PUBLICATIONS

Background: Social anxiety disorder (SAD) is a common mental health problem, and its core cognitive manifestation is the persistent fear of being evaluated, including both negatively (FNE) and positively (FPE). This study aimed to examine the longitudinal relationships of FNE, FPE and SAD and explore their neural basis. Methods: Three samples were retrieved in this study. First, the data of 649 college students who completed a survey and fMRI scan were used to explore the neural basis of FNE, FPE, and SAD symptoms. Next, the data of 450 participants who completed the same survey twice were used to examine the longitudinal relationships of the variables. Finally, the overlapping of the two samples (N = 288) who completed two surveys and the fMRI scan were used to establish a brain-behavior model. Results: Both FNE and FPE predicted SAD, and SAD also predicted FPE. The neural signals of subregions in prefrontal cortex were correlated with the scores of FNE, FPE and SAD. Abnormal prefrontal signals influenced SAD symptoms via fears of evaluation. Conclusions: Our findings explain the behavioral and neural underpinnings of social anxiety from a fear of evaluation angle. This contributes to a better theorical understanding of SAD and clinical practice.

Online word of mouth (WOM) helps consumers learn about sellers’ products/services quality and influences market competition. Some sellers, taking advantage of the anonymity of contributing consumers, fake consumer WOM to boost their products/services ratings. This research uses a game-theoretical model to examine sellers’ dynamic pricing and their review manipulation decisions in an online marketplace. We explore the critical drivers of review manipulation and how fake reviews shape the market outcome. Specifically, the model shows a self-inhibition mechanism of review manipulation, which prevents high-quality sellers and softly discourages low-quality sellers from faking reviews. Fake WOM reduces vertical seller differentiation and intensifies price competition. Moreover, negativity bias in genuine customer reviews enhances self-inhibition, especially for low-quality sellers. Consequently, sellers with extreme (very high or very low) quality do not fake reviews when consumers are more likely to write reviews for unsatisfying products/services.

In a large sample of developed and emerging markets, we show in a conditional setting that globally traded assets such as currencies and international bonds can proxy for global state variables. We find that, differently from market risk, intertemporal risk matters particularly at times when global markets are not in normal economic conditions. Relying on time-variation for prices of risk helps us capture the hedging component, especially the negative one, stemming from proxies like the yen and global sovereign bonds. Our results show that global uncertainty measured by realized world volatility is an important channel for intertemporal risk.

This study investigates the effect of social distancing on the local bias of institutional investors. Using SafeGraph's Social Distancing Metrics data and SEC's EDGAR 13F filings, we find that stay-at-home duration ratio decreases institutional investors’ local holdings and firms’ institutional ownership in the U.S. We also exploit the lockdown orders across various states during the COVID-19 pandemic as exogenous shocks to conduct the stacked regression estimation, which yields a similar result. Our channel analysis using abnormal return indicates that social distancing mitigates local bias by constraining the information advantage of local investors rather than alleviating their cognitive bias.

The COVID-19 pandemic - as a massive disruption - has significantly increased the need for medical services putting an unprecedented strain on health systems. This study presents a robust location-allocation model under uncertainty to increase the resiliency of health systems by applying alternative resources, such as backup and field hospitals and student nurses. A multi-objective optimization model is developed to minimize the system's costs and maximize the satisfaction rate among medical staff and COVID-19 patients. A robust approach is provided to face the data uncertainty, and a new mathematical model is extended to linearize a nonlinear constraint. The ICU beds, ward beds, ventilators, and nurses are considered the four main capacity limitations of hospitals for admitting different types of COVID-19 patients. The sensitivity analysis is performed on a real-world case study to investigate the applicability of the proposed model. The results demonstrate the contribution of student nurses and backup and field hospitals in treating COVID-19 patients and provide more flexible decisions with lower risks in the system by managing the fluctuations in both the number of patients and available nurses. The results showed that a reduction in the number of available nurses incurs higher costs for the system and lower satisfaction among patients and nurses. Moreover, the backup and field hospitals and the medical staff elevated the system's resiliency. By allocating backup hospitals to COVID-19 patients, only 37% of severe patients were lost, and this rate fell to less than 5% after establishing field hospitals. Moreover, medical students and field hospitals curbed the costs and increased the satisfaction rate of nurses by 75%. Finally, the system was protected from failure by increasing the conservatism level. With a 2% growth in the price of robustness, the system saved 13%.

