Digital Commons Network^™

Analysis Of Industry Recognized Credentials That Lead To Quality Employment For High School Graduates, Teri L. Harris

Educational Leadership & Workforce Development Theses & Dissertations

The purpose of this study was to identify to what extent industry recognized credentials (IRCs) earned in secondary school lead to high quality employment for students after high school and which specific IRCs should be integrated into career and technical education (CTE) courses. This author sought to explore if CTE courses in high school could be the vehicle for students to earn IRCs that led to higher quality jobs, higher pay, and a job to support a family. The researcher gathered empirical evidence using existing data from a survey of business leaders in each of the nationally recognized 14 Career …

Pillars Of Purpose: Postsecondary Career And Technical Education Faculty Viewpoints On Retention, Garry Brandon Hensley

Educational Leadership & Workforce Development Theses & Dissertations

The retention of postsecondary Career and Technical Education (CTE) faculty in community colleges is critical for maintaining a skilled workforce to meet evolving labor market demands. This study employs Q methodology to explore the subjective viewpoints of community college CTE faculty regarding factors influencing their job satisfaction and retention. Grounded in Herzberg’s Two-Factor Theory of Motivation and Job Embeddedness Theory, the research integrates the concept of Employee Experience Management (EEM) to provide a comprehensive framework for understanding faculty retention. Data collected from Q sorts and follow-up focus group interviews reveal six distinct factors representing diverse faculty perspectives on job satisfaction, …

Stability Analysis In The Twist-Bend Nematic Liquid Crystal Model, Zhenqiang Li

Mathematics & Statistics Theses & Dissertations

The recently discovered twist-bend nematic liquid crystal (LC) phase is characterized by a nanoscale helical modulation of the nematic director n, forming a conical helix along the z-axis at an oblique angle θ. While many models assume a constant cone angle and equal elastic constants K11 = K22 = K33, this dissertation removes both assumptions by considering a fully anisotropic elastic energy with K11 ≠ K22 ≠ K33, and allowing θ to vary spatially. We analyze the stability of this system under frustrated and free boundary conditions using variational methods. …

Machine Learning For Reactor Power Monitoring With Limited Labeled Data, C. L. Stewart, B. L. Goldblum, R. G. Abbott, L. Appleby, Brett J. Borghetti, V. Hollingshead, J. H. Whetzel

Faculty Publications

Real-time reactor power monitoring is critical for a variety of nuclear applications, spanning safety, security, operations, and maintenance. While machine learning methods have shown promise in monitoring reactor power levels, there is limited research on their efficacy in label-starved environments. The goal of this work is to assess the feasibility of classifying nuclear reactor power level using multisource data in scenarios with limited labels. Data were collected using low-resolution multisensors at four nuclear reactor facilities: two large research reactors and two TRIGA reactors. Within each pair, one reactor dataset served as the source and the other as the target in …

Cost-Effectiveness Of Personalized Policies For Implementing Organ-At-Risk Sparing Adaptive Radiation Therapy In Head And Neck Cancer, Seyedmohammadhossein Hosseinian, Daniel Suarez-Aguirre, Cem Dede, Raul Garcia, Lucas Mccullum, Mehdi Hemmati, Aysenur Karagoz, Abdallah S R Mohamed, Stephen Y Lai, Katherine A Hutcheson, Amy C Moreno, Kristy K Brock, Fatemeh Nosrat, Clifton D Fuller, Andrew J Schaefer

Faculty, Staff and Student Publications

Background and purpose: The principle of adaptive radiation therapy (ART) is to adjust radiation plans in response to anatomical changes during treatment. The purpose of this study was to develop a decision-making model for implementation of personalized ART that balances the costs and clinical benefits of radiation plan adaptations in head and neck cancer (HNC).

