Physical Sciences and Mathematics Commons^™

Advanced Caching And Streaming For Large Scale Point Cloud Data Visualization On The Web, Pravin Poudel

All Graduate Theses and Dissertations, Fall 2023 to Present

Point clouds are widely used in various applications such as 3D modeling, geospatial analysis, robotics, and more. One of the key advantages of 3D point cloud data is that, unlike other data formats like texture, it is independent of viewing angle, surface type, and parameterization. Since each point in the point cloud is independent of the other, it makes it the most suitable source of data for tasks like object recognition, scene segmentation, and reconstruction. Point clouds are complex and verbose due to the numerous attributes they contain, many of which may not be always necessary for rendering, making retrieving …

Computational Study About Noncovalent Bonding Systems Involving Halogen, Chalcogen And Pnicogen Bonds, Jia Lu

All Graduate Theses and Dissertations, Fall 2023 to Present

First terms used in this thesis are introduced and defined as follows. In the periodic table, the elements in the 17^th column are named halogen including fluorine (F), chlorine (Cl), bromine (Br) and iodine (I). The elements in the 16^th column are named chalcogen including oxygen (O), sulfur (S), selenium (Se) and tellurium (Te). The elements in the 15^th column are named pnicogen including nitrogen (N), phosphorus (P), arsenic (As) and antimony (Sb).

After hydrogen bonds (B-H⋅⋅⋅B) are well studied and understood by scientists and researchers, halogen bonds (R-X⋅⋅⋅B) have drawn attention due to the similarities in …

Analysis Of Student Behavior And Score Prediction In Assistments Online Learning, Aswani Yaramala

All Graduate Theses and Dissertations, Fall 2023 to Present

Understanding and analyzing student behavior is paramount in enhancing online learning, and this thesis delves into the subject by presenting an in-depth analysis of student behavior and score prediction in the ASSISTments online learning platform. We used data from the EDM Cup 2023 Kaggle Competition to answer four key questions. First, we explored how students seeking hints and explanations affect their performance in assignments, shedding light on the role of guidance in learning. Second, we looked at the connection between students mastering specific skills and their performance in related assignments, giving insights into the effectiveness of curriculum alignment. Third, we …

Designing And Studying Redox-Active Molecules For Energy Storage And Desalination, Wenda Wu

All Graduate Theses and Dissertations, Fall 2023 to Present

Since the fast growth of the global population, energy scarcity has become a new threat to modern society. Those commonly seen resources like fossil, coal, and natural gas are nonrenewable energies, which cannot be replenished by human-scale time. In contrast, wind, solar, and hydropower are the three main renewable green energies under development with great efforts around the world. However, the intermittent character of these powers raises a new question: how to store them properly on large scales? By 2021, more than 90% of the electricity storage was taken by Lithium-ion batteries (LIBs). The usage of lithium brings concerns about …

Tropical Atlantic Temperature And Hydrologic Shifts During The Paleocene-Eocene Thermal Maximum, Chels Howard

All Graduate Theses and Dissertations, Fall 2023 to Present

The Paleocene-Eocene Thermal Maximum (PETM) is a geologic climate event that is studied heavily due to the similarities to anthropogenic climate change. This event occurred approximately 56 million years ago (Ma) and is characterized by a rapid release of carbon into the atmosphere, which resulted in an increase in temperature and a change in global climate events such as changes in patterns of evaporation and precipitation. We see this effect occurring in modern anthropogenic climate change due to the burning of fossil fuels releasing carbon dioxide (CO₂) into the atmosphere. In this study I use elemental data of …

Fire And Flow: Assessing The Long-Term Effects Of Wildfires And Impact Of High Flow Events On Phosphorus Concentrations In Mountain Streams, Rachel Watts

All Graduate Theses and Dissertations, Fall 2023 to Present

Climate change has led to significant shifts in the Earth’s weather patterns, often leading to longer, more intense droughts, irregular but extreme storms, and more severe wildfires with longer burn durations. These weather pattern changes have frequently led to shifts in ecosystem dynamics, impacting aspects such as nutrient flux, species diversity, and overall habitat health. Regarding nutrient flux specifically, changes in phosphorus (P) concentrations can negatively impact stream systems as elevated levels can lead to toxic algal blooms, which can cause habitat degradation, loss of usable recreational areas, and large fish kills. A common trigger of these P spikes is …

An Integrated Neutrosophic Approach For The Urban Energy Internet Assessment Under Sustainability Dimensions, Walaa M. Sabry

