Digital Commons Network^™

What Types Of Uncertainty Emerge In Instructional Aviation Incidents?, Abigail Henson

Mechanical Engineering Undergraduate Honors Theses

This study utilized narrative reports from the National Aeronautics and Space Administration (NASA) Aviation Safety Reporting System (ASRS) database to examine the types of uncertainty present in near-miss instructional aviation incidents occurring within the United States between 2015 and 2025. The final dataset consisted of 86 reports, with each report containing a set of narrative accounts from both the student pilot and the flight instructor operating under Part 91 regulations. Each instructional event was classified using a combined framework incorporating uncertainty categorizations and the Human Factors Analysis and Classification System (HFACS). Results indicate that epistemological uncertainty was the most common …

Machine Learning Models For Estimating The Consumed And Remaining Useful Life Of Haul Trucks In An Open-Pit Mine In Peru, Marco Cotrina, Jairo Marquina, Mario Sandoval, Jose Mamani, Johnny Ccatamayo

Journal of Sustainable Mining

The purpose of this study was to develop a machine learning-based model to predict the consumed useful life and estimate the remaining useful life of haul trucks in an open-pit mining operation in Peru. A comparative analysis of multiple machine learning models was conducted, including multiple linear regression (MLR), random forest + PSO, support vector regression (SVR), gradient boosting machine (GBM), decision tree + PSO, and artificial neural networks (ANN-MLP). The models were evaluated using performance metrics such as R², RMSE, and MAE, selecting the optimal model to estimate the remaining useful life based on a theoretical lifespan …

Development Of A Spectral Index And Web Application For Automated Marble Quarry Monitoring: A Sentinel-2 Based Approach For Change Detection And Monitoring, Konstantinos Ntouros, Vasileios Drimzakas - Papadopoulos, Georgios Gkologkinas, Georgios Ntouros, Dimitrios Markou

Journal of Sustainable Mining

This study introduces an automated workflow to monitor marble quarry operations using Sentinel-2 satellite data, providing a cost-effective and efficient tool for regulatory oversight focused on environmental sustainability. At the core of this workflow is the Quarry Change Detection Index (QCDI), a new spectral index specifically developed to leverage the unique spectral characteristics of quarry sites, enhancing the detection of land cover changes associated with quarry expansion. To facilitate practical application, a web-based tool was developed using Google Earth Engine and Streamlit. The user-centric design of this platform features an intuitive interface, allowing users to easily select parameters, visualize data, …

Could 'Real Existing Ai' Be Sentient?, Tim Crane

Animal Sentience

Jonathan Birch is rightly sceptical about the idea that today’s AI machines (e.g. LLMs) are candidates for sentience. Nonetheless, he gives three reasons for taking the future possibility of AI sentience seriously. I dispute them all. Birch fails to distinguish the very idea of an artificial mind from computational AI (what I call ‘Real Existing AI’); he does not adequately distinguish a simulation from a replica or copy; and he fails to acknowledge that the appeal to computational functionalism in this context is straightforwardly question-begging.

A Neutrosophic Memory-Integrity Calculus For Contradiction-Preserving Persistent Ai Agents, Rana Muhammad Zulqarnain, Saalam Ali

Neutrosophic Systems with Applications

Persistent AI agents increasingly convert interaction histories into long-lived memory, making memory transformation not retrieval alone—a central reliability problem. NMIC (Neutrosophic Memory-Integrity Calculus) formalizes the integrity of write, merge, consolidation, revision, and retrieval operations over persistent memory. Each proposition is represented through an evidence ledger carrying independent truth, indeterminacy, and falsity degrees together with reliability, provenance, temporal validity, contextual applicability, and inter-evidence dependence. A dependence-normalized hazard aggregation preserves simultaneous support and opposition while making the resulting state invariant to exact evidence duplication. Pure consolidation is governed by five integrity conditions: no support invention, no opposition invention, no manufactured certainty, contradiction …

The Algorithmic Narcissus: Ai Validation And The Atrophy Of The 'Athletic' Social Self, Ilham Phalosa Reswara, Anggi Mayangsari

