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ESG Rating Improvements Through Mergers and Acquisitions: Do Low ESG-Rated Firms Strategically Acquire Their High ESG Rated Peers?
Get PDFThis paper examines the role of ESG ratings in a merger and acquisition (M&A) context. It attempts to answer two research questions: 1) whether mergers and acquisitions help improve the ESG performance of poorly ESG-rated acquiring companies that acquire target firms with a higher ESG ratings, and 2) whether the market places a premium on the acquisition of high ESG-rated firms. To test the related hypothesis, for our first research question, we consider the three ESG pillars (environmental, social and governance performance) along with the ESG combined score and the ESG controversies score and examine the impact of a given M&A deal on ESG-ratings one year after the acquisition, in line with prior research that shows that ESG rating changes take time to be incorporated within the acquiring firm. We find mixed results: only certain ESG rating factors change after an acquisition, whereas others do not. For the second part, we estimate two types of regressions; one that focuses on deal premium and how it relates to the ESG ratings differential between the acquiring firm and the target firm, and one that relates the cumulative abnormal returns (CARs) to the ESG ratings differential. In the first regression, we find significant evidence of the deal premium being positively impacted by the ESG ratings differential, while the second regression shows that the ESG ratings differential does not necessarily lead to higher CARs for acquiring firms
Triple Product p-adic L-functions for Finite Slope Families and A p-adic Gross-Zagier Formula
Get PDFIn this thesis, we generalize the p-adic Gross-Zagier formula of Darmon-Rotger on triple
product p-adic L-functions to finite slope families. First, we recall the construction of triple product
p-adic L-functions for finite slope families developed by Andreatta-Iovita. Then we proceed to
compute explicitly the p-adic Abel-Jacobi image of the generalized diagonal cycle. We also
establish a theory of finite polynomial cohomology with coefficients for varieties with good
reduction. It simplifies the computation of the p-adic Abel-Jacobi map and has the potential to be
applied to more general settings. Finally, we show by q-expansion principle that the special value
of the L-function is equal to the Abel-Jacobi image. Hence, we conclude the formula
Exact Algorithms for Network Fortification and Design Problems under Stochastic Interdiction
No full textNetworks such as telecommunication, transportation, and logistics play a vital role in sustaining social, economic, and industrial operations. However, networks are at risk of natural and man-made disruptions. With the increasing interconnectivity of networks, a disruption in one network can negatively affect other networks. We use network interdiction models to analyze the effects of disruptions on the networks. Interdiction models represent a two-player sequential game between the interdictor, seeking to maximize damage, and the network operator, striving to optimize operations after interdiction. Network interdiction models identify the critical nodes and/or arcs of the network and can be extended to fortify the existing networks or design resilient networks. In this thesis, we study the design of distribution and multicommodity networks and the fortification of spanning trees under stochastic interdictions.
The first paper investigates the distribution network design problem considering the effect of interdictions. Since interdiction outcomes can be uncertain in the real world, we consider the interdiction outcome as an uncertain parameter. We extend the model to consider the correlated facility interdictions where interruptions at one facility affect the nearby facilities. We Benders decomposition algorithm. Improved by two acceleration techniques to solve the model.
In the second paper, we focus on the design of a multicommodity network with interdictions. The designer does not have information about the interdiction resources; therefore, we consider the uncertainty in the number of interdictions by presenting a tri-level stochastic mathematical model. We present a branch-and-Benders-cut (BBC) algorithm enhanced by several acceleration techniques to solve the model.
In the third paper, we study the fortification of minimum spanning tree (MST) and optimum communication spanning tree (OCST) problems under stochastic number of interdictions. The MST aims to connect all nodes in a graph with minimal installation cost while satisfying communication requirements with minimum communication cost. The goal is to find the optimal fortification strategy so that the increase in the MST/OCST costs due to the interdiction of unfortified edges is minimized by presenting a tri-level stochastic model. We use backward sampling framework with acceleration technique to solve the deterministic and stochastic MST/OCST fortification problems
Constrained Predictive Control Strategies for Feedback-Linearized Autonomous Wheeled Vehicles
Get PDFAutonomous vehicles are becoming increasingly widespread in various real-world applications, ranging from manufacturing and transportation to search and rescue operations. To perform these tasks effectively, it is crucial for the vehicle to be capable of solving trajectory tracking, path following, and obstacle avoidance problems. To improve the accuracy of the performed trajectory, the input constraints acting on the robot’s model should be directly included in the control design. Unfortunately, many of the available control algorithms are unable to do so.
