Journal Acknowledgements And Editorial Board, Academy Editors

Journal of the Arkansas Academy of Science

No abstract provided.

Characterizing Variability In The Transient Storage Zones Of Miller Run In Lewisburg, Pa, Sabrina Savidge

Master’s Theses

The transient storage zone processes are investigated in a small second order stream with a 2.2 square kilometer watershed. The presence of transient storage zones in small streams impacts the available flow paths for water and results in a wider range of residence times for water and dissolved chemicals than would be predicted by considering only the main channel flow path. Residence times can be used to quantify the health of a stream as several biogeochemical and ecological processes occur in water slowed by transient storage.

Sections of the studied stream are impacted by varying types of stream restoration practices …

Genetic Algorithm For Solving A Just-In-Time Inventory Model With Imperfect Rework Implemented In A Serial Multi-Echelon System, Hsien-Chung Tsao, Cheng-Chi Chung, Hsuan-Shih Lee, Chih-Ping Lin, Yan-Yun Tu, Ssu-Chi Lin

Journal of Marine Science and Technology–Taiwan

As global industrial competition intensifies, enterprises can achieve substantial competitive advantages in the supply chain management environment by promptly meeting customer demands and efficiently reducing both supply and demand costs. This paper proposes an inventory model for supply chain optimization that considers uncertain delivery lead times and defective products. Solving the model requires solving a nonlinear mixed-integer problem, which traditionally requires considerable time. Solutions to nondeterministic polynomial-time hard problems with high complexity and difficulty are often obtained using heuristic algorithms. Among these algorithms, genetic algorithms have high efficiency and quality. Therefore, we employed a genetic algorithm to solve the proposed …

An Mrf Model-Based Study On The Effect Of Fishes Upon The Characteristics Of Flow Field Of An Aquaculture Tank, Xian-Ying Shi, Zheng-Zheng Huang, Meng Li, Yinxin Zhou, Xiao-Zhong Ren, Hang-Fei Liu, Shu-Peng Du

Journal of Marine Science and Technology–Taiwan

The study aims to investigate the effect of fish movement on the flow field in the aquaculture tank of a recirculating water aquaculture system. Herein, based on the Navier-Stokes equations and the RNG k-ε turbulence model, the flow field in the aquaculture tank with fish movement was numerically simulated using the multiple reference frame (MRF) model and compared with the numerical simulation results of the fishless aquaculture tank. The results revealed that the overall mean flow velocity in the tank decreased significantly when the fish swam counter-currently with a fixed trajectory, and the overall mean flow velocity increased slightly when …

Occurrence Of Predation Of Juvenile Chum Salmon (Oncorhynchus Keta) By Chub Mackerel (Scomber Japonicus) And Spotted Mackerel (Scomber Australasicus) In Miyako Bay, Iwate Prefecture, Japan, Kei Sasaki, Miwa Yatsuya, Daisuke Shimizu, Daisuke Ojima, Shinji Komatsu

Journal of Marine Science and Technology–Taiwan

In recent years, chub mackerel (Scomber japonicus) and spotted mackerel (Scomber australasicus) have started migrating to the coastal areas off Iwate Prefecture earlier than they did previously, resulting in a geographic overlap between hatchery-bred juvenile chum salmon (Oncorhynchus keta) released at sea and the occurrence of predatory mackerel. To clarify whether the mackerels prey on the juvenile chum salmon, we captured adult mackerels by angling during the period when these species overlap in Miyako Bay, Iwate Prefecture. After capture, the stomach contents of the mackerel were examined using a combination of visual examinations and DNA metabarcoding analysis. As a result, …

Sodium Hyaluronate Based Drug Delivery To The Outer And Inner Ear, Emma Barrett-Catton

Graduate Student Theses, Dissertations, & Professional Papers

We have developed two projects using sodium hyaluronate (HA)-based systems for drug delivery to the ear. First, we investigated an HA-based hydrogel for use as a single use antibiotic treatment for otitis externa. Otitis externa, also known as outer ear infection, is a frequent affliction in both humans and animals. The most prevalent treatment for otitis externa is ear drops, but it is difficult to adhere properly to this treatment, causing poor patient compliance and the potential for complications. As a result, we have developed an HA-based hydrogel for use as a single application treatment for otitis externa to increase …

Machine Learning Models To Automate Radiotherapy Structure Name Standardization, Priyankar Bose

