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Framing Extreme Precipitation Events in the Context of Cumulative Emissions
Get PDFHeavy to extreme precipitation events are often-destructive forms of weather that, despite their infrequency, can lead to significant losses of human life and infrastructural damage. Such events are expected to increase in a warmer world as cumulative carbon emissions continue to rise. However, the extent to which this increase occurs varies considerably across scenarios and spatial scales, especially for the most extreme precipitation. The Transient Response to Cumulative CO2 Emissions (TCRE) has proven to be a powerful metric that characterizes the linear response of global mean temperature to cumulative carbon emissions, and previous research has shown its potential applicability to other climate indicators, such as regional temperature and precipitation, and heat extremes. By using simulations from nine Coupled Model Intercomparison Project Phase 5 (CMIP5) models, I intend to quantify extreme precipitation indices of one-day maximum (Rx1day) and five-day maximum (Rx5day) events against cumulative CO2 emissions. I show that the TCRE framework can be applied to represent changes in these precipitation extremes, with validation of this approach at sub-global scales across emissions scenarios. In Chapter 3, I determine whether precipitation extremes respond linearly to cumulative CO2 emissions, at global to local scales, using simple linear regression modelling. In Chapter 4, I conduct a Generalized Extreme Value (GEV) analysis to model the behavior of the most extreme values of Rx1day and Rx5day and evaluate whether trends in location parameter estimates and specified return levels can be approximated by (regional) TCRE values. For Chapter 5, I extend this analysis to estimate remaining carbon budgets (RCBs) associated with avoiding particular extreme precipitation levels. Overall, my results suggest that extreme precipitation work well within a TCRE framework, and that global and sub-global changes can be well approximated by linear responses to cumulative CO2 emissions, though with less robustly linear trends at local scales. My results further highlight that location parameter estimates and return levels of Rx1day and Rx5day scale approximately linearly to increasing cumulative carbon emissions. My findings also show that RCBs are generally small to avoid specified present-day 20-year and 100-year return levels. This suggests that such events are becoming commonplace with global warming
The Impact of Foreign Direct Investment on Non-Performing Loans in Bangladesh
Get PDFNon-performing loans (NPL) and foreign direct investment (FDI) are pivotal financial parameters exerting contrasting effects on economic performance, particularly in developing and emerging economies. Foreign direct investment, known for stimulating economic activity, has the potential to reduce the number of non-performing loans. This paper investigates how foreign direct investment influences non-performing loan dynamics using data from Bangladesh spanning from 1991 to 2022 alongside key macroeconomic variables. The findings indicate that, overall, FDI has a very minimal positive impact on NPL, underscoring that the relationship between foreign investment and banking stability is complex and likely influenced by multiple factors beyond just FDI
Freeze-Thaw Damage Assessment of Internally Insulated Historic Brick Masonry Walls Under Canada’s Future Climates
Get PDFCanada has taken steps to address climate change and protect heritage buildings by setting energy reduction targets and ensuring occupant comfort. Whereas internal insulation systems have emerged as a potential strategy to address these challenges, the use of such systems may also increase the risk to freeze-thaw (FT) damage of the exterior wall assembly and thereby lead to long-term deterioration of historic brick walls due to reduced drying capacity. Current standards provide general heritage preservation advice, but more specific technical guidance is needed to enhance thermal performance, ensure wall durability with interior insulation, and address climate change impacts on the masonry system. The information provided in this study is to contribute to the existing body of knowledge related to the long-term performance of historic masonry walls, by examining the FT damage of internally insulated historic brick masonry walls under a changing climate. In this study, recommendations are provided for optimal insulation selection to minimize freeze-thaw damage.
Typically, a 30-year period is recommended to evaluate the long-term effects of climate change on building envelopes. However, an alternative approach is to select a single moisture reference year (MRY) that can accurately assess moisture stress over time, reducing the time and costs of simulations with multiple climate parameters. This study assessed the reliability of of presently used climate-based indices for selecting an MRY to evaluate the risk to FT damage in internally insulated brick walls. Finding the existing methods inadequate, the study proposed an alternative approach based on hygrothermal simulations.
A parametric analysis was thereafter conducted to identify the key factors influencing FT damage in brick masonry walls. Simulations were conducted over a continuous 31-year period, as well as for each separate year, demonstrating no cumulative impact on annual FT cycles. The study determined that MRYs at the 93rd percentile severity could be employed for evaluating FT in retrofitting design decision-making.
