from front end ultrasonic system to processing computers are presenting significant challenges. Our current research work minimized these issues. We proposed and implemented: (a) Data encoding technique - We combined every …

A Fast Framework For Generating Radioactive Mixture Spectra And Its Application To Remote High-Performance Mixture Identification, Chiman Kwan, Bulent Ayhan, Adam Stavola, Kazi Aminul Islam, Hongfang Zhang, Jiang Li

Electrical & Computer Engineering Faculty Publications

Remote detection of radioactive materials in mixtures using handheld or portal detectors remains a challenge because of factors such as low concentration, environmental interference, sensor noise, and other complications. This work introduces a fast framework for generating realistic mixture spectra. Moreover, we present mixture isotope identification using data generated by the fast framework. Researchers have examined a range of conventional and recent algorithms within the fields of machine learning and deep learning. An application to uranium enrichment-level prediction has been included. Extensive simulation experiments validated the efficacy of the proposed framework.

Adaptive Fusion Neural Networks For Sparse-Angle X-Ray 3d Reconstruction, Shaoyong Hong, Bo Yang, Yan Chen, Hao Quan, Shan Liu, Minyi Tang, Jiawei Tian

Electrical & Computer Engineering Faculty Publications

3D medical image reconstruction has significantly enhanced diagnostic accuracy, yet the reliance on densely sampled projection data remains a major limitation in clinical practice. Sparse-angle X-ray imaging, though safer and faster, poses challenges for accurate volumetric reconstruction due to limited spatial information. This study proposes a 3D reconstruction neural network based on adaptive weight fusion (AdapFusionNet) to achieve high-quality 3D medical image reconstruction from sparse-angle X-ray images. To address the issue of spatial inconsistency in multi-angle image reconstruction, an innovative adaptive fusion module was designed to score initial reconstruction results during the inference stage and perform weighted fusion, thereby improving …

Development And Characterization Of Polysaccharide-Based Controlled Drug Delivery Platforms, Amanda Pepler

Open Access Dissertations

Drug delivery research focuses on overcoming challenges in patient treatment. Most therapeutics are delivered systemically through oral or intravenous routes. However, these administration methods are often inefficient due to therapeutic hydrophobicity, poor bioavailability, and liver metabolic clearance, necessitating frequent dosing. When treatment plans require frequent dosing, patients experience significant fluctuations in therapeutic concentration in the bloodstream, which can increase adverse side effects. These factors contribute to low patient compliance. Therefore, the core focus of drug delivery research is to develop new administration routes for therapeutics. Drug delivery systems (DDS) that can target therapeutics to a localized region offer advantages over …

Beyond The Phonon Gas Model (Pgm): Unraveling The Unique Mechanistic Processes Dictating Thermal Transport In Highly Anharmonic And Disordered Materials, Sandip Thakur

Open Access Dissertations

Thermal transport in nonmetallic solids has traditionally been described by the phonon gas model (PGM), in which heat is carried by weakly interacting phonon quasiparticles in an ordered crystalline lattice. However, this model breaks down in materials characterized by strong anharmonicity, dynamic disorder, or structural complexity, features prevalent in many next-generation materials used in energy conversion, optoelectronics, and thermal management. This dissertation investigates thermal transport in such complex materials, including metal halide perovskites (MHPs), covalent organic frameworks (COFs), metal organic frameworks (MOFs), and 2D-3D heterostructures, through large-scale molecular dynamics (MD) simulations and frequency-resolved spectral analyses.

In MHPs, the work reveals …

Laboratory Investigation Of Shape And Initial Orientation Effects On Surf Zone Object Migration, Temitope E. Idowu, Jack A. Puleo

Civil & Environmental Engineering Faculty Publications

Discarded objects like munitions in marine environments pose public safety risks. The behavior of various density objects deployed at four cross-shore positions in the surf zone of a large-scale 120 m x 5 m x 5 m wave flume were observed under different forcing conditions. Net migration was predominantly directed offshore, with approximately 70 % offshore migration observed near the outer surf zone. Density, shape, and initial orientation were identified as important to object behavior, with density acting as the dominant driver in 67 % of the object pairing scenarios. The influence of shape and initial orientation on net migration …

A Bridge Too Low, John Adam

Mathematics & Statistics Faculty Publications

The article "A bridge too low" in the Physics Teacher journal discusses a low bridge near the River Greta in Keswick, England, with an arch shaped like a semiellipse. It presents questions about the maximum height a person of a certain height can walk under the bridge without hitting their head, the cross-sectional area of the arch, its eccentricity, and perimeter. The article also mentions the approximation by Indian mathematician Srinivasa Ramanujan for the perimeter of an ellipse and invites readers to find the answers online.