Materials and methods: Using retrospective imaging data from 52 HNC patients, a Markov decision process (MDP) model was developed to determine optimal timing for plan adaptations based on the difference in normal tissue complication probability (ΔNTCP) between planned and delivered doses to organs-at-risk. To capture …

Nanosecond Pulsed Electric Field And Plasma Jets For Cancer Therapy, Edwin Ayobami Oshin

Biomedical Engineering Theses & Dissertations

Nanosecond pulsed electric field (nsPEF) employs nanosecond-duration, high voltage pulses to induce oxidative stress, leading to temporary or permanent damage to cells or tissue (also known as reversible and irreversible electroporation), and has been considered a promising approach for cancer therapy. In parallel to this, nanosecond pulsed atmospheric pressure plasma jets (ns-APPJs) have also shown to be effective in inactivating cancer cells or increasing sensitivity of cells to electric fields. ns-APPJs are known to generate reactive chemical agents, including reactive oxygen and nitrogen species (RONS) which induces oxidative stress resulting in cell proliferation, apoptosis, and necrosis. In this dissertation, a …

Redbird Scholar, Vol. 10, No. 2 (Spring 2025), Illinois State University, Office Of The Vice President For Research And Graduate Studies

Redbird Scholar

No abstract provided.

A New Multiple Imputation Method For High-Dimensional Neuroimaging Data, Tong Lu, Peter Kochunov, Chixiang Chen, Hsin-Hsiung Huang, L Elliot Hong, Shuo Chen

Faculty, Staff and Student Publications

Missing data are a prevalent challenge in neuroimaging, with significant implications for downstream statistical analysis. Neglecting this issue can introduce bias and lead to erroneous inferential conclusions, making it crucial to employ appropriate statistical methods for handling missing data. Although the multiple imputation is a widely used technique, its application in neuroimaging is severely hindered by the high dimensionality of neuroimaging data, and the substantial computational demands. To tackle the critical computational challenges, we propose a novel approach, High dimensional Multiple Imputation (HIMA), based on Bayesian models specifically designed for large-scale neuroimaging datasets. HIMA introduces a new computational strategy to …

Model-Predictive Optimal Control Of Ferrofluidic Microrobots In Three-Dimensional Space, E. Olga Skowronek, Luke Silas Baker, Reza Ahmed, Hamid Marvi

Mechanical Engineering Faculty Publications

Ferrofluid microrobots have emerged as promising tools for minimally invasive medical procedures. Their unique properties to navigate complex fluids and reach otherwise inaccessible regions of the human body have enabled new applications in targeted drug delivery, tissue engineering, and diagnostics. This paper proposes a model-predictive controller for the external magnetic manipulation of ferrofluid microrobots in three dimensions (3D). The internal optimization routine of the controller determines appropriate changes in the applied electromagnetic field to minimize the deviation between the actual and desired trajectories of the microrobot. A linear system governing locomotion is derived and used as the equality constraints of …

Solitons, Breathers And Rogue Waves Of The Yajima–Oikawa-Newell Long Wave–Short Wave System, Marcos Caso-Huerta, Bao-Feng Feng, Sara Lombardo, Ken-Ichi Maruno

School of Mathematical & Statistical Sciences Faculty Publications

In this paper, we consider the recently-introduced Yajima–Oikawa–Newell (YON) system describing the nonlinear resonant interaction between a long wave and a short wave. It extends and generalises the Yajima–Oikawa (YO) and the Newell (N) systems, which can be obtained from the YON system for special choices of the two non-rescalable, arbitrary parameters that it features. Remarkably, for any choice of these latter constants, the YON system is integrable, in the sense of possessing a Lax pair. New families of solutions, including the bright and dark multi-solitons, as well as the breathers and the higher-order rogue waves are systematically derived by …

Unraveling Early Onset Disparities And Determinants: An Analysis Of Colorectal Cancer Outcomes And Trends In Texas, Tyler Torres, Elias Arellano Villanueva, Yossef Alsabawi, Demba Fofana, Manish Tripathi

School of Medicine Publications

Introduction: Colorectal cancer (CRC) is the second leading cause of cancer-related deaths in the U.S., with disparities in incidence, survival, and age at diagnosis across racial, ethnic, and socioeconomic groups. The rising incidence of early-onset CRC (< 50 years) has amplified concerns regarding access to care, screening disparities, and outcomes, particularly among minorities. This study examines the impact of race, ethnicity, socioeconomic status (SES), and sex on CRC survival and age at diagnosis in Texas from 1995 to 2016.