By examining potential FT damage under different future climatic conditions and considering various factors, this research offers a decision-making process for internal insulation retrofit projects and proposes solutions when significant risk of FT deterioration is expected
A New Approach to Dating the Canadian Reference Cycle
Get PDFI evaluate the ability of the mixture multiple change-point model to establish Canadian business cycle turning points dates. Following the technique by Camacho, Gadea, and Loscos (2022) I asses the efficacy and feasibility of this dating method in achieving turning point dates by comparing it with the already announced dates by the C.D. Howe Institute Business Cycle Council. Using key monthly economic indicators spanning the last 34 years, I find that this methodology successfully identifies three business cycle dates for Canada, offering valuable insights into the peaks and recessions the Canadian economy has likely experienced in the past. Nevertheless, It yields significant disparities when compared with the chronology established by the Howe Institute
The Effect of Early Bilingualism on Executive Functions: A Training Study with the Early Executive Functions Questionnaire
Get PDFThe bilingual cognitive advantage states that bilinguals exhibit greater executive function (EF) abilities than monolinguals. This advantage has been reported in children as young as 6 months old yet has failed to be consistently replicated. Given that the bulk of the literature has used correlational designs, the present study adopted a training design which aimed to determine whether teaching monolingual children a second language will lead to greater increases in EF than if taught words in their native language. Two groups of children completed a 12-week online training program during which 9 translation equivalents (TEs; experimental condition) or 9 novel words in their native language (control condition) were taught weekly. Participants’ EF was compared pre- and post-intervention using the Early Executive Functions Questionnaire, which assesses working memory (WM), flexibility (FX), inhibitory control (IC), regulation, and cognitive executive function (CEF, which is a factor that loads onto WM, FX, and IC). Word learning was assessed weekly with a forced choice task based on pointing or touch. Results suggest that learning TEs is more difficult than learning new words in one’s native vocabulary. Results also indicate that although the total sample significantly increased in IC, FX, WM, and CEF from pre- to post-intervention there was no time by condition interaction indicating that the groups EF skills grew equivalently. Finally, only learning TEs was associated with improvement in working memory. To conclude, our results do not support a bilingual advantage at 25 months but suggest a link between second language acquisition and WM
Mencius on Xin and Xing
Get PDF‘Human nature’ is a widely discussed ethical concept in both philosophical traditions in the West and the East. In ancient China, Mencius, one of the most influential philosophers, claimed that human nature is good. However, he did not appeal to logic when presenting his argument, instead he provides seemingly anecdotal evidence to his claims. How can we provide a plausible interpretation for Mencius’ claim? One problem arises as one assess his arguments. Mencius held the belief that human nature, attributed to tian (‘Heaven’ or ‘Sky’), lies beyond human influence. However, he also contended that human nature embodies a normative aspect and is inherently virtuous. This raises the question of how Mencius reconciles the innate and normative aspects of human nature.
In my paper, I argue that xin, a concept introduced by Mencius, plays a vital role to understand his argument of human nature is virtuous. In Chinese philosophy, xin, though literally means heart, serves the dual functions of feeling and thinking. According to Mencius, the xin is where moral tendencies are rooted, and our moral actions are motivated. I argue that the very concept of xin, is fundamental for Mencius to reconcile the innate and normative aspects of human nature. It is also key for helping us to understand Mencius’ argument that human nature is good
Measuring the Immunomodulatory Effects of beta-Adrenergic Receptor Drugs on THP-1-Derived M1 and M2 Macrophage-Like Cells.
Get PDFMacrophages play a crucial role in coordinating the immune response and regulating inflammatory balance. They exist as two main phenotypes: M1 macrophages, which are pro-inflammatory and secrete cytokines like TNF to amplify the immune response, and M2, which are anti-inflammatory and promote tissue healing by secreting cytokines like IL-10. The balance between these phenotypes is essential for effective infection control, and disruptions can lead to autoimmune diseases, severe infections, and other inflammatory disorders. Understanding the modulation of these phenotypes is key for developing effective therapies.
This project investigates the interactions between the sympathetic nervous system (SNS) and the immune system by treating a monocyte-like cell line with β-adrenergic receptor (β-AR) agonist and antagonist drugs. While previous studies showed that β2-AR drugs have anti-inflammatory properties in macrophages (Kast, 2000; Szelenyi et al., 2000), research on their effects on M1/M2 macrophage phenotypes and receptor selectivity is limited. This project aims to address these gaps to better understand the interplay between the SNS and immune system.
