Digital Commons Network^™

Use Of Alaçatı Stone Aggregate In Conventional And Self-Consolidating Concretes, Tahir Kemal Erdem

Journal of Sustainable Construction Materials and Technologies

Alaçatı stone or Alapietra stone is widely used mostly for building cladding. However, its production and use in the construction sites generate high amounts of waste (debris), causing environmental pollution in Çeşme which is a touristic district. This study investigated the reuse of Alaçatı stone as lightweight aggregate (LWA) in both conventional and self-consolidating concrete (SCC) mixtures. The concretes were prepared to have three different water-to-cement ratios (w/c), which were 0.38, 0.42 and 0.48. For each w/c, a conventional concrete mixture and two SCC mixtures with 60 or 70 cm slump flow were produced by varying the superplasticizer SP content. …

Mixed Linear Equation–Inequality Systems Over The Pura Vida Neutrosophic Algebra, Muhammad Rayyanu Abdullahi, Abdulhadi Aminu

Neutrosophic Systems with Applications

This paper proposes a neutrosophic extension of max-plus algebra for solving mixed systems of linear equations and inequalities.Classical max-plus algebra is a powerful tool for modeling synchronization in discrete-event systems LastNatpreClose LastNatClose, but it assumes fully deterministic data.To incorporate uncertainty and indeterminacy, we reformulate the framework so that coefficients and variables are expressed as neutrosophic numbers

γ+λI,γ,λ∈ℝ,I∈[0,1],

where I quantifies the degree of indeterminacy.

We redefine the max-plus semiring in this neutrosophic setting, extend solvability and uniqueness results, and adapt the ONEMLP-EI algorithm LastNatpreClose LastNatClose to handle neutrosophic …

Mapping Sustainability To Cybernetic-Generative Artificial Intelligence-Based Education: An Innovative Neutrosophic Orbifold-Lattice Methodology, Mona Mohamed, Ahmed A. Metwaly

Neutrosophic Systems with Applications

The lightning-fast development of artificial intelligence (AI), notably generative artificial intelligence (Gen AI) technologies, has intrigued multiple disciplines, particularly education. In this context, Large Language Models (LLMs) serve as beneficial cognitive resources that address knowledge deficits and provide tailored educational support for learners and staff. For learners, LLMs are regarded as knowledgeable educators who offer prompt, focused responses and rationales to tricky queries. Whereby LLMs for staff, Strength enhancer, automating tedious tasks, and creating intelligent resources.Gen AI’s rapid growth offers enormous obstacles for educational systems, compelling them to discover solutions for these obstacles.

Conceptually, cybernetics is leveraged for bridging the …

Neutrosophic Σ - Baire Spaces, R. Vijayalakshmi, F. Josephine Daisy, M. Simaringa

Neutrosophic Systems with Applications

A Neutrosophic Baire Space extends the concept of Baire Space from classical topology to the realm of neutrosophic topology which deals with sets and spaces where truth, falsehood and indeterminancy are explicitly considered. In this paper the concept of neutrosophic σ - baire spaces are introduced in Neutrosophic topological spaces. Also Neutrosophic σ - dense, Neutrosophic σ - nowhere dense, Neutrosophic σ -first category and Neutrosophic σ - second category sets are defined. Several characterizations of neutrosophic σ - baire spaces are investigated and explained using examples and the conditions under which a neutrosophic topological space becomes a neutrosophic σ …

Advancements In Perfect Matchings Within Neutrosophic Fuzzy Graphs: Theory And Applications, Muhammad Saeed, Fatima Razaq

Neutrosophic Systems with Applications

Graph theory has been widely used in exemplifying relational structure, and a greater generalization to fuzzy and neutrosophic space enables the exemplification of uncertainty, indeterminacy, and inconsistency in complex systems. Some of these extensions include the neutrosophic fuzzy graphs that provide a more detailed description of the loose relations between the vertices and the edges. However, unlike in classical graph theory, where the concepts of matching and perfect matching are well developed, very little has been studied on how the two concepts can be extended to neutrosophic fuzzy graphs. To seal this gap, the current paper develops and defines the …

