Designing a deep learning-based framework for the prediction of lake surface closed curves

Predicting the surface area and shape of lakes is critical for ecological, hydrological, and climatic studies. Accurate predictions enhance the understanding of lake dynamics, facilitate water resource management, and support environmental change assessments. Traditional methods, while foundational, often lack the efficiency, accuracy, and scalability required to handle complex lake systems, necessitating modern, technology-driven approaches. This study introduces a novel methodology for predicting changes in lake surface area and shape, including shoreline dynamics. The approach employs advanced remote sensing techniques and mathematical modeling, integrating Mathematica simulations with Net-Encoder and Deconvolution models. The framework achieved a high accuracy rate of 93% in water pixel extraction. Results indicate significant reductions in surface area, with Lake Eucumbene shrinking by 4.3% and the Salton Sea by 14.54%. The most notable shoreline changes occurred in the southwestern and northern regions of Lake Eucumbene and the southwestern region of the Salton Sea. This research highlights the effectiveness of a remote sensing-based approach for monitoring lake surface dynamics, offering a low-cost, high-accuracy tool for environmental monitoring and climate change impact assessment. Compared to existing methods, the proposed approach provides equivalent accuracy while delivering enhanced operational simplicity and flexibility

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Building as They Come: Comparative Case Studies of Co-constructing Data Visualization Services with Academic Communities

[para. 3-4] "Academic libraries are well-situated to be strong supporters of democratizing and building knowledge and expertise in the use of data and data visualization as they cut across all of academia, regardless of discipline or department. Within the past decade, many academic libraries across North America have added data visualization services to their offerings. This has been done in several ways, from existing librarians with related portfolios like GIS or research data learning new skills to libraries creating new positions with the focus on the portfolio on data visualization. This chapter presents and compares two case studies of building data visualization services at York University Libraries and McMaster University Library. We do so with the hope of sharing practical and relevant knowledge with our readers.

Drawing The Line: An environmental history of the Westcoast Transmission natural gas pipeline, 1948-1982

This dissertation is an environmental history of Westcoast Transmission Company Limited (Westcoast), which built Canada’s first big-inch natural gas pipeline and inaugurated large-scale natural gas usage in British Columbia. The study starts in the late 1940s, when the company was founded, and ends in 1982 when it effectively concluded its first encounter with substantial public resistance to its natural gas pipeline ventures. The dissertation asks to what extent Westcoast shaped human-nature relations and argues that Westcoast’s energy transition was about more than technological innovations and economic questions of supply and demand. Instead, natural gas usage and exploitation were intertwined with gender identity, community building, geopolitical questions, colonial ambition, and the definition of modernity. Relying primarily on three archival collections in two Canadian cities, parts of which are newly available to the public, this dissertation explains how Westcoast developed, operated, maintained, and expanded its complex energy system and sheds light on Canada’s relatively late transition to fossil fuels and the persistent nature of Canada’s fossil fuel reliance

Intestinal Immunolocalization and Insight on the Role of Tachykinin-Related Peptides in the Yellow Fever Mosquito, Aedes aegypti

This study investigates a family of neuropeptides known as tackykinin-related peptides (TKRPs) in the mosquito Ae. aegypti, which is an insect of medical concern owing to its transmission of several arboviruses. As knowledge on TKRPs is limited in this mosquito, this study aimed to create a tk knockout line using CRISPR/Cas9 and characterize the distribution of TKRP immunoreactivity in the midgut over different physiological conditions including starved, sucrose-fed and blood-fed, and across developmental stages. The results demonstrate that TKRP immunoreactivity in the Ae. aegypti midgut is greatest in adult stage mosquitoes. Further, starvation significantly reduced TKRP immunoreactivity in the midgut compared to sugar fed adult mosquitoes, but no change was observed in relation to blood-feeding by females. Overall, this study established the intestinal distribution of TKRPs in Ae. aegypti and identified functional sgRNAs to disrupt the tk gene so that the physiological role of TKRPs can soon be characterized

Patient Experience and Virtualized Healthcare: Thematic analyses of news, scientific literature, and user experience discourses

This dissertation uses mixed methods to examine three discourses of patient experience of virtualized healthcare. The three discourses examined are: (1) a news discourse, (2) a scientific literature discourse, and (3) a user experience discourse. Virtualized healthcare is defined by this dissertation as healthcare activities specifically conducted via mediated communication. Uptake in virtualized healthcare has accelerated as many Ontario practitioners have recently offered this form of care due to the onset of the COVID-19 pandemic, and the Ministry of Health creating temporary “COVID-19 pandemic allowance” for all physicians to bill for virtual care. As a period of initial unregulated use of virtual care by Ontarians ends, there is now an opportunity to take a closer look at the patient experiences of these healthcare services. By analysing the three distinct discourses (each of which is a form of health communication), this dissertation maps central themes that are consistently brought up in discussions of virtualized healthcare and patient experience. Comparing the themes that come up in the discourse genres of scientific literature and news articles provides an understanding of how patients may come to understand the phenomenon of virtualized healthcare. Adding an analysis of user experience discourse to this understanding provides findings of what themes overlap in both public discourses and accounts of personal experiences of virtualized healthcare. The themes found across the three discourses are ultimately developed into three recommendations the implementation or practicing of virtualized healthcare, which are to be tested and evaluated in future research programs. The three recommendations (engaging patients in healthcare innovation, viewing healthcare as a hybrid patient-centric network, and understanding that virtualization requirements of healthcare interactions vary) are ways of thinking about how healthcare can become virtualized, and what affects the potential virtualization of healthcare. These recommendations are evidenced based, proven to not only be observed in user experience discourse, but also in how researchers and the public discuss issues and concepts of virtualized healthcare. Different and overlapping elements of each recommendation are highlighted by each discourse. Each of the recommendations is discussed in terms of its theoretical and practical implications

