Digital Commons Network^™

Neutrosophic Sets In Neural Networks: Theory, Applications, And Challenges, Vladimir Simic, Dragan Pamucar, Hafiz Muhammad Athar Farid

Neutrosophic Systems with Applications

The integration of neutrosophic sets into neural networks presents a novel approach to handling uncertainty, indeterminacy, and falsity in data. Traditional neural networks typically operate under the assumption of precise and complete data, but real-world applications often involve noisy, incomplete, or ambiguous information. Neutrosophic sets extend fuzzy logic by incorporating three components: truth, indeterminacy, and falsity, allowing for a more nuanced representation of uncertain data. This paper explores the theoretical foundations of neutrosophic sets and their integration with neural networks, highlighting the challenges in computational complexity, training, and optimization. The paper also discusses the potential applications of neutrosophic neural networks …

Emergent Operator Logic: A Foundational Framework For Dynamic Reasoning And Generative Intelligence, Mona Gharib, Abduallah Gamal, Muhammad Nawaz, Basma Nasir

Neutrosophic Systems with Applications

This paper introduces Emergent Operator Logic (EOL), a framework that treats propositions as continuous operators $F_p:X \rightarrow X$ on a complete metric state space $( X,d )$ and evaluates truth after action via a continuous valuation $V:X \rightarrow [ 0,1 ]$. Logical composition is realized by three operator-level connectives: sequential $p \circ q$(causal order), parallel $p\parallel q$(1-Lipschitz cooperative blend), and the emergent synthesis $E( p,q ) = \frac12( F_p \circ F_q + F_q \circ F_p )$, which symmetrizes non-commuting actions. We provide a Hilbert-style proof system (sound), an algebraic semantics via E-algebras, and show that the category of E-algebras is …

An Interpretable Hybrid Deep And Reinforcement Learning Paradigm For Glioma Prognosis, Renuga Devi M Ms

Theses and Dissertations

Brain tumors are highly aggressive and lethal types of cancer, particularly gliomas. These cancerous growths show complicated pathophysiological behaviours and with poor prognosis despite therapeutic advances. Due to their biological differences and infiltrating growth, as well as overlapping radiological characteristics, they pose great difficulty in diagnosis, grading, and survival prediction. Artificial intelligence technology, which includes machine learning , deep learning, and reinforcement learning has developed into a new paradigm for the automation of brain tumor diagnostics and personalized treatment. The main goal of this study is to create an integrated AI-based framework that can perform brain tumor segmentation, grading and …

Development Of Deep Fused Neural Architecture For Ancient Tamil Palm-Leaf Manuscript Recognition, Hariharan P Mr

Theses and Dissertations

Digitizing Tamil palm-leaf manuscripts is important for education, communication, and the preservation of cultural heritage. The complex structure of the Tamil script, the wide range of handwriting styles, and the degradation seen in ancient Tamil palm-leaf manuscripts make these texts very difficult to read and understand. Digital Image Processing (DIP), document analysis techniques, and traditional Optical Character Recognition (OCR) are unable to handle noise, background interference, faded ink, and limited labelled data, motivating the need for robust, effective Deep Learning (DL)- based solutions.

As a prerequisite to understanding and designing effective recognition systems for ancient manuscripts, this thesis first examines …

Application Of Microbial Fuel Cells For Leachate Bioremediation: The Effects Of Nutrient Nourishment, Umi Sholikah, Fidela Chosta, Tegar Palyus Fiqar, Eka Masrifatus Anifah, Riza Hudayarizka, Tadashi Hibino

Makara Journal of Science

Leachate contains a high organic content, which can cause environmental pollution. The organic content in leachate can be removed through bioremediation using microbial fuel cell (MFC) systems to remediate pollutants and generate bioelectricity. Organic matter is important to the MFC process as both a substrate and a nutrient source. This study investigated the effects of glucose and sodium phosphate buffer nourishment on BOD, COD, and TSS removal and bioelectricity generation. This study used leachate as a substrate and a graphite rod with a surface area of about 32.98 cm² as an electrode. The reactor uses plastic blocks with a …

