21090 research outputs found
Sort by
Exploring intersectoral collaboration in a community-based climate adaptation initiative: A qualitative case study of the Waterloo Region Heat, Cold and Air Quality Network (WRHCAN)
No full textBackground: An increase in the frequency and severity of extreme weather and poor air quality events due to anthropogenic climate change has resulted in negative implications for human and planetary health. Understanding the role of local public health units in the development and implementation of community-based climate change adaptation initiatives can minimize the health impacts of climate change and enhance community resilience. This study aimed to explore the facilitators and barriers to intersectoral collaboration for Network partners of the Waterloo Region Heat, Cold and Air Quality Network (WRHCAN), a public health led climate adaptation initiative.
Methods: A community engaged research approach informed the study design and community-academic partnership between the University of Waterloo and Region of Waterloo Public Health (ROWPH). A case study methodology was applied to explore the processes of intersectoral collaboration in the context of the WRHCAN. Data sources included a focus group with ROWPH, 13 semi-structured interviews with Network partners, participant observation of two Network meetings, and a document review of selected WRHCAN internal documents. Recruitment was facilitated by ROWPH, and data collection and analysis were guided by the Bergen Model of Collaborative Functioning, a theoretical framework for intersectoral collaboration. All interviews were audio-recorded using Teams and transcribed verbatim. Interview data were thematically analyzed using a hybrid inductive-deductive approach.
Results: Network partners identified several facilitators and barriers, as well as contextual factors influencing intersectoral collaboration in the WRHCAN. For Network partners, facilitators included alignment with WRHCAN objectives, coordination by ROWPH, usefulness of communication products, and information and resource sharing with other Network partners. Barriers included accessibility of information and resources for vulnerable populations, and the need for more tailored training and response by frontline staff. Contextual factors included the housing and affordability crisis impacting Waterloo Region and the need to address the specific challenges of those experiencing substance use challenges, mental health concerns, and/or homelessness.
Conclusion: This study highlighted intersectoral collaboration as an approach that can be leveraged by local public health units in the design and implementation of community-based climate change adaptation initiatives. This study provides insights into the facilitators and barriers experienced by WRHCAN Network partners. The findings from this study can inform future climate change adaptation efforts that utilize intersectoral collaboration
Pretending Architecture: The Journey Towards Verne Station
No full textThis is a record of pretend architecture, a journey of fabricating fantasy in the form of a virtual environment that is an authentic fake. This is an exploration inspired by the many fictional stories that I have encountered in order to create an interpretation of a space station. Framed by the harsh reality of space and contrasted by idealistic viewpoints in film, literature and video games, the end result presented is a far cry from initial expectations. It is a means to an end; a way to explore architecture in outer space with the use of constructs.
Verne Station exists as fragments of experiences; attempts to understand and discover the intoxicating ideals of a limitless frontier ruled by the harshest of living conditions. By use of the machine, one has the ability to create complex virtual environments to simulate and visualize space architecture concepts; a field that has historically been inaccessible to many. By simulating different scales of artificial gravity design, the real-time exploration of designed spaces can facilitate a clearer understanding and more effective visual feedback of potential space architecture designs.
This is a thesis about coming to terms with not arriving at your original destination, the one you imagine and expect to reach, but instead, the real one which you never quite anticipated
Development of a 7-item Dietary Screener Questionnaire for Determining Intakes of Eicosapentaenoic and Docosahexaenoic Acid in Canadians
No full textThe intakes of the omega-3 polyunsaturated acids, eicosapentaenoic acid (EPA, 20:5n-3) and docosahexaenoic acid (DHA, 22:6n-3) are associated with various health benefits. The main dietary sources of EPA and DHA are seafood, but there are non-seafood sources that contain significant amounts of EPA and DHA. Determining the dietary intake of EPA and DHA can be challenging due to the sporadic nature of seafood intake. An online 7-item dietary screener has been used to estimate the DHA intake of pregnant women from both seafood and non-seafood sources in the United States. Using this screener as a template, a Canadian screener for estimating EPA and DHA intakes was developed. In Project 1, EPA values were added and DHA values revised to match those in the Canadian Nutrient File, the questions were reordered to focus on seafood commonly consumed, questions about eggs were expanded to include options for size and omega-3 enrichment, and a French version of the screener was developed. In Project 2, fatty acid quantitation analyses were completed on screener items when the Canadian Nutrient File EPA and DHA data was either missing or not consistent with literature. This included eggs (medium, large, extra-large, omega-3, and omega-3 plus), liver (beef, pork, and chicken), and chicken (regular and organic). For Project 3, the “Canadianized” screener was evaluated for errors using mock. To do this, intake information from the 24-hour dietary recalls in the 2015 Canadian Community Health Survey (CCHS) were extrapolated to bimonthly intakes and entered into the screener. The EPA and DHA intakes estimates from the screener agreed with those from the CCHS 2015 Nutrition, indicating that the Canadian screener is ready for a proper validation study in the future. Once validated, this online tool should be able to improve our ability to estimate the intakes of EPA and DHA by Canadians. In addition, the process that was used to “Canadianize” the screener can be used as a template top adapt the screener for different countries
Utilizing Existing Data to Measure Ecological Connectivity for Planning Southern Ontario’s Urban Growth: A Case Study of the Waterloo Region
No full textUrbanization is an increasing threat to global biodiversity. Urban areas are often thought to preclude native plants and animals but are capable of supporting some species if properly managed. Urban planning tends to focus on maximizing human benefits of the urban landscape; however, urban greenspaces can enhance ecological services for humans and promote natural species diversity. Habitat quantity and quality should be the top priorities when managing urban greenspaces. In urban areas, quantity and quality may be limited by the area available, so other tools are needed to make advancements. Connectivity represents a metric that could help plan urban greenspaces.
