Digital Commons Network^™

Class A Biosolids Production Using Conventional And Low-Cost, Low-Tech Processes At Small Water Resource Recovery Facilities: A Multidimensional Sustainability Assessment, Janna Brown, Robert Handler, Eric Seagren, Jennifer Becker

Michigan Tech Publications

Producing Class A biosolids is a beneficial way to reuse wastewater treatment solids, but most conventional processes are energy-intensive and expensive. There is growing interest in the use of low-cost, low-tech (LCLT) Class A biosolids treatment processes, especially at small water resource recovery facilities (WRRFs). This study used a holistic sustainability assessment to examine the environmental, economic, and social sustainability of conventional and LCLT processes at small WRRFs. The technologies studied were Direct Heat Drying, Composting, Lagoon Storage, Air Drying, and Temperature-Phased Anaerobic Digestion (TPAD). Environmental impacts were determined by conducting life-cycle assessments for all technologies, which is described in …

Palpation Characteristics Of An Instrumented Virtual Cricothyroidotomy Simulator, Melih Turkseven, Trudi Di Qi, Ganesh Sankaranarayanan, Suvranu De

Engineering Faculty Articles and Research

Cricothyroidotomy (CCT) is a critical, life-saving procedure requiring the identification of key neck landmarks through palpation. Interactive virtual simulation offers a promising, cost-effective approach to CCT training with high visual realism. However, developing the palpation skills necessary for CCT requires a haptic interface with tactile sensitivity comparable to human fingers. Such interfaces are often represented by plastic partial mannequins, which require further adaptation to integrate into virtual environments. This study introduces an instrumented physical palpation interface for CCT, integrated into a virtual surgical simulator, and tested on 10 surgeons who practiced the procedure over a training period. Data on haptic …

Development Of Turbulence Models For Prediction Of Hypersonic Flows Using Compressibility Corrections And Uncertainty Quantification With Emphasis On The Wray-Agarwal Model, Aidan Robert Murphy

McKelvey School of Engineering Graduate Student Theses & Dissertations

Accurate prediction of high-speed compressible turbulent flows remains one of the most significant challenges in Computational Fluid Dynamics (CFD), especially in the realm of hypersonic vehicle design. These flow regimes involve significant aerodynamic heating and shock-wave boundary layer interactions (SBLI) which remain a significant challenge for the current CFD prediction capabilities. While Direct Numerical Simulation (DNS) and Large-Eddy Simulation (LES) provide high-fidelity solutions, their computational demands limit their usability, making the Reynolds-Averaged Navier-Stokes (RANS) equations an efficient alternative for many industrial aerodynamic applications. Traditional RANS techniques, while computationally efficient, often provide limited accuracy in high-speed flows due to the presence …

Photolysis Of Emerging Agricultural Applications Under Environmental Conditions, Kun-Pu Ho

McKelvey School of Engineering Graduate Student Theses & Dissertations

Novel agricultural applications are rapidly emerging to protect crops from weeds or pests in the US and worldwide. Isoxaflutole is a proherbicide considered agriculturally significant because it controls weeds that have developed resistance to other commonly used herbicides. In addition, RNA interference (RNAi) technology is regarded as a next-generation biopesticide because the technology has less toxicity to humans and more specificity to pests than traditional pesticides, which pose risks to the environment and the health of humans and livestock. However, we cannot disregard the possible risks of the emerging proherbicides and biopesticides to humans or non-target organisms. Therefore, the study …

Multi-Layer Support For Component-Based Cyber-Physical Systems Applications, Oren Bell

McKelvey School of Engineering Graduate Student Theses & Dissertations

Component-based design is a paradigm meant to aid in development of software applications by modularizing different functionalities of a system. This building-block approach is used extensively in cyber–physical and robotic systems. Common and established solutions to specific problems, such as perception nodes, state estimators, and motion planners, can be developed, verified, and reused as off-the-shelf modules. These modules may be integrated atop today’s heterogeneous hardware platforms, where an application can be distributed across GPUs, FPGAs, and CPU cores. When heterogeneous computational devices share the workload of a collection of components, the very act of integration may inject timing uncertainty. For …

