Digital Commons Network^™

Hydronic Parking Lots: A Solution For Urban Heat Island Mitigation, Sumeet Jinnappa Paramaj

Theses and Dissertations

The Urban Heat Island (UHI) effect has emerged as a critical challenge in modern cities. It contributes to elevated local temperatures, placing significant stress on urban ecosystems and public health. One of the primary drivers of this phenomenon is the widespread use of impervious surfaces, such as dark-colored parking lots, which absorb heat during the day and gradually release it at night. This ongoing cycle results in persistently warmer urban environments. As cities continue to expand and densify, the need for effective, sustainable strategies to reduce urban heat becomes increasingly urgent.

This project aims to explore innovative interventions, particularly in …

Enhancing The Uniform Partition Of Heirs Property Act: The Need For Broader Use And Essential Modifications, Donna Arzanipour

Touro Law Review

The Uniform Partition of Heirs Property Act (“UPHPA”) seeks to address complex and inequitable conflicts that arise when partitioning heirs property. This Note explores the legislative history of the UPHPA, along with the legal and societal significance of the UPHPA, which provides mechanisms to prevent forced sales of family-owned property. Through a comparative analysis of states that have adopted their versions of the UPHPA, this Note considers the law’s potential to protect family land, reduce the risk of displacement, and promote longterm economic stability for communities, especially those in rural and minority populations. While recommending that all states should adopt …

A Patent System For Temple Grandin, Andrew Chin

Touro Law Review

This article explores the intersection of neurodiversity and innovation through the lens of Temple Grandin’s contributions to livestock handling systems and her philosophical alignment with the U.S. patent system. Drawing on cognitive research by Simon Baron-Cohen, archaeological insights from Penny Spikins, and personal reflections, I argue that autistic cognitive styles—characterized by heightened “folk physics” aptitudes and visual thinking—have historically driven technological progress. Grandin’s patented inventions, motivated by sensory-based empathy for animals, exemplify how neurodivergent inventors seek to embed their conceptions of ethical utility into the nation’s technological infrastructure. The article critiques modern patent litigation trends, advocating instead for a system …

And The Oscar For Best “Original” Screenplay Goes To . . . Chatgpt: Does The Use Of Ai In Scriptwriting Devalue Hollywood Writers?, Harrison Stern

Touro Law Review

No abstract provided.

Distinct Composition-Dependent Topological Hall Effect In Mn2-Xznxsb, Md Rafique Un Nabi, Yue Li, Suzanne G.E. Te Velthuis, Santosh Karki Chhetri, Dinesh Upreti, Rabindra Basnet, Gokul Acharya, Charudatta Phatak, Jin Hu

Physics Faculty Publications and Presentations

Spintronics, an evolving interdisciplinary field at the intersection of magnetism and electronics, explores innovative applications of electron charge and spin properties for advanced electronic devices. The topological Hall effect (THE), a key component in spintronics, has gained significance due to emerging theories surrounding noncoplanar chiral spin textures. This study focuses on Mn_2-xZn_xSb, a material crystalizing in centrosymmetric space group with rich magnetic phases tunable by Zn contents. Through comprehensive magnetic and transport characterizations, we found that the high-Zn (x > 0.6) samples display THE which is enhanced with decreasing temperature, while THE in the low-Zn ( …

Reconfigurable Photonic Lattices Based On Atomic Coherence, Jiaqi Yuan, Shun Liang, Qingsong Yu, Chiangbao Li, Yanpeng Zhang, Min Xiao, Zhaoyang Zhang

Physics Faculty Publications and Presentations

The array of coupled optical waveguides, which is also viewed as a photonic lattice, can exhibit abundant photonic band structures depending on the desired spatial arrangements of involved waveguides. Studies of photonic lattices are usually performed in solid-state materials, where the required periodic susceptibilities can be achieved by employing the femtosecond laser direct-writing or optical induction method, and have spawned flourishing achievements in manipulating the behaviors of light. Recently, the concept of electromagnetically induced photonic lattice (EIPL) is proposed under the well-known electromagnetically induced transparency (EIT) in coherently prepared multilevel alkali-metal atomic systems, where the strong coupling beams producing EIT …