Jurnal Psikologi Sosial

Artificial intelligence systems designed around continuous affirmation and minimal friction are becoming increasingly prominent as social and relational partners in everyday life. This article examines how sustained interaction with such frictionless AI systems may reshape the developmental conditions under which social selfhood forms and is maintained. Drawing on Cooley's (1902) looking glass self, Kohut's (1971) concept of optimal frustration, and the developmental literature on social competence, the article argues that frictionless AI may distort the social mirror through which identity forms, remove the manageable frustration that appears necessary for psychological growth, weaken empathic capacity, and erode the social stakes that …

Decision Support System For The Selection Of Thumbprint Recognition Algorithms In Biometric Security Systems, Toqeer Jameel, Muhammad Riaz

Neutrosophic Systems with Applications

This study investigates fingerprint recognition in immigration operations, emphasizing the role of biometric verification in enhancing security, fairness, and operational efficiency in international mobility. To address the uncertainty, vagueness, and imprecision inherent in fingerprint identification, a novel decision-making framework is proposed by integrating interval-valued picture fuzzy (IVPF) information. Fairly aggregation operators are introduced to combine decision makers' evaluations, while extracted fingerprint features are modeled using positive, neutral, and negative membership degrees within the IVPF environment. Objective criterion weights are determined using the criteria importance through intercriteria correlation (CRITIC) method, and individual ranking is performed via the alternative ranking order method …

Context Matters: Evaluating Llm-Generated Knowledge Graph Schemas, Ritvik Garimella, Riju Marwah, Atishay Jain, Khusham Bansal, Amit Sheth

Publications

Knowledge graph (KG) schema engineering is labor-intensive and resists automation at scale. We investigate whether LLMs can generate domain-specific KG schemas of sufficient quality for downstream symbolic reasoning. We propose a tiered contextual framework that varies domain context richness across four levels: zero context, domain scope, task requirements, and data distribution. Generated schemas are evaluated intrinsically on BioRED (600 PubMed abstracts, multi-type entities and relations), where automated tiered schemas match an established KG construction baseline at 79.9% EC, with edge conformance rising from 47.5% at L1 to a stable 78–80% from L2 onward. Extrinsic evaluation on a 50-record MedHop controlled …

Semantic Shields: Automating Critical Infrastructure Defense Via Nlp-Driven Ransomware Profiling, Henry Trowbridge, Ian Zalcberg, Ryan Schley, Carter Yagemann, Natasha Phan, Srikar Maduposu, Vimal Buck

Military Cyber Affairs

Ransomware poses a growing threat to critical infrastructure, where successful attacks can disrupt operational technology (OT) and industrial control systems (ICS) with significant public safety consequences. However, attributing ransomware incidents to specific threat actors remains challenging due to ransomware-as-a-service ecosystems, actor rebranding, and the obfuscation of traditional indicators of compromise. This paper presents Semantic Shields, an NLP-driven attribution framework that leverages BERT-generated semantic embeddings and DBSCAN clustering to profile ransomware actors through the linguistic characteristics of ransom notes. Using a dataset of 295 ransom notes from 189 distinct threat groups, the framework achieved an 87.2% true positive clustering rate and …

Comparison Of Gravity Separation And Flotation For Pyrite Recovery From Tailings, Gülay Bulut, Gönül Göksu Gökçe, Dilruba Karamanlı, Oğuzhan Mert Gürkan, Ergin Sarp Zenzirci, Binnur Kırım, Alim Gül

Journal of Sustainable Mining

Tailings generated during the production of lead, zinc, and copper concentrates contain significant amounts of sulfide minerals, particularly pyrite. Pyrite, which remains in tailings after the recovery of other metals, is one of the main contributors to acid mine drainage (AMD). Therefore, recovering pyrite from tailings is environmentally and economically an important issue. In this study, pyrite recovery from tailings was investigated using flotation and gravity separation methods. Mineralogical characterization and liberation analyses were conducted by Mineral Liberation Analysis (MLA), indicating that pyrite is the major sulfide mineral with over 76% liberation degree even in the coarsest fractions. Flotation tests …

