Digital Commons Network^™

Adaptive Multi-Stage Fuzzy Logic Approach With Dynamic Weight Adjustment For Robust Power Transformer Diagnostics, Dilafruz Rustamovna Abdullabekova, Odiljon Muhammadjonovich Kutbidinov

Technical science and innovation

An adaptive multi-level fuzzy logic framework with dynamic weight adjustment for power transformer fault diagnosis and health index assessment was proposed in this study. A comprehensive analysis of existing transformer diagnostic approaches was performed, and their limitations related to static weighting schemes and uncertainty handling were identified. A hierarchical fuzzy inference structure was introduced, integrating multi-source diagnostic data, including dissolved gas analysis, transformer oil quality indicators, thermal parameters, and electrical measurements. At the first level, individual fuzzy subsystems were developed to evaluate partial condition indices associated with insulation degradation, oil aging, and thermal–electrical stress. At the second level, a global …

Refinement Of Wind Speed Estimation At Turbine Hub Height Using Satellite Data And Regression Analysis Under Complex Terrain Conditions, Isroil Abriyevich Yuldoshev, Tulqin Rustamovich Jamolov, Sa'dullo Sayfiddin Ugli Fazliddinov, Jumanazar Farhodjon OʻGʻL Abdurashidov

Technical science and innovation

Reliable assessment of wind energy potential in regions characterized by complex terrain is often constrained by the limited availability of ground-based meteorological measurements. This study proposes an improved regression-based approach for refining wind speed estimates at the wind turbine hub height of 65 m using satellite-derived data from the NASA POWER database combined with a logarithmic vertical wind profile. The proposed methodology is validated using real operational data from a 750 kW wind power plant located in the mountainous Bostanlyk district of Uzbekistan for the period 2018–2021. The regression analysis demonstrates a strong linear relationship between the extrapolated wind speed …

Manufacturing Of Functional Surface Coatings In The Automotive Industry, Gulirano Saidakhmedova

Technical science and innovation

The development of environmentally sustainable technologies for material processing represents one of the most pressing scientific and technical challenges in modern industrial production. This issue is particularly relevant to the automotive industry, where growing demands for energy efficiency, durability, and environmental safety necessitate the adoption of advanced manufacturing methods. Special attention is given to developing vacuum-based, import-substituting equipment and technologies for producing functional coatings from composite materials with predefined physical, mechanical, and optical properties, including reflective and protective characteristics. Current requirements for mechanical engineering products and production technologies drive the need for innovative approaches to material processing and specialized technical …

Design Of Virtual Impedance Controller For Parallel-Connected Converters In A Microgrid, Manyanda Makoye, Francis Mwasilu, Peter M. Makolo, Jackson Justo

Tanzania Journal of Science

This paper addresses the significant challenge of inaccurate power sharing among Distributed Generators (DGs) in islanded microgrids, which is primarily caused by mismatched feeder and line impedances. Conventional decentralized control solutions often fail to ensure accurate power sharing, especially when line impedances are resistive. To overcome this, the paper proposes a robust, coordinated Virtual Impedance Control (VIC) strategy for DGs. This method implements fixed virtual resistance and virtual inductance to standardize the output impedance characteristics of parallel-connected inverters, thereby minimizing impedance discrepancies and enhancing system stability through increased damping. The theoretical analysis and design of the VIC were validated through …

Elemental And Mineralogical Characterization Of Permo-Carboniferous Coal In The Ngaka Coalfield, Southwest Tanzania: Implications For Coal Quality Variation, John W. Gama, Justina J. Saroni, Remigius L. Gama

Tanzania Journal of Science

Coal from heterolithic seams exhibits distinct chemical characteristics due to interbedded non-coal beds, which influence its quality and usability. Despite extensive research in these settings, the elemental and mineralogical characteristics of the heterolithic sequences of the Mchuchuma Formation (K2) in the Ngaka Coalfield remain poorly documented. This study provides the first detailed assessment of K2 seams across five active mine sites. Eleven representative samples were analyzed using proximate, elemental, and mineralogical techniques, supported by Pearson correlation, binary analysis, and Principal Component Analysis. Proximate analysis revealed that ash content varies across mine sites, with values ranging from 9.17 to 25.03%, volatile …