Moment Ensemble Approaches For Estimation And Robust Quantum Control, Andre Luiz Paes De Lima

McKelvey School of Engineering Graduate Student Theses & Dissertations

Large-scale population dynamics governed by ensemble systems present significant challenges due to their inherently high dimensionality. Recent advances have demonstrated the effectiveness of moment-based methods (particularly those employing polynomial bases such as Legendre and Chebyshev polynomials) when integrated with optimization techniques for control design in ensemble systems. However, these methods have not been extensively explored for enhancing robustness against systematic noise, nor have they been widely applied to complex quantum systems involving multi-parameter configurations and entanglement phenomena. In this work, we extend the application of moment methods in ensemble systems through two projects. Initially by developing a Kalman filter analogue, …

Developing Aerosol Representation In Atmospheric Modeling, Inderjeet Singh

McKelvey School of Engineering Graduate Student Theses & Dissertations

Ambient aerosols affect climate and air quality in the Earth system. Global chemical transport models used to study these impacts require an accurate representation of aerosols in terms of shape, composition, and vertical distribution. For example, despite having a complex morphology, mineral dust is often treated as spherical particles in these models. This can lead to errors in optical properties calculations, trace gas retrievals, and heterogeneous chemistry. Similarly, inaccurate representation of the vertical distribution of smoke particles in these models can lead to uncertainties in air quality assessments. Aerosol composition also plays a critical role in determining radiative forcing and …

Development, Numerical Simulation, And Optimization Of Quiet Supersonic Transport Aircraft And Extension Of The Wray-Agarwal Algebraic Transition Model To Include Compressibility And Crossflow Effects, Dean Ryan-Simmons

McKelvey School of Engineering Graduate Student Theses & Dissertations

In the first part of this thesis, the goal of the research is to implement and extend multi fidelity methods for conceptual design and optimization of a commercial supersonic transport (SST). Most modern commercial aircraft operate at a cruise condition between Mach 0.7 and 0.9, as this is the optimal trade-off of flight efficiency, operational cost, and adherence of noise regulations for overland travel. Advancements in aviation propulsion fuel efficiency and design materials suggests a primary restraint on commercial supersonic transport is noise regulations. At speeds above Mach 1, the shock waves generated by the airframe are strong enough to …

Performance Analysis Of Multivariable Control Structures Applied To A Neutral Point Clamped Converter In Pv Systems, Renato Santana Ribeiro Junior, Eubis Pereira Machado, Damásio Fernandes Júnior, Tárcio André Dos Santos Barros, Flavio Bezerra Costa

Michigan Tech Publications

This paper addresses the challenges encountered by grid-connected photovoltaic (PV) systems, including the stochastic behavior of the system, harmonic distortion, and variations in grid impedance. To this end, an in-depth technical and pedagogical analysis of three linear multivariable current control strategies is performed: proportional-integral (PI), proportional-resonant (PR), and deadbeat (DB). The study contributes to theoretical formulations, detailed system modeling, and controller tuning procedures, promoting a comprehensive understanding of their structures and performance. The strategies are investigated and compared in both the rotating ((Formula presented.)) and stationary ((Formula presented.)) reference frames, offering a broad perspective on system behavior under various operating …

Design And Evaluation Of An Electromagnetic Band Gap Structure For Self-Interference Reduction In Mmwave Full-Duplex Systems, Adewale Kehinde Oladeinde