Sorption Of Lanthanum(Iii) Ions On Impregnated Solid Extractants, Jaloliddin Q. Abdurashidov, Tadjigul R. Odamova, Maksim A. Kamarou, Zuhra Ch. Kadirova, Shahlo Sh. Daminova

CHEMISTRY AND CHEMICAL ENGINEERING

The aim of this work was to obtain solid extractants by impregnating the organic reagent 1-phenyl-1,3-butadione (BNA) onto carriers based on styrene-divinylbenzene copolymers (PAD600, PAD400) as well as activated carbon, and to investigate the sorption isotherms of La³⁺ ions under static conditions. The structure of the sorbents before and after La³⁺ ion sorption was studied using SEM-EDX and IR spectroscopy. The parameters of sorption extraction of La³⁺ ions, including extraction degree, sorbent capacity, and distribution coefficient, were evaluated. It was established that the PAD600-BNA sorbent exhibits the most favorable sorption properties toward La³⁺ ions.

Reconstruction Of Information System Acceptance Model In The Era Of Integrated Artificial Intelligence: A Systematic Literature Review, Ilham, Merlin Apriliyanti

Library Philosophy and Practice (e-journal)

This study aims to explain the rapid development of Artificial Intelligence (AI) which has driven significant transformations in the development and use of information systems. However, most classical information system acceptance models, such as the Technology Acceptance Model (TAM) and  (UTAUT), have not been able to fully explain the unique characteristics of AI-based systems that are autonomous, adaptive, and complex. This study aims to reconstruct the information system acceptance model in the era of integrated AI through a Systematic Literature Review (SLR) approach. This study was conducted using the PRISMA protocol on 130 leading scientific articles indexed by Scopus and …

Sweep Angle Effects Of Flow Over A Seal Whisker-Inspired Undulated Cylinder, Trevor Dunt, Christin T. Murphy, Ondrej Fercak, Raúl Bayoán Cal, Jennifer A. Franck

Mechanical and Materials Engineering Faculty Publications and Presentations

Flow over a seal whisker-inspired undulated cylinder at swept back angles is computationally investigated, comparing the vortex shedding, forces, and wake characteristics to those of an equivalent smooth geometry. Numerous prior studies have demonstrated that undulated cylinders can reduce mean drag and unsteady lift oscillations; however, none have isolated the effects of the sweep angle resulting from whisker positioning in flow. Inspired by the active control seals exert over their whiskers while navigating and sensing in unsteady aquatic environments, this study investigates how such orientation influences the hydrodynamic performance of the geometry. Simulations are performed of flow across a rigid, …

Virtual Humans In Virtual Reality: A Scoping Review On Sociability, Fidelity, And Expression, J K Sangeeth Chandran, Marisa Llorens Salvador, Cathy Ennis

Articles

Introduction:

Virtual reality (VR) systems have evolved significantly over the past decade, enabling immersive experiences with enhanced realism and interactivity. This has motivated an interest in socially oriented applications. As user proxies, Virtual Humans (VHs) play essential roles in such applications. However, despite technological advancements, achieving realistic, expressive, and socially responsive VHs continues to present design and implementation challenges. In this scoping review, we present the state-of-the-art of VR VHs, examining the impact of VHs on the user experience.

Methodology:

We reviewed 59 papers retrieved from five databases across three core themes: the implementation and impact of VH facial expressions, …

Exploiting Chaotic Antenna Arrays For Rf Fingerprint Authentication And Physical Layer Security In Wireless Communications, Joshua K. Thomas Ranstrom

USF Tampa Graduate Theses and Dissertations

Modern wireless networks support a wide range of applications including consumer devices, healthcare monitoring, industrial automation, smart grids, and military platforms, in which transmitted information is often highly sensitive and subject to stringent confidentiality and integrity requirements. As wireless communication operates over an open broadcast medium it exposes systems to physical layer (PHY) threats such as eavesdropping, in which an adversary passively intercepts transmitted information, and spoofing, in which a forged transmission misleads the receiver about device identity. Cryptographic defenses address both threats but require computational assumptions and key management infrastructure that may not scale to dense or resource-constrained deployments. …

Making Water Visible: Neutral, Verification-First Infrastructure For Basin-Scale Water Accountability, Eric Mcculley

Spring Runoff Conference

Every Tool Tells You What the Lake Level WAS.