Impact Of Collodion Thickness On Fission Track Generation Within Lexan Employed For Nuclear Forensic Analyses, Ryan K. Chapman, Zachary P. Meisel, Abigail A. Bickley

Faculty Publications

We report results from a computational study of fission track damage within a Lexan substrate caused by ²³⁵U suspended within a collodion film of varied thickness. MCNP6.3 was used to model energy deposition and displacements per atom within a Lexan film coated by a film of collodion, located within the air-filled 6 inch dry tube of a TRIGA reactor. The collodion layer was varied between 20 and 100 μm thickness, resulting in an 8.8% difference in energy deposition and 12% difference in displacements-per-atom within the Lexan substrate. These findings indicate that collodion adhesive thickness has a moderate impact on …

A Dynamic Systems Framework For Customer Lifecycle Management: From Latent State Discovery To Robust Control Policy, Ali Nasirzonouzi

Northeast Journal of Complex Systems (NEJCS)

Traditional marketing often relies on static strategies that fail to capture dynamic customer behavior. This paper introduces an integrated framework to model and control the customer lifecycle, bridging the gap between empirical data and computational simulation. Using the Customer Personality Analysis dataset, we implemented a five-stage methodology. We first identified three distinct customer segments (At-Risk, Standard, High-Value) using Gaussian Mixture Models. To address the lack of longitudinal data, we calibrated a normative transition model based on customer inertia principles. Our analysis revealed that marketing effectiveness is highly state-dependent; notably, At-Risk customers exhibited a 33.5% lift when targeted with catalogs. Leveraging …

Understanding The Binding Of Nickel(Ii) Bromide To A Zirconium Metal-Organic Cage For Heterogeneous Catalysis, Luci Green

Caroliniana Undergraduate Research Journal

Heterogeneous catalysts offer inherent advantages in improving the sustainability of industrial chemical processes. Their high ease of separation and recyclability has the potential to reduce the cost, waste, and energy consumption of processes that currently rely on homogeneous catalysts. Metal–organic cages (MOCs) are an attractive material for heterogeneous catalysis due to their discrete and highly tunable structures, which can be functionalized by binding these complexes with catalytically active metals. The specific objective of this study is to provide preliminary insight into the binding of nickel(II) bromide (NiBr₂) to zirconium MOCs, which was done by studying the binding of …

Convergence Education In A Stem-Focused University: An Ethnographic Study Of A Cross-College Design And Innovation Program, Greg J. Strimel, Sherylyn Briller, Jackson Otto, Rebecca Martinez, Douglas Pruim, Todd R. Kelley

School of Engineering Technology Faculty Publications

Background

Innovation often transcends the bounds of individual disciplines, yet undergraduate teaching is still largely organized today within single disciplines and respective academic units. Convergence education offers a promising organizational approach to bring transdisciplinary teaching and learning into undergraduate education by aligning institutional structures, pedagogy, and partnerships around meaningful, innovation experiences to address contemporary problems. However, longstanding academic silos in higher education hinder the adoption of convergence-focused pedagogies. Therefore, this study examined the three-year implementation of a novel convergence education initiative developed to foster design and innovation capabilities across multiple academic disciplines. By integrating faculty from the university’s engineering technology, …

Evaluating Fine-Scale Air-Quality Heterogeneity Using A Low-Cost Multipollutant Sensor Network In Twin Cities, Minnesota, Varuni Abhayaratne, Weixing Hao, Chen Ye, Anjana Yatawara, Philip K. Hopke, Jiayu Li, Yang Wang

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

There is growing interest and effort in deploying low-cost sensors (LCSs) to understand fine-scale spatiotemporal heterogeneity of ambient air pollutants in urban areas. The Assessing Urban Air Quality project conducted by the Minnesota Pollution Control Agency deployed 45 low-cost air-quality monitors (AQMesh, Environmental Instruments Ltd.) in the Minneapolis/St. Paul metropolitan area network. Hourly averaged pollutant concentrations or mixing ratios for carbon monoxide (CO), ozone (O₃), nitrogen dioxide (NO₂), nitric oxide (NO), and sulfur dioxide (SO2), as well as particulate matter (PM), including PM1, PM2.5, and PM10, were obtained. The network data from June 2019 to June …