Methods: This retrospective cohort study utilized Texas Cancer Registry (TCR) data, including 235,076 CRC cases diagnosed between 1995 and 2016. Kaplan-Meier analysis and log-rank tests assessed 10-year survival by race and ethnicity and time period (1995-2005 vs. 2006-2016). Kruskal-Wallis tests with Bonferroni correction were used to compare survival years between racial/ethnic groups within each period. Age at diagnosis was analyzed by race and …

Data Driven Multi-Objective Optimization Of The Scheduling For Towing A Floating Offshore Wind Turbine Between Assembly Port And Installation Location Throughout A Year, Frédéric Le Pivert, Adam Roberts, Adan Lopez-Santander, Matthew Craven, Saeid Kazemi

School of Engineering, Computing and Mathematics

High demand for the installation of floating offshore wind turbines over the coming years is likely to place significant pressure on ports and installation vessels. Optimization of the routes between ports and farms and the towing schedule when transporting equipment is therefore critical to reducing operation timescales and carbon emissions. This paper presents two series of multi-objective optimizations for minimizing the timescale and carbon emissions for the case of an IEA 15 MW turbine on a VolturnUS-S platform being wet towed through the English Channel to the Celtic Sea. The study makes use of the Maritime Simulation Laboratory (MSL) Ship …

Volume 34, Number 1, Spring 2025, Office Of Communications, Illinois Wesleyan University

Illinois Wesleyan University Magazine, 2018-present

VOLUME 34 | NUMBER 1 | Spring 2025

ON OUR COVER:

The Shirk Center has been a cherished University landmark for 30 years.

22 30 Years of Shirk. In 2024, the Shirk Center reaced a new milestone with its 30th anniversary. The Center still serves as an essential community hub for campus and Bloomington-Normal.

24 A Hearth for Haiti. Kevin Hineline '88 is working with IWU physics students to develop a new cooking stove for use in the most impoverished and disaster-stricken regions of the world.

28 Setting the Standard. Jeff Becker '01 and Matt Gavin '01 are the minds …

The Roddey Mcmillan Record - April 2025, Winthrop University

The RMR 2020-2029

The Roddey McMillan Record (RMR) is Winthrop’s monthly multicultural student publication. The RMR has been a significant voice of the minority population of the Winthrop community since its creation by Gail Harris in April 1986. It promotes awareness and understanding of issues concerning minorities for the prosperity of the entire Winthrop community. The purpose of the RMR is to shine light on the many diverse cultures at Winthrop, focusing on issues, concerns and happenings of those who represent the multicultural community on campus. The Roddey McMillan Record is named after Dr. Cynthia Roddey and Atty, Sheila McMillan. Cynthia Roddey was …

Multiphase Iterative Algorithm For Mixed-Integer Optimal Control, Chaoying Pei, Sixiong You, Yu Di, Ran Dai

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Mixed-integer optimal control problems (MIOCPs) frequently arise in the domain of optimal control problems (OCPs) when decisions including integer variables are involved. However, existing state-of-the-art approaches for solving MIOCPs are often plagued by drawbacks such as high computational costs, low precision, and compromised optimality. In this study, we propose a novel multiphase scheme coupled with an iterative second-order cone programming (SOCP) algorithm to efficiently and effectively address these challenges in MIOCPs. In the first phase, we relax the discrete decision constraints and account for the terminal state constraints and certain path constraints by introducing them as penalty terms in the …

On The Probability Of Necessity And Sufficiency Of Explaining Graph Neural Networks: A Lower Bound Optimization Approach, Ruichu Cai, Yuxuan Zhu, Xuexin Chen, Yuan Fang, Min Wu, Jie Qiao, Zhifeng Hao

