Digital Commons Network^™

Courts Of New York: A Visual Atlas Of The City’S Public Basketball Spaces, Nathaniel Rattner

Dissertations, Theses, and Capstone Projects

Basketball courts in New York City are recreation facilities, community anchors and part of the city’s cultural image. In the basketball capital of the world, New Yorkers are rarely more than a few blocks away from a court. The visual diversity of these courts, however, is not widely documented in systematic ways.

This project makes that diversity visible to the public, combining open data, aerial imagery and computational analysis to document this important public space across the five boroughs. It is a narrative story and digital atlas of New York City’s public basketball courts, using surface color as a way …

Artificial Intelligence In Higher Education, Opportunities, And Challenges: A Review, Sharifa Alblooshi

All Works

Artificial intelligence (AI) is a growing force of change in higher education, providing assistance to students, teachers, and administrators in teaching, learning, and administration. As AI technologies advance rapidly, they present a combination of significant opportunities and complex challenges. In this study, we examine the role of AI in higher education, highlighting both its positive and negative impacts, as well as current policy gaps and issues arising from its deployment. The literature on the topic was reviewed to determine how AI decisively impacts teaching and learning, the role of AI in assessments and academic integrity, as well as ethics, psychological …

A High-Resolution Daily Precipitation Fusion Framework Integrating Radar, Satellite, And Nwp Data Using Machine Learning Over South Korea, Hyoju Park, Hiroyuki Miyazaki, Menas Kafatos, Seung Hee Kim, Yangwon Lee

Institute for ECHO Articles and Research

Accurate precipitation mapping is essential for effective disaster management; however, individual radar, satellite, and numerical weather prediction products often struggle in the topographically complex terrain of South Korea. This study proposes a high-resolution (~500 m) daily precipitation fusion framework that integrates Korea Meteorological Administration (KMA) radar, Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrievals for GPM (IMERG), and Local Data Assimilation and Prediction System (LDAPS) data. The framework employs a Random Forest model augmented with a monthly Empirical Cumulative Distribution Function (ECDF) correction. Auxiliary predictors are incorporated to enhance physical interpretability and stability, including terrain attributes to represent orographic effects, land-cover …

Adaptive Multi-Agent Intelligence: A Dynamic Data Management System For Enhanced Data Quality And Reconciliation, Anthony Taeyang Vigil

USF Tampa Graduate Theses and Dissertations

Organizations struggle with data quality problems that cost them millions of dollars annually. Manual data cleanup and standardizing, also known as reconciliation, can take skilled analysts days to complete and can still contain human errors. This dissertation addresses this challenge by developing and validating the Origin Data Management System, which automates processes that traditionally require days of manual work. The system achieved up to 100% accuracy in standardizing vendor names and identifying CEO information across real-world test datasets from Jefferson County Public Schools, Forbes, and Fortune Magazines. Processing time dropped from days to minutes—a 96-fold improvement over manual methods. These …

Assessing Urban Flooding And Vegetation Impact In Dubai Creek Following The April 2024 Extreme Rainfall, Dana Alhammadi, Xin Hong

All Works

During April 2024, the United Arab Emirates experienced an unusual phenomenon of an intense rainfall episode between April 14 and 18 that resulted in massive flooding in urban environments, particularly low-lying areas such as Dubai Creek. As a tidal waterway with dense urban development and environmentally sensitive zones surrounding it, Dubai Creek is an ideal site for assessing environmental changes caused by to floods. The study employed pre-flood (14 April) and post-flood (18 April) high-resolution PlanetScope satellite images, in combination with QGIS analysis, to evaluate vegetation health and surface water changes. Quantification of affected areas from flooding was achieved through …

Mapping Post-Rainfall Recovery In Arid Regions Using A Hierarchical U-Net, Xin Hong