Theses and Dissertations

Structure name standardization is a critical problem in Radiotherapy planning systems to correctly identify the various Organs-at-Risk, Planning Target Volumes and `Other' organs for monitoring present and future medications. Physicians often label anatomical structure sets in Digital Imaging and Communications in Medicine (DICOM) images with nonstandard random names. Hence, the standardization of these names for the Organs at Risk (OARs), Planning Target Volumes (PTVs), and `Other' organs is a vital problem. Prior works considered traditional machine learning approaches on structure sets with moderate success. We compare both traditional methods and deep neural network-based approaches on the multimodal vision-language prostate cancer …

Evaluation Of Changes In Cellular Behavior Upon Exposure To Selected Analytes Used In Synthetic Applications, Olivia L. Rose

Graduate Theses, Dissertations, and Problem Reports (ETD)

Metal-organic frameworks (MOFs) are organic crystalline hybrids that have been used for a variety of applications from catalysis to gas storage and drug delivery. Studies have shown that generally, while such frameworks are more biocompatible than similar aspect ratio nanostructures, they are also more prone to transformation and disintegration due to their flexible and fragile nature as resulted from the structure assembly and implemented structure-function relationships. With extensive previous literature showing that materials with nano dimensions such as carbon nanotubes, quantum dots and other nanomaterials have unique physicochemical properties that lead to increased biosystems’ reactivity. With increasing concerns regarding adverse …

Exploring The Usage Of Acid Mine Drainage Sludge As A Soil Amendment For Reclaimed Mine Lands, Brady Watters

Graduate Theses, Dissertations, and Problem Reports (ETD)

Acid mine drainage (AMD) is a form of water pollution generated when water and oxygen come into contact with sulfide minerals, forming metal ions, sulfuric acid, and sulphates in solution. AMD is characterized by its toxic metals content and low pH, both of which are an environmental concern. The treatment of AMD separates the contaminants into a high-water content sludge. This sludge is a pure waste product under current evaluation for alternative uses as well as improvements to storage and treatment processes. This thesis focused on analyzing the potential for AMD sludge to be used in recently reclaimed lands as …

Selective Recovery Of Rare Earth Elements From Acid Mine Drainage Treatment Byproduct, Zeynep Cicek

Graduate Theses, Dissertations, and Problem Reports (ETD)

Rare earth elements (REEs) are critical in emerging clean energy, advanced technologies, and military defense applications due to their unique physical, chemical, optical, and luminescent properties. Given the potential supply chain restrictions imposed by one country's dominance in the global REE market and the consistently growing demand for REEs, developing the US domestic REE supply chain is imperative. Thus, alternative sources are crucial to supply the demand and prevent interruption in manufacturing products that consume REEs. Concurrently, acid mine drainage (AMD) is a long-standing and widespread global challenge the mining industry encounters. AMD is generated in large volumes and continuously …

Longitudinal Oxygen Imaging In 3d (Bio)Printed Models, Ryan Curtis O'Connell

Graduate Theses, Dissertations, and Problem Reports (ETD)

Electron paramagnetic resonance (EPR), and its molecular imaging modality, is a powerful tool to noninvasively map various biological and chemical markers within objects of interest. Reliable data acquisition is a major impeding factor for longitudinal hands-off measurements. Measurements are especially challenging in biomedical applications, as live objects are not static. Frequent changes occur that require constant fine recalibration of the EPR detection system, called the resonator. To enable longitudinal imaging, a technology permitting automatic digital control of resonator coupling, tuning, and EPR data acquisition was developed. Automation was achieved through the utilization of a microcontroller and digital peripheral components such …

Localization Of People In Gnss-Denied Environments Using Neural-Inertial Prediction And Kalman Filter Correction, Lauren N. Cash

Graduate Theses, Dissertations, and Problem Reports (ETD)

This thesis presents a method based on neural networks and Kalman filters for estimating the position of a person carrying a mobile device (i.e., cell phone or tablet) that can communicate with static UWB sensors or is carried in an environment with known landmark positions. This device is used to collect and share inertial measurement unit (IMU) information — which includes data from sensors such as accelerometers, gyroscopes, and magnetometers — and UWB and landmark information. The collected data, in combination with other necessary initial condition information, is input into a pre-trained deep neural network (DNN) which predicts the movement …

Feasibility Of Applying Motion Magnification In Subsurface Defect Detection For Concrete And Fiber-Reinforced Polymer Specimens, Nagavardhani Malineni