Research Collection School Of Computing and Information Systems

The explainability of Graph Neural Networks (GNNs) is critical to various GNN applications, yet it remains a significant challenge. A convincing explanation should be both necessary and sufficient simultaneously. However, existing GNN explaining approaches focus on only one of the two aspects, necessity or sufficiency, or a heuristic trade-off between the two. Theoretically, the Probability of Necessity and Sufficiency (PNS) holds the potential to identify the most necessary and sufficient explanation since it can mathematically quantify the necessity and sufficiency of an explanation. Nevertheless, the difficulty of obtaining PNS due to non-monotonicity and the challenge of counterfactual estimation limit its …

Enmob: Unveil The Behavior With Multi-Flow Analysis Of Encrypted App Traffic, Mengmeng Ge, Ruitao Feng, Likun Liu, Xiangzhan Yu, Sachidananda Vinay, Xiaofei Xie, Yang Liu

Research Collection School Of Computing and Information Systems

In the contemporary digital landscape, mobile applications have become the predominant conduit for internet connectivity and daily tasks. Simultaneously, the advent of application encryption technology has safeguarded users’ privacy. However, this encryption, while fortifying privacy, introduces challenges to security by hindering the effective management of network applications within encrypted data streams. Conventional detection methods for encrypted application traffic, relying heavily on statistical metrics like payload, packet size, and distribution, are constrained to single traffic flows, often yielding results of limited specificity. To address this limitation, our paper introduces an innovative approach that elucidates the multi-flow nature of application behavior traffic …

Verifying Timed Properties Of Programs In Iot Nodes Using Parametric Time Petri Nets, Étienne André, Jean-Luc Béchennec, Sudipta Chattopadhyay, Sebastien Faucou, Didier Lime, Dylan Marinho, Olivier H. Roux, Jun Sun

Research Collection School Of Computing and Information Systems

The analysis of timed properties of programs is a complex task, as it is highly dependent on both the software and the hardware. In this work, we propose a framework for modeling with timed formal models the execution of programs, taking into account the micro-architecture of the machine on which it executes. We model both the program, at the instruction set architecture level, and the hardware, including the processor micro-architecture, using time Petri nets. Our implementation uses the ARM Cortex-M instruction set architecture and a hardware architecture representative of microcontrollers used in IoT nodes. The whole translation is fully automated …

Chatcrs: Incorporating External Knowledge And Goal Guidance For Llm-Based Conversational Recommender Systems, Chuang Li, Yang Deng, Hengchang Hu, Min-Yen Kan, Haizhou Li

Research Collection School Of Computing and Information Systems

This paper aims to efficiently enable large language models (LLMs) to use external knowledge and goal guidance in conversational recommender system (CRS) tasks. Advanced LLMs (e.g., ChatGPT) are limited in domain-specific CRS tasks for 1) generating grounded responses with recommendation-oriented knowledge, or 2) proactively leading the conversations through different dialogue goals. In this work, we first analyze those limitations through a comprehensive evaluation, showing the necessity of external knowledge and goal guidance which contribute significantly to the recommendation accuracy and language quality. In light of this finding, we propose a novel ChatCRS framework to decompose the complex CRS task into …

Towards Understanding Why Fixmatch Generalizes Better Than Supervised Learning, Jingyang Li, Jiachun Pan, Vincent Tan, Kim-Chuan Toh, Pan Zhou

Research Collection School Of Computing and Information Systems

Semi-supervised learning (SSL), exemplified by FixMatch (Sohn et al., 2020), has shown significant generalization advantages over supervised learning (SL), particularly in the context of deep neural networks (DNNs). However, it is still unclear, from a theoretical standpoint, why FixMatch-like SSL algorithms generalize better than SL on DNNs. In this work, we present the first theoretical justification for the enhanced test accuracy observed in FixMatch-like SSL applied to DNNs by taking convolutional neural networks (CNNs) on classification tasks as an example. Our theoretical analysis reveals that the semantic feature learning processes in FixMatch and SL are rather different. In particular, FixMatch …