Cache-Conscious Sparse Matrix Dense Matrix Multiplication On Gpus, Haoqiang Guo

LSU Doctoral Dissertations

Over the past decade, high-performance deep learning has evolved into a critical research domain, driven by the demand for efficient models and high inference throughput. Deep learning architectures have shifted from stacked convolutional layers to transformer-based models, while pruning techniques and graph-structured data have established sparse matrix–dense matrix multiplication (SpMM) as a fundamental kernel—particularly in graph neural networks (GNNs). Modern GPUs, with their massive parallelism and high-bandwidth memory, offer immense potential for accelerating these workloads. While SpMM implementations using the compressed sparse row (CSR) format remain common to avoid conversion overhead, preprocessing-based methods have recently demonstrated superior potential. In GNN …

Integrated Chitosan-Based Coagulation And Microbubble Pre-Treatment For Improved Microplastic Fibre Removal From Water, Nimesha Thathsarani, Mohadeseh Najafi, Mehdi Khiadani, Muhammad Rizwan Azhar, Masoumeh Zargar

Research outputs 2022 to 2026

Microplastic fibres (MPFs) dominate treatment plant influents but are difficult to remove due to their morphology, composition and low density, limiting the effectiveness of coagulation flocculation pre-treatment. Integrating coagulation-flocculation with microbubble introduction offers a sustainable route to improve MPF removal, yet conventional inorganic coagulants often require high dosages, show strong pH dependence, and pose environmental risks. Chitosan is a biodegradable and non-toxic green alternative, but its application is limited due to its low solubility and pH sensitivity. In this study, two amphoteric derivatives of Chitosan have been successfully developed: CMC-CTA, produced by modifying carboxymethyl Chitosan (CMC) with 3-chloro-2-hydroxypropyl trimethyl ammonium …

Thermoeconomic Optimization Of Climate-Adaptive Solar And Wind Multi-Generation Systems Using Artificial Intelligence And Thermal Energy Recovery, Ehsanolah Assareh, Nima Izadyar, Emad Tandis, Mehdi Khiadani, Amir Shahavand, Neha Agarwal, Arian Gerami, Ahmed Rezk, Minkyu Kim, Reza Kord, Tahereh Pirhoushyaran, Mehdi Hosseinzadeh, Saleh Mobayen

Research outputs 2022 to 2026

This study presents a hybrid multi-generation energy system designed to overcome solar intermittency while meeting the global demand for integrated delivery of electricity, water, cooling, and sustainable fuels in the transition to decarbonization. The engineering application integrates solar thermal and wind energy with a modified Brayton cycle, a Steam Rankine Cycle (SRC), and a Thermoelectric Generator (TEG) to simultaneously produce electricity, fresh water via Reverse Osmosis (RO), hydrogen and oxygen via Proton Exchange Membrane Electrolyzer (PEME), and cooling (via absorption chiller) within a unified optimization framework. The system was modeled using Engineering Equation Solver (EES) and optimized via Response Surface …

Accurate Prediction Of Geometrical Parameters Of An Ultra-Broadband Metamaterial Absorber Using Machine Learning, Md. Rezwan Ahmed, Oishi Jyoti, Pritu Parna Sarkar, Mohammod Abdul Motin, Md. Selim Habib, Md. Samiul Habib

Electrical and Computer Engineering Faculty Publications

In this paper, we systematically demonstrate the design and analysis of a new type of ultra-broadband tunable metamaterial perfect absorber (MPA) comprising a top vanadium dioxide (VO2) based patterned resonating patch, a continuous metallic film at the bottom, and an intermediate dielectric substrate having a thickness of only 0.18 at the center working frequency. The simulation results reveal that the absorber achieves a bandwidth of 7.26 THz, ranging from 5.40 THz to 12.66 THz, with more than 90% absorptance and an average absorption of 98.21% under normal incidence of the incoming THz wave. Furthermore, absorptance exceeding 99% is achieved between …