Dissertations and Theses

Full-duplex (FD) wireless is a new technology that allows a device to transmit and receive at the same time and on the same frequency band. It has the potential to double the capacity and spectral efficiency of a wireless link compared to the family of conventional half-duplex wireless systems. The key challenge in implementing FD wireless communication is self-interference (SI): a node's transmitting signal generates significant interference to its receiver. Several previous studies have demonstrated the potential to reduce SI and develop FD radios; however, these studies are mostly limited to sub-6 GHz systems. Previous and on-going research explored various …

Transition And Instability Characteristics Of The Separated Shear Layer Formed At Low Reynolds Number Over Three-Dimensional Irregular Rough Surface, Ganesh Kt Mr

Theses and Dissertations

The present experimental work investigates the effects of irregularly roughened surfaces and imposed streamwise pressure gradients on the topological, transitional, and instability characteristics of the laminar separated shear layer (SSL). Experiments are conducted in the low-speed wind tunnel for 𝑅𝑒𝑡=30000, based on the thickness of the airfoil model, t, and freestream turbulence intensity, fst = 0.8%. The airfoil model used in this study features a semicircular leading edge followed by a constant-thickness flat portion and a pitchable trailing-edge flap. Test conditions include two rough surfaces: sandblasted (SB) and sand-deposited (SD), and flap deflections: 𝛽=−30° and 𝛽=+30°, imposing adverse (APG) and …

Design And Development Of Deep Learning Based Generic Platform For Promoting Precision Agriculture, Srilakshmi A

Theses and Dissertations

Precision agriculture also referred as precision farming or smart farming, is an innovative approach to agricultural management that leverages technology and data to optimize various aspects of the farming process. This approach aims to make farming more effective, sustainable, and profitable by affording farmers with the application tools and information they need to make more informed decisions.

Precision agriculture combines elements of agriculture, technology, and data science to enhance crop production, and resource utilization. Precision agriculture techniques can be highly effective in leaf disease detection within crop fields. Machine learning has been developed incredibly across multiple domains and shown it …

Computational Studies On The Major Antioxidant Enzymes Of Wuchereria Bancrofti Towards The Development Of Anti Filarial Leads, Sureshan M

Theses and Dissertations

Lymphatic filariasis (LF), or elephantiasis, is a mosquito-borne parasitic disease affecting the lymphatic system, primarily in tropical and subtropical regions. It is the second leading cause of long-term disability and a neglected tropical disease. According to WHO, LF threatens 882 million people in 44 countries, with over 9 billion treatments administered. The disease is caused by three nematodes: Wuchereria bancrofti (90% of cases worldwide, including India), Brugia malayi (10% in Southeast and Eastern Asia), and Brugia timori (found in Timor and nearby islands). Lymphatic filariasis (LF) is an ancient disease that continues to challenge scientists and physicians. Current treatments include …

Artificial Intelligence-Based Adaptive Data-Driven Techniques For Disruption Prediction In Tokamak, Priyanka M

Theses and Dissertations

Tokamaks are nuclear fusion reactors designed to generate sustainable energy by confining plasma, yet plasma disruptions remain a major obstacle as they can damage reactor components and interrupt fusion reactions. Addressing this challenge requires reliable models that can classify plasma discharges and predict disruptions in advance. This thesis develops machine learning and deep learning approaches to improve both classification and forecasting.The first contribution is a semi-supervised active learning framework, Nearest Margin-Ranked Batch Mode Active Learning (NM-RBMAL), integrated with an ensemble model.

Unlike traditional classifiers that operate on static training data, this approach continuously adapts to new plasma conditions, reducing model …

Ai Based Early Detection Of Hormonal Imbalance And Poly-Cystic Ovary Syndrome In Young Women, Reka S

Theses and Dissertations

A hormonal disorder, Poly-Cystic Ovary Syndrome (PCOS) usually affects women during the reproductive age. It is characterised by imbalances in hormones, particularly a rise in the female body's androgen level (male hormone) and enlarged ovaries with small cysts. PCOS can cause ovarian cysts, weight gain, acne, excessive hair growth, insulin resistance, and irregular menstrual cycles along with other health problems. While the exact origin of PCOS is uncertain and its symptoms are unclear, diagnosing PCOS in real-world conditions is a difficult task. Therefore, prompt and precise PCOS diagnosis is essential for efficient treatment and for averting long-term issues.

