Physical Sciences and Mathematics Commons^™

Relaxation Maximum-Based Iteration Method For Solving The Generalized Absolute Value Equation, Ximing Fang, Zhidong Wang, Zhijun Qiao

School of Mathematical & Statistical Sciences Faculty Publications

The generalized absolute value equation (GAVE) has wide applications in scientific computing. Establishing a high performance computing method to solve the GAVE is a hot research topic in recent years. In this paper, with the aid of the maximum function, the GAVE is decomposed of two equations, and then we present the relaxation maximum-based (RM) iteration method. To see the feasibility of the method, we discuss the necessary and sufficient conditions for the GAVE to have a unique solution. Next, the convergence analysis of the RM iteration is discussed under some convergence conditions. Moreover, some numerical examples of low and …

The Design And Optimization Of Optical Systems For The Detection And Characterization Of Phytoplankton, Caitlyn M. English

Theses and Dissertations

Phytoplankton are among the smallest organisms found in marine and freshwater ecosystems. Despite their small size, phytoplankton play a vital role in sustaining life on Earth as primary producers. Through oxygenic photosynthesis phytoplankton are also responsible for large scale carbon fixation and oxygen production. The distribution of phytoplankton in a given water resource can exhibit a high degree of variability across time and space, and this is in large part due to their varying sensitivities to environmental conditions such as resource availability and grazing by predator organisms. As such, phytoplankton community structure is widely used as an investigative tool for …

Artificial Intelligence Applications For Grid-Connected Solar Inverters, Utkirjon Ubaydullaev, Sarvinoz Mirzaeva, Hasan Mustafoev

Chemical Technology, Control and Management

The increasing global demand for renewable energy has highlighted the importance of grid-connected solar inverters in ensuring efficient and stable power conversion. However, challenges such as fluctuations in solar energy generation, grid disturbances, and power quality issues necessitate advanced control strategies. The integration of artificial intelligence (AI) into solar inverters presents a transformative solution, enhancing performance, adaptability, and reliability in real-world applications.

This review explores the role of AI techniques, including machine learning (ML), deep learning (DL), fuzzy logic, and reinforcement learning (RL), in optimizing key inverter functionalities such as maximum power point tracking (MPPT), fault detection, power quality enhancement, …

Automating Course Scheduling With Linear Programming And The Python Pulp Framework: First Steps, George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

This article presents a pragmatic approach to automating course scheduling in an academic setting using linear programming.

We explore how linear optimization via current open-source tools can efficiently handle scheduling constraints such as instructor preferences, teaching loads, course section requirements, and specific time slots. Using Python’s PuLP library and matplotlib for visualization, we built a flexible and accessible scheduling system.

Our research prototype balances course assignments while addressing department-specific needs, demonstrating how linear programming can simplify academic scheduling and improve efficiency.

Although this is a research prototype, our results already demonstrate the ability to generate a correct course schedule that …

Analytical Dispatch Strategies For Pumped Storage Hydro: A Conditional Dynamic Programming Approach To Discontinuous Multi-Period Optimization Problems, Jian Liu, Jianwen Zhang, Zaiwu Gong, Donald C. Wunsch, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

The increasing integration of renewable energy sources like wind and solar poses significant challenges to secure and stable grid operation. Energy storage systems, particularly pumped storage hydro (PSH), play a crucial role in balancing power supply and demand. Traditional analytical studies of PSH economic dispatch problems often assume zero lower bounds for generating and pumping rates to simplify analysis and derive analytical solutions for multi-period optimization problems. However, the inherent mechanical design constraints of PSH require non-zero minimum flow rates for efficient operation. We analyze two scenarios, merchants having PSH only and merchants having both PSH and wind farms. In …

Advances In Natural Fiber Polymer And Pla Composites Through Artificial Intelligence And Machine Learning Integration, Md Helal Uddin, Mohammed Huzaifa Mulla, Tarek Abedin, Abreeza Manap, Boon Kar Yap, Reji Kumar Rajamony, Kiran Shahapurkar, T. M.Yunus Khan, Manzoore Elahi M. Soudagar, Mohammad Nur-E-Alam