Escaping Isolation: An Analysis Of Virtual Machine And Container Breakout Vulnerabilities, Felix Iov

Cybersecurity Undergraduate Research Showcase

Cloud computing providers rely on multi-tenant architectures to maximize resource efficiency. This infrastructure depends on virtualization, which provides isolation between clients. This comes primarily in the form of Virtual Machines (VMs) and Containers. However, “breakout attacks” or “escapes” are a critical threat where attackers bypass these isolation layers to gain unauthorized access to the host system and neighboring environments. This paper surveys virtualization escape threats and analyzes three case studies: a runc container escape (Leaky Vessels), a VMware ESXi VM escape (VSOCKPuppet), and an NVIDIA GPU container escape (NVIDIAScape). Each demonstrates different attack surfaces, including file descriptor misuse, kernel driver …

Humanizing Engineering For Preservice Elementary Teachers Through Sustainable Engineering Stories, Jeffrey D. Radloff, Madison M. Morris, Jacob Pleasants

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

Meeting the goals of US K–12 science education reform requires that preservice elementary teachers (PSTs) understand engineering as a specialized discipline and a human endeavor that addresses real-world issues. Yet in their formal education, few PSTs receive opportunities to learn about the humanistic dimensions of engineering: engineers’ personal values and motivations, the sociocultural nature of engineering work, and the societal significance of engineering. Storytelling about engineering offers a practical way to give PSTs access to these ideas. This study examines the impacts of integrating storytelling about engineering in a science methods course for future elementary school teachers. Over a two-year …

Aiw26s: Applied Llms, Chengjie Zheng

Paul English Applied Artificial Intelligence (AI) Institute Publications

This workshop introduces the concept of applied large language models (LLMs), focusing on how users can move from simple prompt-based interaction to building structured, repeatable AI-driven workflows. Participants explore how AI enables faster prototyping, lowers barriers to entry, and expands who can participate in building technology. Through a hands-on demonstration, attendees learn how to transform raw inputs into meaningful outputs such as summaries, key concepts, and actionable steps. The session emphasizes the importance of clear problem definition, iterative refinement, and critical evaluation when working with AI systems.

Comparison Of Anomaly Detection Methods On Event-Based Vision Sensor Data In A High Noise Environment, Will Johnston, Anthony L. Franz, Shannon R. Young, Rachel Oliver, Zachary Theis, Brian Mcreynolds, Michael L. Dexter

Faculty Publications

Event-based vision sensors (EVSs) provide unique frequency analysis opportunities due to their event data output and high temporal resolution. Anomaly detection methods used in hyperspectral analysis can be used on the event frequency spectra to detect targets. However, the introduction of a strong, flickering interfering source can reduce the EVS sensitivity and obscure targets of interest. Previous work presented a method showing that targets could still be detected through an overwhelming source using frequency analysis, background suppression, and statistical filtering. This paper extends that research and compares the ability of five different eigenanalysis anomaly detection methods (principal component background suppression …

Approach To Physics Education Using Local Ai With Rag For Open Educational Materials Generation, Dmitriy Beznosko

All Things Open

The new tool that starts to enter all parts of our life is AI – and it enters education as well. There are two standing large concerns with using AI for education – the safety of students’ data and the AI missing specific knowledge about the given class. The approach of using the Retrieval Augmented Generation (RAG) provides the user data to the locally run LLM model (using Ollama framework, a free and open-source tool that allows you to run large language models locally on your system) as a context for the generation of the OER materials. As this AI …

Digital Transformation And Market Microstructure: Analyzing The Impact Of Algorithmic Trading On National Stock Exchange Of India Price Discovery Mechanisms Through Complex Systems Theory., Mukesh Bhaskar Ahirrao, Harshal Anil Salunkhe, Vishal Sunil Rana

Northeast Journal of Complex Systems (NEJCS)

Abstract

This research examines the evolution of market microstructure at the National Stock Exchange of India (NSE) from 2020 to 2024, a period characterized by substantial growth in algorithmic trading from 35% to 44% of total trading volume. Using market microstructure data and analytical techniques grounded in complex systems perspectives, the study documents temporal patterns in price discovery, liquidity, volatility, and market efficiency associated with this digital transformation.