Neutrosophic Systems with Applications

The urgent issue of climate change has resulted in an increasing preoccupation with the transition to low-carbon energy. The urban energy internet (UEI) enables the efficient utilization of renewable energy via the integration of contemporary energy grids, smart energy services, and cyber-physical systems. Consequently, the assessment of urban energy is vital for the construction of low-carbon cities. This study intends to provide an integrated decision-making approach for addressing assessment challenges related to UEI, with a focus on sustainability. The introduced approach adopts the opinions of three experts to express their opinions through semantic terms in evaluating sustainability factors and evaluating …

An Exhaustive Review Of Neutrosophic Logic In Addressing Image Processing Issues, Samia Mandour

Neutrosophic Systems with Applications

Since the importance of images in our lives and the advancements in computer data gathering methods, anyone can collect a large number of images, but most of them cannot be processed manually. Image processing therefore becomes appealing since various types of data may be represented and processed digitally. Image processing has become the most popular processing method, employed in security camera films, healthcare images, images from remote sensors, and naturalistic image/videos because of fast computers and processors. In order to raise cognitive function and speed up decision-making, image processing is crucial to many information access systems. Since ambiguity now permeates …

Exposure To Climate Change Information Predicts Public Support For Solar Geoengineering In Singapore And The United States, Sonny Rosenthal, Peter J. Irvine, Christopher L. Cummings, Shirley S. Ho

Research Collection College of Integrative Studies

Solar geoengineering is a controversial climate policy measure that could lower global temperature by increasing the amount of light reflected by the Earth. As scientists and policymakers increasingly consider this idea, an understanding of the level and drivers of public support for its research and potential deployment will be key. This study focuses on the role of climate change information in public support for research and deployment of stratospheric aerosol injection (SAI) in Singapore (n = 503) and the United States (n = 505). Findings were consistent with the idea that exposure to information underlies support for research and deployment. …

Forecasting Traffic Speed During Daytime From Google Street View Images Using Deep Learning, Junfeng Jiao, Huihai Wang

Research Collection College of Integrative Studies

Traffic forecasting plays an important role in urban planning. Deep learning methods outperform traditional traffic flow forecasting models because of their ability to capture spatiotemporal characteristics of traffic conditions. However, these methods require high-quality historical traffic data, which can be both difficult to acquire and non-comprehensive, making it hard to predict traffic flows at the city scale. To resolve this problem, we implemented a deep learning method, SceneGCN, to forecast traffic speed at the city scale. The model involves two steps: firstly, scene features are extracted from Google Street View (GSV) images for each road segment using pretrained Resnet18 models. …

Multivariate Analysis Approach To Factor-Affected Tuberculosis Disease, Zuli Agustina Gultom, Farid Akbar Siregar, Mahardika Abdi Prawira Tanjung, Al-Hamidy Hazidar

Knowledge Engineering and Data Science

Tuberculosis is a disease caused by infection with the mycobacterium tuberculosis complex. Tuberculosis attack organ besides the lung, such as the pleura, lining of the brain, lining of the heart, lymph gland, bones, joint, skin, intestines, kidney, urinary tract, and genital. This disease is found in densely populated settlements with poor sanitation, lack of ventilation and sunlight and lack of rest. Moreover, the factors that will be analyzed in this research are Population Density (X1), Number of HIV/AIDS (X2), number of toddlers who experience nutrition (X3), Number of toddlers who experience BCG immunization (X4), number of toddlers who get exclusive …

Systematic Literature Review On Ontology-Based Indonesian Question Answering System, Fadhila Tangguh Admojo, Adidah Lajis, Haidawati Nasir

Knowledge Engineering and Data Science

Question-Answering (QA) systems at the intersection of natural language processing, information retrieval, and knowledge representation aim to provide efficient responses to natural language queries. These systems have seen extensive development in English and languages like Indonesian present unique challenges and opportunities. This literature review paper delves into the state of ontology-based Indonesian QA systems, highlighting critical challenges. The first challenge lies in sentence understanding, variations, and complexity. Most systems rely on syntactic analysis and struggle to grasp sentence semantics. Complex sentences, especially in Indonesian, pose difficulties in parsing, semantic interpretation, and knowledge extraction. Addressing these linguistic intricacies is pivotal for …

Evidence Of Students’ Academic Performance At The Federal College Of Education Asaba Nigeria: Mining Education Data, Arnold Adimabua Ojugo, Christopher Chukwufunaya Odiakaose, Frances Emordi, Rita Erhovwo Ako, Winifred Adigwe, Kizito Eluemonor Anazia, Victor Geteloma

