26800 research outputs found
Sort by
EVALUATING CHALLENGES AND SOLUTIONS FOR BUCKTHORN CLASSIFICATION IN SHADOWED ENVIRONMENTS
Get PDFInvasive species, such as buckthorns, pose significant ecological threats by displacing native vegetation and reducing biodiversity. This study examines the impact of shadows on the classification accuracy of buckthorns using drone-based multispectral imagery collected in a forested area near Michigan Technological University. Shadows impacted approximately 70% of the imagery, notably distorting reflectance in key spectral bands such as near-infrared (NIR) and red edge. The study evaluated vegetation indices like NDVI and the performance of machine learning models, specifically the Random Forest classifier, under these shadowed conditions. Conventional shadow correction techniques, including histogram normalization, Shadow Index (SI), and Inverse Distance Weighting (IDW) interpolation, provided only marginal improvements. The highest classification accuracy achieved was 49.5%, with a Kappa coefficient of 0.24. These findings highlight the challenges of utilizing single-date multispectral imagery in heavily shadowed environments. The study recommends exploring advanced techniques such as Hue Saturation Value (HSV) correction, multi-temporal data fusion, and deep learning approaches to enhance vegetation classification
On graph decompositions: Exploring the stars and stripes problem with odd n-stars
Get PDFThis dissertation addresses the “Stars and Stripes problem, which is a graph decomposition problem. The Stars and Stripes problem can be described as finding uniformly resolvable decompositions when the blocks are either -stars or edges.
In other words, this refers to decomposing a complete graph into spanning sub-graphs, these spanning sub-graphs are either a union of disjoint -stars or a union of disjoint edges. We will call these -star factors and -factors respectively. A given complete graph which satisfies the necessary condition (\ref{ness}) can potentially be decomposed into -factors and -star factors. The goal of this problem is to find a solution for every pair and every complete graph that satisfies the necessary conditions. This dissertation focuses on the odd -star cases of the problem. We present the solutions we have found along with the remaining open cases.
In Chapter \ref{ch2 WC}, we present a method for decomposing a complete graph into cycles using weighted graphs. This method provides solutions for most cases where is large. However, this method requires our decomposition to contain a certain number of -factors. Thus, we cannot find solutions for extreme cases, that is pairs when is very small.
Because the technique in the Chapter \ref{ch2 WC} does not apply to extreme cases, we began to study the most extreme cases, where . In Chapter \ref{ch3 5star}, we give solutions for the case when and . The generalized version, when and is odd, is presented in Chapter \ref{ch4 Nstar}. The results in Chapters \ref{ch3 5star} and \ref{ch4 Nstar} are obtained fundamentally by the same method. However, the constructions in Chapter \ref{ch4 Nstar} are much more complex, and there are many more detailed cases
DEVELOPMENT OF NEAR-INFRARED PROBE FOR THE DETECTION OF NADH AND NAD(P)H IN LIVE CELLS AND D. MELANOGASTER LARVAE
Get PDFA near-infrared fluorescent probe, A, was designed by substituting the carbonyl group of the coumarin dye’s lactone with a 4-cyano-1-methylpyridinium methylene group and then attaching an electron-withdrawing NADH-sensing methylquinolinium acceptor via a vinyl bond linkage to the coumarin dye at the 4-position. The probe exhibits primary absorption maxima at 603, 428, and 361 nm, and fluoresces weakly at 703 nm. The addition of NAD(P)H results in a significant blue shift in the fluorescence peak from 703 to 670 nm, accompanied an increase in fluorescence intensity. This spectral shift is attributed to the transformation of A−π–A−π–D configuration to D−π–A−π–D pyridinium platform in probe AH, owing to the addition of a hydride from NADH to the electron-accepting quinolinium acceptor producing the electron-contributing 1-methyl-1,4-dihydroquinoline donor in probe AH. This conclusion is supported by theoretical calculations. The near-infrared emissive probe offers a highly sensitive and specific method for monitoring NAD(P)H levels across cellular, tissue and whole-organism systems. The ability to detect NAD(P)H variations in reaction to varying stimuli, including nutrient availability and chemotherapeutic stress, underscores its potential as a valuable resource for biomedical research and therapeutic monitoring
STATISTICAL METHODS FOR JOINT ANALYSIS OF MULTIPLE TYPES OF PHENOTYPES IN GENETIC STUDIES
No full textThis dissertation consists of three chapters, with a brief overview of each chapter provided below.
In Chapter One, we proposed a latent variable model pairwise likelihood to jointly model genotypes, multiple types of phenotypes and covariates efficiently. We proposed a new Wald-type test statistic to test the conditional independence between genotypes and phenotypes after adjusting for covariates. This method preserves the ordinal nature of genotypes and ordinal phenotypes and does not require commonly assumptions used in association studies to detect genetic variants that are associated with phenotypes related to human complex diseases, such as assumptions for continuity and linearity, thus provides enhanced flexibility. Additionally, it explores covariance structures and can efficiently handle large-scale data. Simulations demonstrated that the proposed method had well-controlled Type I error rates and higher power than existing methods. Real data analysis based on the Genetics of Kidneys in Diabetes (GoKinD) study identified novel genetic variants that are associated with type 1 diabetes.