A Modular Design And Construction Framework For Enhancing The Sustainability Of National Housing Projects In Egypt, Ayman Ahmed Ezzat, Farah Ibrahim El Sahzly

Mansoura Engineering Journal

Egypt’s national housing sector faces mounting sustainability challenges due to outdated design and construction practices that cause material waste, high costs, and inefficient resource use. This study develops a framework for integrating modular design and construction principles into Egypt’s National Housing Projects (NHPs) to strengthen their social, economic, and environmental sustainability. A mixed-method approach combined a literature review, case studies, and a survey of Egyptian Architectural Design Firms (ADFs). The literature review revealed a global consensus on the benefits of modular design and construction, such as improved efficiency, reduced waste, and faster project delivery, yet found limited adoption in Egypt …

Application Of Reinforcement Learning To Precision Aerial Delivery System Control In Adverse Wind Conditions, Radman Zarbock

The Journal of Purdue Undergraduate Research

Precision aerial delivery systems (PADS) are a subset of airdropped parachute-leveraging package delivery systems that use autonomous guidance, navigation, and control (GNC) to reach targets with high degrees of accuracy. This technology emerged in the 1990s, and strides have been made since to improve the reliability of traditional physics-based controllers that guide PADS. However, these algorithms still struggle to deliver acceptable performance results when PADS are subjected to austere operating environments, such as those with unpredictable wind. Building on a foundational study in 2022 that used artificial intelligence (AI) and machine learning to improve PADS GNC performance, this study aims …

The Automation Of Lamp Biomolecular Assays Using The Eppendorf Epmotion™ 5075 Robotic Pipettor, Shamalaa Sathiasealan

The Journal of Purdue Undergraduate Research

Loop-mediated isothermal amplification (LAMP) is a nucleic acid amplification technique known for its specificity and efficiency in less controlled environments than laboratories. Designing novel LAMP reagents requires strict contamination control, and unlike the more commonly automated polymerase chain reaction, LAMP’s heightened sensitivity poses unique challenges to automation. To address this, we optimized automation procedures for LAMP using a robotic pipettor without altering the core assay itself. This project focuses on minimizing contamination using sealed plates, reducing operator intervention by automating pipetting, and preventing enzyme degradation using cooling plates. Our approach employs pre-slit pierceable seals and cooling blocks to prevent cross-contamination …

Implications Of Noninvasive Blood Pressure Pumps On Mean Arterial Pressure, Sammie Miller

The Journal of Purdue Undergraduate Research

Noninvasive blood pressure pumps are recommended as the primary form of acquiring patient blood pressure to test for blood poisoning. The Philips IntelliVue Patient Monitor MP5, the Philips SureSigns VS4/VS3 Vital Signs Monitor, the Philips EarlyVue VS30 Vital Signs Monitor, the Philips IntelliVue Patent Monitor MP2/X2, the Philips IntelliVue Patent Monitor MMS, and the Philips IntelliVue Patent Monitor MP3/X3 were tested for accuracy. The control condition for hypotension was 95/55 (68) mmHg, normal blood pressure was 120/80 (93) mmHg, and hypertension was 155/85 (108) mmHg. The order of values is arrayed according to diastolic, systolic, and mean arterial pressure. The …

From Test Tracks To Carbon Tracks: Evaluating The Carbon Emissions Of Autonomous Vehicle Development, Sarah Deniz

The Journal of Purdue Undergraduate Research

Autonomous vehicle (AV) development has seen a sharp increase in the past 10 years, and there is a significant energy cost of training AVs to meet safety regulations. The training process includes virtual simulation training as well as physical training on public roads and within private testing facilities. The high energy expenditure associated with AV training has a respective environmental cost that can be quantified as a lifetime “carbon debt” of released carbon emissions. The environmental sustainability of AVs has not been thoroughly studied in currently available literature, and this paper showcases a model that quantifies the total “carbon debt” …

A Proposed Secure Video Using Lightweight Chacha20 Based On Chaotic Map, Hadeel Mohammed Taher, Ali Makki Sagheer