Configx: Modular Configuration For Evolutionary Algorithms Via Multitask Reinforcement Learning, Hongshu Guo, Zeyuan Ma, Jiacheng Chen, Yining Ma, Zhiguang Cao, Xinglin Zhang, Yue-Jiao Gong

Research Collection School Of Computing and Information Systems

Recent advances in Meta-learning for Black-Box Optimization (MetaBBO) have shown the potential of using neural networks to dynamically configure evolutionary algorithms (EAs), enhancing their performance and adaptability across various BBO instances. However, they are often tailored to a specific EA, which limits their generalizability and necessitates retraining or redesigns for different EAs and optimization problems. To address this limitation, we introduce ConfigX, a new paradigm of the MetaBBO framework that is capable of learning a universal configuration agent (model) for boosting diverse EAs. To achieve so, our ConfigX first leverages a novel modularization system that enables the flexible combination of …

Neural Multi-Objective Combinatorial Optimization Via Graph-Image Multimodal Fusion, Jinbiao Chen, Jiahai Wang, Zhiguang Cao, Yaoxin Wu

Research Collection School Of Computing and Information Systems

Existing neural multi-objective combinatorial optimization (MOCO) methods still exhibit an optimality gap since they fail to fully exploit the intrinsic features of problem instances. A significant factor contributing to this shortfall is their reliance solely on graph-modal information. To overcome this, we propose a novel graph-image multimodal fusion (GIMF) framework that enhances neural MOCO methods by integrating graph and image information of the problem instances. Our GIMF framework comprises three key components: (1) a constructed coordinate image to better represent the spatial structure of the problem instance, (2) a problem-size adaptive resolution strategy during the image construction process to improve …

Rethinking Neural Multi-Objective Combinatorial Optimization Via Neat Weight Embedding, Jinbiao Chen, Zhiguang Cao, Jiahai Wang, Yaoxin Wu, Hanzhang Qin, Zizhen Zhang, Yue-Jiao Gong

Research Collection School Of Computing and Information Systems

Recent decomposition-based neural multi-objective combinatorial optimization (MOCO) methods struggle to achieve desirable performance. Even equipped with complex learning techniques, they often suffer from significant optimality gaps in weight-specific subproblems. To address this challenge, we propose a neat weight embedding method to learn weight-specific representations, which captures weight-instance interaction for the subproblems and was overlooked by most current methods. We demonstrate the potentials of our method in two instantiations. First, we introduce a succinct addition model to learn weight-specific node embeddings, which surpassed most existing neural methods. Second, we design an enhanced conditional attention model to simultaneously learn the weight embedding …

Learning-Guided Bi-Objective Evolutionary Optimization For Green Municipal Waste Collection Vehicle Routing, Shubing Liao, Yixin Xu, Yunyun Niu, Zhiguang Cao

Research Collection School Of Computing and Information Systems

Waste management has emerged as a critical issue in modern society, where vehicles are scheduled to visit multiple locations for waste collection and transport. This study focuses on a key problem in waste management: route optimization of waste collection vehicles, and formulate it as a bi-objective vehicle routing problem with stochastic demand (VRPSD), aiming to minimizing both total costs and carbon emissions. Although previous studies have significantly advanced our understanding of solving similar problems, the lack of real-world data and limited problem-solving capabilities still restrict the practical applicability of existing methods. To bridge this research gap, this study designed a …

Sita: Structurally Imperceptible And Transferable Adversarial Attacks For Stylized Image Generation, Jingdan Kang, Haoxin Yang, Yan Cai, Huaidong Zhang, Xuemiao Xu, Yong Du, Shengfeng He

Research Collection School Of Computing and Information Systems

Image generation technology has brought significant advancements across various fields but has also raised concerns about data misuse and potential rights infringements, particularly with respect to creating visual artworks. Current methods aimed at safeguarding artworks often employ adversarial attacks. However, these methods face challenges such as poor transferability, high computational costs, and the introduction of noticeable noise, which compromises the aesthetic quality of the original artwork. To address these limitations, we propose a Structurally Imperceptible and Transferable Adversarial (SITA) attacks. SITA leverages a CLIP-based destylization loss, which decouples and disrupts the robust style representation of the image. This disruption hinders …

