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Corporate resilience and financial performance following climate-induced events: Evidence from the US
Get PDFThis thesis examines the role of environmental, social, and governance (ESG) factors in providing firms with financial resilience to climate-induced events. Using the exhaustive NCEI and EM-DAT database of natural disasters and Refinitive and Bloomberg ESG scores from 2009 to 2019, I show that firms with higher ESG scores experience lower cumulative abnormal returns (CARs) and are then less resilient in terms of performance. This finding contrasts with prior studies that frequently find a positive relation between ESG ratings and corporate resilience. High ESG scores do not bring resilience to firms operating in primary affected states. However, in terms of volatility firms with higher post-ante ESG scores, return on assets (ROA), size, and Tobin’s q are less volatile and thus more resilient to the exogenous shocks of natural events, while undervalued firms are more volatile. The results are persistent across firms operating in various industries and primary affected states
Non-Intrusive Load Monitoring using Machine and Deep Learning Techniques
Get PDFNon-intrusive Load Monitoring (NILM) is a computational technique that extracts individual appliance consumption and operation state change information from the aggregate power consumption made by a single residential or commercial unit. This technique has emerged as a reliable energy management approach that intends to reduce energy wastage and inform customers about their electricity consumption. NILM is considered as both Supervised and Semi-supervised Learning problems. The main contribution of this thesis is three-fold.
First, we evaluated some regression algorithms commonly used in NILM research based on 8 different training and testing scenarios which according to our knowledge covered major demographic factors that affect the appliance usage. The dataset used for the evaluation of the regression models, was collected from a research lab at Grenoble INP, in Grenoble, France. Furthermore, a novel Bayesian optimized Ensemble regressor model for predicting individual appliance consumption from aggregated load data is also proposed. Instead of just using the aggregated power information, the proposed model also uses demographic information from the dataset to estimate accurate consumption output of individual appliances.
NILM research often requires significant labeled data and obtaining such data by installing smart meters at the end of consumers’ appliances is laborious and expensive and exposes users to severe privacy risks. Moreover, most NILM research uses empirical observations instead of proper mathematical approaches to obtain the threshold value for determining appliance operation states (On/Off) from their respective energy consumption value. The second fold of the thesis proposes a novel semi-supervised multilabel deep learning technique based on Temporal Convolutional Networks (TCN) and Long short-term memory (LSTM) for classifying appliance operation states from labeled and unlabeled data. The two thresholding techniques, namely Middle Point Thresholding and Variance Sensitive Thresholding, which are needed to derive the threshold values for appliance operation states, were also compared thoroughly. The proposed models were then evaluated using Redd, Uk-Dale and Refit datasets.
Third, we propose a novel NILM algorithm that utilizes deep learning Temporal Convolutional Networks (TCN) for the regression and classification NILM tasks. Most NILM models cannot simultaneously classify appliance operational status or estimate individual appliance power consumption. The deep TCN layers in the proposed architecture of the third fold of the thesis allow the simultaneous extraction of complex patterns in the data of the power consumption and the operational state of individual appliances. Refit data is used for the evaluation of this model
Hybrid Wearable Signal Processing/Learning via Deep Neural Networks
Get PDFWearable technologies are gaining considerable attention in recent years as a potential post-smartphone platform with several applications of significant engineering importance. Wearable technologies are expected to become more prevalent in a variety of areas, including modern healthcare practices, robotic prosthesis control, Artificial Reality (AR) and Virtual Reality (VR) applications, Human Machine Interface/Interaction (HMI), and remote support for patients and chronically ill patients at home. The emergence of wearable technologies can be attributed to the advancement of flexible electronic materials; the availability of advanced cloud and wireless communication systems, and; the Internet of Things (IoT) coupled with high demand from the tech-savvy population and the elderly population for healthcare management. Wearable devices in the healthcare realm gather various biological signals from the human body, among which Electrocardiogram (ECG), Photoplethysmogram (PPG), and surface Electromyogram (sEMG), are the most widely non-intrusive monitored signals. Utilizing these widely used non-intrusive signals, the primary emphasis of the proposed dissertation is on the development of advanced Machine Learning (ML), in particular Deep Learning (DL), algorithms to increase the accuracy of wearable devices in specific tasks. In this context and in the first part, using ECG and PPG bio-signals, we focus on development of accurate subject-specific solutions for continuous and cuff-less Blood Pressure (BP) monitoring. More precisely, a deep learning-based framework known as BP-Net is proposed for predicting continuous upper and lower bounds of blood pressure, respectively, known as Systolic BP (SBP) and Diastolic BP (DBP). Furthermore, by capitalizing on the fact that datasets used in recent literature are not unified and properly defined, a unified dataset is constructed from the MIMIC-I and MIMIC-III databases obtained from PhysioNet. In the second part, we focus on hand gesture recognition utilizing sEMG signals, which have the potential to be used in the myoelectric prostheses control systems or decoding Myo Armbands data to interpret human intent in AR/VR