In the last two decades of research, Model Predictive Control solutions have been developed to solve the considered control problems for autonomous wheeled vehicles. Nonlinear MPC schemes exploit accurate state predictions, however, the underlying computational demand might not be affordable in strict real-time contexts or when the robot's computation capabilities are limited.
Conversely, linearized MPC approaches, have the important advantage of drastically reducing computational burdens at the expense of more conservative control performance.
This research proposes a novel control paradigm to solve trajectory tracking, path following and obstacle avoidance problems for input-constrained wheeled mobile vehicles. The proposed solutions are applicable to both differential-drive and car-like robots, and they are the result of the combination of Model Predictive Control strategies and feedback linearization techniques.
First, it is shown that if a feedback linearized model of the robot is exploited for predictions in MPC, then the set of admissible inputs for the linearized model is a nonconvex and state-dependant polyhedron, leading to non-convex and computationally expensive optimization problems with local minima issues. Then, a novel worst-case circular approximation of the state-dependent input constraints set is analytically derived and used to design reference tracking controllers that are, by design, recursively feasible and non-conservative.
The proposed predictive control paradigm has been successfully applied in real time to solve trajectory tracking, obstacle avoidance, and formation control problems for mobile robots and autonomous cars.
The effectiveness and benefits of the proposed control framework are shown with simulations and laboratory experiments involving the Khepera IV differential-drive robots and Quanser Qcar, and its performance contrasted with state-of-the-art alternative control solutions
Leveraging Stack Traces for Spectrum-based Fault Localization in the Absence of Failing Tests
Get PDFBug fixing is a crucial task in software maintenance to hold user trust. Although various automated fault localization techniques exist, they often require specific conditions to be effective. For example, Spectrum-Based Fault Localization (SBFL) techniques need at least one failing test to identify bugs, which may not always be available. Bug reports, particularly those with stack traces, provide detailed information on system execution failures and are invaluable for developers. This study focuses on utilizing stack traces from crash reports as fault-triggering tests for SBFL. Our findings indicate that only 3.33% of bugs have fault-triggering tests, limiting traditional SBFL efficiency. However, 98.3% of bugfix intentions align directly with exceptions in stack traces, and 78.3% of buggy methods are reachable within an average of 0.34 method calls, proving stack traces as a reliable source for locating bugs. We introduce a new approach, SBEST, that integrates stack trace data with test coverage to enhance fault localization. Our approach shows a significant improvement, increasing Mean Average Precision (MAP) by 32.22% and Mean Reciprocal Rank (MRR) by 17.43% over traditional stack trace ranking methods
Optimal Allocation of EVs in Electricity Distribution Network to Maintain Uniform Load
Get PDFAn electricity distribution network comprises of parking lots, electric vehicles, distribution grid, transformers, charging infrastructure, and customer locations. This thesis presents an optimization model for the optimal allocation of parking lots within a distribution system to efficiently supply electric vehicle (EV) loads. The model aims to determine the best capacity and size of parking lots to meet peak hour demands while considering constraints on the permanent operation of the distribution system. Using the Particle Swarm Optimization (PSO) algorithm, the study maximizes total benefits, taking into account data and market prices. Results show that installing parking lots could be economically profitable for distribution companies (DISCOs) and could improve voltage profiles.