Gravitational Property Theory, Monika U. Ehrman

Faculty Journal Articles and Book Chapters

Isaac Newton and Albert Einstein defined and redefined the field of physics. Their theoretical work on gravity is still considered a monumental discovery of their respective times. Although born centuries apart, the theory of gravity each advanced is still essential. Newton’s theory is applied on Earth and even throughout much of the solar system, while Einstein’s theory is used in special environments, such as near black holes. Neither theory is discarded, but instead used where appropriate. Likewise, gravitational property theory proposes that simple property theory—the ad coelum doctrine—is still appropriate for simple property, like stationary or vertical objects. And complex …

Podcasting As Open Access: A Review And Discussion Of Potential Impact On Scholarly Communication And Promotion, Allison Symulevich, Matt Torrence, Jason Boczar, Jessica Szempruch

USF Libraries Faculty and Staff Publications

Introduction: Academic podcasts are an informal way for faculty members to share their research expertise with an expanded and international audience. In this article, we argue that podcasts are a form of informal scholarly communication and that libraries can contribute to the successful dissemination of this informal scholarly communication.

Description of Service: At the University of South Florida, episodes of faculty-created academic podcasts are posted to the institutional repository, allowing the dissemination of these podcasts permanently via open access. The open-access nature of these materials makes them freely available to faculty, students, and other scholars; additionally, it helps to improve …

Electrochemical Observation And Ph Dependence Of All Three Expected Redox Couples In An Extremophilic Bifurcating Electron Transfer Flavoprotein With Fused Subunits, Debarati Das, Wassim El Housseini, Monica Brachi, Shelley D. Minteer, Anne Frances Miller

Chemistry Faculty Research & Creative Works

Bifurcating enzymes employ energy from a favorable electron transfer to drive unfavorable transfer of a second electron, thereby generating a more reactive product. They are therefore highly desirable in catalytic systems, for example, to drive challenging reactions such as nitrogen fixation. While most bifurcating enzymes contain air-sensitive metal centers, bifurcating electron transfer flavoproteins (bETFs) employ flavins. However, they have not been successfully deployed on electrodes. Herein, we demonstrate immobilization and expected thermodynamic reactivity of a bETF from a hyperthermophilic archaeon, Sulfolobus acidocaldarius (SaETF). SaETF differs from previously biochemically characterized bETFs in being a single protein, representing a concatenation of the …

Scalable Fabrication Of High-Payload Dendrimer-Based Nanoparticles For Targeted Atherosclerosis Therapy, Huari Kou, Lin Qi, Da Huang, Jiandong Wu, Honglan Shi, Hu Yang

Chemistry Faculty Research & Creative Works

Nanoparticle-based therapeutics hold promise for the treatment of atherosclerosis, but challenges such as low drug-loading capacity and a lack of scalable, controllable production hinder their clinical translation. Flash nanoprecipitation, a continuous synthesis method, offers a potential solution for scalable and reproducible nanoparticle production. In this study, we employed a custom-designed multi-inlet vortex mixer to perform cross-linking reaction-enabled flash nanoprecipitation, facilitating controlled and scalable synthesis of cross-linked Poly amidoamine (PAMAM) dendrimer nanoparticles. Notably, this approach allows simultaneous nanoparticle cross-linking and drug loading in a single step. The mannose moiety enabled specific targeting of macrophages via mannose receptors, enhancing the localization of …

Natural Capital Credit Risk Assessment In Agricultural Lending: An Approach Based On The Natural Capital Protocol, Francisco Ascui, Theodor Florian Cojoianu

Research Collection College of Integrative Studies

It is increasingly recognised that financial institutions have significant impacts and dependencies on natural capital – defined as the stock of the world’s renewable and non-renewable natural resources and ecosystems that yield flows of environmental goods and services, which directly and indirectly underpin the global economy and human wellbeing. Historically, many of these impacts and dependencies have been overlooked. The value of goods and services that nature provides ‘for free’ has often been ignored, and resources have typically been priced at their cost of extraction, rather than the cost of their replacement or substitution, which would promote more sustainable long-term …