The analysis reveals several notable changes in market characteristics. Transaction costs improved significantly, with bid-ask spreads declining by 23.4% and market depth increasing by 18.1%. Price adjustment half-life decreased by 50%, indicating …

Optimizing Controller Speed And Torque To Reduce Drivetrain Stress, Selene J. Welch

SACAD: Scholarly Activities

This project investigates how torque delivery from the motor controller affects mechanical stress on the rear freewheel ratchet mechanism of a three-wheel electric race car. Excessive initial torque has caused accelerated wear and tear on the ratchet mechanism, reducing drivetrain reliability during Kansas ElectroRally competitions. Preliminary testing showed that slower, gradual acceleration prevented malfunction and allowed the vehicle to maintain top speed reliably. Prior research shows that torque-control strategies strongly influence electric-drive performance. Current research focuses on optimizing the controller’s torque and speed using the Alltrax Software ToolKit to reduce drivetrain tension while preserving the car’s performance capabilities.

Analysis Of The Effects Of Magnetic Field, Heat Transfer, And Thermal Radiation On Blood Flow Through Bifurcated Artery To Enhance Tumor Treatments, Abdullahi Isah, Dauda Gulibur Yakubu, Ali Musa

Tanzania Journal of Science

The work presents the effects of some pertinent parameters on blood flow through bifurcated artery to enhance tumor treatments. Combining appropriately the basic equations, together with the fractionalized Maxwell fluid model allow us to determine the velocity, temperature and concentration of blood flow through bifurcated artery. The study adopted the Atangana-Baleanu fractional time derivative on fluid model to describe the non-Newtonian behavior of blood flow. The numerical simulations were performed using the combined Laplace transform and the method of undetermined coefficients and the results obtained with the aid of Mathcad software were simulated and presented graphically. From the graphical results, …

Complex Systems Mapping Of Fiscal Growth Dynamics At Strategic Maritime Chokepoints Using Time-Series Slopes, Rahul Balamurugan, Preethi Nanjundan, Avichal Sharma

Northeast Journal of Complex Systems (NEJCS)

This study examines how maritime and trading states allocate public resources between defence, health, and economic growth around three strategic chokepoints the Strait of Malacca, the Strait of Hormuz, and the Suez Canal. The analysis extends the classic “guns versus butter” framing by treating defence and health spending as co-evolving components of an interconnected fiscal-growth system. Using World Development Indicators data (1999-2024), trend slopes are estimated for military spending (% of GDP), healthcare spending (% of GDP), and GDP growth (annual %). Two derived indicators are computed, a defence-to-health slope ratio (military slope/health slope) and a fiscal-balance proxy (health slope …

Arum–D Nexus: Adaptive Reflexive Urban Metabolism For Complex Construction And Demolition Waste Governance In Bengaluru, Talat Naaz, Rosewine Joy

Northeast Journal of Complex Systems (NEJCS)

Urban material systems exhibit nonlinear dynamics governed by feedback, adaptation, and emergent coupling among institutions, markets, and behaviors. Construction and demolition (C&D) waste in Bengaluru is a great example of such complexity, where fragmented regulation, informal actors, and digital asymmetries coalesce into unstable waste flows and resource leakages. This study conceptualizes Bengaluru’s C&D waste system as a Complex Adaptive System (CAS), where institutional, market, behavioral, and metabolic subsystems co-evolve through nonlinear feedback interactions. A meta-analysis of secondary literature combined with benchmarking of government datasets is used to evaluate two key complexity indicators, i.e., response speed and feedback density. The advancement …

Multiple-Valued Quantum Automata For Robotics, Yuchen Huang

Dissertations and Theses

This dissertation introduces a new type of quantum automata, their encoding and circuit realization. I concentrate on possible applications in robotics. Several methods and application of quantum automata and quantum circuit-based controllers for elementary robotic systems, with a focus on humanoid robot motion, emotion, and behavior generation are illustrated in detail. The research introduces several novel methodologies that bridge quantum computing principles with robotic control, aiming to overcome the limitations of classical deterministic and probabilistic approaches.