β1- and β2-AR expression was confirmed in both macrophage phenotypes, with ADRB1 and ADRB2 genes upregulated in M1-like cells (with IFNγ) and downregulated in M2-like cells. Isoproterenol, a non-selective β-AR agonist, decreased TNF in M1-like cells and increased IL-10 in M2-like cells. These effects were reversed by the non-selective β-AR antagonist bupranolol but not by the β2-selective antagonist ICI 118,551. Terbutaline, a β2-selective agonist, showed similar trends and was reversed by both antagonists. This research demonstrates that SNS activity influences immune responses, promoting an anti-inflammatory profile through β-AR activation. These findings suggest potential therapeutic strategies using β-AR drugs for inflammatory and autoimmune conditions
Atomistic Simulations of Next-generation Durable Aerospace Materials: A Comparison between Nickel and Cobalt Oxides
No full textNickel and cobalt-based superalloys are extensively used in various industrial turbomachinery. In the aerospace industry, these alloys are employed as outstanding materials and quality coatings, exhibiting exceptional mechanical performance within the severe environments of gas turbine engines. The formation of nickel oxide (NiO) and cobalt oxide (CoO) during sliding contributes to lubricious tribofilms reducing friction and wear rates at contact interfaces. Despite their similar rock salt crystal structure with ionic bonds, NiO and CoO display distinct behaviors in mechanical and tribological terms. Experimental studies reveal the superior performance of the third body formed on Co-based alloys compared to that of Ni-based materials. However, computational investigations at the nanoscale are essential to analyze the different behaviors of these metal oxides.
In this dissertation, molecular dynamics (MD) simulations were carried out to evaluate the mechanical behavior of NiO and CoO under uniaxial compressive and tensile loading at room temperature (300 K). The combination of the Second Nearest-Neighbor Modified Embedded-Atom Method and the Charge Equilibration method (2NNMEAM+Qeq) potential was employed for each oxide to accurately present the interatomic forces and interactions within the ionic crystal structure. The compressive and tensile engineering stress-strain findings of the present study highlight the remarkable ability of CoO for high-strength applications where structural stability and resistance to permanent deformation are critical. CoO exhibits stronger performance compared to NiO under higher stress conditions. The use of durable materials and excellent oxide-based coatings could significantly enhance equipment efficiency, leading to fuel consumption reductions in aerospace applications
Deluxe or de-luxe? Exploring the effects of price discounting and brand messaging on luxury perceptions for new brands
Get PDFFor many products, brand managers use price promotions to address challenges such as diminishing growth or excess inventory. Luxury brands face the same challenges, but unlike other brands, they may face constraints in using price promotions given that luxury brands are generally associated with high price points. In particular, managers of new luxury brands may wish to use promotions as a means to acquire new customers; however, they are often concerned that reducing prices may affect consumers’ brand luxury perceptions. Is it possible for luxury brands to maintain luxury perceptions when discounting their products? While past research has shed light on the pros and cons of price promotions, little research has explored how different discount levels (low vs. high) may affect brand luxury perceptions and, more importantly, how brand messaging might buffer luxury brands against potential negative impacts of discounts. I hypothesize that brand messaging—i.e., an agentic (independent) vs. communal (interdependent) positioning—may affect consumers’ luxury perceptions at low and high discount levels, and that perceived strategic fit may drive effects. Across three experiments, participants were presented with display ads in which brand messaging and discount level were varied. While Study 1 yielded null effects, Study 2 revealed a marginally significant discount level x brand messaging interaction on luxury perceptions. Perceptions of strategic fit mediated these results. Finally, Study 3 attempted to replicate study 2 findings and explore the downstream effects of luxury perceptions on purchase intentions. In this study, luxury perceptions were primarily affected by discount level, while purchase intentions were primarily affected by brand messaging. My thesis discusses theoretical contributions, avenues for future research, and managerial implications related to price discounting for luxury brands
Automated Data Preparation using Graph Neural Networks
Get PDFThe process of data preparation is a time-consuming portion of data scientists' work. Being able to automate this work will improve the quality of the machine learning results and free data scientists to shift their focus to the machine learning task at hand. My research presents a system to automate this process by learning from the data preparation steps taken from others working on similar datasets. To automate data cleaning and transformation, datasets and their corresponding notebooks were extracted from Kaggle, their information was abstracted before being uploaded into a knowledge graph. Graph Neural Network (GNN) models were trained on those knowledge graphs, and the most commonly used cleaning and transformation operations for similar datasets were inferred. These operations are offered to the user as recommendations that they can apply to their dataset using the corresponding APIs. These recommendations have outperformed their state-of-the-arts counterparts in terms of time, memory consumption, and accuracy. To detect similarity inclusion dependencies (sIND), knowledge graphs from datasets in the Prague Relational Learning Repository were created. From those knowledge graphs, the columns deemed to have an inclusion dependency were studied until features leading to this dependency were observed. These features were used to create a model that could predict the sIND between columns. The resulting model was able to correctly predict more sIND pairs, in a shorter timespan than its competitor. This holistic platform can easily be integrated into any Data Science Pipeline (DSP) and facilitate the data preparation process for data scientists