The study also explores the impact of battery capacity and charging power rate variations on outcomes, emphasizing the importance of accurately determining these parameters. Additionally, the study highlights the advantages of the proposed approach, including improvements in voltage profiles, reductions in power flow, and enhancements in equipment lifespan. These benefits underscore the potential of the approach to optimize parking lot allocations for EV charging and improve overall distribution network performance and efficiency. Further implementation in suitable locations with appropriate sizes could yield significant technical and financial benefits
A Multimethod Approach to Resilience Against Alcohol Use, Depression, and Suicide among Indigenous Youth in a Northern Quebec Community
Get PDFColonization, historical loss and intergenerational trauma have given rise to mental health disparities among Indigenous communities. For over 100 years, assimilation policies have directly targeted Indigenous youth. While many Indigenous youth have thrived despite the experience of intergenerational trauma and ongoing colonization, problems with alcohol use, depression, and suicide risk continue to be reported. Yet, little research has looked at the temporal sequence of these mental health problems among Indigenous youth. In turn, interventions have often been based off of research among non-Indigenous youth, which have been limited at best. In turn, there is a need to return to Indigenous ways of knowing in order to promote the well-being of Indigenous youth. Using quantitative (Study 1) and qualitative (Study 2) studies, the goal of this dissertation was to develop a community-specific model of alcohol use, depressive symptoms, and suicide resilience among Indigenous youth in one Northern Quebec community. Study 1 (N=110) utilized a longitudinal design to examine change in alcohol use and negative affect (a symptoms of depression) and reciprocal associations in a sample of Indigenous youth. Results demonstrated that when an Indigenous adolescent drank more alcohol than expected at one time point, they reported higher levels of negative affect than expected at the following assessment. This may suggest that drinking alcohol precedes negative affect. Study 2 (N=14) utilized semi-structured interviews with community members to understand alcohol and suicide resilience from an Indigenous perspective. Through the voices of Indigenous people in the community, colonization was identified as the primary problem that led to alcohol use and suicide risk. Complementary to Study 1, most of the participants highlighted that drinking alcohol precedes suicidal ideations and behaviours. Connecting as a community and returning to living off of the land is where the participants believed recovery would be found. Taken together, both studies shed light on understanding alcohol use, negative affect and suicidality rooted in systemic factors and a continued call for supporting Indigenous peoples in revitalizing their cultures
Seismic Performance Assessment of Reinforced Masonry Core Walls with Boundary Elements
Get PDFReinforced masonry shear walls with boundary elements (RMSW+BEs) have emerged as a reliable seismic force-resisting system (SFRS) under the Canadian Standards for the Design of Masonry Structures (CSA S304-14) and the National Building Code of Canada for 2015 (NBCC 2015). While reinforced concrete (RC) core walls are commonly used as an SFRS in RC structures due to their ability to allocate staircases and elevators conveniently. The reinforced masonry core walls with boundary elements (RMCW+BEs) remain underexplored in seismic performance studies. The conservative shear strength calculations in CSA S304-14 limit the height of RM ductile shear walls as specified by NBCC, highlighting the need for further research into RM shear strength with varying design parameters.
This research is divided into two phases. Phase I, titled "Seismic Performance Evaluation of the System-level Response of RMCW+BEs," introduces the Applied Element Method (AEM) as a modeling technique to capture the cyclic behavior of fully grouted RM shear walls and the dynamic response of RM buildings. A new structural layout for RM buildings is proposed, with RMCW+BEs as the primary SFRS. The phase evaluates the seismic response of RMCW+BEs designed per CSA S304-14 provisions and examines the effect of higher modes of vibration on seismic performance of RM structures. The ductility and overstrength of the proposed system are quantified using nonlinear pushover analysis following FEMA P695 guidelines, and incremental dynamic analysis (IDA) is used to assess seismic collapse risk and to develop system-level-based fragility curves.
Phase II, titled "Experimental Investigation of the Component-Level Response of RMCW+BEs," includes diagonal tension tests on 41 masonry assemblages to evaluate the impact of different vertical and horizontal reinforcement ratios on RM shear strength. A quasi-static cyclic test on a C-shaped RMCW+BEs was conducted to simulate the first story response of a 12-story prototype building’s core wall.