None Tell You What It WILL BE.

• Current monitoring is retrospective — USGS gauges and DWR reports show only historical data
• Decision lag: By the time you see the problem, it’s too late to adjust
• 2024–2025 season: GSL dropped to record lows before upstream conservation kicked in
• Agricultural users, municipalities, and GSL all compete for limited supply with imperfect information

Meteorological Monitoring With Real-Time Data Processing Using Uas, Htoo Poe Sar, Cal Coopmans, Sierra Young

Spring Runoff Conference

Background and Motivation 

• Agricultural water management requires information about crop water use and water consumption 
• Remote sensing methods are used to model and estimate environmental processes – but they require weather data 

Utah Prototype For Visualizing Vulnerabilities Downstream Of Dams, Courtney Flint, Michael Englert, Nicolas Holden

Spring Runoff Conference

The climate question 

• Overtopping = 1/3^rd of all dam failures (ASDSO) 
• Drought concerns and supply uncertainty motivate keeping reservoirs full 
• Overtopping likelier if extreme storm when reservoir full 
• Over much of the country the probability of the annual daily maximum rainfall > 100-year event and the preceding 30-day rainfall >10-year event are going up (Hwang & Lall 2024) 

The Reform Of Prior Appropriation And The Fate Of Great Salt Lake, Christopher L. Lant, Rocky Seeley, Beth Parker, Andrew Follett

Spring Runoff Conference

Legal Issues Raised by the Decline of Great Salt Lake

• Violations of Clean Air Act from dust threaten federal intervention
• Petition to list Wilson’s Phalarope as an endangered species

Microplastics As Physical Hubs Amplifying Antiobiotic Resistance Under Mixed-Contaminant Selection In Wastewater Systems, Liyuan Hou

Spring Runoff Conference

Microplastics (MPs) are tiny plastic particles within the range of (0.1 mm to 5 mm) in size

Integrating Hydrology And Human Water Footprints: A Case Study Of The Great Salt Lake Watershed, Rocky Lynne Seeley

Spring Runoff Conference

A new model, comprehensively integrating hydrologic, water footprint, and virtual water data

Balancing Water Needs In Walker Basin, Peter Stanton

Spring Runoff Conference

Walker Lake 

Supported 50% of Mineral County economy 

Traditional homeland of Walker River Paiute Tribe (Agai Dicutta) 

World-class fishery

Annual Loon Festival

Electrochemical Sensors And Processes In Molten Salts: Chlorine Production, Electroanalytical Chemistry, And Thin-Layer Electrodes, Tyler Williams

Theses and Dissertations

Molten salt electrochemistry has historically played a substantial role in the metallurgical industry, and this trend is only accelerating in areas of green steel, direct ore processing, and rare-earth processing. In addition, the recent surge of interest in molten salt nuclear reactors has provided an additional application for molten salt electrochemical processes, including the reprocessing of used nuclear fuels and the development of electrochemical sensors for nuclear applications. In this work, the development of a chloride volatility separation process for used nuclear fuel is advanced; electroanalytical methods are improved for identifying and quantifying unknown analytes with and without the presence …

Mould Risk Prevention In Healthcare Facilities In Hot And Humid Climates Through Acmv Air Supply Parameter Optimisation: Development And Validation Of A Multi-Objective Optimisation Framework, Jiang Kaiyun

Student Works (2020-2029)