environments. Capitalizing on the recent advances in hybrid architectures and Transformers in different applications, we aim to enhance the accuracy of sEMG-based hand gesture recognition by introducing a hybrid architecture based on Transformers, referred to as the Transformer for Hand Gesture Recognition (TraHGR). In particular, the TraHGR architecture consists of two parallel paths followed by a linear layer that acts as a fusion center to integrate the advantage of each module. The ultimate goal of this work is to increase the accuracy of gesture classifications, which could be a major step towards the development of more advanced HMI systems that can improve the quality of life for people with disabilities or enhance the user experience in AR/VR applications. Besides improving accuracy, decreasing the number of parameters in the Deep Neural Network (DNN) architectures plays an important role in wearable devices. In other words, to achieve the highest possible accuracy, complicated and heavy-weighted Deep Neural Networks (DNNs) are typically developed, which restricts their practical application in low-power and resource-constrained wearable systems. Therefore, in our next attempt, we propose a lightweight hybrid architecture based on the Convolutional Neural Network (CNN) and attention mechanism, referred to as Hierarchical Depth-wise Convolution along with the Attention Mechanism (HDCAM), to effectively extract local and global representations of the input. The key objective behind the design of HDCAM was to ensure its resource efficiency while maintaining comparable or better performance than the current state-of-the-art methods
An Improved Approach for Extracting Frequently Extracted Code Idioms
Get PDFSource code refactoring is a process of restructuring or changing the existing codes without changing their external behaviour. This is a continuous process done by the developers to improve code quality, readability, maintainability of the source code, and address technical debt. There have been studies and tools to aid developers to refactor effectively their source code and to understand the motivations behind refactorings applied by developers. We aim to find Code Idioms that developers tend to refactor more frequently and investigate whether there are unique refactored code idioms for production code and test code. We use the RefactoringMiner tool to detect and collect EXTRACT METHOD refactoring from the commit history of the projects and propose a technique to represent the code fragments as structure-preserving context-free independent graphs and apply graph-similarity measure techniques to find similar code idioms among 65,742 EXTRACT METHOD instances. We measure both exact matching and partial matching with constraint checking from the associated metadata of the nodes and edges of the graphs. We divide our data set into production code and test code and found a total of 489 code idiom patterns. We present in detail 22 of the most frequently refactored code idioms. There are unique patterns to production code and test code and patterns shared among them. We limit our study to only Java-based open-source projects and EXTRACT METHOD refactoring, but we believe the approach can be applied to other object-oriented languages or refactorings. The findings can be useful to design an effective refactoring recommender system, help developers gain confidence in refactoring recommendation tools, and help researchers understand refactoring motivations and API usage patterns
Technological Change and Older Workers
Get PDFThis study investigates the influence of age on perceived technological competence and interpersonal skills in the workplace. We aimed to challenge common age-related stereotypes and explore the potential effects of individual variability within the older adult population. Using an online experiment and survey method, we examined participants' assessments of a younger versus older hypothetical worker's technological competence. Our findings revealed no significant difference between the perceived technological competence of older and younger workers, suggesting that age stereotypes may be less prevalent than typically assumed. Contrary to previous research, our data indicated that older participants did not hold negative attitudes towards their in-group members. The study further showed that age did not significantly influence self-reported technological learning satisfaction or self-efficacy about learning technology. Our research highlights the importance of addressing age-related stereotypes to foster a diverse and inclusive workforce. Furthermore, it emphasizes the necessity of creating an environment that supports and encourages employees of all ages to learn and adapt to new technologies, considering the increasingly multigenerational nature of workplaces. The study's findings have significant implications for organizational practices and policies, particularly concerning age diversity and technology-related competency. However, limitations related to sample representation and geographical constraints suggest a need for further research to extend and validate these findings
Investigating Impacts of Wood Harvest on the Canadian Boreal Forest Carbon Store
Get PDFEarth System Models provide important insight into global climate dynamics. These models often require large computational resources to run, inhibiting accessibility and exploration of a wide range of climate-related scenarios. Machine learning can help by creating an emulation of an aspect of an ESM to enable less expensive scenario simulation. I use a Long Short-Term Memory model to emulate forest carbon dynamics in the Community Earth System Model 2 in order to understand the impact of wood harvest on carbon stocks in the Canadian Boreal forest. To validate the emulation, I use available external datasets that explicitly quantify carbon stocks in soil and above-ground biomass. The emulation can predict CESM2 several carbon stock variables accurately (0.89 R Score) and can be explained with important climatic relationships. I then create land-cover scenarios to simulate no wood harvest for the years 1984-2019. These scenarios show that 584 Mt C were lost to wood harvest over this period, with an additional 172 Mt C attributed to regrowth from wood harvest over the same period. The LSTM model I use in this study provides a more flexible approach to investigating land-use change impacts on carbon stocks by harnessing the power of both machine learning models and process-based ESMs. This approach can help understand land-use change scenarios that are not considered in large inter-model comparison efforts
