Digital Commons Network^™

Relationships Between The Parameters Of The Vegetable Oil Refining Process And Energy Consumption, Umidjon Abdimajitovich Ruziev, Marufjon Kobuljonovich Shodiev

Technical science and innovation

Vegetable oil refining is a multi-stage thermochemical process that is closely related to energy consumption, process parameters, product quality, and food safety requirements. Despite the extensive scientific literature on oil quality and pollutant formation, the quantitative relationship between purification parameters and relative energy consumption has not been sufficiently studied. In this work, a systematic analysis and engineering synthesis were carried out based on existing studies. According to the analysis results, the deodorization process accounts for 52–56% of the total thermal energy consumption, with a relative value ranging from 160 to 380 kJ/kg, depending on the enterprise's capacity and heat utilization …

Theoretical And Practical Foundations Of Healthcare System Digitalization, Sanjarbek Bekturdiev Sharifboyevich Phd, Muxlisa G‘Ulom Qizi G‘Ofurova

Technical science and innovation

The digital transformation of healthcare represents a key direction in improving the accessibility, quality, and efficiency of medical services. This study examines the main components of eHealth, including telemedicine, electronic health records, cloud technologies, and mobile applications, as well as the role of international organizations in promoting national digital health strategies. Particular attention is given to the current state of digital healthcare in the Republic of Uzbekistan, where significant progress has been made in developing infrastructure and implementing information systems, although challenges related to integration, standardization, and regulatory support remain. The work identifies the main trends in the development of …

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 …

Development Of New Methods For Calculating Pressure And Energy Losses For Pumping Station Water Intakes, Faxriddin Jaylovovich Nosirov, Oleg Yakovlevich Glovatsky, Jurabek Abdyrahmon O'G'Li Urolov, Abduqodirkhon Samatkhonovich Abdullakhaev, Anvar Mamur Ugli Uzokov

Technical science and innovation

The aim of this study is to improve the efficiency of water resource management methods. Improvements to the water intake of the Karshi Main Canal are considered using calculations of pressure losses at the entry and exit of liquid into the flow, which differ from pressure losses in a stationary medium. The article considers some of the methods for using renewable energy sources, where the change in gravitational potential energy does not depend on the way of using energy and the operating scheme is a gravitational energy pump. The implementation of control and monitoring systems is necessary to ensure optimal …

Studying The Performance Of Photovoltaic Installations In The Pvsyst Software Package, Zukha Islamovna Juraeva, Isroil Abriyevich Yuldoshev I.A.Y., Islom Rakhmatovich Juraev

Technical science and innovation

This article simulates the operation of photovoltaic installations consisting of photovoltaic panels of crystalline and thin-film technologies. The calculations were performed in the PVSyst software package 7.4.8 version. In the calculations, the input parameters were environmental factors, the angle of inclination of the panels to the horizon. In calculating the values of the solar radiation flux density, the program selects the METEONORM climate database in accordance with the geographical area of the Tashkent city. The tilt angles of the photovoltaic panels of the installation were set manually by selecting specific values of the characteristic tilt angles in the range from …

Modern Promising Directions For The Development Of Alternating Current Converters, Shukhrat Badretdinovich Umarov, Ubaydullo Abdushukur Ugli Boqijonov

Technical science and innovation

The relevance of this work is explained by the presentation of modern trends in the development of powerful AC converters, which are widely used in various industrial processes, agriculture, and other sectors. Today, the most widely used method for creating powerful AC converters is the use of double energy conversion systems (DECS). The article shows the electrical diagram and functional tasks of the DECS system, as well as the technical and schematic capabilities of the pulse width modulation (PWM) inverter.  It highlights the importance of effectively using independent (autonomous) filters to improve the hybrid and matrix structure, as well as …

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 …

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 …

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 …

Proxyconnec: A Lora-Based Proactive Safety Communication System For Off-Grid Environments, Shruti Bhandari, Roza Shaimurat

ATU Scholars Symposium

Remote environments lacking cellular or satellite coverage present significant safety challenges. ProxyConnec was developed as a point-to-point communication system using ESP32 microcontrollers and REYAX RYLR998 LoRa modules to provide off-grid monitoring.