Reinforced concrete block shear walls (RCBSWs) have been used as an effective seismic force resisting system in low- and medium-rise buildings for many decades. However, attributed to their complex nonlinear behavior and the composite nature of their constituent materials, accurate prediction of their seismic performance, relying solely on mechanics, has been challenging. This study adopts multi-gene genetic programming (MGGP)— a class of bio-inspired artificial intelligence, to uncover the complexity of RCBSW behaviors and develop simplified procedures for predicting the full backbone curve of flexure-dominated RCBSWs under cyclic loading. A piecewise linear backbone curve was developed using five secant stiffness expressions associated with: cracking; yielding; 80% ultimate; ultimate; and 20% strength degradation (i.e., post-peak stage) derived through mechanics-controlled MGGP. Based on the experimental results of large-scale cyclically loaded fully-grouted RCBSWs, compiled from previously reported studies, a variable selection procedure was performed to identify the most influential variable subset governing wall behaviors. Subsequently, the MGGP stiffness expressions were trained and tested, and their accuracy was compared to that of existing models employing various statistical measures. In addition, the predictability of the developed backbone model was assessed at the system-level against experimental results of two two-story buildings available in the literature. This study demonstrates the power of the MGGP approach in addressing the complexity of the cyclic behavior of RCBSWs at both component- and system-level—offering an efficient prediction tool that can be adopted by relevant seismic design standards pertaining to RCBSW buildings.

The amount of data accumulated by utility companies is growing in volume and variety each year. Such data can be a very valuable asset to utilities, but because it is generally viewed only through traditional tables and charts, it has not yet been used to its full potential. A utility can make better use of its data by employing machine learning techniques to recognize patterns that are hidden when traditional techniques are used. Association rule analysis is one of numerous machine learning techniques, that has been widely used in business and some engineering fields to identify important feature correlations previously hidden due to the volume of data under analysis, but it is not widely adopted in the asset management sector. This project applies association rule analysis to a historical transmission line outage event database through a resilience-based asset management lens, distinguishing patterns and developing rules that relate to the occurrence of input features together with long-duration outages.

In the past decades, the United States of America and Canada have witnessed a continuous increase in the frequency and magnitude of climate change-induced natural disasters. These events include droughts, floods, wildfires, and most recently, tornadoes. In 2016, climate change induced damage was estimated to be $8.6 billion in Canada, while in the United States of America, floods are becoming one of the costliest and highest in occurrence of all climate change induced hazards, costing an average of $8 billion dollars annually. Also, hurricanes such as hurricane Sandy cost over $67 billion dollars of total damage, while more recently. hurricane Florence resulted in an estimated damage of $5 billion so far. It is thus clear that the effect of climate change is already costing North Americans billions of dollars annually, at an increasing rate. Coupled with climate change, the expansive developments of urban areas are causing a significant increase in flood-related disasters worldwide. However, most flood risk analysis and categorization efforts have been focused solely on the hydrologic features of flood hazards (e.g., inundation depth and duration) without considering the resulting long-term consequences in terms of losses and recovery time, and thus the community’s flood resilience. The aim of this study is to develop a flood resilience classification system at a community level that can be used in the development of disaster managerial insights and risk mitigation measures, to better prepare urban areas from future flood risks. This data-driven model will categorize communities using Machine learning classification techniques, bypassing the complexity and probabilistic nature of physics-based models.

Increased severity and frequency of climate-induced disasters (CID) is affecting the resilience of cities worldwide. Canadian insurers are facing natural disaster-induced claims of approximately $1 billion annually, whereas it was closer to $400 million in the previous decade. Moreover, annual national liabilities of the Disaster Financial Assistance Arrangements have increased from $100 million annually in late 1990s to $500 million in 2009–2010 and reached approximately $2 billion in 2013–2014. To maintain their basic functions, Canadian cities have to maximize the resilience of their critical infrastructure systems under CID. In their previous work, the authors proposed a framework that focused on historical power outage data to quantify city resilience. The framework showed that CID are the main cause of power outages in North America. Subsequently, the aim of this work is to assess previous CID in an attempt to enhance the resilience of the power system as one of the most critical infrastructure systems in Ontario. The first part of this work involves employing descriptive data analysis to derive meaningful information from a CID database. Following that, an unsupervised machine learning technique will be employed to assess CID probability in Ontario on the spatial level. Finally, the resilience of hydroelectric power generators across Ontario will be deliberated based on the spatial analysis performed. This work is considered a step in CID assessment and prediction, based on historical hazard data, global climate models, and climate change measures, in an attempt to maximize city resilience and mitigate CID-induced risks on cities.