Clinicians typically …

Effects Of Eogo In Metakaolin-Based Geopolymer, Chaewon Lee, Hoyoung Lee, Jinwoo An, Boo Hyun Nam

Civil Engineering Faculty Publications

Geopolymer concrete uses a geopolymer binder instead of traditional Portland cement; thus, it reduces carbon emissions by a significant amount. In this study, Edge-Oxidized Graphene Oxide (EOGO), a carbon-based nanomaterial, was added into a metakaolin-based geopolymer, and its effect on the mechanical and rheological properties of the mixture was investigated. EOGO was added into the mixture at 0% (control), 0.1%, 0.5%, and 1% of the metakaolin mass. Several experiments were conducted to characterize the properties of the metakaolin–EOGO (MKGO) geopolymer, including its compressive strength, free–free resonance column (FFRC), void content, water absorption, setting time, flow, and rheology. It was found …

Investigation Of Dependency Parsing Techniques For Digital Document Analysis Through Deep Learning Approach, Rekah D Ms

Theses and Dissertations

Digital document dependency parsing is a significant task in natural language processing. Dependency parsing supports verification of the grammatical correctness of a sentence besides enabling extraction of relevant documents. Inappropriate extraction of features may result in falsely parsing a document, leading to decreased accuracy. Machine learning methods have been employed to perform feature extraction. However, selecting pertinent features was never achieved which minimizes the time consumption and overhead.

Hence, novel machine learning and deep learning techniques have been designed in our work for accurate and computationally efficient digital document analytics through dependency parsing. Four different contributions have been proposed for …

Cyclone Intensity Prediction In The Bay Of Bengal Using Deep Learning Methods, Senthil Kumar J

Theses and Dissertations

The Bay of Bengal region's coastlines have been badly devastated by tropical cyclones, as the region experiences an average of five to six cyclones per year, with about two to three of these intensifying into tropical storms or severe cyclones. Thus it necessitates to study the accurate and efficient forecasting of their intensity to improve preparedness and response to natural disasters. The present study compares and examines three distinct approaches to cyclone intensity prediction using historical datasets from 1998 to 2020: hybrid optimisation, deep learning-based, and empirical approaches.The predicted accuracy, computational effectiveness, and feasibility for real-time scenarios of each model …

Investigations Of Secure Memory For Vlsi Based Crypto System, Vijay Sai R Mr

Theses and Dissertations

Semiconductor technology is growing very rapidly in their architectural developments, involving the usage of processor and memory. Presence of memory, in general is a vital commodity in various devices which are almost embedded into human activity, from robust work stations to handy mobile phones. Security of data stored in memory is very important, and hence, observation must be made that these valuable data should not be thwarted by malicious means. Security in cache memory is a major issue in memory related applications such as smart cards and bio-metric implementations.

Cache, is a small and limited memory located between central processing …

Exploring Longitudinal Stability Of Spatiotemporal Sequential Patterns By Means Of Autoencoder Schemes For Eeg Based Personal Identification, Muhammed E. Oztemel

LSU Doctoral Dissertations

Robust personal identification remains a critical and challenging task in the digital era. Electroencephalography (EEG) offers a unique biometric modality that captures individual brain dynamics through complex neural signals. This dissertation proposes autoencoder (AE) based feature extraction and subject identification through these features. EEG recordings are first transformed into topographic maps to represent spatial brain activity. Consecutive topomaps are then concatenated to capture temporal transitions across frames. Convolutional autoencoders (CAEs) are used to learn spatial and temporal patterns, while domain-adaptive AEs are designed to model evoked potential based responses. Additionally, self-attention mechanism is incorporated to enhance feature representation. To analyze …