Iraqi Journal for Computer Science and Mathematics

Multimedia content has become a necessary part of our daily lives in many domains, including medical imaging, surveillance, and entertainment. Among all multimedia categories, video is mainly critical because of its high content density and large size. For that, ensuring the security of video has become a necessity for transmission and storage. This paper proposed an enhancement to the Chacha algorithm integration with a hybrid chaotic map for generating keys to secure video. The main motivation for the selection ChaCha algorithm is that it is fast, simple, and appropriate for widespread applications, whereas the motivation for using chaos is to …

Control Techniques And Design Of Load-Side Controls For The Mitigation Of Late-Time High-Altitude Electromagnetic Pulse, Connor A. Lehman, Rush Robinett, Wayne Weaver, David G. Wilson

Michigan Tech Publications

This paper introduces a novel control archetype designed to mitigate high-altitude electromagnetic pulse (HEMP) (Formula presented.) disturbances on the power grid, as well as information on performance and specifications of different control laws for the controller archetype. This method of protection has been overlooked in the literature until now. A controlled voltage supply is placed on the load-side of a transformer, diverting unwanted power from the transformer core to prevent saturation. The controlled voltage source is modeled using four control laws: an integral controller (capacitor), Linear Quadratic Regulator (LQR), an energy storage minimized feedforward control law, and a Hamiltonian feedback …

Probing The Mechanisms Of Reinforcement Learning: Reinforcement Learning, Ventral Striatal Astrocytes, And The Dynamic Coordination Of Information Seeking With Learning, Fatih Sogukpinar

McKelvey School of Engineering Graduate Student Theses & Dissertations

While reinforcement learning has been a vital component in artificial intelligence and machine learning, there exist many open questions about its implementations and how to improve them, in both minds and machines. Among these are i) the contribution of non-neuronal cell types to reinforcement learning, and ii) information-seeking behavior during reinforcement learning. In this thesis, we studied these main topics pertaining to reinforcement learning. In the first chapter, we examined the role of astrocytes in reinforcement learning, and in the second, we investigated human information seeking during reinforcement learning. Neurons in the human and animal brain have been known to …

Evaluation And Calibration Of Coupled Low-Cost Particulate Matter Sensors, Tyler Cargill

McKelvey School of Engineering Graduate Student Theses & Dissertations

Low-cost sensors (LCS), if used appropriately, are useful instruments to elucidate human exposure to particles suspended in the air (i.e. particulate matter, PM). Characterizing this exposure is crucial, as exposure to PM2.5 (i.e. particles with aerodynamic diameter ≤ 2.5 μm) negatively affects the respiratory, cardiovascular, and other organ systems and is the leading environmental burden for global mortality. Similarly, exposure to particles with aerodynamic diameters greater than 2.5 μm and less than or equal to 10 μm, here forth termed as PMCoarse, is known to cause health ailments for the upper respiratory system. This dissertation focuses on the MODULAIRTM-PM (MOD-PM), …

Transforming Co2 Into Value-Added Products: Liquefied Petroleum Gas And Solid Carbon Materials, Kaiying Wang

McKelvey School of Engineering Graduate Student Theses & Dissertations

The escalating climate crisis, driven by anthropogenic carbon dioxide (CO2) emissions, necessitates the development of advanced Carbon Capture and Utilization (CCU) technologies. A critical distinction within CCU lies between transient carbon cycling, which converts CO2 into fuels, and permanent sequestration, which transforms it into durable, solid materials. This dissertation addresses fundamental catalytic challenges across both pathways to develop economically viable and technologically sound solutions for CO2 valorization. This work seeks to answer two primary research questions: (1) How can the catalyst temperature mismatch and stability limitations in the tandem conversion of CO2 to Liquefied Petroleum Gas (LPG) be overcome to …

An Integrated Multivariate Econometric Modeling Framework For Risky Driving Behavior Related Crashes: Evaluating Crash Risk And Severity Across Zones, Pabitra Kumar Roy