Electrophoresis- Driven Lateral Flow Immunoassay and Aptamer Selection

Electrophoresis has become an indispensable tool in Biochemistry and Molecular Biology, essential for analyzing proteins and nucleic acids. My work focuses on new bioanalytical applications of electrophoresis: lateral flow immunoassay (LFIA) and the selection of oligonucleotide aptamers. Electrophoretically-driven LFIA (eLFIA) is a new technique aiming to enhance diagnostic sensitivity of LFIA in both antigen and serological tests. While previously applied to Hepatitis B and C, I aimed to extend eLFIA's scope to analyze the SARS-CoV-2 spike protein, demonstrating a 77% reduction in the limit of detection compared to conventional LFIA. Shifting focus to aptamers, I utilized capillary electrophoresis (CE), with the highest partitioning efficiency, to address challenges in aptamer selection. I determined the optimum target concentration and developed bulk affinity assays workflow that quantitatively assesses the progress of selection. Understanding these parameters can significantly influence aptamer selection efficiency and can guide researchers in designing assays and developing novel diagnostic tools

Integrative and Multi-scale Deep Learning for 3D Point Cloud Transmission Corridor Scene Segmentation: Noise Filtering, Attention-Fused Feature Integration, and Panoptic Network

LiDAR technology plays a crucial role in mapping natural and built environments across various civic and military applications. It enables the acquisition of high-density 3D point clouds with pulse repetition frequencies ranging from 100Hz to 2MHz. However, the increased overlap with atmospheric points has posed challenges in noise filtering and 3D point cloud quality. This dissertation proposes the Noise Seeking Attention Network (NSANet), a novel solution integrating psychological theories of feature integration and attention engagement. NSANet achieves a 4.10% increase in F1-Score and a 7.30% improvement in recall by employing multiscale context, global physical priors, and local spatial attention for noise filtering, surpassing previous techniques. The study explores the relationship between vegetation height and plantation guidelines, identifying spatial layout consistency in utility layouts and transmission objects. This insight drives the development of three semantic analysis approaches: Semantic Utility Network (SUNet), Fusion-Semantic Utility Network (Fusion-SUNet), and Panoptic-Semantic Utility Network (Pan-SUNet). Encouraged by the performance improvements of SUNet and Fusion-SUNet, Pan-SUNet achieves outstanding results, boasting a 94% F1-Score for pylons, 99% for ground, vegetation, and powerlines, and demonstrating high precision in 3D object detection. Experiments conducted on Teledyne Optech's Galaxy T1000 dataset, which features diverse voltage transmission lines, validate the effectiveness of Pan-SUNet, particularly when combined with the RandLA baseline. Significant improvements are observed, including an increase from 80% to 85% in F1-Score for pylons, 98% to 99% for ground, 93% to 97% for powerline, 75% to 78.3% for other objects, 86% to 88% for buildings, while maintaining 98.2% for high vegetation and 93% for medium vegetation. The key contribution of this research is a significant advancement beyond basic object classification, as it not only identifies the class of an object but also distinguishes between different instances of the same class. This instance segmentation is critical for utility network modelling and simulation. The research emphasizes the importance of external cues such as contextual reasoning, spatial cognition, and physical priors for multiscale fusion in scene understanding systems. Moreover, the study's adaptability to integrate proposed contributions into existing networks enhances their overall performance, making them network-agnostic

Performance Assessment and Retrofit Strategies for Unreinforced Masonry Structures

This research project provides a detailed investigation into the seismic assessment of Unreinforced Masonry (URM) heritage structures, emphasizing their cultural and historical importance and vulnerability to earthquakes. It focuses on advanced seismic evaluation methods, including 3-D finite element modeling, to address specific challenges such as the unique characteristics of these buildings, like distributed stiffness and mass, and the absence of diaphragm action. The project aims to bridge knowledge gaps in the dynamic, nonlinear behavior of URM structures during seismic events, and involves comparative evaluation of different computational modeling methods. Additionally, it addresses the calibration and validation of computational models, analysis of dynamic response and failure characteristics, and evaluates retrofitting strategies to mitigate seismic risks. The research aligns with international preservation conventions and aims to contribute to the effective seismic risk mitigation strategies for unreinforced masonry heritage buildings

Applications and Implications of AI in East Asian Libraries

This presentation was delivered at the Council of East Asian Libraries, Association for Asian Studies Annual Conference 2024.Artificial intelligence (AI) has entered library areas such as search and information discovery, collection management, academic publishing, information literacy, and references services. With the rise of generative AI such as ChatGPT, AI becomes a hot topic in academia. A deeper understanding of this technology and how it affects academic libraries becomes more important than ever. The objective of this presentation is to investigate and reflect critically the application of AI in East Asian libraries. To address the issues of privacy, data security, academic integrity, and threats to job, it is critical to involve information professionals in the development and stewardship of this technology