Graduate Theses, Dissertations, and Problem Reports (ETD)

Irrespective of size and complexity, every civil infrastructure needs certain scrutiny regarding its structural health to ensure its serviceability during its lifetime. In olden times such scrutiny was done with the aid of destructive testing methods using sensors that required cumbersome and expensive installations and led to the destruction of at least part of the tested specimen. However, in recent times, many non-destructive methods, such as acoustic emission testing, electromagnetic testing, and laser testing methods have emerged, leaving the specified tested specimen undisturbed. With the advancement in sensor technology like motion magnification and with the help of access to high-speed …

State Estimation, Covariance Estimation, And Economic Optimization Of Semi-Batch Bioprocesses, Ronald Hunter Alexander

Graduate Theses, Dissertations, and Problem Reports (ETD)

One of the most critical aspects of any chemical process engineer is the ability to gather, analyze, and trust incoming process data as it is often required in control and process monitoring applications. In real processes, online data can be unreliable due to factors such as poor tuning, calibration drift, or mechanical drift. Outside of these sources of noise, it may not be economically viable to directly measure all process states of interest (e.g., component concentrations). While process models can help validate incoming process data, models are often subject to plant-model mismatches, unmodeled disturbances, or lack enough detail to track …

3d Printed Microfluidic Devices For Advanced Fluid Manipulation In Biomedical Applications, Kathrine Curtin

Graduate Theses, Dissertations, and Problem Reports (ETD)

Microfluidics are valuable devices in the biomedical field because of their remarkable ability to manipulate small volumes of fluid and facilitate reactions. Microfluidics have several significant advantages, including their portability, low cost, fast reaction times, and high surface area-to-volume ratios. Microfluidics be used for a multitude of applications, including bioanalysis, cellular assays, organ-on-a-chip, and separations. Microfluidics have great potential to overcome many challenges in conventional chemical and biological applications. However, complex fabrication procedures in silicon, glass, thermoplastics, and (polydimethylsiloxane) PDMS devices make rapidly optimizing and prototyping devices challenging. Microfluidic fabrication techniques have been a long-standing challenge in successfully translating these …

Machine Learning For Biosensors, Gayathri Anapanani

Graduate Theses, Dissertations, and Problem Reports (ETD)

Biosensors have become increasingly popular as diagnostic tools due to their ability to detect and quantify biological analytes in a wide range of applications. With the growing demand for faster and more reliable biosensing devices, machine learning has become a valuable tool in enhancing biosensor performance. In this report, we review recent progress in the application of machine learning to biosensors. We discuss the potential benefits of using machine learning in biosensors, including improved sensitivity, selectivity, and accuracy. We also discuss the various machine learning techniques that have been applied to biosensors, including data preprocessing, feature extraction, and classification and …

Missile Modeling And Simulation Of Nominal And Abnormal Scenarios Resulting From External Damage, James Manuel Floyd Iii

Graduate Theses, Dissertations, and Problem Reports (ETD)

This thesis presents the development of a six-degree-of-freedom flight simulation environment for missiles and the application thereof to investigate the flight performance of missiles when exposed to external damage. The simulation environment was designed to provide a realistic representation of missile flight dynamics including aerodynamic effects, flight control systems, and self-guidance. The simulation environment was designed to be modular, expandable, and include realistic models of external damage to the missile body obtained by adversarial counteraction.

The primary objective of this research was to examine missile flight performance when subjected to unspecified external damage, including changes in trajectory, stability, and controllability, …

Optimal Path Planning For Aerial Robots Using Genetic Algorithm, Anna Puigvert I Juan

Graduate Theses, Dissertations, and Problem Reports (ETD)

This thesis presents a path optimization solution for a robot in two different constrained 3-dimensional (3D) environments. The robot is required to travel from its current position to a goal position following minimum cost paths (optimal paths). The first environment has 3D obstacles that interfere with the robot’s path. The path cost for this environment accounts for the minimum distance traveled by the robot from the start to the goal position while avoiding obstacles. The second environment is the atmosphere of Venus, specifically a flyable region of this atmosphere with characteristics similar to Earth’s. This environment has strong westward winds …

Computational Mechanisms Of Face Perception, Jinge Wang

Graduate Theses, Dissertations, and Problem Reports (ETD)