To explore the utility of connectivity tools for cities in Southern Ontario, resistance maps were developed for Kitchener, Ontario based on four animals (bats, deer, shrews and snakes) using 2019 aerial data. Scenarios were developed based on potential changes to the city by increasing either the number of habitat cells by 5% or 10% (showing potential backyard and small greenspace restorations) or the number of buildings cells to meet projected growth targets. These were created by selecting cells randomly and reassigning values based on desired fragmentation of the land type. The resulting resistance maps were analyzed using an “omnidirectional” method developed for Circuitscape that enabled landscape level analysis of connectivity.
Urban connectivity differed for the four species based on the dispersal capability of each species with bats and deer having the most connectivity with maximum resistance values of 0.53 and 0.76 respectively and shrews and snakes the least connectivity with maximum resistance values of 1.07 and 1.33. Connectivity decreased with increasing urbanization, showing a gradient of increasing current as building density increased and urban green spaces decreased. All urban greenspaces, from yards to natural areas, were important for landscape connectivity and need to be maintained if not enhanced. Buildings represented the primary barrier for all species other than bats (due to their ability to fly over them). Roads and paved areas also posed barriers to all species and represented the strongest barrier for bats. Mitigation methods should be considered for these areas, with greenspaces planned through highly built areas.
Of the three models, increased building density had the largest effect on habitat connectivity, changing the resistance values by 25-33% for deer, shrew, and snakes. Bats species only had a 5-6% increase in resistance because buildings are less of a barrier to bats. The models with increasing habitat amounts were difficult to visually differentiate from the 2019 baseline, and changes in resistance value were less than 1%. These maps did show some benefits for urban species. This was expected due to the larger number of cells changed in the increased building density scenario. Planning mitigation efforts around densification should be the top priority for maintaining connectivity, but creating and maintaining greenspaces should not be forgotten as increasing habitat provides benefits beyond connectivity.
Overall, these results were expected, but this analysis did show the utility of connectivity mapping for Kitchener. Connectivity analysis is potentially a valuable tool for urban planners in Southern Ontario cities if habitat quantity and quality are already being maximized and with the caveat that connectivity planning should not justify the removal of existing habitat patches and care should be taken to avoid undervaluing small patches
Sociétés numériques 2025 – Rapport des résultats pour le Canada
No full textCe rapport contient les résultats détaillés, sous forme d'infographies et de tableaux, du sondage d'opinion publique canadien 2025 intitulé « Sociétés numériques », mené dans le cadre du module plus large du Programme international d'enquêtes sociales (ISSP) 2024.Nous remercions le Conseil de recherches en sciences humaines du Canada (CRSH) qui a financé cette enquête dans le cadre de son programme de subventions Savoir (subvention n° 435-2023-0100)
Smart Light Therapy Glasses for Sleep, Cognition, and Mental Wellness
No full textFor the past century, people have gradually transitioned to spending their time indoors where, compared with the outdoors, light levels are significantly lower during the day and higher at night. This mismatch of lighting has been associated with related health problems like rising depression rates and sleep issues. As such, bright-light therapy has been established as a treatment for mood and sleep disorders. However, existing devices face major barriers to adoption; light boxes require people to stay in one place for a long time, and wearable products often lack social acceptability. As a result, the research literature has been constrained to short interventions with limited exploration of dose, duration, and individualized understanding of response to light. This thesis presents the design, engineering, and clinical evaluation of a pair of smart light therapy glasses (Lumos glasses) developed to improve convenience, social acceptability, and comfort. The hardware provides the foundational infrastructure for an intelligent, data-driven approach to personalized circadian health. The glasses use a nanotechnology lens with wavelength-based reflection, which allows key light therapy components to be compacted into a classic glasses shape. A hardware-software platform was developed featuring calibrated light therapy optical systems, on-device sensors for reliable wear detection, melanopic ambient light detection, an FCC-approved Bluetooth module with a custom antenna, as well as a compatible