Digital Commons Network^™

Balancing War And Peace In Taekwondo Athlete Development: A Multi-Criteria Coaching Decision Support Framework Using Leadership Scale For Sports And Fuzzy Ahp, King Harold A. Recto, Hazel Jade L. Antonio, Jhyrald Anthony P. Dalida

Electronics, Computer, and Communications Engineering Faculty Publications

Coaching effectiveness is frequently evaluated through competitive outcomes, with the assumption that coaching behaviors directly influence athlete performance. This study examined the relationship between perceived coaching behaviors and individual win percentage among collegiate Taekwondo athletes using the Leadership Scale for Sports (LSS). Descriptive statistics, correlation analysis, and regression analysis were conducted across five coaching dimensions: Training and Instruction, Democratic Behavior, Autocratic Behavior, Social Support, and Positive Feedback. Results showed that none of the coaching dimensions demonstrated a statistically significant relationship with win percentage. Although coaching behaviors did not significantly predict win percentage, the findings suggest that athlete development is shaped …

Genwriter 2.0: A Hybrid Case-Based And Llm Rewriting Approach For Mitigating Implicit Gender Cues In Text, Shweta Soundararajan, Sarah Jane Delany

Conference papers

Gendered language is the use of words or phrases that indicate an individual's gender. Although useful in some contexts, gendered language can reinforce gender stereotypes and introduce bias, particularly in machine learning models for tasks involving people such as recruitment or occupation classification. When textual content about individuals, such as biographies, contain gender cues, models can learn spurious associations between gender and other characteristics of individuals, such as profession, potentially resulting in unfair outcomes such as reduced hiring opportunities for women.

To address this challenge, we propose GenWriter 2.0, a hybrid approach that integrates Case-Based Reasoning (CBR) with Large …

A Fuzzy–Neutrosophic Suitability Index For Selecting An Appropriate Reasoning Model Under Vagueness, Incompleteness, And Conflict, Nada A. Nabeeh, Ahmed Samy

Neutrosophic Systems with Applications

Fuzzy reasoning and neutrosophic reasoning are both used to handle uncertainty, but they are not intended for the same uncertainty structure. Fuzzy reasoning is suitable when uncertainty appears mainly as gradual vagueness, where a value may belong to a concept such as ``high risk'' or ``good performance'' to a certain degree. In this case, a membership value is often sufficient. Neutrosophic reasoning is more suitable when the problem also contains incomplete information, undecided evidence, or conflict between sources. In such cases, one membership degree may be too limited because it cannot represent support, rejection, and indeterminacy separately. This study introduces …

Evaluating Generative Ai-Based User Interfaces Using An Integrated Neutrosophic Multi-Criteria Decision-Making Framework, Nada Mohamed, Alshaimaa A. Tantawy

Neutrosophic Systems with Applications

User Interface (UI) design can be seen as an essential aspect of human-computer interaction (HCI) and makes communication easier between people and technology. In today's digital economy, interface quality has become one of the most important business concerns, since it has a direct impact on customer satisfaction and retention while affecting revenue. Although creating user-centered and accessible interfaces is crucial, doing so is a difficult and time-consuming process, which leads to burnout for many usability professionals. Although conventional artificial intelligence (AI) was utilized for design assessment and automation, the arrival of generative AI technology has created new possibilities for automated …

Congestion Avoidance And Control In Internet Router Based On Fuzzy Aqm, Hanan M. Kadhim, Ahmed A. Oglah

Engineering and Technology Journal

The internet has made the world a little community, linking millions of people, organizations, and equipment for different purposes. The great impact of these networks in our lives makes their efficiency a vital matter to take care of, and this needs handling some problems including congestion. In this paper, the fuzzy-PID controller is used to control the nonlinear TCP / AQM model. This controller adjusts congestion of the computer network and commits controlled pressurized signaling features. Many experiments were carried out using different network parameter values, various queue sizes, and additional disturbances to verify the robustness and efficiency of the …