In Chapter Two, we developed a robust and flexible Tweedie-imputation method to tackle zero inflation and high taxa variability in microbiome data. Our approach preserves index variability, imputes missing values using dataset-derived averages, and adjusts for sequencing depth. Simulations showed its superiority over existing methods and its robustness. Real data analysis identified more significant taxa than other existing methods.
In Chapter Three, we proposed a regression-based method that utilizes the latent variable model and the pairwise likelihood to detect genetic variants that are associated with phenotypes related to human complex diseases. This method can efficiently model a large number of multiple types of phenotypes, genotypes, and covariates. Comparing with the method proposed in Chapter One, this method offers better interpretations since it can quantify the genetic effect on phenotypes. Simulations confirmed that the proposed method has well-controlled Type I error rates and high power
LIFE CYCLE ASSESSMENT AND LIFE CYCLE COST ASSESSMENT OF A FLOATING TREATMENT WETLAND IN A WASTEWATER LAGOON LOCATED IN COPPER HARBOR, MICHIGAN
Get PDFA full-scale floating treatment wetland was implemented in a wastewater treatment lagoon in Copper Harbor, Michigan. Copper Harbor is a small, rural community located in a cold climate. Many studies have focused on the performance of floating treatment wetlands in mesocosm studies in warmer climates, but there is not a lot of research conducted on the feasibility and performance of full-scale floating treatment wetlands in wastewater lagoons, specifically in cold climates. The 2024 treatment season was the first season with the floating treatment wetland implemented in the wastewater lagoon. A preliminary assessment of the cost and environmental impacts of a full-scale floating treatment wetland system using the Beemats technology system was analyzed using a life cycle assessment and life cycle cost assessment. The results of these analyses showed that although the floating treatment wetland system had a relatively high upfront cost ($28,500) and additional labor requirements (approximately 200 hours a year), the carbon emissions of these systems are relatively low. The Beemats and the plants in the system released approximately 1,020 kg CO2eq emissions in 2024. With the plants able to mitigate approximately 968 kg CO2eq emissions, the net emission for the entire FTW system is 50 kg CO2eq. The plants are able to mitigate 95% of emissions from the FTW system. In future work, the nutrient uptake capacity of these systems should be calculated to ensure that they are capable of sufficient nutrient removal. This will help determine if floating treatment wetlands can further reduce greenhouse gas emissions through carbon sequestration, the reduction of aluminum sulfate addition, or electricity use
MEASURING OCCUPANT CONTROLLED VENTILATION AND COOKING FREQUENCY TO AID IN MODELING ENERGY PERFORMANCE OF NORTHERN MICHIGAN HOMES
Get PDFThis research investigates the real-world interplay between energy efficiency and indoor air quality. We explore how home ventilation strategies, heating systems, and weatherization levels in rural homes interact. As part of a larger study on air quality, 17 homes participated in the study. Specifically, this research shows the results and methods for monitoring cooking frequency, kitchen range hood use, and bathroom fan use over two, month-long study periods to build accurate energy and contaminant transport models of homes that were studied. Energy audits to document home characteristics were conducted, including blower door testing and detailed qualitative data regarding the homes’ energy performance and factors influencing indoor air quality. Energy models were created based on these energy audits. The single most common form of kitchen ventilation was no mechanical ventilation, although almost every kitchen had an operable window. The studied homes were far from uniform with widely ranging utilization of kitchen range hoods (0-139 min/day average utilization) and bathrooms exhaust fans (0-264 min/day). The envelope leakage was also quite diverse with some homes being as tight as 3 ACH50 whereas others as loose as 45 ACH50. Future work will include contaminant modeling to show the indoor air quality impacts of the home characteristics, pollutive events inside the home, as well as the ventilation strategies that were employed by the homeowners
PHASE-FIELD METHOD OF FRACTURE IN FIBER-REINFORCED POLYMER COMPOSITES: THEORY, MODELING, AND VALIDATION
No full textFiber-reinforced polymer composites (FRPCs) are widely used in aerospace, automotive, and civil engineering due to their high specific strength, stiffness, and durability. However, their anisotropic and heterogeneous nature makes fracture prediction challenging, with damage modes classified as intralaminar failure (matrix cracking, fiber breakage, fiber-matrix debonding) and interlaminar delamination. Delamination, driven by weak through-thickness strength and high interlaminar stresses, is a predominant failure mechanism in FRPCs. While cohesive zone models (CZMs) are commonly used for delamination, they are computationally expensive and inefficient for capturing intralaminar fractures where crack paths are unknown. To address this, we developed a unified phase field-based cohesive zone model (PF-CZM) capable of predicting both intralaminar and interlaminar fractures while quantifying mode-I () and mode-II () fracture energies. The model was validated against experimental data, demonstrating its accuracy in capturing delamination and matrix cracking. However, failure in FRPCs involves complex interactions between these damage modes, particularly in multi-layered laminates with varying fiber orientations, where mechanical property mismatches and interfacial decohesion create displacement field discontinuities. To overcome this, we developed an anisotropic interface-regularized phase field approach in 2D, incorporating a