cloud-connected mobile app. More than 100 units were manufactured and deployed in a double-blind, randomized, placebo-controlled crossover clinical trial. Participants receiving bright-light showed significant improvements in sleep disturbance (PROMIS) and psychomotor vigilance (PVT) relative to active dim-light control. Stratified analyses revealed that participants with darker eyes generally exhibited more significant improvements under bright-light compared to dim-light control. In contrast, mood (using the Montegomery Asberg Depression Rating Scale) and working memory (word-pair recall) reached statistical significance only after accounting for eye color. Exploratory models also showed that daily use and exposure to ambient light were correlated with improved outcomes, while higher baseline severity strongly predicted room for change. Age and sex contributed smaller, secondary effects. For working memory, dark-eyed participants showed significant Phase~1 gains under bright-light, while other subgroups demonstrated positive associations between daily usage and recall performance. This yielded meaningful insights that adherence and individual pigmentation influence optimal light dosage. This work demonstrates that mobile sensor-driven light therapy can overcome long-standing adherence barriers and enable in-depth research about dose–response and personalization. By combining engineering innovation with clinical validation, the Lumos Smart Glasses provide a foundation for next-generation circadian health technologies that are practical, effective, and scalable
Translocation-Induced Shape Transitions in Vesicles using a Neural Network-Based Solver for the Helfrich Model
No full textThis thesis discusses our efforts to model the translocation of an enclosed lipid bilayer membrane (vesicle) through a circular pore. First, we will discuss the study of lipid bilayers, introduce the standard model for representing the energy of a membrane, and provide background on the many theoretical and experimental efforts in the field of membrane modeling. We then review the relevant theoretical and practical considerations
regarding the simulation of vesicles and translocation, and implement a neural network-based solver for a scalar phase field. We will proceed to detail our efforts to characterize each constraint imposed on the vesicle throughout the translocation and model them within the context of the solver. Following this, we provide a variety of visual snapshots of the translocation process showing different classes of translocation and the resulting behavior of each. Equally important is the quantitative analysis of the energy landscape traversed by the vesicle, where we chart the induced bending energy imposed upon it by the narrow pore. Additionally, we introduce two types of external effects that modify the energy landscape and illustrate their impact on the total vesicle energy throughout its passage. We then map the results out onto the relevant parameter space to give a picture of where the thresholds between qualitatively different behaviors lie. As a final demonstration of our model’s capabilities, we estimate the time of passage of the vesicle by modeling it diffusively using the energy landscape to calculate the effect of narrower pores on the time to translocate. This model successfully demonstrates explicit phase transitions between stable vesicle states and maps out the energy landscape throughout the unstable regime under the effects of translocation
Semantically Consistent Alignment for Novel Object Discovery in Open-Vocabulary 3D Object Detection
No full text3D object detection is a fundamental task in the autonomous driving perception pipeline, where identifying and localizing objects within the surrounding environment is critical for safe and robust decision-making. However, traditional 3D object detectors are limited by their reliance on a closed set of training categories, rendering them incapable of recognizing novel or out-of-distribution objects encountered in open-world driving scenarios. To address this limitation, the field of open-vocabulary (OV)-3D object detection has emerged, aiming to generalize beyond predefined label sets by leveraging vision-language models (VLMs) to align 3D object proposals with semantically rich 2D language-informed features.
Despite promising results, a major challenge in OV-3D object detection lies in achieving robust cross-modal alignment between 3D and 2D features, which is often compromised by noisy annotations, occlusions, and resolution inconsistencies that disrupt semantic coherence. In this thesis, we present OV-SCAN, a novel framework for Open-Vocabulary 3D object detection that enforces Semantically Consistent Alignment for Novel object discovery. OV-SCAN introduces a two-stage strategy: (1) discovering precise 3D annotations for novel objects using vision-language supervision, and (2) filtering out semantically inconsistent or low-quality 3D–2D training pairs that arise from annotation errors and sensor limitations.