Knowledge Engineering and Data Science

One main objective of higher education is to provide quality education to its students. One way to achieve the highest level of quality in the higher education system is by discovering knowledge for prediction regarding enrolment of students in a particular course, alienation of traditional classroom teaching model, detection of unfair means used in online examination, detection of abnormal values in the result sheets of the students, and prediction about students’ performance. The knowledge is hidden among the educational data set and is extractable through data mining techniques. The present paper is designed to justify the capabilities of data mining …

Eeg Classification While Listening To Murottal Al-Quran And Classical Music Using Random Forest Method, Heni Sumarti, Fahira Septiani, Agus Sudarmanto, Wahyu Caesarendra, Rizki Edmi Edison

Knowledge Engineering and Data Science

This study is aimed to classify the brain activity of adolescents associated with audio stimuli; murottal Al-Quran and classical music. The raw data were filtered using Independent Component Analisys (ICA) and followed by band-pass filter in Python on the Google Colab Extraction was processed with Power Spectral Density (PSD) and the Random Forest Method in Weka Machine Learning was used for classification. The research results showed the same results between the two types of stimulation, namely the order of brain waves from highest to lowest were delta, alpha, theta and beta. The average brain waves of teenagers when given murottal …

Deep Learning Approaches With Optimum Alpha For Energy Usage Forecasting, Aji Prasetya Wibawa, Agung Bella Putra Utama, Ade Kurnia Ganesh Akbari, Akhmad Fanny Fadhilla, Alfiansyah Putra Pertama Triono, Andien Khansa’A Iffat Paramarta, Faradini Usha Setyaputri, Leonel Hernandez

Knowledge Engineering and Data Science

Energy use is an essential aspect of many human activities, from individual to industrial scale. However, increasing global energy demand and the challenges posed by environmental change make understanding energy use patterns crucial. Accurate predictions of future energy consumption can greatly influence decision-making, supply-demand stability and energy efficiency. Energy use data often exhibits time-series patterns, which creates complexity in forecasting. To address this complexity, this research utilizes Deep Learning (DL), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU) models. The main objective is to improve the accuracy of …

Recurrent Session Approach To Generative Association Rule Based Recommendation, Tubagus Arief Armanda, Ire Puspa Wardhani, Tubagus M. Akhriza, Tubagus M. Adrie Admira

Knowledge Engineering and Data Science

This article introduces a generative association rule (AR)-based recommendation system (RS) using a recurrent neural network approach implemented when a user searches for an item in a browsing session. It is proposed to overcome the limitations of the traditional AR-based RS which implements query-based sessions that are not adaptive to input series, thus failing to generate recommendations. The dataset used is accurate retail transaction data from online stores in Europe. The contribution of the proposed method is a next-item prediction model using LSTM, but what is trained to develop the model is an associative rule string, not a string of …

Comparison Of Machine Learning Algorithms For Species Family Classification Using Dna Barcode, Lala Septem Riza, M Ammar Fadhlur Rahman, Yudi Prasetyo, Muhammad Iqbal Zain, Herbert Siregar, Topik Hidayat, Khyrina Airin Fariza Abu Samah, Miftahurrahma Rosyda

Knowledge Engineering and Data Science

Classifying plant species within the Liliaceae and Amaryllidaceae families presents inherent challenges due to the complex genetic diversity and overlapping morphological traits among species. This study explores the difficulties in accurate classification by comparing 11 supervised learning algorithms applied to DNA barcode data, aiming to enhance the precision of species family classification in these taxonomically intricate plant families. The ribulose-1,5-bisphosphate carboxylase-oxygenase large sub-unit (rbcL) gene, selected as a DNA barcode locus for plants, is used to represent species within the Amaryllidaceae and Liliaceae families. The experimental results demonstrate that nearly all tested models achieve accurate species classification into the appropriate …

Insight Into The Binding Of Argon To Cyclic Water Clusters From Symmetry-Adapted Perturbation Theory, Carly A. Rock, Gregory S. Tschumper

Chemistry Faculty Research & Creative Works

This work systematically examines the interactions between a single argon atom and the edges and faces of cyclic H (Formula presented.) O clusters containing three–five water molecules (Ar(H (Formula presented.) O) (Formula presented.)). Full geometry optimizations and subsequent harmonic vibrational frequency computations were performed using MP2 with a triple- (Formula presented.) correlation consistent basis set augmented with diffuse functions on the heavy atoms (cc-pVTZ for H and aug-cc-pVTZ for O and Ar; denoted as haTZ). Optimized structures and harmonic vibrational frequencies were also obtained with the two-body–many-body (2b:Mb) and three-body–many-body (3b:Mb) techniques; here, high-level CCSD(T) computations capture up through the …