Air conditioning mechanical ventilation (ACMV) supply parameters—such as airflow rate, temperature, and humidity—significantly influence indoor thermal and hygrometric conditions, influencing airflow patterns and mould proliferation. In healthcare facilities, mould poses significant health risks, particularly for immunocompromised patients, contributing to respiratory issues, allergic reactions, and infections. While ACMV systems are central to infection control and indoor air quality, conventional designs often fall short in hot and humid climates. Existing practices typically prioritise high air change rates (ACH) without sufficient dehumidification, underestimate humidity control, and assume spatial homogeneity, overlooking microclimates conducive to localised mould growth. To address these limitations, this research develops …

Recent Advances In Graphene-Based Field-Effect Transistor Biosensors For Disease Biomarker Detection And Clinical Prospects, Deeksha Nagpal, Anup Singh, John Link, Abijeet Mehta, Ashok Kumar, Vinay Bhudhraja

Health & Biomedical Sciences Faculty Publications

Field-effect transistor (FET) biosensors using graphene have become one of the most promising biosensing platforms for the early diagnosis of diseases with features such as high sensitivity, label-free detection and application compatibility with point-of-care systems. Herein, we critically discuss recent advances in graphene FET (GFET) biosensor development toward clinically relevant biomarkers associated with representative diseases including cancer, neurodegenerative disease, infectious disease, and inflammatory conditions. Recent progress was reviewed to evaluate GFET architectures, surface functionalization methods, and detection quality. The biomarkers explored were clusterin in Alzheimer’s disease, thrombin in coagulopathy, estrogen receptor α (ER-α) in breast cancer, Carcinoembryonic antigen in lung …

Secure Machine Learning In Networking Systems, Wenwei Zhao

USF Tampa Graduate Theses and Dissertations

With the rapid integration of Machine Learning (ML) into networking systems, ensuring the security and trustworthiness of these intelligent frameworks has become a paramount concern. While ML offers unprecedented capabilities in spectrum management and collaborative learning, it also introduces novel vulnerabilities that can be exploited by sophisticated adversaries. This dissertation investigates and addresses critical security challenges across three key dimensions of ML-driven networking: adversarial spectrum sensing, the security of privacy-preserving unlearning processes, and efficient post-attack model recovery.

First, we address the threat of adversarial spectrum attacks in cognitive radio networks, where malicious nodes manipulate sensing reports to disrupt spectrum access. …

Multi-Modal Tensor Fusion For Alzheimer’S Disease Recognition, Mason Li, Tiffany Le, Jiajing Huang, Yuxin Wen

Engineering Faculty Articles and Research

Accurate and early diagnosis of Alzheimer’s disease (AD) is critical for effective intervention, disease monitoring, and patient care. Traditional diagnostic approaches rely on a single modality, such as clinical assessments, neuroimaging, or genetic markers, which may fail to capture the complex, multifaceted nature of AD. Multimodal learning has therefore been explored to integrate complementary information across data sources. However, conventional fusion strategies, including early feature concatenation and late decision-level fusion, often model modalities independently and fail to capture high-order cross-modal interactions. To address these limitations, we propose a multimodal tensor fusion network (MTFN) that integrates heterogeneous data sources, including visual …

Pheasant Forever Loan Program, Will Benkelman

Spring Runoff Conference

Bear River Watershed Community Conservation Program

Our Mission: Pheasants Forever (PF) and Quail Forever’s (QF) mission is to conserve pheasants, quail, and other wildlife through habitat improvements, public access, education, and conservation advocacy.

Logan River Watershed Draft Environmental Impact Statement, Zan Murray

Spring Runoff Conference

Agenda:

Program Overview: PL-566 & NEPA

Project Need and Purpose

Project Alternatives

Preferred Alternative Costs and Benefits

NEPA Timeline/Current Schedule

Questions

Cache Water District Conservation Program, Autumn Matthews

Spring Runoff Conference

Conservation and Education Programs.