All Works

The United Arab Emirates (UAE) experienced an extreme rainfall event between April 15 and 17, 2024, and that resulted in severe flooding in its coastal regions. Dubai was among the most affected regions. This study applies a hierarchical deep learning model on PlanetScope imagery to detect flood inundation, quantify flood extent by land cover, and examine short-term recovery dynamics. While earlier work detailed the methodological development of a hierarchical U-Net model (Hong et al., in press), here we emphasize its application for monitoring resilience trajectories in an arid urban environment. Results show that approximately 22 km² of land was …

Generative Ai Use And Self-Learning In Higher Education: The Role Of Learning Difficulties, Dana Saleh, Areej Elsayary

All Works

The rapid development of GenAI tools and their adoption in education have shown promising potential to personalize learning experiences. However, their effectiveness is influenced by factors such as familiarity, frequency of use, and the impact on self-learning. This study investigates the undergraduate students' familiarity with Generative AI (GenAI) tools, their frequency of use, and the perceived impact of GenAI on self-learning, with particular consideration of differences between students with and without learning difficulties. Prompt engineering is also included as a secondary aspect of students' GenAI experience. The research employed a quantitative survey design, utilizing validated scales to measure familiarity, usage …

Mapping Urban Vegetation Changes Using Planetscope Imagery And Gis: A Case Study Of The 2024 Dubai Flood, Sumayya Almansoori, Xin Hong

All Works

In April 2024, unexpected heavy rainfall triggered one of the most severe flooding events in Dubai’s recent history, causing widespread concern for urban infrastructure and green spaces. This study evaluates the flood’s impact on urban vegetation in South Dubai using high-resolution PlanetScope satellite imagery and the Normalized Difference Vegetation Index (NDVI). Vegetation conditions before and after the flood (April 14 and April 18–19, 2024) were quantified and compared using NDVI analysis within QGIS to assess changes in vegetation health and coverage. Results indicate significant declines in vegetation health in areas dominated by intensive turf management, such as Damac Hills and …

Integrating Adversarial Scenarios Into Llm Security Labs: An Experience Report On A Hands-On Approach, Dominic A. Wilson

Journal of Cybersecurity Education, Research and Practice

This paper presents an exploratory case study detailed as a pedagogical experience report on integrating adversarial Large Language Model (LLM) scenarios into a graduate cybersecurity curriculum. In addition to prompt injection, sophisticated techniques such as jailbreaking and model inversion pose emerging threats that traditional computer security curricula often lack. We present the design and implementation of a structured, hands-on module addressing this gap, utilizing a custom Retrieval-Augmented Generation (RAG) platform with local open-source LLMs. A cohort of 16 graduate students participated in this two-week pilot module, engaging in "red team" activities to actively exploit model alignment and privacy vulnerabilities. The …

Developing A Multimodal Approach To Channel Characterization On Youtube, Shadi Shajari

Theses and Dissertations

YouTube has become a dominant arena for global information sharing, where creators and audiences interact through content, comments, and engagement dynamics. Understanding how these interactions shape a channel’s identity requires a comprehensive characterization of both audience behavior and content structure. This dissertation develops a unified framework for characterizing YouTube channels through multimodal analysis that integrates behavioral modeling, dimensionality reduction, clustering, and content-based characterization to provide a holistic view of audience and editorial patterns. The framework begins by analyzing the structural relationships within co-commenter networks, where users who repeatedly comment on the same videos are connected to capture patterns of interaction …

Oer Review For Open Programming: Java I - Creating An Oer Textbook For Programming Fundamentals, Peter Arsenault

Open Educational Resources Publications

This report describes the creation and implementation of a seven‑chapter Open Educational Resource (OER) for Bentley University’s CS 180 – Programming Fundamentals course, developed from the author’s teaching notes, custom examples, and course materials from Fall 2024. The project aimed to provide current, accessible, digital‑first learning resources aligned with modern programming tools, supported by generative‑AI editing in NotebookLM and open‑source formatting tools such as pandoc and Marp. Implemented during Fall 2025, the OER received highly positive student feedback, particularly regarding its clarity, accessibility, and cost savings, and it is slated for further refinement—including updates for Java 25, expanded examples, and …