The system implements a proactive heartbeat model in which a beacon device transmits a signal every 1,000 milliseconds. A base station monitors this connection using a 5,000 millisecond watchdog timer. If communication is interrupted, the system immediately triggers audible and visual alerts. Unlike conventional tracking devices that depend on manual SOS activation, this design treats unexpected signal loss as a potential safety event.

The manufacturer rates the selected LoRa …

Automatic Three Phase Balancing In Utility Applications, Ryan Morris

ATU Scholars Symposium

Balancing three-phase power has become more important as modern loads like EVs and large agricultural fans create fast and uneven changes in distribution systems. Manual balancing is slow, inconsistent, and often only done during certain “seasons,” which leaves long periods of imbalance. This paper reviews existing automatic methods such as the Fast-Switching Relay method, the Practical Balancing Algorithm, and the Phase-EQ system and highlights their benefits and limitations. Based on this analysis, a new method called the Predicted Practical Balancing Algorithm (PPBA) is proposed. The PPBA combines the stability of threshold-based switching with historical data to predict when imbalances are …

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 …

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 …

Preliminary Study Of Circular Lithium-Ion Battery Thermal Management System With Intermittent Cooling, Muhammad Luthfi, Leo Van Gunawan, Yudhy Kurniawan, Sabda Ikhwal Rahmatullah, Alfarikh Muhammad Daen

Journal of Mechanical Engineering Science and Technology (JMEST)

The active battery thermal management system is important to maintain the lithium-ion battery temperature within the optimum and safe range. However, current research has not considered the potential for excessive energy consumption due to the continuous operation of a fluid flow source, such as a fan. This research aims to investigate the effect of a novel approach called intermittent cooling on the cooling efficacy and energy consumption of the circular-configuration BTMS. Both transient computational fluid dynamic simulation and experiment were conducted in this work with 30 pieces of 18650 Lithium-Ion batteries positioned in a staggered circular configuration. From the result, …

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 …

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 …

Traditional And Machine-Learning Equalization Techniques For Bandwidth-Limited Short-Reach Optical Communication Channels, Abdullah Khawatmi

Thesis/ Dissertation Defenses

This thesis investigates equalization techniques for bandwidth-limited short-reach optical communication systems, with a focus on Visible Light Communication (VLC) and Step-Index Plastic Optical Fiber (SI-POF) links. Commercial light-emitting diodes and photodiode receivers impose severe bandwidth constraints, inter-symbol interference, and noise sensitivity, which fundamentally limit achievable data rates. The work addresses these impairments through systematic evaluation of traditional digital signal processing–based equalizers and modern machine-learning-based post-equalization methods.

The primary aim of this thesis is to enhance the achievable data rate and reliability of commercial short-reach optical links while maintaining practical computational complexity. Specifically, the objectives are to (i) design and experimentally …

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 …

Threat-Analysis Oriented Digital Twinning Of Ml-Powered Future Autonomous Weapon Systems, Thomas Neubert

Doctoral Dissertations and Master's Theses

Warfare is undergoing a rapid transformation with the integration of artificial intelligence (AI) and machine learning (ML) into autonomous weapon systems (AWS) for perception, decision support, and control. As these systems become more software-defined, their cyber attack surface expands across sensing, communications, autonomy logic, and human-machine interfaces. As human oversight diminishes, ensuring the cybersecurity, resilience, and reliability of these systems becomes critical to mission success. This thesis investigates how a digital twin-driven threat modeling framework that integrates system-centric analysis with adversary-informed methodologies can support structured cybersecurity vulnerability evaluation and defensive strategy development associated with ML-powered AWS. First, the study analyzes …

A Platform For Acquiring And Classifying Low-Noise Electrocardiogram Signals For Applications In Cardiovascular Monitoring, Begmamat Berdimurodovich Dushanov, Narzullo Mamatov Dr.

Technical science and innovation

The early screening and continuous monitoring of cardiovascular diseases need effective acquisition and smart processing of electrocardiogram (ECG) signals. In this article, we introduce a compact platform designed for the acquisition of low-noise ECG signals and classification of the signals using a one-dimensional convolutional neural network (1D-CNN). Our compact platform consists of a low-noise analog front-end (AFE), including an instrumentation amplifier and a chain of analog filters, along with a data acquisition component designed to ensure effective suppression of baseline wander and high frequencies. Our compact platform consumes a low amount of power and can therefore be used for continuous …

Breath-Based Detection Of Liver Cancer Biomarkers Using An Swcnt-Fet Nanobiosensor: Quantumatk, Mohamed Mohieb Rashdan

Thesis/ Dissertation Defenses

There are still no tests to detect liver cancer in the early stages because it is a slow-growing and invasive type.