Research outputs 2022 to 2026

Natural Fibre Polymer (NFP) and Polylactic Acid (PLA) composites have received a lot of interest in a variety of sectors because they are environmentally friendly, renewable, and sustainable. Over the last decade, researchers have investigated the aspects of NFP/PLA composite development and optimization for a wide range of applications, including packaging materials, automotive components, construction materials, textile and apparel, biomedical devices, agricultural and horticultural applications, electronics, and consumer electronics. Furthermore, using Artificial Intelligence (AI) and Machine Learning (ML) methodologies has increased these polymer materials and associated technologies in their search for new potential ways to further progress in NFP and …

Regularized Methods For Tensor Recovery And Processing, Katherine J. Henneberger

Theses and Dissertations--Mathematics

The rapid growth of high-dimensional data has exposed the limitations of traditional vector and matrix-based methods for data analysis. These methods often struggle with computational inefficiencies, loss of critical cross-dimensional correlations, and challenges inherent in high-dimensional data. Tensors—multidimensional arrays—offer a robust framework for modeling and analyzing complex data. Tensor methods have proven valuable in tasks such as dimensionality reduction, feature extraction, and data compression, underpinning advancements in machine learning, computer vision, signal processing, and remote sensing.

This thesis focuses on two challenges in tensor analysis: tensor recovery and tensor processing. Tensor recovery addresses the reconstruction of incomplete or corrupted tensors. …

Experimental Investigation On Hydrogen-Rich Syngas Production Via Gasification Of Common Wood Pellet In Bangladesh: Optimization, Mathematical Modeling, And Techno-Econo-Environmental Feasibility Studies, Md Sanowar Hossain, Mujahidul Islam Riad, Showmitro Bhowmik, Barun K. Das

Research outputs 2022 to 2026

Since hydrogen produces no emissions, there is increasing interest in its production throughout the world as the need for clean and sustainable energy grows. Bangladesh has an abundance of biomass, particularly wood pellets, which presents a huge opportunity for gasification to produce hydrogen. Gasification of mahogany (Swietenia mahagoni-SM) and mango (Mangifera indica-MI) wood is performed in a downdraft gasifier to evaluate the impact of particle size, equivalence ratio, and temperature on hydrogen gas composition and gasifier performance. Under the optimal conditions determined by central composite design-response surface methodology (CCD-RSM) optimization, gasification of SM and MI wood can greatly increase hydrogen …

J-Necora: A Framework For Optimal Resource Allocation In Cloud-Edge-Things Continuum For Industrial Applications With Mobile Nodes, Marco Pettorali, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi

Computer Science Faculty Research & Creative Works

In the Industrial Internet of Things (IIoT) landscape, where the Cloud-to-Things Continuum (C2TC) paradigm is now a reality, industrial applications need to cope with highly heterogeneous network and computing resources. Moreover, many industrial applications also involve Mobile Nodes (MNs). Efficient allocation of network and computing resources to meet the stringent requirements of such applications is often a very challenging task. In this paper, we propose J-NECORA (Joint NEtwork and COmputing Resource Allocation), a comprehensive analytical framework to derive the optimal joint allocation of network and computing resources in the C2TC, that guarantees the application requirements, even in the presence of …

Harnessing The Power Of Gradient-Based Simulations For Multi-Objective Optimization In Particle Accelerators, Kishansingh Rajput, Malachi Schram, Auralee Edelen, Jonathan Colen, Armen Kasparian, Ryan Roussel, Adam Carpenter, He Zhang, Jay Benesch