Data Altruism: Eu Solution And Path Of Localization In China, Teng Wu

Bulletin of Chinese Academy of Sciences (Chinese Version)

Data altruism transcends the profit-seeking, monopolistic and competitive nature of the market mechanism. It is driven by innovation and encourages data subjects and holders to share information guided by the public interest. As a new type of data application and service model, it aims to achieve a virtuous cycle of the data ecosystem while optimize the utilization of data resources. Tracing back to the source, the theoretical foundation of data altruism from the ethical theory of altruism, and the concept of its budding was supported by the data for good. Analysis shows that the specific scheme of the EU data …

Significance, Challenges, And Policy Recommendations For Strengthening Database Development To Support Ai For Science In China, Kaihua Chen, Hongxin Liu, Rui Guo

Bulletin of Chinese Academy of Sciences (Chinese Version)

With the rapid emergence of research intelligence driven by big data and artificial intelligence (AI), high-quality, openly shared scientific databases have become a strategic focal point for scientific innovation and enhancing technological competitiveness. Major countries around the world are increasingly recognizing the foundational role of scientific databases in advancing basic research. While continuously strengthening their own scientific data infrastructure through a series of initiatives, they have simultaneously imposed restrictions and suppression on the development of AI technologies in China, including those involving research data. Against this backdrop, building an autonomous and controllable scientific data ecosystem to support research intelligence is …

Identifying And Overcoming Some Operational Limitations Of Reconfigurable Intelligent Surfaces In 5g And Beyond Wireless Networks, Souvik Deb

Doctoral Theses

Reconfigurable intelligent surfaces (RIS) can dynamically reshape the propagation environment to enhance signal strength, spectral efficiency and reliability in 5th generation (5G) cellular as well as device to device (D2D) communications. However, to reap such benefits, a range of practical and operational challenges need to be addressed for effectively utilizing RIS in realistic urban environments. This includes maintaining line of sight (LoS) between the RIS and the communicating devices for reliable signal reflection in millimeter wave (mmWave) communication, reducing high channel estimation overhead for communication using multipath rich channels and preventing violation of strict latency constraints due to high complexity …

Visual Relocalization Method Combining Region Classification And Local Feature Enhancement, Yining Wang, Yanli Liu, Guanyu Xing

Journal of System Simulation

Abstract: Visual relocalization tasks have important application value in fields such as digital twin and augmented reality. The current mainstream methods still face challenges such as mismatch between coordinate regression scale and receptive field and insufficient attention to local information. A visual relocalization method that combines region classification and local feature enhancement is proposed. The coordinate regression problem in large space is transformed into a multi-region classification problem and a coordinate regression problem inside a small scene, which significantly reduces the uncertainty of coordinate regression and makes the network globally have a large receptive field. A conditioning layer using deep …

Addressing The Void Of Ai Policies In Education For Students With Specific Learning Disabilities, Mikyung Shin, Fatmana Deniz, Latesha Watson, Cynthia Dieterich, Kathy B. Ewoldt, Friggita Johnson, Jennifer E. Kong, Sung Hee Lee, April Whitehurst

Education Faculty Articles and Research

The purpose of this study was to identify the current state of artificial intelligence (AI) policies in U.S. education and propose actionable recommendations through large language model–based topic modeling and Delphi surveys. Out of 12 policy documents released between 2015 and 2025, only two documents (National Center for Learning Disabilities, 2024; W.A. v. Clarksville/Montgomery County School System, 2024) specifically addressed learning disabilities. Policy documents addressing topics such as AI-driven risk assessment, data protection, legal risk management, and ethical guidelines covering other disabilities and general AI in education policy were provided as baselines that could be discussed and validated through …

Inverse Kinematics 3d Human Modeling Simulation Based On Multi-View Vision, Guoyu Fang, Yanze Li, Kai Chen, Xiaodong Zhao, Zizhuo Hu, Mingshi Yang, Wanqing Wu, Zichen Wang, Wenkai Guo