Mapping The End Of Coal: How Policy Strength, Ownership, Finance Shape Global Plant Retirements, Jiwon Kim, Theodor Florian Cojoianu, Theodor F. Cojoianu, Maretno Agus Harjoto, Dariusz Wojcik

Research Collection College of Integrative Studies

This study examines how national environmental performance, institutional investor divestment from fossil fuels, investment in renewable energy, and ownership type influence the global phase-out of coal power plants, and whether these effects are constrained by geographical boundaries. Using data on coal plant retirements across 90 countries, we test whether domestic and cross-border factors shape the timing and likelihood of asset closure. We find that companies headquartered in countries with stronger environmental performance are more likely to retire domestic but not foreign plants. While institutional investors divestment is often assumed to exert cross-border influence, our findings show that divestment pressure by …

The Climate-Biodiversity-Pollution Nexus: The Pricing Of Environmental Credit Risks For European Industrial Polluters, Dominik Hirschbühl, Andrej Ceglar, Theodor Cojoianu, Tina Emambakhsh, Yifan Qi, Caterina Rho, Elsie Hu, Marco Petracco, Fabrizio Biganzoli, Alfred De Jager, Laura Garcia Herrero, Andrea Mandrici, Carlo Pasqua

Research Collection College of Integrative Studies

This study examines how euro area banks factor pollution-induced biodiversity risks into lending decisions, using data from 832 banks and 5,000 major polluters. Our results show that banks are increasingly pricing these risks by adjusting loan-to-value ratios and interest rates. Banks adjust lending conditions in line with EU pollution and biodiversity protection legislation, particularly for companies with large pollution footprints near biodiversity-protected areas or those contributing to Environmental Quality Standards failures of downstream surface waters. The former is driven primarily by banks’ adoption of biodiversity policies and public commitments to the Equator Principles, while the latter is a result of …

Meteorology, Weather And War In South East Asia: Malaya C. 1940-1960, Fiona Williamson

Research Collection College of Integrative Studies

This article interrogates the positioning of British colonial meteorology in Malaysia and Singapore from the 1940s to 1960. This period spanned a global conflict and an internecine war, effecting profound sociopolitical changes from which neither Malaysia nor Singapore would emerge the same. The meteorological services were essential to Britain's armed conflicts, providing vital weather information to the army, navy and, especially, the air forces, as well as supporting the aviation and shipping industry often in difficult and dangerous circumstances. This article argues that British military policy in South East Asia and the specific concerns of the colonial government in Malaya …

Leveraging Deep Learning Models And Social Media Data For Enhanced Situation Awareness In Disaster Management, Ademola Abdulganiyu Adesokan

Doctoral Dissertations

"In recent years, social media has become a crucial source of real-time data for disaster management, supporting emergency responses when traditional channels like 911 are overcrowded and overwhelmed. It offers authorities valuable data for developing effective strategies, especially when swift actions are essential to save lives. However, the informal language, ambiguous meanings, and irrelevant content on social media pose challenges to accurate classification and hinder the efficient extraction of disaster-relevant information, leading to inefficiencies in emergency response efforts.

This research focuses on seven key questions: i) How can we detect, classify, and analyze hate and offensive tweet emotions during large-scale …

Collateral Damage And Individual Rights In Armed Conflict, Charles P. Trumbull Iv

Fordham International Law Journal

No abstract provided.