Relative Rate Observer-Based Online Tuning Mechanism For Single-Input Interval Type-2 Fuzzy Pid Controllers, Oqba Aldreiei, Cenk Ulu, Mert Can Kurucu, Müjde Güzelkaya

Turkish Journal of Electrical Engineering and Computer Sciences

The characteristics of the footprint of uncertainty (FOU) in interval type-2 membership functions (IT2-MFs) are crucial to the performance and robustness of interval type-2 fuzzy controllers (IT2-FCs). However, existing IT2-FC design approaches mostly use fixed FOU structures. This study proposes an online membership function (MF) adjustment mechanism for a single-input interval type-2 fuzzy PID controller (SIT2-FPID)  that adjusts the FOU of the antecedent MFs and weights of the consequent MFs, respectively, to achieve high performance and robustness. The proposed online adjustment mechanism consists of a relative rate observer (RRO), a two-input rule-base adjustment system, and a first-order smoothing filter. The …

Empowering Edge Intelligence Through Reparameterized Lightweight Transformers And Distributed Inference, Hosein Esmaeili, Mohammad Ali Afshar Kazemi, Reza Radfar, Nazanin Pilevari

Turkish Journal of Electrical Engineering and Computer Sciences

Deploying advanced transformer-based models on resource-constrained edge devices remains a significant challenge due to their high memory footprint and substantial compute requirements. In this paper, we propose a reparameterized transformer framework that integrates High-Rank Factorization (HRF) during training, layer merging at inference, and dynamic, load-balanced distributed inference across multiple devices. To further reduce resource usage, our framework supports mixed-precision quantization down to 4-bit, enabling flexible accuracy–latency–energy trade-offs. Experimental evaluations on the ESC-50 environmental sound dataset demonstrate that our method matches or exceeds the performance of larger baseline models while using 20–30% fewer parameters, achieving up to 48% latency reduction in …

Identifying The Causality And Criticality Of Factors Influencing The Promotion Of Asian Island-Hopping Cruise Tourism In Taiwan, Hsiao-Chuan Liu, Gin-Shuh Liang, Feng-Ming Tsai, Yu-Ling Lien

Journal of Marine Science and Technology–Taiwan

Following the COVID-19 pandemic, the global tourism industry has steadily recovered since 2022. Island-hopping cruises have gained increasing attention for enhancing regional connectivity and diversifying marine travel experiences; however, their development in Asia remains limited compared with that in Europe and the Caribbean due to fragmented policy coordination, inadequate port infrastructure, and uneven technological readiness. Although Taiwan possesses a geographic advantage and can serve as a strategic hub linking Asian island destinations, systematic planning and stakeholder coordination remain insufficient, resulting in unclear mechanisms that drive or constrain island-hopping cruise tourism. Existing studies mainly focus on market demand or passenger behavior, …

A Resilience Early Warning Assessment Of The Maritime Supply Chain: A Case Study Of China’S New Energy Vehicle Exports, Xiuqian Chen, Liangyong Chu, Mengyao Wang, Jiayin Du, Yiming Zhang, Xiyao Xu

Journal of Marine Science and Technology–Taiwan

Enhancing resilience through early warning is a critical strategy for mitigating disruption risks in the maritime supply chain (MSC). This study proposes a novel early warning assessment framework to determine the resilience of the MSC. It is referred to as a resilience early warning system. An evaluation index system for shipping enterprises is developed based on four dimensions: withstand capacity, adaptive capacity, learning capability, and the external environment. A resilience assessment model that uses the Bayesian best-worst method (BBWM) and the extension cloud model (ECM) is established to quantify MSC resilience. An early warning evaluation model based on a Bayesian …

Doing Less With More: A First-Principles Exploration Of The Suitability Of Agentic Computing Over Alternative Architectural Choices, Ritvik Garimella, Biplav Srivastava, Amit Sheth

Publications

There is growing interest in automating business activities with Agentic Artificial Intelligence (AI) due to latter's seeming ease of use. Never has it been easier, or costlier, to do less with more. However, little is known about when agents are preferable to established alternatives such as local computation, Representational State Transfer (REST), the Simple Object Access Protocol (SOAP), and the Model Context Protocol (MCP), particularly when development speed, performance, and operational cost are considered. We investigate this question using a controlled mathematical task that compares seven methods on a benchmark of 1,000 arithmetic expressions where semantics of operator precedence has …