Application of machine learning methodology to detect the potential for fluvial hazards to occur along river networks
Get PDFFluvial hazards of river mobility and flooding are often problematic for road infrastructure and need to be considered in the planning process. The extent of river and road infrastructure networks and their tendency to be close to each other creates a need to be able to identify the most dangerous areas quickly and cost-effectively. In this study we propose a novel methodology utilizing random forest machine learning methods and hydro geomorphic expertise to provide easily interpretable fine scale fluvial hazard predictions for large fluvial networks. The developed tools provided these predictions at reference points every 100 meters along the fluvial network of three watersheds within the province of Quebec, Canada and used variables focused on river conditions and to proxy hydro geomorphic processes such as sediment transport. Training/validation data was collected in four forms: field data, results from hydraulic and erosion models, government infrastructure databases, and hydro geomorphic evaluations using the 1-m DEM and satellite/historical imagery. First a subset of the reference points was manually classified then divided into training (75%) and validation (25%) datasets. Then the training dataset was used to train supervised random forest models. The validation dataset combined with extensive validation indices indicated the models were capable of accurately predicting the potential for hazards to occur. Metrics are extracted from the model to determine which variables are most important to predict each hazard. Finally, a methodology is proposed for a top-down hazard analysis of extensive fluvial networks to identify the most at-risk infrastructure/communities
Solutions Journalism vs. Solutions-Oriented: Intentionality of Canadian Alternative Media in Building Climate Solutions Frames
Get PDFAlternative media fill a unique role in the Canadian media landscape, reaching marginalized and local communities, and as early adopters of innovative journalism practices such as solutions journalism. Solutions journalism can be an effective tool to combat audience fatigue with problem-oriented news. This is especially relevant for issues like climate change, which often receives “doom and gloom” coverage. This study aims to understand how alternative media in Canada are using solutions journalism to cover climate change and other environmental stories, via an explanatory sequential mixed-methods research design. A content analysis of all climate change and other environmental stories published by six Canadian alternative media outlets in 2022 was conducted to determine the proportion of articles using solutions journalism, and the types of solutions included. This was followed by interviews with authors of selected solutions journalism articles to further understand the frame-building process of solutions journalism. Interviews were analyzed using thematic analysis. It was found that 38% of climate and environmental articles used some degree of solutions-oriented reporting, though only 12% were fully solutions journalism. This was further explained by interview results, which identified internal and external constraints to implementing solutions journalism. Though not all journalists recalled intentionally applying a solutions journalism framework, solutions-oriented reporting is prioritized in alternative media newsrooms, is perceived as beneficial to audiences and engagement, and is made easier through newsroom support
Natural Disasters and Agricultural Commodity Prices: Global Evidence
Get PDFUsing the prices of seven agricultural commodities over the period from 1980 to 2019, this study employs both an event study and GARCH modelling to capture whether and how natural disasters occurring in the main production centres of certain agricultural commodities affect their returns and price volatilities.
In a first step, we examine how natural disasters affect the prices and volatilities of the affected commodities. In a second step, we employ a series of ordinary least squares (OLS) regressions to examine what factors (e.g., disaster, commodity, and country characteristics) affect the abnormal return and abnormal volatility. Our study thus provides important insights for traders, hedgers, producers, and purchasers of agricultural commodities who are concerned about the rising risk of climate-induced events and how they may affect the agricultural commodity markets
Feasts and Fasts: Christina Rossetti’s Public Banquet and the Nineteenth-Century Politics of Femininity
Get PDFMore often than not, critical readings of Christina Rossetti’s “Goblin Market” tend to frame the poem as a revision of the female tale of temptation by focusing on the goblin men and fruit as their primary object of interpretation. This essay instead proposes to analyze Rossetti’s poem through its “food plots” consisting of more neglected elements and activities happening simultaneously around the goblin market. The feasts and culinary activities of Rossetti’s heroines in Speaking Likenesses, the poet’s response to Carroll’s Alice’s Adventures in Wonderland, are considered alongside those of “Goblin Market” in order to extricate the ways in which these so-called food plots represent scenes of celebration of feminine hunger and industriousness in a rapidly expanding world of commodities. Drawing mainly from works by scholars such as Michael Parrish Lee, Helena Michie, Ronjaunee Chatterjee, and John Ruskin, the essay intends to demonstrate how Rossetti makes use of representations of food consumption and of the labor surrounding its production in her application to imagine a world where the Victorian woman is free to be industrious in the development of her sense of individuality