We validate the effectiveness of OV-SCAN through comprehensive experiments on autonomous driving benchmarks, where our framework consistently outperforms existing methods in the OV-3D object detection task. Overall, OV-SCAN underscores the critical role of semantic consistency in cross-modal alignment and demonstrates its potential as a scalable solution for discovering and localizing novel objects in real-world autonomous driving scenarios
NP-hardness of testing equivalence to sparse polynomials and to constant-support polynomials
No full textGiven a list of monomials of a n-variate polynomial f over a field F, and an integer s, decide whether there exists an invertible transform A and a b such that f(Ax + b) has less than s monomials. This problem is called the Equivalence testing to sparse polynomials (ETsparse). It was studied in [GrigorievK93] over Q, in this work, they give an exponential in n^4 time algorithm for the problem. The lack of progress in the complexity of the problem over last three decades raises a question, is ETsparse hard? In this thesis we give an affirmative answer to the question by showing that it is NP-hard over any field.
Sparse orbit complexity of a polynomial f is the smallest integer s_0 such that there exists an invertible transform A such that f(Ax) has s_0 monomials. Since ETsparse is NP-hard hence computing the sparse orbit complexity is also NP-hard. We also show that approximating the sparse orbit complexity upto a factor of s_f^{1/3-\epsilon} for any \epsilon \in (0,1/3) is NP-hard, where s_f is the number of monomials in f. Interestingly, this approximation result has been shown without invoking the celebrated PCP theorem.
[ChillaraGS23] study a variant of the problem which focus on shift equivalence. More precisely, given f over some ring R (the input has the same representation as in ETsparse) and an integer s, does there exists a b such that f(x + b) has less than s monomials. It is called the SETsparse problem, [ChillaraGS23] showed that SETsparse is NP-hard when R is an integral domain which is not a field; we extend their result to the case when R is a field.
Finally, we also study the problem of testing equivalence to constant-support polynomials; more precisely, given a polynomial f as before and with support \sigma, does there exists an invertible transform A such that f(Ax) has support \sigma -1. We call this problem ETsupport. We show that ETsupport is NP-hard for \sigma >= 5 and over any field
Development and Optimization of Terahertz Detection Systems
No full textTerahertz (THz) technology, occupying the spectral range between microwaves and infrared radiation (0.1–10 THz), has rapidly emerged as a compelling focus of scientific and engineering research. Characterized by its non-ionizing nature, strong penetration through non-metallic materials, and high sensitivity to molecular composition and water content, THz radiation offers significant potential across a broad spectrum of applications.
THz detection systems form the foundational core of THz technology platforms, serving the critical function of converting incident terahertz radiation into quantifiable electrical signals. The effectiveness and applicability of THz technology depend heavily on the performance of these detection systems. However, current THz detection technologies remain limited in several critical aspects, including sensitivity, accuracy, temporal resolution, and scalability. Specifically, weak interaction between THz waves and detectors often restricts signal detection, while environmental noise and material inconsistencies reduce measurement fidelity. Furthermore, the relatively slow response times hinder the real-time capture of dynamic biological and communicational processes. These challenges pose substantial barriers to the development of high-performance THz systems, constraining the practical implementation of THz technology's immense potential across various fields.
In response to these challenges, this research presents a comprehensive exploration of advanced THz detection strategies, introducing multiple technological innovations aimed at achieving accurate, sensitive, fast, compact, and cost-effective THz detection solutions. Central to this work is the design of a novel graphene-integrated microbolometer, forming the core of a THz Microbolometer Array Imaging System (MAIS). This microbolometer features an optimized structural design and tailored material composition, significantly improving responsivity, detectivity, and response time. This innovative microbolometer design not only sigfinicantly enhances the THz detection performance, but also establishes a solid foundation for advancing THz imaging applications. Complementing detection methodology development, a Micro Circular Log-Periodic Antenna (MCLPA) was designed and optimized using a custom-developed Evolutionary Neural Network (ENN). This algorithm-driven approach enables efficient optimization of the sophisticated antenna design, resulting in a compact structure with broad bandwidth, high gain, and optimal impedance matching. This ENN-driven MCLPA represents a significant breakthrough in THz antenna engineering, introducing a transformative design paradigm that synergistically integrates algorithmic intelligence with structural innovation.
In conclusion, this work significantly contributes to overcoming key limitations in THz detection by integrating advancements in novel device architecture, advanced material engineering, and innovative algorithm-driven design methodology. These innovations collectively enhance sensitivity, accuracy, response speed, system compactness and cost-effectiveness, representing a considerable step forward in the performance of THz detection systems. Beyond technical improvements, the results provide a solid foundation for practical implementation in biomedical and other high-impact applications. Overall, the contributions made herein substantially advance the development of THz technology and offer promising pathways for its transformative application across scientific, industrial and clinical fields