Designing Classifact: Towards A Transparent And Secure Platform For Stakeholder-In-The-Loop Data Annotation, Philippine Waisvisz

Communications of the IIMA

AI systems depend on human judgment, yet many annotation workflows are either designed for data-science specialists or managed through external commercial platforms. The first approach may demand more technical skills than relevant stakeholders possess. The second can require organizations to transfer data, expertise, and governance to an outside provider. Both can limit the involvement of people who understand what data means in its real-world context. Human-in-the-loop approaches introduce human judgment. Stakeholder-in-the-loop annotation focuses on selecting and organizing people whose contextual knowledge fits the AI application.

This paper presents Classifact, a transparent and secure platform for stakeholder-in-the-loop data annotation and validation. …

Comparative Analysis Of Takagi-Sugeno And Mamdani Fuzzy Inference Architectures With Anfis-Based Automated Rule Generation For Eeg-Based Cognitive State Monitoring, Amina Radončić, Mehrija Hasičić, Jasmin Kevrić

Communications of the IIMA

Fuzzy inference systems have demonstrated considerable promise for EEG-based cognitive state monitoring in neurodegenerative conditions. However, two design decisions significantly influence system performance and clinical applicability: the choice of inference architecture (Takagi-Sugeno vs Mamdani) and the method of rule and membership function generation (manual expert-driven vs data-driven automated). This paper presents a comparative analysis of both dimensions in the context of an EEG-based Alzheimer’s disease monitoring system operating on the ds004504 OpenNeuro dataset (88 subjects: 36 AD, 23 FTD, 29 HC). A Takagi-Sugeno system, implemented as a hybrid FSM-Fuzzy architecture, is compared against a Mamdani equivalent across four axes: inference …

An Interval-Valued Spherical Fuzzy Critic–Waspas Framework For Prioritizing Healthcare Delivery Models To Enhance Patient Satisfaction Under Uncertainty, Mariam Hamada, Ahmed Samy, Mohamed M. Abdelhafeez, Shrouk El-Amir

Neutrosophic Systems with Applications

Selecting an appropriate healthcare delivery model is important for improving the quality of healthcare services and enhancing patient satisfaction. However, this decision is complex because it involves several criteria, uncertainty, and different expert opinions. To handle this uncertainty, this paper uses Interval-Valued Spherical Fuzzy Sets (IVSFSs), which allow experts to express their evaluations more flexibly. This paper proposes an integrated interval-valued spherical fuzzy CRITIC-WASPAS approach to prioritize healthcare delivery models. The CRITIC method is used to determine the objective weights of the evaluation criteria, while the WASPAS method is used to rank the healthcare delivery models. Expert evaluations are expressed …

A Neutrosophic Event-Graph Legal Ai System For Detecting Contradictions In Witness Testimonies Under Egyptian Law, Shimaa Abdelghany Attalla, Alaa Elmor, Nada Hesham, Abduallah Gamal

Neutrosophic Systems with Applications

Witness testimony is an important source of evidence in criminal proceedings, but it may contain contradictions, incomplete details, or conflicts with other case-file materials. This paper proposes a neutrosophic event-graph legal AI framework for detecting materially contested claims in witness testimonies under the Egyptian criminal-procedure context. The framework converts testimony and related records into structured claims containing actor, action, object, time, location, source, and modality. These claims are then connected through an event graph and evaluated using neutrosophic components of support, indeterminacy, and opposition. The system produces source-grounded legal-review alerts when a claim has sufficient opposition from other claims or …

Microwave Thermal Pre-Treatment To Improve Nickel Extraction From Lateritic Ore, Johana Borda, Daniel Sosa, Robinson Torres