The dissertation first presents a quantum-circuit-based framework for generating non-repetitive and expressive (e)motions in a humanoid robot actor, using superposition and entanglement to produce …

Ai Method For Classification Of Diagnosis Of Near-Infrared Breast Lesion Images, Kaiquan Chen, Fangyang Shen, Honggang Wang, Zhengchao Dong, Jizhong Xiao, Ming Ma, Afroza Aktar, Christopher Chow, Wenxiong Zhang

Publications and Research

In near-infrared optical breast lesion screening and diagnosis systems, high-speed four-dimensional scanners can dynamically acquire tens of thousands of lesion images within a five-minute period. Currently, manual computer annotation is required to generate standard samples from these scanned breast lesion images, a process that depends heavily on physicians with clinical expertise. On average, a single physician can annotate only approximately ten samples per working day. As a result, this process is time-consuming and labor-intensive, and the collected samples often suffer from low accuracy, large variability, and limited diagnostic reliability. Several AI-based annotation tools, such as QuPath, HALO AI™, and X-AnyLabeling, …

Solving Wordle Using Information Theory, Talal Aladaileh, Donald Stephens, Mallak Alqaisi, Congyu Wu

Northeast Journal of Complex Systems (NEJCS)

Wordle, a popular word-guessing game, challenges players to identify a five-letter secret word through iterative guesses and feedback on letter placement. The players must figure out the secret word within six guesses. After each guess, the letters will be color-coded based on different criteria. Optimizing the choice of guesses is critical for maximizing success within the limited attempts allowed. In this study, the application of Shannon entropy is explored as a strategy for selecting words that maximize information gain at each step of the game. By quantifying the uncertainty reduction achieved by potential guesses, this method prioritizes words that are …

A Multi-Layer Complex Adaptive System Framework For Ai-Driven Robo-Advisory Services, Jesty Mariam Philip, Mohit Boralkar, Alwin Joseph

Northeast Journal of Complex Systems (NEJCS)

The rapid integration of Artificial Intelligence (AI) into investment advisory services has changed financial decision-making, giving rise to adaptive robo-advisory systems capable of real-time analysis, personal recommendations, and autonomous portfolio optimization. Existing research evaluates these systems primarily through technological performance or investor adoption, overlooking the complex feedback-driven interactions that emerge when AI analytics, data environments, and human behavior operate together. This study addresses this gap by conceptualizing AI-enabled robo-advisors as a multi-layered Complex Adaptive System comprising historical data, real-time data, AI analytics, investor perception, and decision-making layers. A simulation model grounded in machine learning dynamics, behavioral finance, and complexity theory …

Regional Drought Modulation By Enso And Iod As Indicated By The Standardized Precipitation Index, Arpit Tiwari, Preethi Nanjundan, Tanu Sharma, Ravi Ranjan Kumar, Satyaban Bishoyi Ratna

Northeast Journal of Complex Systems (NEJCS)

Understanding the modulation of drought by large-scale ocean–atmosphere teleconnections is crucial for strengthening drought prediction and resilience in India. This study investigates the influence of the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) on meteorological drought characteristics across India from 1950 to 2024 using the Standardized Precipitation Index (SPI) at a 12-month timescale. Drought events were quantified in terms of frequency, duration, severity, and intensity and linked to ENSO–IOD variability through composite, correlation, and mediation analyses. Results reveal that El Niño events consistently correspond to widespread and severe droughts, particularly over central and southern India, with drought …

Evaluating Square Gradient Theory For Correlation And Prediction Of Surface Tension, J. Richard Elliot