Zero Trust Architecture As A Risk Countermeasure In Small-Medium Enterprises And Advanced Technology Systems, Ahmed M. Abdelmagid, Rafael Diaz

Engineering Management & Systems Engineering Faculty Publications

The growing sophistication of cyberattacks exposes small- and medium-sized businesses (SMBs) to a widening range of security risks. As these threats evolve in complexity, the need for advanced security measures becomes increasingly pressing. This necessitates a proactive approach to defending against potential cyber intrusions. Emerging technologies, such as blockchain, artificial intelligence, and Zero Trust security framework, offer crucial tools for strengthening the digital infrastructure of SMBs. The Zero Trust architecture (ZTA) holds significant promise as a critical strategy for protecting SMBs. While existing literature explores the implementation of ZTA in various business settings, discussions specifically addressing the financial, human resource, …

Ai-Generated Messaging For Life Events Using Structured Prompts: A Comparative Study Of Gpt With Human Experts And Machine Learning, Christopher Lynch, Erik Jensen, Ross Gore, Virginia Zamponi, Kevin O'Brien, Brandon Feldhaus, Katherine Smith, Joseph Martínez, Madison H. Munro, Timur E. Ozkose, Tugce B. Gundogdu, Ann Marie Reinhold, Hamdi Kavak, Barry Ezell

VMASC Publications

Large Language Models (LLMs) play an increasingly integrated and pivotal role in generating diverse types of texts, such as social media messages, emails, narratives, and technical reports, among other textual communication forms. As AI-generated messaging filters into human communication, a systematic exploration of their effectiveness for mimicking human-like communication of life events is needed. In this study, we employ a zero-shot structured narrative prompt to generate 24,000 life event messages for birth, death, hiring, and firing events using OpenAI's GPT-4. From this dataset, we manually classify 2880 messages and evaluate their validity in conveying these life events through the form …

Survey: A Study On Image Encryption Using Dna In Bioinformatics, Rana M. Zaki, Zaed S. Mahdi, Matheel E. Abdulmunim

Journal of Soft Computing and Computer Applications

One area of study between computer science and biology is bioinformatics, which deals with methods for collecting, processing, storing, and evaluating biological data. Sequences of RiboNucleic Acid (RNA), DeoxyriboNucleic Acid (DNA), and proteins make up biological data, which has a wide range of uses in domains such as feature extraction, data segmentation, data security, and more. In cryptography, DNA sequences are used as data carriers, enhancing the unique properties of biomolecules. This approach involves using DNA sequences to enhance the security of confidential data that must be transmitted over networks or stored securely. Several DNA-based security techniques have been developed, …

New Feature Selection Using Principal Component Analysis, Zaid Mundher Radeef, Soukaena Hassan Hashem, Ekhlas Khalaf Gbashi

Journal of Soft Computing and Computer Applications

Dimensionality reduction techniques streamline machine learning by reducing data complexity, improving model accuracy, and cutting computational costs. They remove noise and irrelevant features, making models faster and more efficient. These techniques also enhance data visualization and interpretation by condensing data into manageable, insightful dimensions. Ultimately, dimensionality reduction leads to simpler, more interpretable models without sacrificing critical information, making it a cornerstone of efficient data analysis and machine learning applications. Theoretically, feature extraction tends to create new features that encapsulate more information by combining multiple existing features, resulting in more concentrated and informative features. In contrast, feature selection involves choosing a …

Development Of A Hybrid Methodology Of Deep Learning And Machine Learning For Lung Nodule Detection In Medical Computed Tomography Images, Zaed S. Mahdi, Rana M. Zaki, Alaa Kadhim Farhan, Negar Majma