Rapid Reaction Studies On The Chemistry Of Flavin Oxidation In Urocanate Reductase, Niusha Delavari

Masters Theses

Urocanate reductase (UrdA) is a bacterial flavin-dependent enzyme that reduces urocanate to imidazole propionate, enabling bacteria to use urocanate as an alternative respiratory electron acceptor. Elevated serum levels of imidazole propionate are associated with the development of type 2 diabetes and, since UrdA is only present in humans in gut bacteria, this enzyme has emerged as a significant factor linking the health of the gut microbiome and the metabolic disorder insulin resistance. Here we investigated the chemistry of flavin oxidation by urocanate in the isolated FAD domain of UrdA (UrdA’) using anaerobic stopped-flow experiments at 4 °C. This analysis unveiled …

Estimating Ocean Observation Impacts On Coupled Atmosphere-Ocean Models Using Ensemble Forecast Sensitivity To Observation (Efso), Chu-Chun Chang, Tse-Chun Chen, Eugenia Kalnay, Cheng Da, Safa Mote

Mathematics and Statistics Faculty Publications and Presentations

Ensemble Forecast Sensitivity to Observation (EFSO) is a technique that can efficiently identify the beneficial/detrimental impacts of every observation in ensemble-based data assimilation (DA). While EFSO has been successfully employed on atmospheric DA, it has never been applied to ocean or coupled DA due to the lack of a suitable error norm for oceanic variables. This study introduces a new density-based error norm incorporating sea temperature and salinity forecast errors, making EFSO applicable to ocean DA for the first time. We implemented the oceanic EFSO on the CFSv2-LETKF and investigated the impact of ocean observations under a weakly coupled DA …

Development Of A Physics-Informed Neural Network For Prediction Of Blood Flow, Marcello Vittorio Mattei Di Eugenio

Theses and Dissertations

Abstract—Objective: We propose a new neural network architecture that accepts point clouds and outputs 3D velocity profiles for aneurysm geometries. Methods: We generated a synthetic aneurysm 3D flow dataset using CFD and used it to train our model architecture and compare it with other popular architectures like U-net and PointNet. We incorporate tools for improving model performance such as incorporating a distance function, a physics-informed loss to enforce the law of mass conservation, and the Huber loss to learn patterns across heterogeneous velocity components of multiple dimensions. Results: The tools implemented together with our architecture achieved the best performance on …

The Relationship Between Biofouling Adhesion And Frequency Of Mechanical Cleaning To Control The Recruitment To Clear Coatings, Dylan Thomas Eggers

Theses and Dissertations

There is a need to control biofouling on sensors and transparent windows in the marine environment. Clear silicone coatings offer a method to reduce the adhesion strength of marine organisms to the surface, however, these also require mechanical cleaning to maintain the surfaces free of fouling. This research evaluated the performance of eighty seven formulations and took the top two candidates to investigate a) the frequencies of wiping required by a brush to maintain two coatings and uncoated glass surfaces free of fouling and b) the effects of clear silicone coatings fouling release properties on required frequencies. The panels were …

Algorithm Selection Using Edge Ml And Case-Based Reasoning, Rahman Ali, Muhammad Sadiq Hassan Zada, Asad Masood Khatak, Jamil Hussain

All Works

In practical data mining, a wide range of classification algorithms is employed for prediction tasks. However, selecting the best algorithm poses a challenging task for machine learning practitioners and experts, primarily due to the inherent variability in the characteristics of classification problems, referred to as datasets, and the unpredictable performance of these algorithms. Dataset characteristics are quantified in terms of meta-features, while classifier performance is evaluated using various performance metrics. The assessment of classifiers through empirical methods across multiple classification datasets, while considering multiple performance metrics, presents a computationally expensive and time-consuming obstacle in the pursuit of selecting the optimal …

Predicting New Crescent Moon Visibility Applying Machine Learning Algorithms, Murad Al-Rajab, Samia Loucif, Yazan Al Risheh

All Works

The world's population is projected to grow 32% in the coming years, and the number of Muslims is expected to grow by 70%—from 1.8 billion in 2015 to about 3 billion in 2060. Hijri is the Islamic calendar, also known as the lunar Hijri calendar, which consists of 12 lunar months, and it is tied to the Moon phases where a new crescent Moon marks the beginning of each month. Muslims use the Hijri calendar to determine important dates and religious events such as Ramadan, Haj, Muharram, etc. Till today, there is no consensus on deciding on the beginning of …