The intertwined history of artificial intelligence and neuroscience has significantly impacted their development, with AI arising from and evolving alongside neuroscience. The remarkable performance of deep learning has inspired neuroscientists to investigate and utilize artificial neural networks as computational models to address biological issues. Studying the brain and its operational mechanisms can greatly enhance our understanding of neural networks, which has crucial implications for developing efficient AI algorithms. Many of the advanced perceptual and cognitive skills of biological systems are now possible to achieve through artificial intelligence systems, which is transforming our knowledge of brain function. Thus, the need for …

Structural Health Monitoring Using Machine Learning And Synthetic Data, Michail Tzimas

Graduate Theses, Dissertations, and Problem Reports (ETD)

Structural health monitoring spans many decades of research across multiple engineering fields. However, typical monitoring processes for damage detection of complex structures usually prohibit real-time or fast detection of debilitating damage to the structure. One of the major issues of real-time detection of damage is the enormity of data that needs to be processed, which is worsened by the relative inability of fast relaying of data to structural engineers. With the rapid advancement of Machine Learning, both issues can be overcome, and detection of failure is achieved with non-invasive techniques. This dissertation explores the applicability of Machine Learning as a …

Weigh-In-Motion Data-Driven Pavement Performance Prediction Models, Mohhammad Afsar Sujon

Graduate Theses, Dissertations, and Problem Reports (ETD)

The effective functioning of pavements as a critical component of the transportation system necessitates the implementation of ongoing maintenance programs to safeguard this significant and valuable infrastructure and guarantee its optimal performance. The maintenance, rehabilitation, and reconstruction (MRR) program of the pavement structure is dependent on a multidimensional decision-making process, which considers the existing pavement structural condition and the anticipated future performance. Pavement Performance Prediction Models (PPPMs) have become indispensable tools for the efficient implementation of the MRR program and the minimization of associated costs by providing precise predictions of distress and roughness based on inventory and monitoring data concerning …

A Theoretical And Computational Model For Aquaponics Systems, Isabella M. Digiulio

Honors Theses

Aquaponics, a sustainable farming technique combining the principles of aquaculture and hydroponics, suffers from uncertainty in its optimal operating conditions, preventing large-scale adoption of this technology. Relying on experimentation or full-scale testing to address these uncertainties is an approach that is material, labor, time, and cost intensive. As a result, aquaponics would benefit from a complete computational model of the system. This would allow for information to be gained without long and expensive experiments, but rather with knowledge of system operations and a modeling software package. In this study, the Activated Sludge Model 1 (ASM1) matrix framework, traditionally used in …

Cellulose Nanofiber/Poly(Acrylic Acid)-Based Hydrogel For Colorimetric Biomarker Sensors, Nichaphat Passornraprasit

Chulalongkorn University Theses and Dissertations (Chula ETD)

This dissertation develops innovative CNF/PAA hydrogel-based colorimetric sensors for non-invasive biomarker detection, focusing on urea for chronic kidney disease (CKD) and sarcosine for prostate cancer (PCa). Hydrogels serve as a matrix for enzyme and indicator encapsulation due to their high absorption capacity and unique three-dimensional network, with CNF enhancing mechanical properties. The research is divided into two parts. Part I involves preparing a GO/CNF/PAA hydrogel via non-toxic radiation crosslinking, featuring high swelling capacity and enhanced laser absorptivity due to GO. This hydrogel, used as a patch for on-skin detection, shows vivid color changes to distinguish CKD patients from healthy individuals, …

Schizo-Net: A Novel Schizophrenia Diagnosis Framework Using Late Fusion Multimodal Deep Learning On Electroencephalogram-Based Brain Connectivity Indices, Nitin Grover, Aviral Chharia, Rahul Upadhyay, Luca Longo

Articles

Schizophrenia (SCZ) is a serious mental condition that causes hallucinations, delusions, and disordered thinking. Traditionally, SCZ diagnosis involves the subject’s interview by a skilled psychiatrist. The process needs time and is bound to human errors and bias. Recently, brain connectivity indices have been used in a few pattern recognition methods to discriminate neuro-psychiatric patients from healthy subjects. The study presents Schizo-Net , a novel, highly accurate, and reliable SCZ diagnosis model based on a late multimodal fusion of estimated brain connectivity indices from EEG activity. First, the raw EEG activity is pre-processed exhaustively to remove unwanted artifacts. Next, six brain …