Journal of Sustainable Mining

Two heat treatment processes for a nickeliferous laterite sample are presented, one by the conventional muffle route and the other by microwave. The heating was carried out in order to improve the dissolution of nickel in an acid medium. The study describes the changes observed in the mineral for the increase in temperature as a consequence of radiation in the microwave and in the muffle. The changes in the mineral crystalline phases were analyzed by X-ray diffraction. The leaching media consisted of a 1 M sulfuric acid solution at ambient conditions for 7 h. The interaction between the reagent and …

A Framework For Automated Quantity Extraction From .Ifc Models And Normalized Bid Comparison, Asmaa Mohamed Farouk

Theses and Dissertations

Accurate bid comparison remains a major challenge in construction tendering due to

differences in Bill of Quantities formats, item naming conventions, and pricing methods across

contractor submissions. These variations require time-consuming manual work and often lead to

subjective decisions. This research presents a computational framework that uses Building

Information Modeling through the .ifc file format, combined with text analysis techniques for

automated bid normalization.

The developed system uses a dual-component approach. Phase 1 extracts quantities from .ifc

model files using 3D geometric calculations, automated element classification. Phase 2 uses text

mining with a domain-specific dictionary of construction terms to interpret …

Analysis Of The Throttle Settings Under Uncertain Information, Latafat Gardashova, Nihad Afandi

Chemical Technology, Control and Management

Although classical fuzzy logic controllers are capable of modelling non-linear control systems, they fail to consider the reliability of linguistic information, sensor measurements, and expert knowledge. In this paper, an intelligent controller based on the use of Z-numbers is developed for steam-turbine throttle control. Linguistic information and its confidence degree are considered simultaneously in such a controller. The temperature and pressure values are taken as input variables, while the throttle rotation is selected as the controller output variable. At first, the Z-number representation system is constructed to include the credibility of linguistic measurements and control rules. Then, a Mamdani Type-1 …

Performance Evaluation Of Controllers Using Fuzzy Delphi And Ahp Techniques, Kamala. R. Aliyeva, Nihad Mehdiyev, Shamil Mehdi

Chemical Technology, Control and Management

This study introduces an enhanced decision-support framework that integrates the Fuzzy Delphi method with the Analytic Hierarchy Process (AHP) to improve controller tuning and performance evaluation in uncertain environments. Traditional tuning techniques typically depend on crisp expert judgments and deterministic performance indices; however, industrial control systems are characterized by nonlinear behaviors, uncertain parameter variations, and subjective expert evaluations that are often vague or inconsistent. The Fuzzy Delphi procedure is applied to systematically gather, filter, and consolidate expert insights, enabling a refined set of performance criteria such as stability margins, robustness to disturbances, settling time, overshoot, control effort, and energy consumption …

Biki: Design Overhaul, Meg Rawson

Computer Science and Engineering Senior Theses

For children from bilingual households, kindergarten can represent both an introduction to formal mathematics education and a period of increased exposure to the English language. Students may therefore be learning foundational mathematical concepts while simultaneously developing the English vocabulary used to describe those concepts. BiKi (Bilingual Math for Kindergartners) was developed to help bridge this gap through an educational application that combines visual, auditory, and interactive learning activities to reinforce mathematical vocabulary in both English and a student’s home language.

This work builds upon the existing BiKi application while maintaining its original educational goals and intended audience. The primary focus …

Key Technologies And Their Application Of Vertical Large Models For Geological Guarantee In Coal Mining, Liu Zaibin, Fan Tao, Liu Borui, Chen Changyuan, Li Guihong, Li Wei, Jing Xiaotian, Li Xiping, Du Yiming

Coal Geology & Exploration

Background General-purpose large language models (LLMs) have remarkable capabilities in natural language understanding and complex-task reasoning. However, when applied to geological guarantee in coal mining, these models still suffer from several inherent limitations, including insufficient professional geological knowledge, limited insights into industrial terminology, and inadequate integration of engineering logic and rules. Consequently, they face challenges in accurately capturing domain-specific knowledge and reasoning mechanisms required for geological interpretation, early warning of disasters, and decision-making for disaster prevention and control. These issues lead to limited applicability and reliability of general-purpose LLMs. On the other hand, geological guarantee in coal mining involves multi-source …