Age Estimation In Short Speech Utterances Based On Bidirectional Gated-Recurrent Neural Networks, Ameer A. Badr, Alia K. Abdul-Hassan

Engineering and Technology Journal

Recently, age estimates from speech have received growing interest as they are important for many applications like custom call routing, targeted marketing, or user-profiling. In this work, an automatic system to estimate age in short speech utterances without depending on the text is proposed. From each utterance frame, four groups of features are extracted and then 10 statistical functionals are measured for each extracted dimension of the features, to be followed by dimensionality reduction using Linear Discriminant Analysis (LDA). Finally, bidirectional Gated-Recurrent Neural Networks (G- RNNs) are used to predict speaker age. Experiments are conducted on the VoxCeleb1 dataset to …

Integrated Construction Uncertainty Quantification Framework Icuqf, Felix Delmonte

Mechanical and Civil Engineering Faculty Publications

This study develops ICUQF, an integrated uncertainty framework for digitally instrumented construction megaprojects that unite probabilistic simulation, fuzzy reasoning, Bayesian networks, AI-driven anomaly detection, BIM-linked digital twins, and organizational learning into a single operational architecture. The framework classifies uncertainty into aleatory variability, epistemic gaps, data-centric and model-centric risks, and cognitive or cultural sources, mapping each class to computational treatments such as Monte Carlo engines for stochastic variability, fuzzy intervals and p-boxes for imprecise expert judgments, and hierarchical Bayesian models for coherent evidence aggregation and online updating. ICUQF specifies data pipelines that connect IoT sensing, edge processing, cloud storage, and Common …

Load Balancing Algorithms For Cloud Computing Systems, Mark Rakesh Christian

Electronic Theses and Dissertations 2020 - Present

Cloud computing depends on load balancing to allocate user requests among virtual machines (VMs), affecting response time, utilization, and scalability. Round Robin, Equally Spread Current Execution, and standard Throttled scheduling are widely used; however, standard Throttled selects among eligible VMs using an implementation-dependent order with no explicit preference. This thesis proposes the Weighted Throttled Load Balancing (WTLB) algorithm, which introduces an explicit, deterministic selection priority among eligible VMs, implemented by modifying the Throttled scheduler within CloudAnalyst.

Evaluated under an identical six-region, multi-data-center configuration against Round Robin, ESCE, and standard Throttled, WTLB preserves the aggregate response-time, processing-time, and cost profile of …

A Multidimensional Conceptual Framework For Supportive Outdoor Environments In Healthcare Settings, Mona Ali, Dalia A. Beheiry, Rowaida Kamel, Hammam Serageldin

HBRC Journal

The integration of supportive outdoor environments (SOEs) in healthcare settings has gained increasing attention for their potential to enhance patient recovery, support staff well-being, and contribute to holistic therapeutic outcomes. This scoping review examines design recommendations for inclusive outdoor environments in healthcare settings that impact user health and experience. Guided by the Joanna Briggs Institute (JBI) framework for scoping reviews, a comprehensive search was conducted across five databases (Google Scholar, PubMed, ScienceDirect, JSTOR, Sage), yielding 697 records. Following screening and applying the inclusion and exclusion criteria, 18 articles were selected for analysis. This scoping review identified consistent evidence linking outdoor …

Integrated Construction Risk Assessment Framework, Felix Delmonte

Mechanical and Civil Engineering Faculty Publications

An integrated risk assessment architecture for construction projects synthesizes data governance, BIM, Digital Twins, and hybrid analytics into a continuous feedback loop that links sensing, semantic modelling, probabilistic simulation, and organizational learning. The framework distinguishes aleatory from cognitive uncertainty and prescribes layered methods: Monte Carlo and Bayesian updating for measurable variability; fuzzy inference and consensus aggregation for linguistic and expert judgements; and graph-based discovery for dynamic cyber-physical topologies represented as time-varying graphs 𝐺𝑡. Governance primitives, aligned with ISO 19650, enforce provenance, interoperability, and staged readiness thresholds that prevent premature prescriptive outputs until model convergence criteria are met. BIM and GIS …