Journal of System Simulation

Abstract: In autonomous driving simulation and industrial virtual reality simulation, there is a high demand for accuracy and robustness in 3D human body modeling. However, current joint-based human modeling approaches suffer from issues such as continuous modeling jitter, local distortion, and poor adaptability to occlusion, which degrade model quality and limit the development of practical applications such as intelligent driving and digital factories. To address these challenges, this paper proposes a multi-view vision-based inverse kinematics 3D human modeling method using a vector quantized variational autoencoder(IK-VQ-VAE). By integrating joint training with an automatic variational gradient descent approach, the proposed method achieves …

Pdr-Stgcn: An Enhanced Stgcn With Multi-Scale Periodic Fusion And A Dynamic Relational Graph For Traffic Forecasting, Jie Hu, Bingbing Tang, Langsha Zhu, Yiting Li, Jianjun Hu, Guanci Yang

Faculty Publications

Accurate traffic flow prediction is a core component of intelligent transportation systems, supporting proactive traffic management, resource optimization, and sustainable urban mobility. However, urban traffic networks exhibit heterogeneous multi-scale periodic patterns and time-varying spatial interactions among road segments, which are not sufficiently captured by many existing spatio-temporal forecasting models. To address this limitation, this paper proposes PDR-STGCN (Periodicity-Aware Dynamic Relational Spatio-Temporal Graph Convolutional Network), an enhanced STGCN framework that jointly models multi-scale periodicity and dynamically evolving spatial dependencies for traffic flow prediction. Specifically, a periodicity-aware embedding module is designed to capture heterogeneous temporal cycles (e.g., daily and weekly patterns) and …

Cross-Domain Crowd Counting Model Based On Frequency Domain Enhancement, De Zhang, Zishan Liang, Ningning Liu

Journal of System Simulation

Abstract: Crowd counting takes video surveillance data as input and can be applied to the construction of city digital twin platforms, virtual city modeling and smart city management, etc. However, when there are data domain differences between the application scenario and training scenario, counting performance often significantly decreases. A cross-domain crowd counting model based on frequency domain enhancement is proposed. To alleviate the distribution differences between domains, a frequency domain feature enhancement module and a domain invariant frequency domain adapter module are constructed: the former uses discrete cosine transform to extract key statistical features to enhance spatial representation ability, while …

Evaluation Of Mcf-7 Breast Cancer Cell Cytotoxic And Antioxidant Activities Of Peptide Fractions From Symbiotic Bacteria Of Jellyfish Catostylus Sp., Eka Sry Wahyuni, Ahyar Ahmad, Muhammad Nasrum Massi, Sofa Fajriah, Randi Rimpung, Muh. Akbar Ardiputra, Harningsih Karim, Irda Handayani

Karbala International Journal of Modern Science

Marine-derived symbiotic microorganisms are recognized as a promising source of bioactive compounds with potential therapeutic applications, yet research on jellyfish-associated bacteria remains limited. This study examines the bioactivity of peptide fractions derived from symbiotic bacteria isolated from the jellyfish Catostylus sp., collected from the coastal waters of South Sulawesi, Indonesia, with a focus on their anticancer and antioxidant properties. Following sample collection, the symbiont bacteria were isolated, enzymatically hydrolyzed, and purified before their biological activity was evaluated. Preliminary cytotoxicity screening using the brine shrimp lethality assay revealed that the extracellular peptide fraction (5–10 kDa) and intracellular peptide fraction (3–5 kDa) …

Exploitation Prioritization And Residual Risk Assessment Based On Hybrid Mcdm Model, Zibo Wang, Yaofang Zhang, Sicai Lv, Yingzhou Wang, Hongri Liu, Bailing Wang