This study describes the use of a single-walled carbon nanotube-based field-effect transistor (SWCNT-FET) whose ability to detect hexanal, a volatile organic compound (VOC) found to be elevated in liver cancer, has been demonstrated through direct measurements from exhaled breath. The device is designed using QuantumATK with the semi-empirical Extended Hückel Hamiltonian framework in non-equilibrium Green’s function (NEGF) which shows realistic contact physics by using metallic SWCNT electrodes instead of common metal films. Through the analysis of two zigzag channels …

Robustness Of Fuzzy Artmap To Adversarial Attacks And Progressive Adversarial Training For Streaming Learning, Shane Cairns

Miners Solving for Tomorrow Research Conference

Incremental learners deployed on streaming data must remain robust to evolving adversarial perturbations, yet most adversarial-robustness studies assume offline multi-epoch training with repeated access to historical data. We investigate adversarial robustness in Fuzzy ARTMAP, a prototype-based Adaptive Resonance Theory model that supports single-pass learning without replay. We propose WB-Softmax, a differentiable relaxation that aggregates category-level activations into class-level scores for gradient-based attacks. WB-Softmax PGD achieves 89–100% attack success on vanilla models, exceeding transfer and query-based baselines. We then study adversarial training under true streaming constraints by comparing offline versus online adversarial example generation and standard versus selective updates. Offline adversarial …

Triple Active Bridge Implementation And Control, Nehemiah Milton

Miners Solving for Tomorrow Research Conference

The Triple Active Bridge (TAB) is a three-port power converter that allows power flow in both directions with galvanic isolation. This has a wide range of applications, like high-frequency DC-DC conversion, electric vehicles, microgrids and renewable energy systems. To control the TAB during operation, two phase shift parameters between the bridges are adjusted. In this presentation, I will showcase the software and simulation optimizations which extend previous work by reproducing hardware results which align with simulations. I will also go over the knowledge I’ve gained and the skills acquired through this project.

Analysis Of Autonomous Vehicle Survivability To 5g Communication Errors, Sydney Clark

Miners Solving for Tomorrow Research Conference

Autonomous vehicles rely on low-latency, high-reliability data exchange for real-time perception and control. Disruptions such as packet loss, latency variation, protocol-level errors, and malicious interference can pose significant safety risks to both passengers and surrounding environments. This project aims to evaluate, quantify, and predict the survivability of autonomous vehicle systems to communication errors, with focus on 5G network environments. The impact of these communication impairments on vehicle stability and control will be investigated through high-fidelity cyber-physical simulation of the vehicle and its surrounding environment. Experiments designed to capture varying network conditions will be used to assess a broad range of …

A Wearable Mxene-Based Sweat Sensor For Real-Time Monitoring Of Inflammatory Biomarkers, Ariel Pilger

Miners Solving for Tomorrow Research Conference

Many conventional biosensing approaches rely on invasive sampling or bulky benchtop instrumentation, limiting their use in continuous and portable applications. This project focuses on the development of wearable sweat-based biosensors that enable non-invasive, continuous, and portable monitoring of physical, chemical, and biological markers. The system will be designed to target markers present in sweat and transduce the biochemical interactions into measurable electrical signals. These signals will be processed through integrated electronics to produce clear, interpretable outputs for users and medical professionals. Supporting circuitry including filters, amplifiers, and an independent power supply will be implemented as necessary to ensure signal accuracy, …

Tuning And Performance Of Pid Controlled Low Complexity Systems, Timothy Evans

Honors Theses

Proportional integral derivative (PID) controllers are used for precise position and orientation control in systems such as autonomous underwater vehicles (AUVs). This project supports the University of Southern Mississippi’s (USM) Robotics Club’s RoboSub AUV effort by developing, troubleshooting, and manually tuning PID controllers to characterize tracking performance and settling time across systems of increasing complexity. Initially, the project hypothesized that tracking performance would be reduced and settling times would increase as system complexity advanced from one degree-of-freedom (DOF) to two DOF. However, prior research was found that suggests that for small disturbances around an equilibrium state, separate PID-controlled DOFs can …