Data Science Faculty Publications

Particle accelerator operation requires simultaneous optimization of multiple objectives. Multi-objective optimization (MOO) is particularly challenging due to trade-offs between the objectives. Evolutionary algorithms, such as genetic algorithms (GAs), have been leveraged for many optimization problems, however, they do not apply to complex control problems by design. This paper demonstrates the power of differentiability for solving MOO problems in particle accelerators using a deep differentiable reinforcement learning (DDRL) algorithm. We compare the DDRL algorithm with model-free reinforcement learning (MFRL), GA, and Bayesian optimization (BO) for simultaneous optimization of heat load and trip rates in the continuous electron beam accelerator facility. The …

Potsdam: Pareto Optimization Targeting Security, Data, And Mediation, J Peter Brady

Dartmouth College Ph.D Dissertations

Given the growing amount and variety of data handled by modern systems, it is crucial to guarantee the accuracy and protection of input data without errors or malicious intentions. The need to improve security in software programs often conflicts with the assurance of maximum performance, making developers and maintainers hesitant to incorporate more testing.

LangSec (Language-Theoretic Security) is a security approach that treats input validation as a formal language recognition problem, ensuring that only well-defined, unambiguous inputs are processed to eliminate exploitable parsing flaws. This dissertation explores integrating LangSec principles with Pareto optimization to enhance safety and robustness in digital …

Application Of Genetic Algorithm In Container Vessel Stowage Planning With Carbon Tax Considerations, Ming-Feng Yang, Wei-Hao Su, Ko-Meng Hu, Yu-Hsuan Li

Journal of Marine Science and Technology–Taiwan

This study developed a method for optimizing stowage planning for container vessels, a crucial aspect of international trade logistics. Over 80% of global trade depends on containerized transportation; thus, effective stowage planning is essential for minimizing transportation costs and enhancing operational efficiency. In the proposed hybrid optimization approach, integer programming is combined with a genetic algorithm to generate optimal stowage plans. The key factors considered in this method include load capacity limits, stacking constraints, and carbon tax regulations. The proposed method involves maximizing space utilization while minimizing logistics costs, with particular emphasis on reducing port dwell times. The findings of …

Swarming Segregation: Leveraging Swarm Intelligence And Regionalization As Instruments For School District Desegregation, Jeffrey Wooten

Theses and Dissertations

Even after Brown led to the South briefly having the most diverse schools in the nation, schools throughout the Northeast have remained the most segregated in the nation for decades. While federal jurisprudence has made compelling desegregation pursuant to the Equal Protection Clause more challenging, New Jersey has a particularly favorable landscape to address severe segregation. With a highly diverse, densely populated public enrollment, favorable state constitutional precedent, and a history of successfully compelling desegregation, New Jersey is fertile ground exploring regional desegregation. Scholars, judges, and even plaintiffs in ongoing litigation (Latino Action Network v. N.J.) have called for New …

Methods For Finding High Quality And Optimal Solutions Of Binary Quadratic Optimization Problems, James Haas

Graduate Research Theses & Dissertations

BiqAlps is an extension of the BiqCrunch project that leverages its bounding procedures within a computer cluster environment. The primary goal of this work was to investigate whether distributing the branch-and-bound tree search across multiple CPUs could improve solve times. While some problem instances demonstrated speedups of up to fivefold, others showed no measurable improvement, revealing that parallelization benefits are problem-dependent.

Beyond parallelization, BiqAlps introduces enhanced heuristics for generating high-quality initial solutions and refining existing ones. Additionally, the framework explores the impact of propagating triangle inequality cuts to child nodes during search, with the aim of accelerating bounding processes and …

Optimization Of Derivation Jobs And Modernization Of I/O Integration Tests For The Atlas Epxeriment, Arthur Charles Kraus

Graduate Research Theses & Dissertations

The High-Luminosity LHC (HL-LHC) is a phase of the LHC that is expected to start toward the end of the decade. With this comes an increase in data taken per year that current software and computing infrastructure, including I/O, is being prepared to handle. The ATLAS experiment's Software Performance Optimization Team has areas in development to improve the Athena software framework that is scalable in performance and ready for wide-spread HL-LHC era data taking. One area of interest is optimization of derivation production jobs by improving derived object data stored to disk by about 4-5% by eliminating the upper-limit on …