How Visual Stimuli Evoked P300 Is Transforming The Brain–Computer Interface Landscape: A Prisma Compliant Systematic Review, Jai Kalra, Prashasti Mittal, Nirmiti Mittal, Abhishek Arora, Utkarsh Tewari, Aviral Chharia, Rahul Upadhyay, Vinay Kumar, Luca Longo

Articles

Non-invasive Visual Stimuli evoked-EEGbased P300 BCIs have gained immense attention in recent years due to their ability to help patients with disability using BCI-controlled assistive devices and applications. In addition to the medical field, P300 BCI has applications in entertainment, robotics, and education. The current article systematically reviews 147 articles that were published between 2006-2021*. Articles that pass the pre-defined criteria are included in the study. Further, classification based on their primary focus, including article orientation, participants’ age groups, tasks given, databases, the EEG devices used in the studies, classification models, and application domain, is performed. The application-based classification considers …

Controllability-Constrained Deep Neural Network Models For Enhanced Control Of Dynamical Systems, Suruchi Sharma

Master's Theses

Control of a dynamical system without the knowledge of dynamics is an important and challenging task. Modern machine learning approaches, such as deep neural networks (DNNs), allow for the estimation of a dynamics model from control inputs and corresponding state observation outputs. Such data-driven models are often utilized for the derivation of model-based controllers. However, in general, there are no guarantees that a model represented by DNNs will be controllable according to the formal control-theoretical meaning of controllability, which is crucial for the design of effective controllers. This often precludes the use of DNN-estimated models in applications, where formal controllability …

Intrinsic Motivation By The Principles Of Non-Linear Dynamical Systems, Phu C. Nguyen

Master's Theses

The design of appropriate control rules for the stabilization of dynamical systems can require quite substantial domain knowledge. Modern AI methodologies, such as Reinforcement Learning, are often used to mitigate the need for such knowledge. However, these can be slow and often rely on at least some hand-designed reward structure, and thus human input, to be more effective. Here, we propose an alternative route to construct rewards requiring only minimal domain knowledge, essentially relying on the structure of the dynamical system itself. For this, we use truncated Lyapunov exponents as rewards to calculate the stabilizing controller from samples. Concretely, the …

Micro-Scale Laser-Induced Fluorescence Thermometry For Multiphase Flow In Porous Media, Samuel J. Simmons

Master's Theses

In this thesis the use of Laser-Induced Fluorescence (LIF) thermometry was evaluated as a temperature measurement for multiphase flow in porous media. This optical temperature measurement technique utilizes the temperature-dependent emissive properties of fluorescent dyes to measure temperature. This research evaluates the accuracy, spatial resolution, and temporal resolution of LIF thermometry compared to existing temperature measurements. In this research water-soluble and oil-soluble fluorescent dyes are evaluated in terms of their temperature sensitivity. The ability of these dyes to measure temperature is compared to an Ansys FEA simulation of a fixed temperature gradient. For multiphase flow, the fluorescent dyes were both …

Exploring Ph Gradient Phenomena In Non-Linear Electrokinetic Microfluidic Devices, Azade Tahmasebi

Dissertations, Master's Theses and Master's Reports

Electrokinetic microfluidics is a versatile technology utilized within lab on a chip (LOC) devices for diagnostic and analytical applications; advantages include reduced resource demands, flexibility, and simplicity of use. Dielectrophoresis (DEP) is a precision nonlinear electrokinetic tool utilized within microfluidic microdevices to induce polarization and control bioparticle motions for applications that range from hemoglobin separations to cancer cell isolation and detection. Despite promising results, undesired side phenomena can occur in electrokinetic systems which impede reproducibility and accuracy. These unfavorable phenomena have not been comprehensively explored in the literature. Prior preliminary research suggests the fundamental phenomena originate from microelectrodes utilized in …

Neuromorphic Computing Applications In Robotics, Noah Zins

Dissertations, Master's Theses and Master's Reports

Deep learning achieves remarkable success through training using massively labeled datasets. However, the high demands on the datasets impede the feasibility of deep learning in edge computing scenarios and suffer from the data scarcity issue. Rather than relying on labeled data, animals learn by interacting with their surroundings and memorizing the relationships between events and objects. This learning paradigm is referred to as associative learning. The successful implementation of associative learning imitates self-learning schemes analogous to animals which resolve the challenges of deep learning. Current state-of-the-art implementations of associative memory are limited to simulations with small-scale and offline paradigms. Thus, …