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Image-Based PV Soiling Quantification and Defect Detection Using Machine Learning
No full textSolar energy, a rapidly growing renewable energy source, has garnered significant global attention in recent years. Achieving high efficiency and maintaining the optimal performance of PV panels is crucial. In addition to the material properties and design of the solar cells, PV efficiency is also significantly affected by system losses and degradation.
Soiling loss, an important system loss, cannot be improved solely through design modifications and requires periodic inspection and cleaning. In this thesis study, a novel image-based method for estimating soiling loss has been proposed, utilizing key feature extraction and linear regression techniques. Two datasets were collected for this purpose: an in-lab simulation dataset and a dataset obtained from an outdoor PV testing field. The proposed method was tested with both datasets using measured soiling loss/power loss as a gold standard. The method achieved an r-squared value of 0.98 and the root mean squared error of 0.01, which showed its significant potential for cost-effective soiling monitoring purposes.
In addition to soiling loss, this study also addresses the problem of PV cell defects, which can come from degradation. A computer vision-based method is developed for detecting PV defects. The method utilized the State-of-the-Art (SOTA) object detection algorithm You Look Only Once V8 (YOLOV8), with U-net architecture and feature pyramid network to improve the accuracy. In addition, the model is compressed with Layer-Adaptive Magnitude-based Pruning to improve the computational efficiency, Additional improvement including the adoption of a better loss function inner-CIoU and the activation function MiSH. To test the proposed method, an open-source Electroluminescent PV defect dataset PVEL-AD was used. The method is compared with several existing algorithms in terms of accuracy and efficiency. The proposed method outperformed all reported work in accuracy and ranked No.2 only in efficiency. It reached mean Average Precision under IoU of 50% (mAP50) of 93.1%, and mean Average Precision under IoU from 50% to 95% (mAP50:95) of 68.7%, which improved about 8-15% comparing to the best existing algorithm. The model���s detection speed is 85.3 Frame per Second (FPS) which ranked in 2nd place among all the existing works. In addition, the model is trained on multiclass detection, the fastest method with FPS of 94.34 only trained on selected classes.
In summary, the novel methods developed in this thesis provide effective tools for estimating soiling loss and detecting defects in PV panels. With improved efficiency and accuracy, these developments have the potential to significantly improve the overall efficiency and maintenance of solar energy systems
Role of Ventral Hippocampus in the Contextual Control of Avoidance
No full textAnimals react to aversive situations with a complex set of behaviors that reduce the likelihood of predation. Of course, animals have the capacity to learn from their experience; this allows them to anticipate and defend against future threats. On the one hand, Pavlovian conditioned responses, such as freezing behavior in rats, adaptively generalize to a variety of threats and environments in which they might be encountered. On the other hand, instrumental learning, such as making an active escape or avoidance response to avoid a painful event, enables animals to develop new behavioral strategies to avoid future threats. Compared to their Pavlovian counterparts, the neural and behavioral mechanisms of instrumental avoidance responses are poorly understood. In particular, whether avoidance responses exhibit a tendency to generalize across many contexts is unknown. To address this question, I assessed the context-dependence of instrumental avoidance learning in male and female rats. These studies used a two-way signaled active avoidance (SAA) task, in which animals can avoid an aversive footshock by shuttling in response to a warning signal. In the first set of experiments, I determined whether a learned avoidance response transfers to a new context and whether the contextual control of avoidance requires the hippocampus, a brain area that has been implicated in this form of learning. This work revealed that shuttle-box avoidance was context-dependent and decreased outside of the training context; inactivation of the ventral (VH), but not dorsal (DH) hippocampus, with the GABAA agonist, muscimol, eliminated the context-dependence of the response. In another set of experiments, I examined avoidance responses to the training context itself���so called inter-trial responses (ITRs). Inactivation of the VH decreased ITRs, whereas chemogenetic activation of the VH with ���designer receptors exclusively activated by designer drugs��� dramatically increased the number of ITRs. Together, these studies reveal that the VH has a role in both determining the context in which a threat-induced avoidance response is emitted and in promoting ITRs in the aversive context itself. The neural circuits by which the VH mediates these two functions of context in avoidance requires further investigation
Quantitative Analysis of Strain Response Measured by Low-Frequency Distributed Acoustic Sensing During Hydraulic Fracturing