Improving System-Level Outcomes Via Artificial Intelligence Decision Support In Kidney Utilization, Casey I. Canfield, Cihan H. Dagli, Daniel Burton Shank, Krista Lentine, Mark Schnitzler, Henry Randall, V. Sriram Siddhardh Nadendla, Brendon Cummiskey

Engineering Management and Systems Engineering Faculty Research & Creative Works

Transplantation provides patients suffering from end-stage kidney disease a better quality of life and long-term survival. However, over 20% of deceased donor kidneys are not utilized and never transplanted. While this is sometimes medically appropriate, this also reflects missed opportunities. We are designing Artificial Intelligence decision support for the kidney offer process to support both demand at the transplant center and supply at the organ procurement organization. This includes (1) developing deep learning models, (2) evaluating the effect of explainable interfaces, (3) improving fairness in the model output, (4) identifying factors that influence adoption decisions, and (5) conducting a randomized …

Spatial Analysis And Machine Learning Integration For Nutritional Status Mapping Using Ann And Random Forest Models, Desi Anis Anggraini, Fachrul Kurniawan, Fresy Nugroho, Meidya Koeshardianto, Mohammad Iqbal Bachtiar

Knowledge Engineering and Data Science

Nutritional problems among children under five remain a major public health challenge. This research seeks to create a spatially oriented system for evaluating and mapping nutritional status utilizing Artificial Neural Network (ANN) and Random Forest (RF) algorithms. Data obtained from the Sumenep District Health Office included age, weight, height, and gender variables. Both models were trained using a 70:30 data ratio and evaluated with accuracy, precision, recall, and F1-score metrics. The ANN model achieved an accuracy of 95.8%, while the RF model reached 97.7%. Classification results were visualized through a Geographic Information System (GIS) to illustrate spatial distribution and identify …

Time Series Forecasting With Lstm: An Extensive Content Analysis, Andri Pranolo, Xiaofeng Zhou, Yingchi Mao

Knowledge Engineering and Data Science

This paper presents a comprehensive bibliometric and content review of the trend, architecture, and application of long short-term memory (LSTM) models for time series forecasting. The study aims to provide insights into the overall statistics and distribution of papers focused on LSTM for forecasting. Additionally, the research questions address the most highly cited papers based on LSTM approaches in forecasting, the most productive journals in this field, and identifying trends, gaps, summary tasks and their performance, datasets availability, and future research directions for LSTM in forecasting. This paper is a comprehensive review of LSTM for forecasting from 2017 to 2023 …

From Data To Insight: A Machine Learning Approach In Classifying Dairy Cow Productivity Level And Identifying Important Influencing Variables, Fatkhurokhman Fauzi, Achmad Fauzan, Rhendy K P Widiyanto, Khairil Anwar Notodiputro, Bagus Sartono

Knowledge Engineering and Data Science

Identifying influential predictor variables is crucial for enhancing model interpretability in supervised classification. This study applies Permutation Variable Importance (PVI), a model-agnostic approach, to evaluate variable relevance after model fitting. Using data from the 2024 Indonesia Dairy Cow Productivity Survey, this research investigates five classification techniques: (1) Support Vector Machine (SVM), (2) Neural Network (NN), (3) k-Nearest Neighbors (kNN), (4) Naïve Bayes Classifier (NB), and (5) Logistic Regression (LR), to identify which method(s) yield the best performance based on evaluation metrics such as accuracy, sensitivity, and specificity. PVI is employed to identify the most influential predictor variables within the best-performing …

A Hybrid Soft Voting And Stacking-Based Meta-Learning Approach For Sentiment Analysis Of Bangkalan Batik, Moh. Imron Wahyudi, Lailil Muflikhah, Rizal Setya Perdana

Knowledge Engineering and Data Science

Sentiment analysis is an important field in Natural Language Processing (NLP) that focuses on processing consumer opinions to gain useful insights. The information generated from sentiment analysis can be used as a basis for business decision-making, service quality evaluation, and the formulation of more effective marketing strategies. In the local context, Bangkalan Batik, as one of Madura's distinctive cultural products, has high economic value and cultural identity. However, consumer reviews available online, for example through Google Maps, are still rarely utilized optimally by MSMEs as a source of strategic information. Therefore, this study was conducted to develop a sentiment classification …