Explainable Machine Learning For Biomedical Diagnostics: Optical Imaging And Eeg Signal Analysis, Fozia Rajbdad

LSU Doctoral Dissertations

The growing convenience of complex biomedical data begins new roads for better disease detection and functional identification via artificial intelligence (AI). Nevertheless, conventional analysis methods often rely on basic metrics that drop sensitive biotic differences, and various AI systems are difficult to infer, limiting their clinical reliability and practical use. There is a growing need for explainable, physiologically relevant computational models that can extract key biomarkers from diverse biomedical data sources. This dissertation addresses this problem by obtaining explainable machine learning and deep learning procedures for studying biomedical signals and optical imaging data.

This dissertation is divided into two parts; …

Singulars: Performing The Reverse Turing Test, Halim Madi

ELO (un)supervised 2026

Singulars is an ongoing series of performance systems in which I co-create poetry with a language model trained on an anthology of English poetry alongside my own writing. Across three works—carnation.exe, versus.exe, and reinforcement.exe—I stage live reinforcement loops in which my poems and the model’s responses compete for audience votes. The audience functions as an embodied feedback mechanism, shaping the evolution of both the machine and the human poet in real time.

This paper examines what happens when a poet becomes both author and training data. Drawing from creativity research, metacognition, and social cognition, I reflect …

Comparing Text Score Strategies For Online Music Making, Craig Pedersen, Lindsay R. Vickery, Stuart James

Journal of Network Music and Arts

This paper investigates a range of approaches to using text scores in online and networked music performance, focusing on their alignment with strategies proposed by Wilson (2020) for aesthetic and technical approaches to networked music performance. Text scores, emerging from the experimental music movement of the 1960s, communicate musical ideas through words rather than traditional notation, taking instructional, allusive, and hybrid forms. Although there are many aesthetic and pragmatic approaches to text score composition, the temporal openness of many such works makes the medium well-suited to the latency-challenged practice of telematic performance. The study evaluates four text scores—Craig Pedersen’s July …

Cnkg: Harnessing Large Language Models For Cognitive Neuroscience Knowledge Graph Construction, Ali Sarabadani, Kheirollah Rahsepar Fard, Hamid Dalvand

Turkish Journal of Electrical Engineering and Computer Sciences

Textual resources are among the most valuable sources of information in cognitive neuroscience (CN) for understanding and investigating brain activity and cognitive processes. Extracting and constructing knowledge graphs (KGs) from these texts can facilitate medical research by providing deeper insights into neurological diseases and brain function. In recent years, the use of large language models (LLMs) in natural language processing (NLP) has become increasingly widespread, significantly enhancing the extraction of meaningful information from large volumes of text. This study proposes a novel approach for constructing and evaluating a specialized knowledge graph, termed the cognitive neuroscience knowledge graph (CNKG), from scientific …

Noise-Optimized Routes For Air Taxi, Waleed Raza

Doctoral Dissertations and Master's Theses

Community noise is a primary barrier to the public acceptance and deployment of Advanced Air Mobility (AAM) and Urban Air Mobility (UAM) air taxi operations. This dissertation develops a coupled siting, routing, and noise optimization framework that links vertiport placement to its downstream acoustic consequences, demonstrated through a Daytona Beach case study. Candidate vertiports are screened and selected using accessibility, safety, demand, and feasibility criteria, and the selected sites form a directed network of 20 routes. Each trajectory is evaluated with a physics-based acoustic pipeline reporting Lmax, SEL, and EPNL at school, hospital, and residential receptors, showing that received exposure …

Managing Risks Under Nuclear Power Plant Projects: A Latent Similarity-Guided Contract Customization, Mariam Elazhary, Islam H. El-Adaway