Optimization Of Serum And Salivary Cortisol Interpolation For Time-Dependent Modeling Frameworks In Healthy Adult Males, Nathaniel T. Berry, Travis Anderson, Christopher K. Rhea, Laurie Wideman

Rehabilitation Sciences Faculty Publications

Cortisol is an important marker of hypothalamic-pituitary-adrenal function and follows robust circadian and diurnal rhythms. However, biomarker sampling protocols can be labor-intensive and cost-prohibitive. Objectives: Explore analytical approaches that can handle differing biological sampling frequencies to maximize these data in more detailed and time-dependent analyses. Methods: Healthy adult males [N = 8; 26.1 (±3.1) years; 176.4 (±8.6) cm; 73.1 (±12.0) kg)] completed two 24 h admissions: one at rest and one including a high-intensity exercise session on the cycle ergometer. Serum and salivary cortisol were sampled every 60 and 120 min, respectively. Six alternative sampling profiles were defined by downsampling …

Optimizing Ai Language Models: A Study Of Chatgpt-4 Vs. Chatgpt-4o, Md Nurul Absar Siddiky, Muhammad Enayetur Rahman, Md Fayaz Bin Hossen, Muhammad Rezaur Rahman, Md. Shahadat Jaman

Electrical & Computer Engineering Faculty Publications

This paper presents a comparative analysis of OpenAI's GPT-4 and its optimized variant, GPT-4o, focusing on their architectural differences, performance, and real-world applications. GPT-4, built upon the Transformer architecture, has set new standards in natural language processing (NLP) with its capacity to generate coherent and contextually relevant text across a wide range of tasks. However, its computational demands, requiring substantial hardware resources, make it less accessible for smaller organizations and real-time applications. In contrast, GPT-4o addresses these challenges by incorporating optimizations such as model compression, parameter pruning, and memory-efficient computation, allowing it to deliver similar performance with significantly lower computational …

Flux-Weakening Control Methods For Permanent Magnet Synchronous Machines In Electric Vehicles At High Speed, Samer Alwaqfi, Mohamad Alzayed, Hicham Chaoui

Electrical & Computer Engineering Faculty Publications

Permanent magnet synchronous motors (PMSMs) are widely favored by manufacturers for use in electric vehicles (EVs) because of their many benefits, which include high power density at high speeds, ruggedness, potential for high efficiency, and reduced control complexity. However, since the Back Electromotive Force (EMF) increases proportionally with the motor’s rotational speed, it must be carefully controlled at high speeds. Flux-weakening (FW) control is required to avoid excessive electromagnetic flux beyond the power source and inverter’s voltage restrictions. This paper aims to compare various FW control strategies and analyze their effectiveness in maximizing the speed of PMSMs in EV applications …

Niobium Titanium Nitride Thin Film Deposition, Optimization, And Characterization, Hudson Horne

UNF Graduate Theses and Dissertations

Superconducting thin film devices such as Josephson junctions are a staple of cutting-edge quantum and classical computing architectures. Functional devices require optimized materials with properties suited to the device application; properties like superconducting critical temperature, critical current density, resistivity, and surface roughness are important depending on the intended device application. Niobium titanium nitride (NbTiN), a fcc transition metal nitride, is a promising material for these applications, with a superconducting critical temperature among the highest of the superconductors described by Bardeen, Cooper, and Schreiffer (BCS) theory. NbTiN has excellent mechanical and electrical characteristics and is easily deposited via reactive magnetron sputtering …

Minimizing Cpu Utilization For Job Scheduling Problems By The Advanced Round Robin Method: A Pragmatic Perspective, Haribhau R Bhapkar, Pankaj R Chandre, Parikshit Mahalle