Journal of Soft Computing and Computer Applications

Deep learning and machine learning play an important role in the medical field, helping doctors make accurate, fast and effective diagnosis. Despite the progress achieved in the use of modern technologies in detecting cancerous nodes, current studies still suffer from some challenges and limitations that must be addressed to obtain high efficiency in identifying cancerous nodes. These challenges include using image pre-processing, combining deep learning and machine learning techniques, and constantly adapting to clinical changes, in order to address this. A hybrid methodology has been proposed for detecting cancerous nodules in the lung in medical Computed Tomography (CT) images. It …

Enhancing Image Classification Using A Convolutional Neural Network Model, Zena M. Saadi, Ahmed T. Sadiq, Omar Z. Akif, Marwa M. Eid

Journal of Soft Computing and Computer Applications

In recent years, with the rapid development of the current classification system in digital content identification, automatic classification of images has become the most challenging task in the field of computer vision. As can be seen, vision is quite challenging for a system to automatically understand and analyze images, as compared to the vision of humans. Some research papers have been done to address the issue in the low-level current classification system, but the output was restricted only to basic image features. However, similarly, the approaches fail to accurately classify images. For the results expected in this field, such as …

Improved Rapidly-Exploring Random Tree Using Firefly Algorithm For Robot Path Planning, Dena Kadhim Muhsen, Firas Abdulrazzaq Raheem, Yuhanis Yusof, Ahmed T. Sadiq, Faiz Al Alawy

Journal of Soft Computing and Computer Applications

In robotics, efficient path planning makes robots work independently and move through changing environments over time. This study combines the Rapidly-exploring Random Tree (RRT) architecture with the Firefly Algorithm (FA) to make robot’s path-planning better. The proposed ERRT-FA, which stands for "Enhanced RRT with Firefly Algorithm", generates better routes using Firefly social habits. Plan routes using Firefly social habits can effectively aid in exploring configuration space. The role of the FA is to enhance the RRT algorithm by providing an optimized exploration of the search space, ultimately leading to optimizing the path found by the RRT algorithm and better paths …

First-Principles Study Of Ferroelectric Properties And Co2 Reduction Reaction Capabilities In Two-Dimensional Monolayers And Heterostructures, Mo Li

Dissertations

Two-dimensional (2D) materials hold significant potential for CO₂ reduction reactions (CO₂RR) due to their high surface-to-volume ratio. However, achieving high selectivity for desired products and overcoming limitations posed by scaling relationships remain challenging. Recent studies suggest that ferroelectric (FE) materials with switchable out-of-plane polarization (OOP) can effectively tune the adsorption behavior, thermodynamics, and kinetics of CO₂RR, offering promising solutions to these challenges. Using density functional theory (DFT) and the Berry phase approach, this work expands the family of 2D ferroelectrics by theoretically identifying Y₂CO₂, Y₂CS₂, and Sc …

Determination Of Electrochemical Parameters For Predicting Reaction Mechanism And Algorithmic Approaches To Pain Assessment, Huize Xue

Dissertations

This dissertation introduces novel advancements in electrochemical kinetics and pain assessment, structured into two main parts. The first part focuses on the comprehensive analysis of the kinetic and mechanistic aspects of electrochemical reactions, utilizing a combination of experimental techniques and simulation methods. A new software tool, Envismetrics, was developed using Python to facilitate the analysis of complex electrochemical data, including cyclic voltammetry (CV), chronoamperometry (CA), and hydrodynamic voltammetry (HDV). The software was rigorously tested and validated with well-characterized redox systems such as the ferricyanide/ferrocyanide couple, dimethylamine borane (DMAB), and Per- and Polyfluoroalkyl Substances (PFAS). It was successfully used to determine …

Single-Step Synthesis Of Activated Carbon From Arabica Spent Coffee Ground Using K2co3 As Activator Agent, Ghina Ivana Mieldan, Yuliusman Yuliusman

Journal of Materials Exploration and Findings

Activated carbon is a nanomaterial that is often used as an effective adsorbent. Activated carbon raw materials can use biomass, such as coffee grounds, which can be found along with the growth of public interest in coffee drinks. Chemical activators are used for activation to increase biomass carbon's adsorption capacity. Using K₂CO₃ activator to increase the specific surface area of activated carbon is more harmless than KOH. The use of spent coffee grounds as carbon source and food additive K₂CO₃ as an activator can make food-grade activated carbon that can be used for food. …