Conservation Goals:

Increase community awareness through outreach, workshops, and community programs that support water conservation.

Expand and improve programs that encourage water savings and more efficient water use across the community.

Help residents save money by promoting water efficiency and reducing unnecessary water use.

The Writing On The Wall: The Rise Of Applied Ai And The Life-Or-Death Choice Every Ceo Must Make Now, Gary Sheng, Ron Roberts

Digital Laboratory: Publisher of Internet Journal

Applied AI is putting AI to its highest and best use: running your organization as autonomously as possible so you can deliver value for humanity while maximizing the scale of the value. The economy is splitting. Organizations that adopt applied AI are expanding their capacity, accelerating their impact, and pulling ahead. Those that don't are quietly becoming irrelevant, not because they're doing bad work, but because the gap between what they can do and what the moment requires is widening every day. This is not a technology question. It is a leadership question. This paper is written for organizational leaders, …

Photoplethysmography For Measuring Cognitive Load In Xr Environments: A Systematic Review, Alya Alshehhi, Qammer H. Abbasi, Rami Ghannam

School of Engineering Technology Faculty Publications

Photoplethysmography (PPG) is a low-cost, low-power biosensing technology with growing applications in education, particularly for monitoring cognitive load in eXtended Reality (XR) learning environments. Measuring cognitive load is critical for preventing overload and optimising immersive learning, yet existing approaches such as self-reports or Electroencephalography (EEG) are often intrusive, costly or impractical for real-time use. This systematic review is the first to synthesize a decade of research (2015–2025) on the use of PPG for cognitive load measurement in XR. Twenty-three studies were identified and analysed according to PRISMA guidelines, with attention to sensor placement, integration with other modalities, research design, and …

Marine Vehicle Dynamics Using Koopman Operator Theory With Hybrid Observables, Mikhalib A L Green

LSU Master's Theses

Accurate modeling of marine vehicle dynamics remains challenging due to strong nonlinear hydrodynamic effects, environmental disturbances, and sensitivity to configuration changes, particularly for small-scale platforms. Classical physics-based models require extensive parameter identification and often exhibit degraded performance outside narrow operating regimes, while purely data-driven approaches may lack structure or impose high computational cost. This thesis presents a data-driven Koopman operator framework with hybrid observables for modeling the dynamics of unmanned marine vehicles. The proposed approach combines structured monomial observables with a learned neural network embedding to construct a lifted state representation in which the nonlinear vehicle dynamics are approximated by …

Large-Scale File Fragment Classification Via Multi-View Learning, Samuel Hildebrand

LSU Master's Theses

File reassembly is one of the most fundamental tasks in digital forensics, enabling recovery of data from potentially damaged storage media even when file system metadata is unavailable. This thesis reviews more than two decades of work in the realm of file carving, with a particular focus on fragmented file carving, which remains a focus of research, and file fragment classification, a principal component of fragmented file carving. This thesis serves a literature review of both file carving and fragmented file carving, surveys the massive amounts of data needed for the task of fragment classification and the datasets that serve …

Geometry-Driven Controls On Hydrothermal Natural Hydrogen Generation From Magnetite Mineral, Kaveh Moghanirahimi, Lionel Esteban, Valeriya Shulakova, Muhammad Ali, Stefan Iglauer, Alireza Keshavarz

Research outputs 2022 to 2026

Despite recent efforts to investigate hydrothermal natural hydrogen production from magnetite, the effects of sample geometry and surface area on hydrogen yields remain inadequately characterized. This study compares hydrogen production from natural magnetite prepared as an intact slab versus fine powder under identical experimental conditions (pH 9, 200 °C, 103.4 bar, 60 days). The results show that magnetite powder yielded 0.052 mmol g−1 rock hydrogen, approximately fivefold higher than the slab. Comprehensive characterization of the magnetite slab revealed increased surface roughness after reaction. Quantitative XRD analysis showed substantial phase conversion from magnetite to hematite, consistent with oxidation of Fe2+ to …