Critical Core Technology Breakthroughs In Large-Scale Models: Industrialization Strategies And Policy Implications, Zhongqi Wu, Yinshan Liu, Tao Dai, Xiaolong Zheng

Bulletin of Chinese Academy of Sciences (Chinese Version)

As a pivotal direction for breakthroughs in key core technologies within the artificial intelligence domain, large-scale models hold strategic significance in securing national scientific and technological sovereignty. This study employs a multidimensional framework encompassing “technological breakthroughs, industrial transformation, and governance policies” to systematically investigate the developmental trajectories and industrialization bottlenecks of large-scale models. At the technological level, while large-scale models exhibit exponential growth in parameter scale and computing power demands, they face critical challenges including the scarcity of high-quality data, insufficient transfer learning capabilities, and reliability-explainability trade-offs. Industrially, these models are reshaping the global industrial chain landscape through a dual-track …

Explaining Safety Challenges And Their Relationships Using A Qualitative-Fuzzy Dematel Approach: A Surface Mine Case Study, Neda Molamehdizadeh, Gholam Hossein Halvani, Hossein Ebrahimi, Ali Asghar Farshad, Seyedeh Melika Kharghani Moghadam, Saber Moradi Hanifi

Journal of Sustainable Mining

Safety in surface mining operations is a major organizational management challenge due to the inherently hazardous nature of mining activities. This study aimed to explain safety challenges and identify their interrelationships through a combined qualitative-fuzzy DEMATEL approach in a surface mine in Yazd Province, Iran. The research was carried out in two sequential phases: first, key safety challenges were identified through qualitative interviews with employees, supervisors, and safety experts; then, the relationships among these challenges were analyzed and prioritized using the fuzzy DEMATEL technique. The main challenges identified included insufficient specialized training, inadequate safety equipment, weak organizational safety culture, and …

Learning Casual Structures From Aviation Accident Narratives Using Natural Language Processing And Graph-Based Knowledge Representation, Stephanie Ramsey, Katherine Hoffsetz, Madeline Gorman, Logan Lambeth

Discovery Day - Daytona Beach

Understanding the complex causal relationships underlying aviation accidents is critical for improving safety and preventing future incidents. However, much of this information exists in unstructured narrative reports, making large-scale analysis difficult. This project aims to automatically extract and model causal chains from National Transportation Safety Board (NTSB) accident narratives using a combination of traditional natural language processing (NLP) techniques, transformer-based architectures, and graph-based knowledge representation. Traditional NLP methods, including named entity recognition, dependency parsing, and rule-based pattern matching, will be used to identify structured cause–effect relationships. These approaches will be compared with transformer-based models, including a lightweight encoder for classification …

Comparative Analysis Of Artificial Intelligence (Ai) Implications For Active Learning And Assessment At Various Curricular Levels Of Stem And Health Sciences In Developed Versus Underprivileged Countries, Vinay Munlapudi, Santanu De

FDLA Journal

Today’s all-pervasive emergence of artificial intelligence (AI) necessitates a comprehensive evaluation of its role in education, especially for occupational disciplines in health sciences and STEM. AI has reshaped student learning and instructors’ navigation of the evolving, post coronavirus infectious disease (COVID-19) global academic landscape. This study aimed at encapsulating evidence-based AI implications for student-centered, active learning and assessment at various curricular levels of science, technology, engineering, mathematics (STEM) and health sciences education in developed versus underprivileged or less developed countries. An extensive, scoping review of 65 recent, scholarly publications was conducted over six months through diverse search engines (PubMed, Google …

Prompt-Driven Tabletop Robotic Manipulation With A Structured Llm Interface And Visual Verification, Tomas Franco

Master's Theses

Creating a tabletop robotic manipulation system that connects language goals to robot actions is a challenging problem. Large language models can interpret mission objectives and reason through actions, but when placed directly in control of hardware they can produce hallucinated commands that result in unsafe behavior without the proper safeguards.

This thesis presents a constrained LLM-guided manipulation system built around the Quanser Qarm, a four degree of freedom manipulator with a mounted Realsense RGB-D camera and a gripper with readable current. Every decision made by the LLM planner is routed through a gated command interface that restricts the model to …