Multi-Layered Framework For Llm Hallucination Mitigation In High-Stakes Applications: A Tutorial, Sachin Hiriyanna, Wenbing Zhao

Electrical and Computer Engineering Faculty Publications

Large language models (LLMs) now match or exceed human performance on many open-ended language tasks, yet they continue to produce fluent but incorrect statements, which is a failure mode widely referred to as hallucination. In low-stakes settings this may be tolerable; in regulated or safety-critical domains such as financial services, compliance review, and client decision support, it is not. Motivated by these realities, we develop an integrated mitigation framework that layers complementary controls rather than relying on any single technique. The framework combines structured prompt design, retrieval-augmented generation (RAG) with verifiable evidence sources, and targeted fine-tuning aligned with domain truth …

Design Of A 10-Ton Capacity Solar-Powered Cold Storage At The Cituis Fish Auction Site, Tangerang, Banten, Indonesia, Wardi Wardi, Muhammad Arif Budiyanto

Journal of Materials Exploration and Findings

This study presents the design of a 10-ton capacity solar-powered cold storage system for the Cituis Fish Auction Site (TPI) in Tangerang, Indonesia, as a sustainable solution to support the coastal fisheries sector. The system comprises two temperature zones: an anteroom at 5°C and a freezer room at -30°C, both integrated with a hybrid system utilizing solar panels and batteries. The total power consumption of the cooling system reaches 27.51 kW. The simulation results, obtained using HOMER Pro software, indicate that the hybrid PV system can reduce CO₂ emissions by up to 16,754 kg per year. From an economic …

Implementation Of Reliability Centered Maintenance On Crude Booster Pump, Dzulfikar Ali Hafizh, Maya Agustianingrum Putri St., Ph.D., D. Rizal, Adzra Qanita

Journal of Materials Exploration and Findings

Crude oil booster pump is one of the critical equipment in oil refinery operations. In aging plants that have been operating for more than 25 years, where reliability and maintenance issues on equipment often cause operational disruptions and even unplanned shutdowns. This study aims to develop a structured reliability-centered maintenance (RCM) strategy for crsude oil booster pump systems that is also in line with the principles of asset integrity management system (AIMS). This methodology follows the RCM II approach, starting with system function analysis, failure mode identification, and criticality assessment through Failure Mode, Effect, and Criticality Analysis (FMECA) to determine …

Application Of Grouted Steel Pipe Micropile Composite Structure In Highway Landslide Treatment, Wu Bing, Liang Yao, Li Jie, Zhao Honghu

Journal of China & Foreign Highway

To optimize the calculation methods and application schemes for reinforcing landslides using grouted steel pipe micropile composite structures,the composition of single-pile composite structures and the combination forms of pile group structures were described.By taking the Bishop method as an example,a stability analysis method and the anti-sliding principle of side (sliding ) slopes reinforced with grouted steel pipe micropile composite structures were proposed,and a calculation formula for the allowable shear resistance of the composite pile was derived.By taking a landslide case from a section of Lijiang‒Panzhihua Expressway as an example,landslide stability analysis and landslide thrust calculation were carried out.The grouted steel …

Research On Construction Monitoring Of Continuous Rigid Frame Bridge Based On Knn Algorithm, Liu Jianlin, Pan Kai, Yang Gang, Wang Junming

Journal of China & Foreign Highway

To address common issues in bridge construction monitoring,such as sensor damage and failure in strain and displacement measurements,this study took the Nanliu Jialing River Bridge as a case and proposed a method based on the K-nearest neighbor (KNN ) algorithm to reconstruct missing construction monitoring data.ANSYS Workbench was used to establish a finite element model (FEM ) of the bridge construction process to verify the effectiveness of the KNN algorithm in the presence of missing bridge construction monitoring data.The results show:① KNN algorithm can assist in filling short-term data gaps caused by damage to strain and displacement sensors during bridge …