Role Of Authentication Factors In Fin-Tech Mobile Transaction Security, Habib Ullah Khan, Muhammad Sohail, Shah Nazir, Tariq Hussain, Babar Shah, Farman Ali

All Works

Fin-Tech is the merging of finance and technology, to be considered a key term for technology-based financial operations and money transactions as far as Fin-Tech is concerned. In the massive field of business, mobile money transaction security is a great challenge for researchers. The user authentication schemes restrict the ability to enforce the authentication before the account can access and operate. Although authentication factors provide greater security than a simple static password, financial transactions have potential drawbacks because cybercrime expands the opportunities for fraudsters. The most common enterprise challenge is mobile-based user authentication during transactions, which addresses the security issues …

Patterns In Winter Stonefly Distribution Along A River Continuum And Land-Use Gradient In Northwest Arkansas Streams, Zachary Tipton

Graduate Theses and Dissertations

Freshwater ecosystems are facing a crisis with extinction rates of aquatic species exceeding those of their terrestrial counterparts by up to fivefold. This decline is predominantly attributed to evolving land use patterns within watersheds, leading to chemical and physical transformations in freshwater habitats. Northwest Arkansas (NWA) represents one of the fastest-growing regions in the United States, undergoing substantial shifts in land use. Consequently, the status of aquatic life in this region remains uncertain. Addressing this concern, the latest Arkansas Wildlife Action Plan emphasizes the necessity of distribution and population data to guide conservation efforts for Species of Greatest Conservation Need …

The Study Of The Polarization Domains Of Mbe-Grown Barium Titanate Thin Films And Nanodots Using Pfm, Mohammad Zamani-Alavijeh

Graduate Theses and Dissertations

Polarization domains are the fundamental elements in ferroelectric materials that contribute to their properties. A natural polarization domain, which possesses a specific polarization direction or state, typically ranges in size from one unit cell to a few micrometers. Therefore, studying polarization domains requires the fabrication of materials and the measurement of ferroelectric properties at the nanoscale. In this study, molecular beam epitaxy (MBE) is employed to fabricate barium titanate (BTO) crystals, and piezoresponse force microscopy (PFM) is used to measure the properties of polarization domains at the nanoscale. In the beginning, this study identifies the reflection of high-energy electron diffraction …

Modeling The Peak Ground Acceleration And Assessing The Economic Loss And Casualty Due To Potential Earthquakes Of Various Magnitudes In The New Madrid Seismic Zone, Mahmud Afroz

Graduate Theses and Dissertations

The New Madrid Seismic Zone (NMSZ) is one of the active tectonic zones capable of producing big earthquakes in the southcentral USA. The objective of this study was to assess the economic losses and casualties that might be caused by various magnitudes of seismic events in the NMSZ. The manifestation is the estimation for each magnitude which is using the Peak Ground Acceleration (PGA) that might occur during an earthquake. It is calculated using an attenuation function that correlates the intensity of local ground movement, the earthquake magnitude, and the distance from its epicenter. The assessment of casualties was conducted …

The Spatial And Temporal Distribution Of Large Rock Blocks And Control On Landscape Evolution In The Ozarks, Chelsea Moran

Graduate Theses and Dissertations

Geologists often use landscape form to infer landscape processes through time. While climate and tectonics shape geomorphic form, the potential range of spatial or temporal scales that can shape any specific landscape can render landscape process-form based hypotheses too general for consideration. Contributions by mathematical modeling have helped bridge the gap between inferring processes from form, notably in how sediment transport dynamics shape hillslopes. However, few models encapsulate the movement of large rock blocks ( >2 meters across) and their potential impact as hillslope sediment transport disruptors. The Upper Buffalo River watershed (BRW) in the Ozarks of northern Arkansas has …

Towards Long-Term Fairness In Sequential Decision Making, Yaowei Hu

Graduate Theses and Dissertations

With the development of artificial intelligence, automated decision-making systems are increasingly integrated into various applications, such as hiring, loans, education, recommendation systems, and more. These machine learning algorithms are expected to facilitate faster, more accurate, and impartial decision-making compared to human judgments. Nevertheless, these expectations are not always met in practice due to biased training data, leading to discriminatory outcomes. In contemporary society, countering discrimination has become a consensus among people, leading the EU and the US to enact laws and regulations that prohibit discrimination based on factors such as gender, age, race, and religion. Consequently, addressing algorithmic discrimination has …