Turkish Journal of Electrical Engineering and Computer Sciences

Exploitation is one of the most significant ways to launch attacks using vulnerabilities. The increasing number of vulnerabilities and limited allocation of security resources make it impossible to eliminate all exploitations. Because not every vulnerability can be fixed, it is necessary to rank exploitations and subsequently assess the residual risk, which is defined as the remaining threat potential after each elimination. In this paper, a structured and flexible decision support framework based on a hybrid multicriteria decision-making model is proposed for prioritizing exploitations and assessing residual risk. Metrics are treated as criteria in the model. The hybrid model is developed …

A Deep Learning-Based Real-Time No-Reference Image Decolorization Network With Perceptual Preservation, Mengjuan Zhao, Yitao Liang, Weiya Shi, Juan Xia

Turkish Journal of Electrical Engineering and Computer Sciences

Currently, grayscale images are preferred as input data for some specific vision tasks. Decolorization is the transformation of a color image into a grayscale image. Efficient decolorization algorithms can improve the overall task efficiency, while perceptual preservation in decolorization can provide more information for further processing. In recent research, traditional methods focus on preserving contrast or detail information with little attention to perceptual features. Deep-learning methods are beginning to consider perceptual preservation, but they run inefficiently. In addition, the decolorization methods lack the optimal target grayscale images for reference. Therefore, we propose a new deep learning-based real-time no-reference decolorization network …

Noninvasive Condition Monitoring For Eccentricity Fault Detection In Large Hydro Generators, Atena Tazikeh Lemeski, Di̇dem Tekgün, Ozan Keysan, Kemal Leblebi̇ci̇oğlu, Murat Göl

Turkish Journal of Electrical Engineering and Computer Sciences

Eccentricity faults in electric machines remain a critical concern, as they generate uneven magnetic forces that increase vibration and noise, ultimately raising the risk of premature motor failure. This study proposes a method for the early detection of dynamic eccentricity (DE) faults in hydropower plants through an advanced optimization-based parameter identification technique integrated with finite element analysis (FEA). Finite element modeling (FEM) is first used to analyze an existing salient-pole synchronous generator (SPSG) from a hydroelectric power plant in Türkiye. The effects of DE faults on the SPSG’s magnetic equivalent circuit parameters are then examined under various fault severities. A …

A Deep Learning Model For Accurate Tomato Leaf Disease Identification, Maheen Shahzad, Muhammad Abdullah Javed, Erum Ashraf, Hafiz Ishfaq Ahmad, Sabeen Masood

Turkish Journal of Electrical Engineering and Computer Sciences

Recent advances in machine learning and deep learning have greatly improved how we detect plant diseases, making diagnoses more accurate, faster, and easier to scale. However, many existing solutions depend on large, pretrained models that need powerful hardware, which limits their use in the field, especially in areas with limited resources. To tackle this, we designed a custom lightweight convolutional neural network (CNN) built from scratch using 20,000 carefully selected images from the PlantVillage tomato dataset. Our model uses Squeeze-and-Excitation (SE) blocks and Swish activation functions to boost performance, reaching an accuracy of 97.7% while using far fewer computing resources …

Influence Of Sinr And Noise Variance On Outage Probability For Mimo-Noma System In 5g And Beyond, Sadiq Ur Rehman, Jawwad Ahmed, Muhammad Zubair, Syed Sajjad Hussain Rizvi

Turkish Journal of Electrical Engineering and Computer Sciences

Nonorthogonal multiple access (NOMA) communication presents a promising solution to the limitations of traditional orthogonal multiple access techniques, offering potential improvements in achievable rates. Multiple-input multiple-output (MIMO), when combined with NOMA (MIMO-NOMA), further enhances these benefits by leveraging the diversity advantages of multiple antennas. Looking ahead, the future of wireless communication hinges on deploying heterogeneous networks (HetNets), facilitating the coexistence of various wireless access networks in a hierarchical fashion. However, the advent of 5G and 6G communications brings shorter channel coherence times, rendering channel reciprocity unreliable. Consequently, conventional channel estimation methods relying on uplink (UL) pilots for downlink (DL) transmission …