The southern Saskatchewan region has been recognized for persistent droughts due to its continual rainfall deficiency. To estimate drought severity, a first-order and five-state Markov chain model is used in this study. Long-term daily precipitation records of three stations (Broadview, Last Mountain CS, and Swift Current CDA) were considered for the analysis. The Standard Precipitation Index (SPI) was used to classify the drought severity into five different classes (no drought, mild drought, moderate drought, severe drought, and extreme drought) to develop Markov models. Transition Probability Matrices (TPM) were calculated for each of the stations, and the chances of drought occurrence were estimated from the steady state matrix. Expected drought duration, mean first passage time of drought, and mean recurrence time of drought were also calculated from TPM to understand the drought characteristics in the long run. The results indicate that mild drought has more than 35% chance of occurrence, and the expected drought duration of extreme drought can exceed over a month. Furthermore, for extreme drought, the mean recurrence time and the mean first passage time can be varied from 102.09 to 168.20 months and 14.47 to 20.20 months, respectively. The Markovian approach is an efficient method to estimate and understand the drought characteristics, providing crucial information in agricultural sectors, planning, and other related professional fields to adapt and plan accordingly.

An effective infrastructure asset management (AM) system is crucial for utilities, city officers, government agencies, and other asset-owning organizations to facilitate navigating the numerous challenges associated with operating and managing infrastructure assets. In this paper, the AM system itself is represented as a complex network (comprised of nodes and links) that describes the major components necessary for its operation within an organization and the information connections between such components. ISO 55001, a widely accepted international standard, specifies the requirements for an effective AM system and outlines the criticality levels of different system components - reflected in the corresponding network by the link weights. The main challenges facing managing an AM system (i.e., metamanagement) pertain to (1) information asymmetry (i.e., not relying on consistent information for decision making) between AM system components; and (2) information overload (i.e., excessive information undermining decision making) within the AM system components. These challenges cause systemic risks (possibility of dependence-induced disruptions) within the system network due to the connectedness of system components. Systemic risks can be mitigated through built-in network resilience by restructuring the system component connections. Such network reconfiguration presents a complex nonconvex optimization problem with multiple potential solutions depending on the number of new connections to be added to the AM system, the length of those connections, and the target risk mitigation level. Through this metamanagement (managing the management system) lens, a genetic algorithm approach was employed to explore the optimal AM network configurations considering different objective functions. These objective functions were based on different complex network measures including the betweenness-, closeness-, and eigenvector-centrality, as well as the vulnerability index. The devised objective functions were employed to the cases of adding 1 to 15 links only to limit network overconnectedness (i.e., information overload). Considering all objective functions evaluated, adding a small number of links (fewer than five) provided a significant reduction in systemic risk (18% to 49%). Finally, managerial insights are presented to explain how to employ the developed approach to mitigate the systemic risks within an organization's AM system based on different metrics valuations and stakeholder inputs.

Bisphenol A (BPA) is one of the most common contaminants of emerging concerns (CECs), which pose a threat to human health. Conventional wastewater treatment plants (WWTPs) are considered as the major pathway of BPA entering the aqueous environment. To control and mitigate BPA contamination in the aquatic environment, predicting BPAs fate at WWTPs is critical. In this study, three machine learning models, including shared layer multi-task neural network (MLT-NN), genetic programming (GP), and extra trees (ET) are used to predict the effluent BPA concentration at twelve municipal WWTPs across Canada. Additionally, the theory of networks is adopted to analyze the interdependencies among the influencing factors of BPA removal. It is found that the proposed models can provide reasonable BPA effluent concentration predictions. They have advantages in alleviating data sparsity and imbalance, improving model interpretability, and measuring predictor importance, which is valuable for the modeling of BPA and many other CECs. The network analysis results imply there are moderate interdependencies among various influencing factors of BPA removal. Factors that significantly affect BPA effluent concentration and are thus important for BPA removal are identified. The results also show that BPA is unlikely to be removed at primary treatment plants, while BPA removal could be achieved through secondary or tertiary treatment. This study presents an integrated framework for the modeling and analysis of BPA at WWTPs, which can provide direct and robust decision support for the management of BPA as well as other emerging contaminants in municipal wastewater.