ASEAN Journal on Science and Technology for Development

CPU scheduling issues include minimizing waiting time for processes, ensuring fairness in resource allocation, and optimizing throughput. Balancing these objectives can be challenging, as improving one aspect may negatively impact another, making it essential to design efficient scheduling algorithms. This work presents an innovative approach to enhance CPU utilization through the development of a Result-Based Round Robin Scheduling Algorithm. Traditional Round Robin Scheduling methods often face challenges in efficiently allocating CPU time, leading to suboptimal system performance. In response to these limitations, the proposed method introduces a result-oriented strategy that dynamically adjusts time quantum allocations based on the execution progress …

Quantified Neutrosophic Set (Qtns)-Based Mcdm Algorithms For Sustainable Material Selection For Anti-Microbial Bio-Fabricated Textile Manufacturing, Muhammad Saeed, Neha Andaleeb Khalid, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

This paper proposes a modified structure for the neutrosophic set called the Quantified Neutrosophic Set (QtNS) with a parameterized setting. Unlike conventional approaches, the QtNS provides a quantified environment for the indeterminacy by its dependence on truthness and falsity components. This innovative approach quantifies the uncertainty and improves the assessment process via expert-guided opinions, customising it according to the specific situations in real-world decision-making scenarios. Some QtNS operations along with useful characteristics are addressed. Furthermore, two algorithms, QtNSUI and QtNSAO, are developed for the proposed operations of union, intersection, AND, and OR based on QtNS. In the world of sustainable …

Development Of A Hybrid Methodology Of Deep Learning And Machine Learning For Lung Nodule Detection In Medical Computed Tomography Images, Zaed S. Mahdi, Rana M. Zaki, Alaa Kadhim Farhan, Negar Majma

Journal of Soft Computing and Computer Applications

Deep learning and machine learning play an important role in the medical field, helping doctors make accurate, fast and effective diagnosis. Despite the progress achieved in the use of modern technologies in detecting cancerous nodes, current studies still suffer from some challenges and limitations that must be addressed to obtain high efficiency in identifying cancerous nodes. These challenges include using image pre-processing, combining deep learning and machine learning techniques, and constantly adapting to clinical changes, in order to address this. A hybrid methodology has been proposed for detecting cancerous nodules in the lung in medical Computed Tomography (CT) images. It …

Real-Time Feedback-Driven Framework For Automated Cybersickness Mitigation, Md Jahirul Islam

Master's Theses

As technologies are becoming more advanced day by day, the embracement of virtual reality (VR) technology among users is also increasing in daily activities for various purposes, and subsequently, the barrier between the real and virtual world is fading. Despite the versatile uses, cybersickness (CS) is a major problem which is induced among users due to the immersive VR experience. There is a plethora of research findings and methods to measure the users’ CS such as virtual reality sickness questionnaire (VRSQ), simulator sickness questionnaire (SSQ), fast motion scale questionnaire (FMS), and others. Recently, machine learning approaches have also been adopted …

Optimal Algorithm For Managing On-Campus Student Transportation, Youssef Harrath, Jood Alyusuf, Zeena Ghulam, Muna Aldoseri

Research & Publications

This study analyzed the transportation issues at the University of Bahrain Sakhir campus, where a bus system with an unorganized and fixed number of buses allocated each semester was in place. Data was collected through a survey, onsite observations, and student schedules to estimate the number of buses needed. The study was limited to students who require to move between buildings for academic purposes and not those who choose to ride buses for other reasons. An algorithm was designed to calculate the optimal number of buses for each time slot, and for each day. This solution could improve transportation efficiency, …

(R2100) Optimality Conditions Of A Topsis Optimization Model And Its Application On Interval-Valued Data, Sudipta Roy, Sandip Chatterjee

Applications and Applied Mathematics: An International Journal (AAM)

The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is widely used in the field of multi-criteria decision analysis. Despite its popularity and widespread application, little attention has been given to the mathematical foundation that underlies the TOPSIS algorithm. The existing literature on this subject is far from comprehensive, leaving many aspects of the algorithm unexplored. This paper aims to address this gap in the literature by delving into the optimization problem associated with TOPSIS. Unlike traditional interval analysis theory, which only covers a limited scope, our approach extends to a broader range of scenarios and offers …