Agentstudio: A Toolkit For Building General Virtual Agents, Longtao Zheng, Zhiyuan Huang, Zhenghai Xue, Xinrun Wang, Bo An, Shuicheng Yan

Research Collection School Of Computing and Information Systems

General virtual agents need to handle multimodal observations, master complex action spaces, and self-improve in dynamic, open-domain environments. However, existing environments are often domain-specific and require complex setups, which limits agent development and evaluation in real-world settings. As a result, current evaluations lack in-depth analyses that decompose fundamental agent capabilities. We introduce AgentStudio, a trinity of environments, tools, and benchmarks to address these issues. AgentStudio provides a lightweight, interactive environment with highly generic observation and action spaces, e.g., video observations and GUI/API actions. It integrates tools for creating online benchmark tasks, annotating GUI elements, and labeling actions in videos. Based …

Predictive Modelling For Vessel Traffic Flow: A Comprehensive Survey From Statistics To Ai, Deshan. Chen, Chen. Huang, Tengze. Fan, Hoong Chuin Lau, Xinping. Yan

Research Collection School Of Computing and Information Systems

Recognizing the specific complexities of vessel traffic flow, this comprehensive survey exclusively addresses the predictive modelling in maritime transportation, tracing the evolution from conventional statistical approaches to modern artificial intelligence (AI) techniques. The survey examines a broad range of predictive targets, including vessel volume, trajectories, velocities, destinations and traffic patterns. Through bibliometric analysis utilizing Citespace, the central research themes and technological trends characterizing the vessel traffic flow prediction domain have been identified and discussed. Our analysis indicates a clear trend towards AI-based models, highlighting their increasing dominance in enhancing predictive accuracy and efficiency. Additionally, we highlight persistent challenges, such as …

A Functional Software Reference Architecture For Llm-Integrated Systems, Alessio Bucaioni, Martin Weyssow, Junda He, Yunbo Lyu, David Lo

Research Collection School Of Computing and Information Systems

The integration of large language models into software systems is transforming capabilities such as natural language understanding, decision-making, and autonomous task execution. However, the absence of a commonly accepted software reference architecture hinders systematic reasoning about their design and quality attributes. This gap makes it challenging to address critical concerns like privacy, security, modularity, and interoperability, which are increasingly important as these systems grow in complexity and societal impact. In this paper, we describe our emerging results for a preliminary functional reference architecture as a conceptual framework to address these challenges and guide the design, evaluation, and evolution of large …

Prioritizing Speech Test Cases, Zhou Yang, Jieke Shi, Muhammad Hilmi Asyrofi, Bowen Xu, Xin Zhou, Donggyun Han, David Lo

Research Collection School Of Computing and Information Systems

As Automated Speech Recognition (ASR) systems gain widespread acceptance, there is a pressing need to rigorously test and enhance their performance. Nonetheless, the process of collecting and executing speech test cases is typically both costly and time-consuming. This presents a compelling case for the strategic prioritization of speech test cases, which consist of a piece of audio and the corresponding reference text. The central question we address is: In what sequence should speech test cases be collected and executed to identify the maximum number of errors at the earliest stage? In this study, we introduce PRiOritizing sPeecH tEsT …

Bigcodebench: Benchmarking Code Generation With Diverse Function Calls And Complex Instructions, T.Y. Zhuo, M.C. Vu, J. Chim, ..., David Lo

Research Collection School Of Computing and Information Systems

Task automation has been greatly empowered by the recent advances in Large Language Models (LLMs) via Python code, where the tasks ranging from software engineering development to general-purpose reasoning. While current benchmarks have shown that LLMs can solve tasks using programs like human developers, the majority of their evaluations are limited to short and self-contained algorithmic tasks or standalone function calls. Solving challenging and practical tasks requires the capability of utilizing diverse function calls as tools to efficiently implement functionalities like data analysis and web development. In addition, using multiple tools to solve a task needs compositional reasoning by accurately …