Objective: During the height of the COVID-19 pandemic, personal support workers (PSWs) were heralded as healthcare ‘heroes’ as many of them cared for high-risk, vulnerable older populations, and worked in long-term care, which experienced a high number of COVID-19 outbreaks and deaths. While essential to the healthcare workforce, there is little understanding of PSW working conditions during the pandemic. The aim of our study was to examine the working conditions (including job security, work policies, and personal experiences) for PSWs in the Greater Toronto Area during the COVID-19 pandemic from the perspectives of PSWs. Methods: This study used a mixed-methods design. From June to December 2020, we conducted a survey of 634 PSWs to understand their working conditions during the COVID-19 pandemic. Semi-structured interviews with 31 survey respondents were conducted from February to May 2021 to understand in greater depth how working conditions were impacting the well-being of PSWs. Results: We found PSWs faced a range of challenges related to COVID-19, including anxiety about contracting COVID-19, reduced work hours, taking leaves of absences, concerns about job security, and losing childcare. While the COVID-19 pandemic highlighted the PSW workforce and their importance to the healthcare system (especially in the long-term care system), pre-existing poor work conditions of insecure jobs with no paid sick days and benefits exacerbated COVID-19–related challenges. Despite these hardships, PSWs were able to rely on their mental resilience and passion for their profession to cope with challenges. Conclusion: Significant changes need to be made to improve PSW working conditions. Better compensation, increased job security, decreased workload burden, and mental health supports are needed.

There are numerous regional, national, and international efforts to establish mechanisms to curb pollution and emissions. In this paper, we mainly focus on emissions trading systems. Using a static Cournot oligopoly game, we investigate a multi-product multi-pollutant market in which several supply chains compete in a non-cooperative manner in their product markets. This study characterizes conditions under which an efficient solution procedure converges to the unique Nash equilibrium of the oligopoly. Meanwhile, its partners establish a cooperative triopoly game within each supply chain in a nonsuperadditive characteristic function form. Drawing on the cooperative game theory literature, we provide a closed-form solution for a rational distribution of joint rewards between the supply chain's partners.

How do formal time allocations in teams affect team learning trajectories and performance? We argue that allocating more time for transition phases induces steeper learning trajectories that engender a positive group atmosphere, which in turn improves team performance by improving coordination quality. We tested our hypotheses in a laboratory experiment in which teams worked on a creative design task over multiple iterations. Using a latent growth modeling approach, we found that teams with shorter action and longer transition phases during prototyping had lower initial performance but steeper learning trajectories, which indirectly led to better final team performance.

This paper presents data from a study conducted in twenty groups of three participants each. Data were collected from sixty participants during a lab visit which was video recorded from several angles. Upon arrival to the lab and following informed consent, participants were told that they would be a part of a group decision-making task and were given instructions for a procedure titled “the desert survival task” Lafferty and Pond (1974). Participants were then connected to several electrodes on their upper body and palm for the collection of their electrocardiogram, respiration and electrodermal activity throughout the group task. Participants then performed the task together. The collection of physiological data from all group members was conducted simultaneously and in synchrony with the video recording. The video recordings of the group interactions were later coded by trained psychology students for positive affective behaviors made by participants (smiling and laughing) throughout the group task. Self-report measures (trait anxiety and social phobia) were collected prior to the group task from all participants. This multimodal dataset thus integrates behavioral, self-report, and physiological measures from group members, which are important for understanding group dynamics. These data will allow verification, replications, and additional analyses of the data from new perspectives.