Effect Of Alkyd And Polyester Resin Compositions On Corrosion Resistance, Blistering, And Adhesion In Utilization Of Oily Sludge As Anti-Rust Coating Material, Gerets Land Kakalang, Yohanes David Kristianto, Johny Wahyuadi Mudaryoto

Journal of Materials Exploration and Findings

Oil sludge is a waste derived from upstream and downstream activities of the oil and gas industry which is estimated at 10,000 tonnes generated from all PERTAMINA downstream activities spread across various fields, processing units and depots throughout Indonesia. Oil sludge has the same characteristics as asphalt, where asphalt in previous studies can be used as an anti-rust coating, so that the handling of oily sludge can be topped up by reusing and having its own added value. The purpose of this research is to utilise waste oily sludge as an alternative anti-rust coating material and compare alkyd resin and …

A Comparative Study Of Conventional And Statistically Active Corrosion Methods For Corrosion Growth Assessment Of A 24-Inch Gas Pipeline, Rudi Rinaldi, Jaka Fajar Fatriansyah

Journal of Materials Exploration and Findings

Component failures in oil and gas pipelines can have fatal consequences, leading to operational downtimes and environmental damage. Knowledge of the corrosion growth rate is fundamental to pipeline integrity management, as it is essential for risk assessment and decisions related to asset management. This article aimed to compare two approaches for the corrosion growth estimation of the 24-inch offshore gas pipeline: the conventional method versus the Statistically Active Corrosion (SAC) method. This article is based on the in-line inspection (ILI) results of two consecutive assessments from 2020 to 2023 of the entire 73 km of the pipeline. The results show …

Developing A Roadmap For Green Port In Timor-Leste, Emanuel Da Silva Maia

World Maritime University Dissertations

No abstract provided.

Automated Segmentation Of The Ulnar Nerve In Mri Using Deep Learning Techniques, Akhil Nagulapalli

Theses

Cubital Tunnel Syndrome (CuTS), a condition caused by compression of the ulnar nerve, results in numbness, tingling, pain, and even muscle atrophy, affecting fine motor skills and diminishing patient quality of life. Accurate diagnosis of CuTS is challenging, as current diagnostic methods—including clinical exams, nerve conduction studies, and unaided MRI—often lack the precision to reliably identify the nerve and detect compression in its early stages. Deep learning-based segmentation offers a promising solution, enabling precise and automated identification of nerve structures in MRI images, which could significantly improve diagnostic accuracy and support timely intervention.

A novel deep learning model for segmenting …

Adopting Association Rule Learning For Detecting And Ranking Influential Users On Hashtags, Layal Abu Daher, Islam Elkabani, Rashed Zantout

BAU Journal - Science and Technology

Social Media platforms are networks of relationships in which users are considered as nodes in a graph and relationships between users are considered edges. These graphs symbolize online groups or communities. It has been a challenge for researchers to study the evolution of such communities due to their rapid and dynamic development. The contribution of our work is twofold: (i) studying the effectiveness of a set of Direct and Indirect Influence Measures that identify the factors that urges users to participate in certain communities; (ii) proposing an Association Rule Mining technique to identify influential users on a dataset of a …

Cropsync: Ai-Powered Sustainable Crop Management, Ziad Doughan, Ibrahim Mneimneh, Zouheir Nakouzi, Noor Al Khaib, Samer Damaj, Jamal Chaaban, Hamza Mrad, Sari Itani

BAU Journal - Science and Technology

CropSync is a smart agriculture system that uses AI and IoT technologies to enable sustain- able crop management and precision farming. The system aims to address the challenges faced by the agriculture sector, such as increasing food production to meet global population demands while minimizing environmental impact. CropSync integrates sensors, cameras, and cloud-based analytics to provide farmers with real-time insights and recommendations for optimizing crop cul- tivation. The system upholds engineering professional and ethical standards, considering broader social, environmental, and economic implications. From a social perspective, CropSync improves food security and enhances farmers’ livelihoods through increased productivity and efficient re- …

Designed For Limb Loss: An Artificial Intelligence-Based Bionic Prosthetic Arm Transition From Simulation To Reality, Amira J. Zaylaa Dr., Imane Haidar, Ziad Doughan, Mohammad Moumnieh, Hamza Mrad, Ali M. Haidar