No full textThis master's thesis investigates the pivotal role of strain measurements in hydraulic fracturing operations, employing Low-Frequency Distributed Acoustic Sensing (LF-DAS) technology to monitor strain changes during treatments. A significant gap in the existing research is addressed by systematically analyzing strain decay beyond the fracture domain corridor. The thesis investigates the impact of parent-well depletion and completion design on hydraulic fracture geometry, employing a decline factor of the strain decay curve as a key analytical tool. This analysis is supported by a geomechanics model, providing a comprehensive understanding of the dataset. Furthermore, the study conducts a comprehensive analysis of Hydraulic Fracture Test Site-2 (HFTS-2), considering the maximum cumulative strain change and decline factor of the strain decay curves. The thesis outlines a well-structured workflow for processing and analyzing LF-DAS cross-well strain data
Experimental Studies on Measurements in Oil-Gas-Water Flow and Turbulent Flow Field in Wind Generated Water Waves
No full textThis thesis presents experimental studies in multiphase flows with two main topics: (1) measurements in oil-gas-water flow using the fiber optic reflectometer (FOR) technique; (2) turbulent flow field in wind generated water waves.
The application of the single-probe fiber optic reflectometer (FOR) technique has been investigated to determine the velocities and size of oil droplets rising in a static water column. The droplet velocity, residence time, and chord length measurements were validated by comparing with the results from high-speed images using the bubble image velocimetry (BIV) technique and the image gradient method. Subsequently, the application of the FOR technique has been extended to oil-gas-water three-phase flows by investigating the accuracy of phase discrimination and measuring the velocity and size of bubbles and droplets. The technique was expanded to identify water, air bubbles, and oil droplets and to quantify the velocity and size of bubbles and droplets in an oil-gas-water three-phase flow through the processing of acquired signals.
In the second part of this thesis, the turbulent flow field in wind generated waves has been studied. The experiments were performed in a wind-wave-current flume with three freestream wind speeds using a particle image velocimetry (PIV) technique. The Bond number and the shear velocity-fetch based Reynolds number were found to correlate the wind wave regimes well. The turbulent dissipation rates were determined based on spatial gradient of instantaneous velocities and one-dimensional velocity spectrum in temporal space. In addition, the turbulent kinetic energy (TKE) budget including its production, dissipation, advection, and turbulent transport was presented. The production-dissipation ratio increased significantly as the wind speed increased, likely attributed to the increased roughness over the substantial coverage of micro-breaking waves. Subsequently, the turbulent flow filed beneath the water surface has also been investigated under the same experimental conditions used for airflow measurements. The friction velocities were estimated from both air- and water- side measurements with the mean velocity profile and the eddy-correlation methods. The result from the comparison of different methods is useful in determining the scaling of water side turbulence such as dissipation rate of turbulence kinetic energy with the air side velocity measurements, or vice versa
The Role of Metformin in the Prevention and Treatment of Breast Cancer: Insights From Preclinical Studies
No full textMetformin, a commonly used medication for type II diabetes, has been reported to decrease breast cancer risk. However, results from observational and clinical trials show mixed results, questioning the efficacy of metformin for breast cancer treatment. In addition, determining what patient populations may experience optimal responses to the drug is warranted. Our goal was to improve our understanding of the circumstances under which metformin may exhibit maximal efficacy for breast cancer prevention and treatment. First, we assessed the effectiveness of metformin on tumor outcomes in ovary-intact and ovariectomized rodents. Our findings support metformin treatment being more effective in the postmenopausal setting, having observed prevention of new tumors and reduced tumor burden. The second objective was to elucidate how metformin affects mammary adipose tissue. We have previously shown that metformin reduces M2-like aromatase-expressing macrophages in the tumor border. Here, we investigated if treatment lowered inflammatory markers in tumor-distant mammary tissue. We found a small reduction in inflammatory markers in metformin-treated mammary adipose. However, RNA seq analysis from adipose tissue from treated and control rats showed no changes in gene expression. Our final aim centered on determining the optimal timeframe for metformin treatment during the menopause transition for improved tumor outcomes. We found that metformin treatment in the first four weeks post-ovariectomy was needed to improve tumor outcomes. This work underscores the significance of menopausal status and timing within the menopause transition in influencing the efficacy of metformin for treating mammary tumors and emphasizing the need for a targeted approach to metformin treatment for breast cancers
Nitrogen-Containing Organic Bases for Molecular Electronic and Biological Investigations
No full textThis dissertation shows the efforts towards tailor-made nitrogen-containing organic base molecules for modular electronic studies as well as biological investigations. The first part features the development of two different kinds of aniline-derived conjugated molecules and their applications as molecular models for single-molecule junction studies. The second part of this dissertation describes the development of a series of polyamine-based detergents and their applications in native mass spectrometry studies of membrane proteins.