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Global energy is transforming, with nuclear power emerging as a pivotal player in achieving sustainable and low-carbon energy goals. The nature of nuclear technology introduces unique challenges, such as stringent safety and environmental requirements. Existing studies focus on general risk identification, such as supply chain delays, cost overruns, and public perception issues. However, these studies fail to address integrating these risks into tailored contractual provisions, which are critical for navigating the unique challenges of nuclear projects. The goal of this paper is to examine how standard construction contracts can be tailored to better accommodate the risks inherent in nuclear power …

Examining Data Richness In Undergraduate Students’ Reflections: A Linguistic Analysis Of Three Data Collection Methods, Kirsten A. Davis, Anne Wrobetz

School of Engineering Education Faculty Publications

Researchers select data collection methods for a variety of reasons. In our research on experiential education programs, we perceived that when we collected students’ reflections using different methods, the data we received was different. However, we found few existing studies to help us characterize what was different across our data sets to inform our decisions about which methods to use. Thus, the purpose of our study was to compare data richness across three data sets from previous studies of engineering students studying abroad, each collected using a different method (written diaries, interviews, video diaries). We used linguistic inquiry as a …

Advancing Social Media Analytics And Personalized Generation Via Transfer Learning, Discourse-Aware Modeling, And Collaborative Modeling, Gibson Nkhata

Graduate Theses and Dissertations

Social media platforms have become central to information exchange, shaping public opinion across social, political, and economic domains. However, the massive volume of user-generated content, combined with its informal, nuanced, and often noisy nature, presents significant challenges for automated analysis and generation. Tasks such as stance detection, rumor verification, and personalized content generation are further complicated by sarcasm, evolving discourse structures, and diverse user preferences. Addressing these challenges requires models that can effectively leverage linguistic nuance, conversational dynamics, and collaborative user signals. Transfer learning has emerged as a powerful paradigm for improving performance in low-resource and complex language understanding tasks. …

Veribrief: A Multi-Agent Retrieval-Augmented Generation System For Policy Decision Support, Imane Bahji

Electrical and Computer Engineering ETDs

VeriBrief is a multi-agent retrieval-augmented generation (RAG) system for evidence-grounded economic policy analysis. The system orchestrates a five-stage LangGraph pipeline, retrieval, research, analysis, synthesis, and critique, to produce cited, structured responses while detecting out-of-scope queries. An empirical evaluation on eight questions drawn from official U.S. macroeconomic releases compared VeriBrief against a single-pass RAG baseline. The multi-agent system achieved 100% refusal precision on unanswerable analytical queries versus 0% for the baseline. Unsupported claims fell substantially on answerable factual questions. A context-propagation defect discovered during evaluation was diagnosed and corrected. Limitations include failure of the evidence gate on policy-counterfactual queries and a …

Aspirational Mirrors: A Conceptual Model Of Alumni-Led Near-Peer Outreach For Precollege Engagement In Ocean Engineering, Habibi Palippui, Juswan Sade

Journal of Pre-College Engineering Education Research (J-PEER)

Precollege students in Indonesia, especially in rural and coastal regions, often lack exposure to engineering disciplines due to social, geographic, and informational barriers. This essay proposes a conceptual model of alumni-led near-peer outreach as an affective and culturally grounded mechanism to engage high school students in ocean engineering. Drawing from theories of social similarity, possible selves, and engineering identity, the model conceptualizes alumni as “aspirational mirrors” who influence students not merely through information but also through identification. The model outlines four primary mechanisms—social connection, narrative engagement, aspirational mirroring, and early identity activation—and also accounts for contextual variables such as school …

Generative Endurance Logic: An Axiomatic Framework For Reasoning About Outcome-Generating Objects Under Constraints, Hafiz Burhan Ul Haq, Muhammad Nauman Irshad