Mapping Of Product Sales Potential Based On Brands In The East Kalimantan Region Using Hybrid Analytical Framework, Achmad F O Gaffar Mr, Mulyanto Mulyanto Mr, Arief Bw Putra Mr, Muhammad Taufiq Sumadi Mr, Emmilya Umma Aziza Gaffar

Knowledge Engineering and Data Science

In geographically dispersed markets, operational costs should be reflected in sales planning to support accurate performance evaluation. However, such considerations are often neglected in practice. This study proposes a hybrid analytical framework to map brand-based product sales potential, with and without operational cost consideration, using historical sales data from PT Karya Inti Total Anugerah (PT KITA) in East Kalimantan. The framework integrates spatial, statistical, and machine learning techniques. Principal Component Analysis (PCA) is used to reduce the dimensionality of variables related to travel distance, total sales, and units sold, where travel distance represents the primary contributor to operational costs. K-Means …

Feature Engineering And Anchor Optimization For Enhancing Faster R-Cnn Detection Of Low-Contrast Steel Surface Defects, Herdianti Darwis, Sitti Nurhalimah, Huzain Azis

Knowledge Engineering and Data Science

Detection of defects on low-contrast steel surfaces, especially crazing and rolled-in-scale, remains a major challenge due to their visual similarity to background patterns. Although state-of-the-art methods have achieved high accuracy through complex architectural adjustments, the contribution of preprocessing techniques has not been thoroughly investigated. This study investigates pre-processing-based improvements to Faster R-CNN by combining Bilateral Filtering to reduce noise, CLAHE to enhance local contrast, CIoU Loss for more effective bounding box regression, and customized anchor settings for irregular defect configurations. Evaluated using the NEU-DET dataset, our BF-CIoU Faster R-CNN model achieved a mAP@50 score of 72.32%, with an AP of …

Comparative Analysis Of Deep Learning Algorithms For Predicting Enso Based On Non-Sequential Sampling Procedure Algorithms, Ria Faulina, Siti Hadijah Hasanah, Nuramaliyah Nuramaliyah, Ika Nur Laily Fitriana

Knowledge Engineering and Data Science

The El Niño–Southern Oscillation (ENSO) is a major climate phenomenon that significantly influences global weather patterns, particularly rainfall and temperature variability across different regions. Accurate ENSO forecasting is therefore essential to support disaster risk mitigation and strategic decision-making in climate-sensitive sectors such as agriculture, fisheries, and water resource management. This study investigates the performance of deep learning approaches for ENSO prediction using a non-sequential sampling procedure on historical climate data. Three models are comparatively evaluated: Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and a hybrid CNN–LSTM architecture. The results demonstrate that the hybrid CNN–LSTM model outperforms the standalone CNN …

Cognitive Eeg Differentiation With Hypnosis-Based Noise Reduction And K-Harmonic Means For Personalized Brainwave Modeling, Ahmad Azhari, Dimas Chaerul Ekty Saputra

Knowledge Engineering and Data Science

This study investigates the integration of hypnosis-based noise reduction and K-Harmonic Means (KHM) clustering for personalized brainwave modeling using Electroencephalography (EEG) data. EEG signals were collected from 100 participants using a Neurosky Mindset sensor at the FP1 (prefrontal) location, with each subject performing nine standardized cognitive tasks such as breathing, memory recall, and mathematical problem-solving. Hypnosis was applied not as a filtering method but as a behavioral protocol to standardize subject conditions and minimize physiological and environmental noise. The EEG signals were sampled at 128 Hz and analyzed using KHM clustering with K=4K = 4K=4, resulting in a Silhouette Score …

Comparative Performance Of Vgg16 And Efficientnetb0-Based Transfer Learning For Brain Tumor Classification, Huzain Azis, Rizqi Ananda Jalil, Abdul Rachman Manga'