First, a general introduction of nitrogen-containing organic bases is presented in Chapter I. Example molecules and their applications including pharmaceuticals, metalorganic frameworks (MOFs) and conducting polymers, specifically polyaniline (PANI), are discussed.
In Chapter II, the design and synthesis of a ladder-type polyaniline-inspired single-molecule switch are presented. With exceptional electrochemical stability rendered by the ladder-type constitution, the molecule can be converted between three distinct molecular states characterized by varying levels of protonation and oxidation. In collaboration with the Schroeder Group at UIUC, we demonstrate its superior conductance and multi-state switching capabilities through electrochemical scanning tunneling microscope break-junction (STM-BJ) experiments. Our results suggest that ladder-type molecules are promising candidates for advanced single-molecule electronics. This work also sheds light on the mechanism of electronic conductivity of redox-active conductive polymers.
Chapter III describes the motivations of a model study involving oligo(para-phenylene)s within a graphene single-molecule junction for mechanistic studies of RCM in the synthesis of ladder polymers or oligomers. The design and synthetic efforts towards the target molecules are the main foci of this chapter.
Next, in Chapter IV, synthesis and purification towards spermine-derived detergents are described. These detergents are employed as additives/co-detergents in native mass spectrometry (MS) studies of various membrane proteins. Their effectiveness in achieving reduced average protein charge states and preserving membrane proteins in more intact states has been demonstrated. Our research in this area laid the groundwork for the molecular design principles of charge-reducing detergents with enhanced protein solubility and improved compatibility with commercial detergents. These principles are crucial for conducting native mass spectrometry studies of membrane protein complexes.
Lastly, Chapter V concludes the entire dissertation by providing an overview of the findings presented in the preceding chapters. Additionally, future research perspectives that promise to shed further light on both fields are discussed
Building 0D and 2D Porous Metal-Organic Nanomaterials for Efficient Photo-Induced Energy Transfer
No full textThe concept of host-guest chemistry has been raised to study the interaction between a system and attached small molecules. These strong interactions have enabled the use of framework materials in catalytic reactions with high selectivity and turnover numbers. Recently, the introduction of metal/metal clusters has revitalized this field due to improved stability and binding capabilities. Metal-organic frameworks (MOFs), renowned for their high crystallinity, porosity, and well-determined structures, have been extensively used for host-guest studies. However, the traditional 3-dimensional bulk MOFs create a diffusion barrier that hinders the applications. To overcome this, chemists have formulated the field of 0-dimensional molecular cages, termed ���0D porous coordination cages���, which maintain homogeneity and exhibit explicit confinement in all dimensions, and the field of 2-dimensional MOF-derived nanosheets, termed ���2D MOF nanosheets���. These cages facilitate efficient catalysis by avoiding 3D stacking of pores.