Dissertations and Theses

Rapid advancements in crash modeling have yet to fully integrate multiple behavior-driven crash types and their severity outcomes within a single, scalable framework, specially in a way that captures the sequential nature of these behaviors where one risky action may amplify another. This study proposed an integrated multivariate econometric framework to jointly model crash frequency and severity outcomes for three major behaviorally driven crash types: alcohol-related, distraction-related, and aggressive-driving-related crashes. Specifically, using Oregon's 2022 census block group-level crash data, we propose an Integrated Multivariate Negative Binomial – Generalized Ordered Probit Fractional Split (IMNB–GOPFS) model to analyze these dimensions simultaneously while …

Global Carbon Mitigation From Bacnet Adoption In Commercial Building Automation Systems 1995-2030, Fei Han, Weiwei Mo

Faculty Publications

Building Automation Systems (BAS) play a critical role in improving energy efficiency in commercial buildings, yet their long-term carbon mitigation potential has not been comprehensively quantified at a global scale. As the dominant open communication standard enabling interoperability in modern BAS, BACnet has played a central role in expanding automation adoption worldwide. This study develops a cohort-based modeling framework to estimate historical (1995–2025) and projected (2026–2030) CO₂e mitigation from BACnet-based BAS adoption across four regions: the United States, Canada, Europe, and the Rest of World (ROW). The model combines regional commercial floor area, baseline electricity and natural-gas energy intensity, BAS …

Nashville Bus System Efficiency, Koryn Harms

Student Scholar Symposium

Nashville’s recently approved “Choose How You Move” plan creates a dedicated funding source for transportation improvements in the city. A key component of the plan is to increase transit service throughout Nashville to make it more accessible and reliable. In order to optimize the use of the funding, an analysis of existing conditions would allow officials to locate and target specific areas in need of improvement. The aim of this research is to determine the efficiency of different bus routes in Nashville based on ridership and on-time rates. The analysis will focus on the eight primary bus routes in the …

Fasttree-Guided Genetic Algorithm For Credit Scoring Feature Selection, Rashed Bahlool, Nabil Hewahi Prof., Youssef Harrath Dr.

Research & Publications

Feature selection is pivotal in enhancing the efficiency of credit scoring predictions, where misclassifications are critical because they can result in financial losses for lenders and exclusion of eligible borrowers. While traditional feature selection methods can improve accuracy and class separation, they often struggle to maintain consistent performance aligned with institutional preferences across datasets of varying size and imbalance. This study introduces a FastTree-Guided Genetic Algorithm (FT-GA) that combines gradient-boosted learning with evolutionary optimization to prioritize class separability and minimize falserisk exposure. In contrast to traditional approaches, FT-GA provides fine-grained search guidance by acknowledging that false positives and false negatives …

Geolocation Algorithms And On-Orbit Calibration For Atmospheric Waves Experiment, Eric Mckinney, Keith Blonquist, Zack Hatfield, Connor Waite, Brooke Wursten, Pedro Sevilla, Ludger Scherliess, Harri Latvakoski, Greg Cantwell

Space Dynamics Laboratory Publications

1 - Overview

• Motivation: Atmospheric gravity waves can degrade navigation, tracking, communications, and geolocation system signals. They can also affect satellite/debris orbit and reentry predictions.
• NASA's Atmospheric Waves Experiment (AWE) is currently on ISS measuring atmospheric gravity waves in the OH airglow layer at ~87 km altitude
• Impact: Improved space weather understanding, estimation, and forecasting

Cybernest: A Step Forward For Training Cybersecurity Professionals, Justin Mott

Theses and Dissertations

The growing complexity of cyber threats has exposed a critical gap between academic cybersecurity education and the operational demands of the workforce. This thesis presents a pilot study for a use-case of CyberNEST, a simulation-based training platform that integrates adversary emulation and threat-informed learning to create realistic, hands-on experiences for cybersecurity students.(17) The system was designed to replicate Security Operations Center (SOC) environments and incident response workflows, allowing participants to investigate, analyze, and respond to simulated attacks under authentic conditions. This pilot-study performed training simulations using the CyberNEST platform, developed by Brigham Young University, with students, collecting data from pre- …

Power Solutions For Large Loads: Mastering Electrical Supply For New Industrial Facilities, Chris Boyer, William Bourgeois, Skyler Bryant

Mavs Open Press Open Educational Resources - Archive

The next 25 years will see unprecedented growth in U.S. electricity demand, driven primarily by data centers for AI and industrial electrification. This surge presents unique challenges for power generation, especially for large loads requiring hundreds of megawatts to gigawatts from a single location. Conventional grid expansion is inadequate, prompting the need for innovative, flexible, and sustainable solutions. This book explores the opportunities, challenges, and hybrid generation strategies to support the evolving landscape of large-scale electric loads.