Knowledge Engineering and Data Science

The classification of brain tumors using Magnetic Resonance Imaging (MRI) images is essential for early diagnosis but remains challenging due to tumor diversity. This study evaluates the effectiveness of two distinct architectural approaches for feature extraction: VGG16, representing a classic sequential design, and EfficientNetB0, a modern architecture optimized for parameter efficiency through compound scaling. Using a dataset of 2,870 MRI images categorized into four classes, we implemented a static transfer learning strategy by freezing all pre-trained ImageNet weights to act as fixed feature extractors. Features were extracted from specific layers, the final pooling layer for VGG16 and the Global Average …

Ahp–Python Framework For Multicriteria Modeling Of Rice Production In Asean, Mayang Anglingsari Putri, Risqy Siwi Pradini, Anuraga Jayanegara, Alexander Dimas Yonanta Putra

Knowledge Engineering and Data Science

Rice production is a key indicator of food security and agricultural stability in Southeast Asia, especially among Association of Southeast Asian Nations (ASEAN) countries. Despite shared regional goals, disparities in rice production remain, and previous studies mainly rely on descriptive statistics, lacking structured multicriteria decision-making frameworks and computational tools for cross-country comparisons. This study addresses these gaps by proposing an integrated Analytic Hierarchy Process (AHP)–Python framework to evaluate and rank ASEAN rice production from 2013 to 2022. Three criteria are used: Total Production Volume (K1), Production Growth Trend (K2), and Recent Year Performance (K3), capturing both long-term consistency and short-term …

Stable Numerical Solution Of An Elliptic Pde Inverse Problem Subject To Incomplete Boundary Conditions, Qasim Abd Ali Tayyeh

Knowledge Engineering and Data Science

This study addresses the challenging problem of solving inverse elliptic Partial Differential Equations (PDE) with incomplete boundary data, data available only on a part of the domain boundary. The aim is to develop a robust, effective numerical framework that consistently recovers parameters and/or sources from incomplete, ill-posed data. In the case of a variational problem discretized by the Finite Element Method (FEM) and solved by an adjoint-based optimization strategy, the framework uses Tikhonov regularization. Morozov's Discrepancy Principle is used to determine regularization parameters that achieve the best balance between accuracy and stability. Even with 5% noise in the measurement data, …

A Bounded Custom Gpt Structures Operational Knowledge For Small-Industry Decision Support, Ikhwan Arief, Alizar Hasan, Nilda Tri Putri, Hafiz Rahmann

Knowledge Engineering and Data Science

Small manufacturing and craft-based firms increasingly use Generative Artificial Intelligence (GenAI) through public chat interfaces, low-cost tools, and informal experimentation. However, these firms often make operational decisions with incomplete records, tacit owner knowledge, fragmented spreadsheets, and limited managerial capacity. Under such conditions, open-ended chatbots may generate fluent but unsafe recommendations by overlooking missing information, contradictory evidence, feasibility constraints, and implementation constraints. This study presents Asisten Cerdas Industri Kecil as a bounded Custom GPT artifact for operational diagnosis, priority selection, and short-horizon action planning in small industries. Using a Design Science Research approach, the study develops a documented artifact corpus comprising …

Enhanced Biofuel Cells Based On A Hybrid Enzymatic/Bimetallic Composite For Complete Lactate Catalytic Electrooxidation, Jefferson Honorio Franco, João Victor Bonaldo, Shelley D. Minteer, Adalgisa R. De Andrade

Chemistry Faculty Research & Creative Works

We describe complete lactate electrooxidation in an enzymatic biofuel cell that combines the catalytic action of the bimetallic composite Ru@Pt-CNT and the enzyme oxalate oxidase (OxOx). The Ru@Pt-CNT/OxOx hybrid electrode was 2.0-fold more catalytically active than the electrode containing the bimetallic composite only. During chronoamperometric experiments, the hybrid electrode achieved a 35% higher maximum current density (2.65 ± 0.15 mA cm^-2) than the Ru@Pt-CNT electrode. Electrochemical impedance spectroscopy showed that the hybrid electrode had lower charge transfer resistance than the Ru@Pt-CNT electrode, confirming that OxOx had a high affinity for lactate during the bioelectrocatalytic reaction on the electrode …