traction-separation-based interfacial constitutive model to capture bulk-interface fracture interactions. This framework effectively modeled matrix crack initiation, propagation, and delamination, where traditional models struggled. To account for out-of-plane deformations and crack twisting in off-axis laminates, we extended this approach to 3D, enabling accurate predictions of delamination and intralaminar-interlaminar interactions in FRPC laminates under complex loading. Finally, we advanced our model by incorporating thermomechanical coupling and the viscoelastic behavior of the polymer matrix. This enhanced framework captures time-dependent deformation, thermal effects, and anisotropic fracture evolution in CFRPs, considering temperature-displacement-driven damage, viscoelastic dissipation, and crack propagation. Our work provides a comprehensive approach to fracture modeling in FRPCs, offering critical insights into failure mechanisms and contributing to the design of more durable, high-performance composite structures
DEVELOPMENT OF A PROTOTYPE FOR AN EARLY ALERT SYSTEM THAT MEASURES STRAINS IN TIMBER ROOF TRUSS MEMBERS
No full textRoof collapses due to excessive loads pose a significant structural concern in cold regions often leading to property damage, financial loss and life-threatening situations. Roof geometry plays a crucial role on how snow accumulate and how structures respond under extreme conditions. Traditional monitoring methods rely on visual inspections and seasonal snowfall estimate, which often fail to capture real time structural response leading to unexpected failures. To address this limitation, the study proposes the development of a strain based early warning system that utilizes the axial capacity of timber roof trusses to monitor the performance of roof trusses under snow loads.
The study examines the influence of architectural styles, truss configuration, material variability and snow load magnitude on the structural response of timber truss. Different roof designs interact with snow accumulation in different ways, influencing load distribution and failure risks. Through Monte Carlo simulation, the study evaluates how strain monitoring can be optimized for both balanced and unbalanced snow loads. A threshold based on the axial capacity of critical member is proposed incorporating safety factors that adjust the nominal strength of the member.
Findings indicate that a fixed threshold is insufficient for ensuring a reliable detection across varying structural and environmental conditions. Higher material variability increases the likelihood of missed alarms. The results emphasize the need for an adaptive warning system that capable of distinguishing between safe high load condition and impending failure. By integrating architectural design consideration, real time strain monitoring and reliability-based threshold selection, the study contributes to development of proactive structural health monitoring solution for snow prone regions
MICROBIAL INACTIVATION AND SPRAY DRYING FOR THE RECOVERY OF SINGLE-CELL PROTEIN FROM CHEMICALLY DECONSTRUCTED POLYETHYLENE TEREPHTHALATE
Get PDFPolyethylene terephthalate (PET) is one of the most widely used polymers in plastics packaging. However, the increasing demand for PET has created challenges in waste disposal. Previous research has shown that PET can be deconstructed using ammonium hydroxide (CDPET). Microorganisms capable of utilizing the product as a sole carbon source can be converted to single-cell protein (SCP). A custom microbial inactivation unit was developed to ensure the safety and stability of generated SCP. Our study evaluated the effect of exposure time (45–60 minutes) at 140 °C with the goal of maximizing microbial inactivation. Our findings suggest that microbial inactivation can be achieved at 140 °C in 60 minutes. Since SCP is generated at relatively low concentrations, spray drying was investigated as a means to achieve rapid drying of heat-inactivated cell biomass. We analyzed spray drying parameters, such as inlet air temperature, feed flow rate, and nozzle pressure, to maximize SCP yield. Overall, the results of this study provide an effective method for recovering SCP from CDPET, with potential applications in the food and feed industries
DEVELOPING INEXPENSIVE SENSORS THAT CAN DETECT ROOF COLLAPSE FROM SNOW LOADS
Get PDFIn cold regions in the United States, residential timber structures are susceptible to roof collapses due to heavy snow accumulation. The recent updates in ASCE 7-22 have introduced reliability-targeted ground snow loads, leading to significant increases in design snow loads for certain areas. Despite this development, existing buildings in these regions designed under historical code requirements may be under-designed for snow load. This fact provides motivation to owners of these structures to pursue effective monitoring and mitigation strategies to prevent structural failures. This research focuses on the development of an early warning system utilizing cost-effective slope monitoring sensors to estimate snow loads on residential and commercial timber roofs. This study focuses on beam-supported flat roofs. By continuously assessing roof slope changes, the system aims to provide real-time alerts for potential structural overloads due to excessive snow. Alerts will prompt owners to remove snow from the roof. The study also incorporates Monte Carlo sampling methods to calibrate the proposed system, characterizing its sensitivity across various scenarios. Furthermore, the research addresses some implications of climate change, which has led to more frequent and severe snowstorms in mid-latitude regions. The findings aim to inform design considerations and safety protocols, aligning with the enhanced requirements outlined in ASCE 7-22. The proposed system offers a proactive and condition-based approach to structural safety, potentially reducing the risk of roof collapses and enhancing the resilience of residential buildings in snow-prone areas