A Comprehensive Review Of Dental Diseases Detection And Classification Based On Artificial Intelligence Techniques, Nermeen N. Noaman, Yasmin M. Alsakar, Naira E. Elazab, Waleed M. Mohamed, Mohamed E. Ezzat, Mohammed M. Elmogy

Mansoura Engineering Journal

In dentistry, many diseases, such as gum, cavities, and oral cancer, affect people of all ages. Early treatment and diagnosis are crucial for minimizing dental diseases' effect on overall health and saving money in the long run. Traditional dental diagnosis methods, such as manual probing and visual inspection, are time-consuming and can be subject to human errors. Hence, a computer-aided diagnosis system based on computer vision and artificial intelligence (AI) techniques is needed. The considerable progress in computer vision and AI techniques offers many possibilities in dental diagnosis based on dental X-ray imaging modalities. Dental X-rays are used to diagnose …

Evaluating Traffic Safety And Geometric Characteristics Using Machine Learning Ensemble Techniques: A Case Study Of Egyptian Rural Multi-Lane Divided Roads, Sania R. Elagamy, Ahmed N. Awaad, Usama E. Shahdah, Sherif M. El-Badawy, Marwa E. Elbany, Eman K. Ali

Mansoura Engineering Journal

Crash prediction models are essential for evaluating traffic safety by analyzing crash occurrence, frequency, or severity. Recently, machine learning techniques have gained prominence in statistical regression modeling and data analysis. This study assesses the effectiveness of machine learning in predicting crashes on Egyptian rural multi-lane divided roads using limited regional data. Supervised machine learning ensemble techniques were applied to predict crash-prone segments (classification) and estimate the total number of crashes per segment (regression). A comparative analysis aims to identify the most suitable method. The Synthetic Minority Oversampling Technique (SMOTE) addressed data imbalance, while K-means Clustering (KC) enhanced regression model accuracy, …

Integration Of Renewable Energy Networks Into Smart Grid Infrastructure: A Matlab/Simulink-Based Simulation Approach, Saturday Noghayin Osaretin

Electrical Engineering Theses

The rising demand for sustainable and resilient energy systems has accelerated the transformation from traditional power grids toward smart grids integrated with renewable energy networks. This thesis explores the integration of solar energy sources into smart grids to replace conventional fossil-fuel-based generation. A simulation model is developed using MATLAB/Simulink to evaluate the operational performance, stability, and adaptability of the proposed smart grid architecture. Key challenges, including intermittency, synchronization, and bidirectional power flows, are identified, and corresponding solutions, such as energy storage systems, are proposed to address these issues. Simulation results demonstrate enhanced grid stability, reduced carbon footprint, and improved power …

Hybrid Machine And Deep Learning Framework For Secure Fog-Based Vanets, Hadeer Kamal, Amira Y. Haikal, Mahmoud M. Saafan

Mansoura Engineering Journal

As a conventional implementation of intelligent transportation systems (ITS), a Vehicular Ad-hoc Network (VANET) facilitates smart communication between vehicle nodes and network infrastructures. Nevertheless, the distributed and dynamic characteristics of VANETs make them vulnerable to numerous cyberattacks like DOS and spoofing. Rapid detection of these threats is crucial. It is essential to respond promptly to mitigate threats before they impact vehicle coordination or infrastructure functionality. In this context, fog computing presents an effective solution. Transferring computational tasks from centralized cloud servers to fog nodes allows for quicker detection within the VANET environment. This research utilizes computational resources for swift and …