University Research

The ESD EOS [Elliott, Suresh, and Dohohue, IECR 1990, 29, 1476] is incorporated into the open-source Square Gradient Theory (SGT) library published by Mejia et al. [Mejia, Müller, and Chaparro-Maldonado, J. Chem. Inf. Model. 2021, 61, 1244]. The combined formalism is evaluated for accuracy compared to experimental measurements and empirical correlation methods. Earlier evaluations of the ESD EOS have shown it to provide accuracy like other EOSs for vapor pressure, vapor–liquid, liquid–liquid, and solid–liquid equilibria where the databases covered roughly 500 compounds and 100–200 mixtures each. For surface tension, a database of measured values for roughly 1000 compounds has been …

Quantifying Dag-Ness: A Multi-Component Framework, Erik Csikos

Northeast Journal of Complex Systems (NEJCS)

Directed acyclic graphs (DAGs) are important structures across many disciplines, including mathematics and network science. They are especially useful in modeling causal and hierarchical relationships in a wide variety of applications. The primary
features of a DAG, directedness and acyclicity, are traditionally treated as binary. A graph is either DAG or it is not. Because of this, graphs are not usually interpreted as partially acyclic or partially directed. To address this gap in understanding, we
introduce a framework, consisting of five components, that will provide a continuous measure of DAG-ness. This measure will take into account edge acyclicity, node cyclicity, …

Preliminary Sonification Of Enso Using Traditional Javanese Gamelan Scales, Sandy Hs Herho, Rusmawan Suwarman, Nurjanna J. Trilaksono, Iwan P. Anwar, Faiz R. Fajary

Northeast Journal of Complex Systems (NEJCS)

Sonification—the mapping of data to non-speech audio—offers an underexplored channel for representing complex dynamical systems. We treat El Niño-Southern Oscillation (ENSO), a canonical example of low-dimensional climate chaos, as a test case for culturally-situated sonification evaluated through complex systems diagnostics. Using parameter-mapping sonification of the Niño 3.4 sea surface temperature anomaly index (1870–2024), we encode ENSO variability into two traditional Javanese gamelan pentatonic systems (pelog and slendro) across four composition strategies, then analyze the resulting audio as trajectories in a two- dimensional acoustic phase space. Recurrence-based diagnostics, convex hull ge- ometry, and coupling analysis reveal that the sonification …

Reducing Systemic Bias In Behavioral Targeting Using Explainable Ai: The Harmonia Complex Systems Approach, Ruchira Deokar, Preethi Nanjundan, Jossy P. George, Carlos Gershenson

Northeast Journal of Complex Systems (NEJCS)

Behavioral targeting is a key part of the modern advertising web's algorithmic engine. However, it is unclear whether optimization processes worsen bias, promote unchecked spread in filter bubbles or lower overall users' trust levels. This paper introduces HARMONIA (Holistic Adaptive Regulatory Model for Optimizing Non-transparent Intelligent Advertising), a comprehensive, data-driven Explainable Artificial Intelligence (XAI) framework aimed at transforming behavioral targeting via transparency, interpretability, and adaptive ethical regulation. This paper conducted a comprehensive Explorative Data Analysis (EDA) on the public Criteo Display Advertising Dataset, which contains over 45 million records, to identify patterns in high-dimensional user-ad interaction space. This analysis uncovered …

Relation Between Swelling Potential And Swelling Pressure For Different Types Of Expansive Clay Of New Cairo, Sherouk Hany Mohamed, Amr Radwan, Mohammed Rabie, Hussein Mostafa

HBRC Journal

The purpose of this paper is to present correlations that may help in predicting swelling pressure (P_S) and swelling potential (SP) for expansive clay, which can contribute to solving engineering problems such as cracking, shifting or other structural damage, that may occur, if the behavior of this type of soil is not taken into consideration. Therefore, in this research, five different samples of expansive clay soils were collected from different site locations in New Cairo, Egypt. Then, these samples were dried, pulverized and passed through sieve No. 40 (0.425 mm). The samples were prepared initially at maximum dry …