Background: Health systems are increasingly implementing policy-driven programs to incentivize performance using contracts, scorecards, rankings, rewards, and penalties. Studies of these “Performance Management” (PM) programs have identified unintended negative consequences. However, no single comprehensive typology of the negative and positive unintended consequences of PM in healthcare exists and most studies of unintended consequences were conducted in England or the United States. The aims of this study were: (1) To develop a comprehensive typology of unintended consequences of PM in healthcare, and (2) To describe multiple stakeholder perspectives of the unintended consequences of PM in cancer and renal care in Ontario, Canada. Methods: We conducted a rapid review of unintended consequences of PM in healthcare (n = 41 papers) to develop a typology of unintended consequences. We then conducted a secondary analysis of data from a qualitative study involving semi-structured interviews with 147 participants involved with or impacted by a PM system used to oversee 40 care delivery networks in Ontario, Canada. Participants included administrators and clinical leads from the networks and the government agency managing the PM system. We undertook a hybrid inductive and deductive coding approach using the typology we developed from the rapid review. Results: We present a comprehensive typology of 48 negative and positive unintended consequences of PM in healthcare, including five novel unintended consequences not previously identified or well-described in the literature. The typology is organized into two broad categories: unintended consequences on (1) organizations and providers and on (2) patients and patient care. The most common unintended consequences of PM identified in the literature were measure fixation, tunnel vision, and misrepresentation or gaming, while those most prominent in the qualitative data were administrative burden, insensitivity, reduced morale, and systemic dysfunction. We also found that unintended consequences of PM are often mutually reinforcing. Conclusions: Our comprehensive typology provides a common language for discourse on unintended consequences and supports systematic, comparable analyses of unintended consequences across PM regimes and healthcare systems. Healthcare policymakers and managers can use the results of this study to inform the (re-)design and implementation of evidence-informed PM programs.

Background: Midwives have long workdays and work many weeks on call. There is a concern that these extended work schedules can negatively affect their intention to stay in the profession. Purpose: This study provides evidence on Canadian midwives’ preferences for and experiences with policies and guidelines which limit the hours of work and weeks per year preferred to be on call, and examines the relationship between preferences and midwives’ intention to stay in the profession. Methods: Data come from our 2018 pan-Canadian survey of midwives. Descriptive statistics of 720 midwives’ preferences and experiences are provided. In the correlations followed by the OLS regressions, 596 midwives’ data are used to test the associations between preferences and intention to stay in the profession. STATA (version 15) is used. A thematic analysis of 274 midwives’ responses to the open-ended survey question is conducted to give voice to midwives on what can be done for retention. Results: Three quarters of the 720 respondents prefer policies and guidelines to limit hours of work in a 24-hour period, though less than half have policies and guidelines on hours of work. More than half prefer to have fewer on-call weeks or never to be on call, less than a third prefer same number of on-call weeks, and only 2% prefer more weeks to be on call. Midwives are currently working on average 33 weeks per year on call. OLS regression analysis shows that ‘met preference’ for hours of work and on-call weeks are positively associated with intention to stay. In responding to the open-ended survey question, midwives recommend limiting the consecutive hours of work and on-call weeks to manageable hours and weeks to retain them in the profession. Conclusion: Midwives whose preferences are met are the ones intending to stay in the profession. There is, however, a large number of midwives with ‘unmet needs’ preferring to have policies and guidelines to limit the hours but do not have that currently, and would like to work fewer weeks on call than currently. These are the midwives who are not intending to stay in the profession.

Despite the prevalence of bus transit networks in urban public transportation, they are very susceptible to disruptions that can have negative implications, accompanied by severe socioeconomic consequences and service quality consequences. In addition, since bus transit system is comprised of stops, routes, and passengers, any disruptions in the bus network can trigger chain reaction disruption scenarios, leading to cascade failures across the entire network. Unlike the existing literature, the present study evaluates the robustness of bus transit network considering the varied hourly operation. The robustness assessment is evaluated using a weighted network considering losses in bus frequency and passenger flow, both are essential to inform service quality improvement. Some of the key findings include the observed significant variation in the hourly performance of the network compared to the daily performance when a failure occurs. Moreover, the network is more robust during on-peak hours and mid-peak compared to off-peak hours. However, the network is sensitive to losing service frequency compared passenger flow when a failure happens. These insights could be used to inform a robustness-based spatiotemporal design of bus transit networks.

More than 4 billion unique users are now using different social media platforms. This provides firms with a new avenue to connect and converse with their targeted customers in a dialogue fashion and create better customer engagement. Although customer engagement has been studied, no study to date has focused on the concept of customer engagement in social media through the lens of users' participation in (co)creation of content on social media. To that end, this study strives to conceptualize and validate the concept of customer engagement with a social media post, proposing a research model which investigates the effects of popularity, discourse logic and argument frame on customer engagement with a social media post. This study also investigates to what extent those effects vary by the product lifecycle stage and social media platform type, using secondary data extracted from Instagram and Twitter on a group of products (i.e., vehicles) at different lifecycle stages to address its research objective. This study's findings would help firms make better decisions on enhancing their customer engagement on social media platforms.