In my PhD research, I aim to develop viable synthetic methodology for 0D and 2D metalorganic nanomaterials. Embedding photoactive ligands into 0D cages and 2D MOF nanosheets provides an approach to introducing catalytic ability. This can be achieved through introducing coordination functional groups such as carboxylate and azolate groups, converting various photoactive molecules into suitable ligands while maintaining their activities. Moreover, the selection of coordination/metal clusters and the host-guest interaction in these materials can lead to distinct activities. This work launched studies on the fabrication of 0D cages and 2D MOF nanosheets and their photo-catalytic reactions. The objective of this research is to study the design rationales of 0D and 2D MOF nanomaterials and inspire the discovery of novel catalysts with high selectivity and activity
CRISPR-CAS12 Nanoparticles Alter Macrophage Function to Improve Inflammation After Traumatic Brain Injury in Mice
No full textOne of the first physiological and cellular events after a traumatic brain injury (TBI) is inflammatory-driven progressive neurodegeneration. The major players in this process are the brain resident immune microglia/macrophage cells (MG/Ma), which release inflammatory cytokines to cause an excess amount of inflammation. However, the largest obstacle to current TBI treatment is the challenge of targeting these MG/Ma cells. We designed a lipid nanoparticle (LNP) that can overcome this problem by locally delivering a clustered regularly interspaced short palindromic repeats (CRISPR) gene editing system specifically targeting the mitogen-activated protein kinase 9 (MAPK9) gene to polarize resident MG/Ma from their proinflammatory to anti-inflammatory state.
First, the CRISPR technology was combined with the LNPs to create a CRISPR-LNP system that was conjugated to an ionized calcium-binding adapter molecule 1 (Iba-1) antibody to increase specificity to central nervous system macrophages. We investigated the delivery efficacy of the CRISPR-LNP to reach the injured brain by comparing intravenous or intranasal administration. Then, we used a standardized cortical impact device to inflict a traumatic brain injury in adult male C57BL/6J mice. The data was visualized and analyzed using an advanced in vivo imaging system (IVIS), immunofluorescence analysis, and immunohistochemical staining with in situ hybridization.
Intranasal administration of Iba-1Ab-CRISPR-LNP produced greater specificity in binding to the injury compared to systemic administration. IVIS indicated that systemic administration led to a more significant accumulation of LNPs in the injured cortex. We found that the treatment reduced MG/Ma activation indicated by a change in macrophage morphology consistent with a reduction in the quantity of pro-inflammatory MG/Ma cells. We also found a decrease in the number of pro-inflammatory cytokines (IL-1�� and TNF��) and TUNEL-positive cells following CRISPR-LNP treatment compared with the vehicle group after 1-day post-TBI.
Our results showed that the Iba-1Ab-CRISPR-LNP reduced the MG/Ma activation indicated by morphology changes and the quantity of apoptosis of all cells after TBI. This work lays the foundation for developing a specific transformational therapeutic approach for TBI patients
Enhance PET Degrading Enzyme Performance Through Immobilization on Magnetic Nanoparticles
No full textGlobal plastic generation has reached 460 million tons in 2019 of which 353 million tons ended up as waste. PET is a widely generated and consumed thermoplastic due to its mechanical and chemical properties. It makes up to 67% of the packaging plastic. Due to its resistance to chemical and mechanical changes it becomes a challenge to handle PET waste. PET requires harsh conditions to decompose into its monomers of TPA and EG. Efforts have been made to recycle PET by degrading it to its monomers or reshaping and upcycling it from there.
Currently, the three major ways of recycling PET comprise of chemical recycling, mechanical recycling and using biological systems for recycling. Chemical recycling of PET is achieved through solvolysis and pyrolysis which is accompanied by release of toxins in the atmosphere. Mechanical recycling does not break down PET to its monomers but reshapes the PET so it can be reused. However, this only recycles PET to a certain extent, after which it can no longer be reshaped and reused since it loses mechanical strength. Biodegradation has become an attractive alternative to chemical and mechanical recycling due to its cost-effectiveness and milder reaction conditions, which makes it a more environment-friendly approach.