A Novel Approach To Maximum Weighted Traffic Flow Method For Effective Signal Control, Zülal Hi̇lal Yildiz Budak, Seyi̇t Alperen Çeltek, Aki̇f Durdu

Turkish Journal of Electrical Engineering and Computer Sciences

Traffic signal management is a critical challenge due to its environmental, economic, and public health impacts. The maximum weighted flow method (MaxWeightedFlow) was developed to optimize traffic flow at isolated and coordinated urban intersections. This study proposes a new method, the novel MaxWeightedFlow, which includes two key strategies to enhance the classical approach. The first strategy reduces computational burden by estimating vehicle approach times based on instantaneous speeds, improving real-time performance. The second employs regression analysis to optimize the alpha parameter, representing the vehicle waiting coefficient. The proposed approach, the novel MaxWeightedFlow, was evaluated using real-world traffic data from Kilis, …

Improving Rail System Signaling Efficiency Through Ai-Based Driving Profile Generation: A Comparative Performance Analysis, Mehmet Taci̇ddi̇n Akçay, Abdurrahi̇m Akgündoğdu

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, a dataset comprising 3600 discrete operational snapshots (rather than continuous time-series data) derived from real-field operations is used to obtain a high-accuracy driving profile equation using a second-degree Polynomial Regression method. This equation demonstrates the model’s interpretability. The performance metrics obtained with the second-degree polynomial regression model’s equation are as follows: a coefficient of determination (R2) of 0.84, a Pearson Correlation Coefficient of 0.91, and an RMSE of 11.13. These results indicate the effectiveness of artificial intelligence-based approaches in improving the efficiency of the railway signaling system. The same dataset is also utilized with other machine learning …

Distribution System Reliability Evaluation Considering Protection Coordination Using Petri Nets, Rani Kumari, Bhukya Krishna Naick

Turkish Journal of Electrical Engineering and Computer Sciences

Maintaining reliable and high-quality power delivery becomes increasingly complex with expanding power grids. The lack of protection coordination poses a significant threat, compromising overall system reliability. This research addresses this challenge by proposing a method for coordinating protective devices within the distribution system, specifically during network faults. The proposed approach utilizes a stochastic timed Petri net (STPN) based methodology to model protective device coordination across various fault scenarios. This technique effectively captures the dynamic behavior and interactions of protective equipment, allowing for the anticipation of potential disturbances. This proactive insight facilitates preventative measures to address prewarning situations, thereby preventing cascading …

Analysis Of A Few Quantum Algorithms And Circuits Related To Boolean Functions, Suman Dutta

Doctoral Theses

Boolean functions are fundamental to computation and presently play a crucial role in quantum information processing. This thesis presents two facets of Boolean functions in the context of quantum computing: (I) extending and applying the theoretical framework of Forrelation to cryptographic analysis, and (II) designing efficient quantum circuits for multi-controlled Toffoli gates and Boolean circuit implementations. Given two Boolean functions $f$ and $g$, Forrelation, introduced by Aaronson (2010), measures the correlation between the truth table of $f$ and the Walsh-Hadamard transform of $g$ at the corresponding points. Here, we revisit the Forrelation framework to study several cryptographically significant spectra of …

Combinatorial & Algebraic Approaches In Analyzing Mutually Unbiased Bases (Mubs) And Their Approximations, Rakesh Kumar

Doctoral Theses

Mutually Unbiased Bases (MUBs) are an important concept in quantum information theory. Two orthonormal bases in a $d$-dimensional complex Hilbert space $\mathbb{C}^d$ are said to be mutually unbiased if the absolute value of the inner product between any pair of vectors, one from each basis, is $1/\sqrt{d}$. A set of $r$ orthonormal bases is called mutually unbiased if every pair of bases in the set is unbiased. It is known that at most $d + 1$ mutually unbiased bases can exist in $\mathbb{C}^d$, and a set achieving this bound is termed a \emph{complete sets of MUBs} in $\mathbb{C}^d$. We can …