Neutrosophic Systems with Applications

This paper introduces Generative Endurance Logic (GEL), a formal framework for studying objects through the outcomes they can produce. In many cases, an object cannot be judged only by a fixed truth value, score, or utility value. A rule, model, action, or strategy may behave well in one situation but fail when the context changes or when small perturbations occur. GEL addresses this issue by treating each object as a generator of outcomes. Each object a is linked to a generation map Ga:X×Ω→Y, where X is the context space, Ω is the …

Real-Time Adaptive Control Of Machining Parameters In Cnc Milling Using An Ensemble Digital Twin And In-Process Vibration Monitoring For Enhanced Surface Integrity, Ali Qasim Abdulwahid

Al-Esraa University College Journal for Engineering Sciences

Achieving consistent surface integrity in CNC milling under varying materials and degraded tool conditions remains a critical challenge because fixed cutting parameters often induce regenerative chatter, accelerate tool wear, and cause surface and subsurface damage. This paper presents an integrated solution that combines an Ensemble Digital Twin (EDT), real-time in-process vibration monitoring, and a hybrid adaptive controller to maintain high surface quality while preserving productivity. The EDT fuses a physics-based milling dynamics model with a recurrent LSTM surrogate through a context-aware meta-learner that weights model outputs by process state (e.g., tool wear, engagement). A compact real-time monitoring pipeline computes a …

An Interactive Multi-Objective Programming Approach For Optimizing Fully Trapezoidal Spherical Fuzzy Linear Programming Problem With Application, Sultan S. Alodhaibi, Hissah Ibrahim Almuzini, Hamiden Abd El-Wahed Khalifa

Neutrosophic Systems with Applications

In this paper, a linear programming framework with completely uncertain parameters is investigated by employing trapezoidal spherical fuzzy numbers (TrSFNs). The proposed formulation incorporates a spherical fuzzy (SF) decision environment in which the optimization process simultaneously maximizes the degree of positive membership while minimizing the corresponding neutral and negative membership degrees. By utilizing the concept of the α -cut associated with TrSFNs, the original fully fuzzy linear programming problem is transformed into an interval-valued linear programming model with confidence levels. To rank and compare the resulting interval objective values, an interval ordering approach based on the decision maker's preferences—considering the …

Interval-Valued Neutrosophic Dombi Bonferroni Mean Aggregation Operators In Medical Diagnosis And Sustainable Energy, Maryam Faisal, Muhammad Nadeem, Muhammad Kamran

Neutrosophic Systems with Applications

Medical diagnosis is one of the most difficult fields in which decisions must be made due to the fact that medical information often has characteristics of uncertainty, incompleteness, imprecision and even contradiction. Traditional aggregation and decision-making methods are often not well suited to such complexities, and may result in less reliable diagnostic outcomes. In order to overcome these drawbacks, the authors propose a new approach using a novel representation of Interval-Valued Neutrosophic Sets (IVNSs), the Dombi operational laws, and Bonferroni Mean (BM) aggregation operators. The proposed framework is specifically aimed at coping with uncertainty, indeterminacy and falsity all at once …

Exploiting Uncertainty Of Computational Methodology In Optimizing User Interface In Human-Computer Interaction, Nada Mohamed, Alshaimaa A. Tantawy

Neutrosophic Systems with Applications

Human-computer interaction (HCI) evaluation and optimization of user interfaces (UIs) constitute a complex multi-criteria decision-making challenge, marked by conflicting evaluation dimensions, subjective expert judgments, and inherent uncertainty in user experience assessment. Traditional evaluation approaches, such as heuristic expert reviews and user satisfaction surveys, rely on sharp, binary classifications that fail to capture the gradual and overlapping nature of human cognitive and affective states. This limitation necessitates a more robust uncertainty-aware methodology that can model the true complexity of HCI evaluation. This paper proposes a hybrid mathematical model that integrates various Multi-Criteria Decision Making (MCDM) techniques of Entropy, and Simple Additive …