The major challenge that comes with biodegradation is the aggregation of the enzymes at higher concentration which inhibits the enzyme activity. To overcome this challenge, there have been attempts to immobilize the enzymes on scaffolds which provides an even distribution and prevents aggregation. With PET degrading enzymes, there have been attempts to immobilize PETase on nanoparticles previously which enhanced the activity of the enzyme. However, none of the existing systems could achieve complete depolymerization of PET. We have proposed a method to immobilize both PET degrading enzymes, FAST PETase and MHETase on iron oxide nanoparticles to enhance activity and achieve complete depolymerization of PET. In addition to this, we also incorporated a carbohydrate binding module (CBM) into the system which has shown to enhance activity by helping the enzyme come in closer proximity to the substrate.
It has been observed that even a small amount of MHETase boosts the PET degradation and the limiting step in conversion of PET to MHET is PETase. We found that 1:20 ratio of pure MHETase to FAST PETase is the optimum ratio for PET degradation. To achieve this ratio on the nanoparticles, we tried sequential addition and compared it with simultaneous addition. The sequential addition helped increase the amount of MHETase immobilized and the total enzyme loading. This two-enzyme system on nanoparticles showed a 2.5-fold increase in TPA release compared with the free enzyme system. We were able to show reusability of the bioconjugates using simple magnetic separation. The enzyme bioconjugates also showed higher stability when stored at 4��� as compared to free enzyme.
With CBM, the enzyme did show higher activity than the samples without CBM initially, however during the course of the assay, it did not show as much enhancement towards the end. This could be due to product accumulation in close proximity of the enzyme which inhibits the activity thereafter. The system could still be optimized further for shorter duration of assays.
With the current system consisting of FAST PETase and MHETase on iron oxide nanoparticles, we were able to achieve complete depolymerization of PET with additional advantages of reusability and enhanced stability through immobilization on nanoparticles. The knowledge from this work could guide other multi-enzyme systems with non-equimolar requirement to enhance activity through immobilization
Target Identification Using Single Cell RNA-Seq: Algorithms and Applications
No full textTarget identification is a crucial step in the drug development process, significantly affecting the success rate and efficiency of bringing new therapies to market. Recent advancements in single-cell RNA sequencing and computational tools have accelerated the identification and validation of therapeutic targets by enabling a deeper understanding of disease mechanisms at the cellular level. However, challenges such as inadequate understanding of the molecular basis of certain diseases and limitations in current single-cell data analysis methods, particularly in capturing gene regulatory relationships, continue to hinder the full exploitation of these technologies in precision medicine.
To this end, we aim to enhance target identification in scRNA-seq, crucial for unraveling cellular differentiation and disease mechanisms. Firstly, we develop ���scInTime���, a computational method that capitalizes on single-cell trajectory data and gene regulatory networks to accurately identify master regulators of cellular differentiation. This algorithm aims to overcome the existing challenges in mapping cell fate decisions, a critical step in advancing personalized medicine. Secondly, we propose to undertake an integrated scRNA-seq data analysis to investigate the association between pyroptosis and the severity of COVID-19. This research is expected to shed light on the immune response to SARS-CoV-2 and identify potential targets for therapeutic intervention. By focusing on the mechanisms underlying severe COVID-19 cases, we anticipate contributing to the global effort in combating the pandemic. Thirdly, we address metabolic diseases, specifically investigating the role of hepatocyte adenosine kinase in fat deposition and liver inflammation. Here, we aim to elucidate the molecular pathways that lead to excessive fat storage and inflammation in the liver, offering targets for the treatment of metabolic syndromes. Finally, we conduct a sex-based study on the role of RSPO3 in estrogen-mediated sex differences. Understanding the molecular bases of sex differences in diseases is critical for the development of gender-specific therapies and this study will contribute to that knowledge base.
Overall, our goal is to leverage scRNA-seq for precise target identification, addressing significant gaps in the understanding of cellular differentiation and disease. This thesis is designed to set the stage for a series of investigations that will collectively advance our knowledge in the field and lead to novel therapeutic strategies