BAU Journal - Science and Technology

Limb or organ loss of human remains challenging especially in the world of incessant reliance on touch-based screens and tasks. Thereby, patients can barely withstand and cope with the augmenting restrictions that they encounter due to this loss. Modern means and technologies, such as advanced and artificial parts reduced restrictions on patients with disabilities or a lost limb or organ. Hand prostheses, for instance, provided a powerful tool for improving the functional capabilities of human limbs, thereby improving the quality of life of the user. However, patients using prosthetic arms are still encountering numerous problems, such as, suffering from intact …

All-Solid-State Sodium-Ion Batteries: A Leading Contender In The Next-Generation Battery Race, Rui-Jie Zhu, Ze-Chen Li, Wei Zhang, Akira Nasu, Hiroaki Kobayashi, Masaki Matsui

Journal of Electrochemistry

All-solid-state lithium-ion batteries (LIBs) using ceramic electrolytes are considered the ideal form of rechargeable batteries due to their high energy density and safety. However, in the pursuit of all-solid-state LIBs, the issue of lithium resource availability is selectively overlooked. Considering that the amount of lithium required for all-solid-state LIBs is not sustainable with current lithium resources, another system that also offers the dual advantages of high energy density and safety— all-solid-state sodium-ion batteries (SIBs) —holds significant sustainable advantages and is likely to be the strong contender in the competition for developing next-generation high-energy-density batteries. This article briefly introduces the research …

Contributions To Journal Of Electrochemistry, The Flagship Journal Of Chinese Chemical Society Electrochemical Committee, Will Be Listed As A New Recommendation Policy For China Youth Awards Of Electrochemist, Editorial Office Of J.Electrochem.

Journal of Electrochemistry

No abstract provided.

Dendrite-Free Strategies For Aqueous Zinc-Ion Batteries: Structure, Electrolyte, And Separator, Gang Wu, Wu-Hai Yang, Yang Yang, Hui-Jun Yang

Journal of Electrochemistry

Continued growth in energy demand and increased environmental pollution constitute major challenges that need to be addressed urgently. The development and utilization of renewable, sustainable, and clean energy sources, such as wind and solar, are crucial. However, the instability of these intermittent energy sources makes the need for energy storage systems increasingly urgent. Aqueous zinc-ion batteries (AZIBs) have received widespread attention due to their unique advantages, such as high energy density, cost-effectiveness, environmental friendliness, and safety. However, AZIBs face significant challenges, mainly the formation of zinc dendrites that seriously affect the stability and lifetime of the batteries, leading to battery …

Electrochemical-Method-Induced Strong Metal-Support Interaction In Pt-Cnt@Sno2 For Co-Tolerant Hydrogen Oxidation Reaction, Shen-Zhou Li, Zi-Jie Lin, Qi-An Chen, Zhao Cai, Qing Li

Journal of Electrochemistry

Inducing the classic strong metal-support interaction (SMSI) is an effective approach to enhance the performance of supported metal catalysts by encapsulating the metal nanoparticles (NPs) with supports. Conventional thermal reduction method for inducing SMSI processes is often accompanied by undesirable structural evolution of metal NPs. In this study, a mild electrochemical method has been developed as a new approach to induce SMSI, using the cable structured core@shell CNT@SnO₂ loaded Pt NPs as a proof of concept. The induced SnO_x encapsulation layer on the surface of Pt NPs can protect Pt NPs from the poisoned of CO impurity in …

Mechanisms And Influential Factors Of Rock Bursts In Tunneling Roadways Of Extra-Thick Coal Seams, Peng Yujie, Wang Qiang, Cao Anye, Xue Chengchun, Lyu Guowei, Hao Qi, Liu Yaoqi, Bai Xianxi, Li Dong

Coal Geology & Exploration

Objective Rock bursts occur frequently in tunneling roadways of extra-thick coal seams, significantly constraining safe coal mining. Investigating their mechanisms and influential factors is crucial to preventing and controlling such disasters in tunneling roadways of extra-thick coal seams. Methods Focusing on the tunneling roadway of mining face 250107-1 for an extra-thick coal seam within a coal mine in Gansu Province, this study established the discriminant indices for rock burst-induced roadway instability. By simulating the energy distribution patterns of surrounding rocks in the tunneling roadway using the FLAC^3D software, this study revealed the zonal energy characteristics of the surrounding rocks …