Recursive Neutrosophic Superhypergraphs With Illustrative Applications, Takaaki Fujita, Ajoy Kanti Das, Suman Das, Sankar Prasad Mondal, Volkan Duran

Neutrosophic Systems with Applications

Finite hypergraphs generalize ordinary graphs by permitting each hyperedge to join any nonempty set of vertices, and thus provide a natural model for truly multiway interactions. To represent hierarchical and multi-layer structure, SuperHyperGraphs iterate the powerset operation so that set-valued entities created at one level can be treated as vertices at higher levels. Independently, recursive hypergraphs allow edge recursion: an edge may contain not only vertices but also lower-level edges, yielding nested (and possibly self-referential) incidence controlled by a specified recursion depth. In this work we introduce and axiomatize Recursive Neutrosophic SuperHyperGraphs, a unified framework that combines vertex …

Fuzzy-System-Based Correction Of Nonlinearities In Complex Dynamic Plants, Zokhid Ergashboyevich Iskandarov, Tukhtamurod Khayitmurodovich Avezov

Chemical Technology, Control and Management

A method for synthesizing a fuzzy compensating element from the static characteristic of a nonlinear plant is presented. The method transforms the characteristic into a rotated coordinate system, identifies zero crossings and local extreme, maps the characteristic points back to the original coordinates, and uses them to determine the antecedent membership functions and singleton consequents of a zero-order Sugeno inference system. Unlike heuristic rule tuning, the proposed procedure derives the rule base directly from the geometry of the nonlinear characteristic. An adaptive reconfiguration mechanism based on a mismatch indicator is also formulated for operating conditions in which plant parameters vary …

Investigation Of The Problems Of Intelligent Control Of The Process Of Low-Temperature Separation Of Natural Gas., Shaxzod Nurmuhammad O‘G‘Li Amirov

Chemical Technology, Control and Management

This article analyzes the problems of intelligent control of the process of low-temperature separation (LTS) of natural gas and modern approaches to solving them. Since the process complexity of gas separation at low temperature as well as multidimensional technological parameters have nonlinear control properties, a principled technological scheme for intellectual control of technological parameters such as gas humidity, temperature, pressure, consumption and diethylenglicol (DEG) consumption of the LTS process has been developed. The analysis and literature review show that intellectual control methods allow for stable operation of the technological process, reduced energy consumption, reduced heat exchange, and reduced gas hydrate …

(R2187) Analysis Of Neurological Impairments In Hospitalized Patients Using Cubic Neutrosophic Sets, B. Anitha, M. Lavanya

Applications and Applied Mathematics: An International Journal (AAM)

This study introduces an MCDM-based framework for identifying neurological diseases in hospitalized patients using symptom-based evaluations. A team of interns, guided by the chief doctor, was responsible for determining each patient’s precise condition from the presented neurological symptoms. To enhance diagnostic accuracy, the interns employed the TOPSIS and WASPAS methods to assess and rank the potential disease options. The combined analysis yielded a clear identification of the highest ranked disease for every patient, highlighting the effectiveness of these MCDM techniques in supporting clinical decision making.

Building Trustworthy Information Systems: A Unified Framework For Comparative Risk Detection, Parisa Momeni

USF Tampa Graduate Theses and Dissertations

Risk detection in large scale information systems increasingly depends on heterogeneous data generatedby both centralized and distributed ecosystems. While centralized systems provide curated and validated reports, distributed environments produce large-scale and real-time observational evidence. Existing computational approaches analyze these ecosystems in isolation, limiting systematic comparison of risk repre-sentations across heterogeneous sources.

This dissertation presents a unified computational framework for comparative risk detection across centralized and distributed information systems. The framework provides a domain independent methodology for transforming heterogeneous risk reporting data into comparable multidimensional representations. To enable interpretable comparison of heterogeneous risk distributions, this work introduces the Geometric Overlap Score …