Research On Autonomous Deviation Correction Of Tunnel Boring Machines And Parameters Based On Machine Learning, Zhang Jun, Li Maopeng

Journal of China & Foreign Highway

In order to solve the problem of realizing the autonomous deviation correction of tunnel boring machines (TBMs ), a TBM deviation correction control method that integrated the random forest (RF) algorithm with the genetic algorithm (GA) was proposed based on actual engineering data.The method combined a prediction model with an optimization model,using target deviation values as input to invert and output the required TBM deviation correction parameter values,thereby further improving the automation level of TBM deviation correction.By comparing it with the actual data,the feasibility of the model was verified.The results show that the RF algorithm-based prediction model achieves an R2 …

Exploring Different Metal-Oxide Cathode Materials For Structural Lithium-Ion Batteries Using Dip-Coating, David Petrushenko, Thomas Burns, Paul Ziehl, Ralph E. White, Paul T. Coman

Faculty Publications

In this study, a selection of active materials were coated onto commercially available intermediate modulus carbon fibers to form and analyze the performance of novel composite cathodes for structural power composites. Various slurries containing polyvinylidene fluoride (PVDF), active material powders, 1-methyl-2-pyrrolidone (NMP) and carbon black (CB) were used to coat carbon fiber tows by immersion. Four active materials—lithium cobalt oxide (LCO), lithium iron phosphate (LFP), lithium nickel manganese cobalt oxide (NMC), and lithium nickel cobalt aluminum oxide (NCA)—were individually tested to assess their electrochemical reversibility. The cells were prepared with a polymer separator and liquid electrolytes and assembled in 2025-coin …

Learning Structure With Multivariate Information Bottleneck And Exploration Of New Methods In Sequential Decision Making, Volodymyr Makarenko

Master's Theses

Research in useful information extraction has been motivated by the increasing demand to extract insights from unstructured data, and by the need to store and transmit great volumes of information, often originating in unstructured data such as videos. Research in rate distortion and information bottleneck paved the path for understanding and guiding the design of lossy encoders, capable of extracting relevant information. Independently, research in deep representation learning has enabled numerous applications for unstructured high-dimensional data such as images. However, the interpretability of the deep learning methods remained limited. Several desired properties of learned representations have been suggested, including disentanglement. …

Autonomous Uav Swarm Formation Utilizing Gradient-Driven Contour Mapping For Radiation Source Localization, Edgar Amalyan

UNLV Theses, Dissertations, Professional Papers, and Capstones

This thesis presents a drone swarm for radiation mapping to aid source localization. The Department of Energy advocates employing UAVs for this task, but existing approaches remain inefficient and impractical in real-world scenarios. Three custom drones are built and flight-tested. A control algorithm to follow a contour, a constant-intensity path, is designed using a gradient fit. By knowing the source’s direction, the drone swarm can fly in the optimal trajectory at every step, leaving nothing to assumption. A program is created that implements formation flight and autonomous navigation. It is tested via a software-in-the-loop simulation utilizing radiation sources and detectors …

The Dropbot: Design And Development Of A Custom Drone For Precision Water Drop Penetration Time (Wdpt) Testing, Mugundan Prakash

UNLV Theses, Dissertations, Professional Papers, and Capstones

Assessing the hydrophobic characteristics of soil is vital for understanding soil wettability or soil-water interactions, particularly in post-wildfire environments where water repellency can significantly impact ecosystem recovery, water infiltration, and erosion control. One key metric in soil wettability studies is the Water Drop Penetration Time (WDPT) test, which evaluates the hydrophobicity of soil and guides land treatment strategies. This thesis presents the design and development of DropBot, a custom-built drone platform engineered for the precise delivery and analysis of water droplets in WDPT tests.The DropBot, a custom drone, integrates a lightweight, 3D-printed frame with a self-leveling platform, enabling consistent droplet …