Addressing Inference Time Of Machine Learning Models In Embedded Systems, Samuel Black

UNLV Theses, Dissertations, Professional Papers, and Capstones

Embedded Systems are used for a wide range of specialized computing purposes including surveyal, safety, security, and quality of life. Many areas that embedded systems are used in require the use of machine learning models. Constraints can be placed on embedded systems. Timeliness of execution, user satisfaction, security, power, and resource limitations must be considered when designing for embedded systems. Neural networks excel at complex tasks that are otherwise intractable, but their relatively high computational cost poses a challenge for inclusion in embedded systems. Neural network architectures should be optimized to reduce the total number of operations performed while maintaining …

Ohmic Contacts For Fabrication Of Sic Cmos Devices, Anthony Di Mauro

Graduate Theses and Dissertations

Today’s world of electronics is dominated by semiconductor devices which utilize silicon as their substrate material. Though, silicon is not ideal for semiconductor devices in high-voltage or high-temperature applications. Additionally, the efficiency of silicon devices becomes drastically reduced in nonideal operating conditions. Therefore, finding alternatives to silicon?based devices has been a topic for decades now. A few great candidates to replace silicon devices for said applications include Silicon Carbide (SiC), Gallium Nitride (GaN), and Aluminum Arsenide (AlAs). SiC has grown its reputation as the best candidate, when compared to other potential alternatives, due to its wide bandgap, high operating frequency, …

Novel Nanocomposite Of Carbonized Chitosan-Zinc Oxide-Magnetite For Adsorption Of Toxic Elements From Aqueous Solutions, Dalia A. Ali Eng, Ganna Gaber Ismail Eng.

Chemical Engineering

Herein, a novel nanocomposite (carbonized chitosan-zinc oxide-magnetite, CCZF) was developed to effectively remove toxic elements in water remediation. Combining the high adsorption capacities of chitosan with the magnetic properties of magnetite and the chemical stability of zinc oxide, the combination of these unique properties makes it an efficient and versatile material that offers a sustainable solution for water purification. The (CCZF) nanocomposite was synthesized through the coprecipitation method and characterized using various techniques, including scanning electron microscopy (SEM), transmission electron microscopy (TEM), Brunauer–Emmett–Teller (BET) analysis, X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, and zeta potential analysis. The results showed …

Stochastic Predictive Modeling Of Social Media Stocks And Reliability Of Hard Disk Drives, Sameer Anand

USF Tampa Graduate Theses and Dissertations

In recent years, social media has emerged as a highly appealing investment sector due to its substantial returns. Our research focuses on creating a predictive model for the financial performance of social media companies. We gathered data from five companies in the S&P 500: Meta, Etsy, Match, Pinterest, and Alphabet. We developed a non-linear, data-driven stochastic analytical model that employs real data, incorporating six financial indicators and four economic indicators, to forecast the weekly closing prices of these social media companies with an accuracy of 94.7%. Additionally, we ranked the statistically significant indicators and their interactions based on their percentage …

Machine Learning-Driven Process Analysis And Optimization In Solid-State Welding And Fusion-Based Additive Manufacturing, Radif Uddin Ahmed

Master's Theses

In the modern era of advanced manufacturing, optimizing process parameters is pivotal in ensuring the quality and reliability of sophisticated component fabrication. This study presents a novel, data-driven approach to parameter optimization in two cutting-edge manufacturing techniques: Friction Stir Welding (FSW) and Laser Powder Bed Fusion (LPBF). By leveraging machine learning methodologies, this research addresses the critical challenge of efficiently determining optimal process parameters, a task traditionally relying on time-consuming and resource-intensive trial-and-error methods. This study will lead to a robust data-driven framework for process analysis of more advanced manufacturing techniques like the Additive Friction Stir Deposition (AFSD) process. Friction …