The research demonstrates enhanced performance of RMCW+BEs under design-level earthquakes, meeting the ductility, overstrength, and deformation capacity requirements for a ductile SFRS. The findings support the adoption of RMCW+BEs as an effective SFRS in future North American masonry design standards
Cyber-security enhancement of wide-area monitoring, protection, and control systems
Get PDFWide-area monitoring, protection and control (WAMPAC) systems have emerged as a promising solution to improve situational awareness of power grid operators. WAMPAC systems collect system-wide measurements through communication infrastructure, synchronize using global positioning system (GPS) in phasor measurement units (PMUs), and utilize them to evaluate teh system operation condition and make appropriate real-time protection and control decisions. Despite teh provided advantages, teh reliance of WAMPAC systems on information and communication technologies (ICTs) makes them prone to various cyber attacks. Teh socioeconomic impacts of teh real-world cyber-attacks on power grids such as teh 2015 Ukraine power grid attack have prompted teh national level institutions to initiate several road-maps emphasizing teh necessity of new adoptions toward cyber security enhancement in teh North America power grids. To take step toward dis adaptation, dis thesis initially investigates teh vulnerability of wide-area applications in integrated power and gas systems (IPGSs) and proposes a preventive defense strategy and online neural network detection scheme. Afterward, dis thesis emphasizes on teh vulnerability of data aggregation standards and protocols in WAMPAC systems to time-synchronization attacks (TSAs). During prevention phase, dis thesis proposes a robust optimization model to obtain communication configuration between PMUs and control centers in order to minimize TSA consequences. Following teh prevention phase, dis thesis proposes an integrated TSA detection and mitigation scheme. Teh TSA detection scheme is a convolutional neural network (CNN) model which captures teh temporal correlation of data quality information to identify and localize TSAs. In teh mitigation phase, a new robust state observer mitigation scheme is proposed for wide-area control applications. Eventually, dis thesis emphasizes on vulnerability of grid supporting functions in inverter-based resources (IBRs) to a resonance cyber-attack. As a countermeasure, dis thesis proposes a new wavelet-enabled wide neural network model which not only detects resonance FDIAs on grid-connected IBRs, but also distinguishes them from normal system events. Numerical results from multiple test benchmarks demonstrate dat teh proposed prevention, detection, and mitigation schemes in dis thesis not only improve teh security of wide-area control applications but also do not compromise normal system performance
Essays on Non-GAAP Reporting
Get PDFThis dissertation consists of two essays, which explore the quantity of non-GAAP metrics, and one proposal, which investigates non-GAAP forward-looking metrics.
The first essay examines the determinants of the quantity of non-GAAP metrics disclosed in quarterly earnings releases using a hand-collected sample of non-GAAP disclosures from 2016 to 2020. Results show that managers are likely to disclose a larger quantity of non-GAAP metrics when their firms have more complex accounting reports and more extensive intangible assets. These findings suggest that when firms’ information environment is relatively poor, investors likely have a greater demand for additional information, and managers provide more non-GAAP metrics to respond. In a subsample where firms have missed analysts’ expectations, I find that when firms just miss the expectations, they are more likely to use a greater quantity of non-GAAP metrics, suggesting that managers’ self-serving incentives play a role in distracting investors’ attention by information overload.
The second essay explores the impact of the quantity of non-GAAP metrics on analysts’ forecast accuracy and dispersion. Results show that analysts’ forecast accuracy is increasing, and their dispersion is decreasing for firms with a larger quantity of non-GAAP metrics (or categories). Among the twelve non-GAAP categories, non-GAAP revenue, non-GAAP operating income, and non-GAAP tax rate are associated with more accurate and less dispersed earnings forecasts; however, return on invested capital increases the disagreement among analysts and leads to less accurate earnings forecasts. Furthermore, I find that a greater quantity of non-GAAP metrics/categories is particularly beneficial to analysts who have less general experience and cover more industries in their portfolios.
Last, I propose a study to explore non-GAAP forward-looking measures and primarily examine managers’ decisions to issue different quantities of non-GAAP forward-looking measures. In contrast to extant prior research on non-GAAP historical measures, studies on non-GAAP forward-looking measures are scant. This proposal intends to fill the void. In addition, to the extent that current regulations give managers broad discretion to issue forecasts that exclude certain recurring expenses and to rely on the “unreasonable efforts” exception to omit GAAP reconciliations, the findings of this study will also be of interest to standard setters