PUBLICATIONS

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.

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.

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 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.

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.

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.

Over the last few decades, there has been an increased demand for resilient low-impact development (LID) systems for stormwater management. During extreme uncertain events, a resilient LID system is expected not only to handle immediate stressors but also to rapidly adapt through changing and regulating itself to ensure continuous functionality. This study presents a new resilience quantification approach applicable to different LID systems. To demonstrate its utility, the developed approach was applied on a bioretention system. A set of equations for the LID system's functionality was developed, integrating an analytical probabilistic approach (APA) and the stormwater management model (SWMM) continuous simulation output. These equations were subsequently used to evaluate resilience indices such as robustness, rapidity, serviceability, and the LID system's reliability for different LID area ratios and surface depression storage depths. Both APA and SWMM exhibited similar resilience index values of 0.66-1.0 and 0.73-1.0, respectively. The overall reliability index values ranged from 60.50% to 100% when using SWMM and 56.67% to 100% when using APA, reflecting their consistency in predicting excellent system performance throughout the simulation period. However, the average rapidity index value prediction with APA was lower compared to SWMM. This slight variation was due to event-by-event hydrological simulation in APA, unlike the time step-by-time step continuous simulations in SWMM. The developed approach and findings of this study provide policy-makers with a consistent methodology to design resilient LID systems and empower decision-makers to strategize investment focused on optimized LID resilience-based designs.

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.

In this paper, a hybrid model is developed to simulate solute transport in discrete fracture networks (DFNs) considering mass exchange between the fracture and the surrounding matrix. The developed model considers three mechanisms: advection and dispersion along the fracture, molecular diffusion within the fracture and into the matrix, and adsorption within the matrix. Furthermore, the developed model can predict solute transport at fracture intersections and at the discrete fracture network outlet for both constant and pulse injections at inlet boundaries. The developed model predictions are compared to those of an existing analytical model; the results indicate the former is approximately 250 times faster than the later, and this efficiency increases with network complexity. In addition, the developed model is compared to a computational fluid dynamics (CFD) model employing Navier Stokes equations, and the comparison indicates the former has a lower dispersion compared to the later. Nonetheless, the developed model can approximately predict solute transport simulated by the CFD model when assuming a higher value of dispersivity. Finally, the results in a high density DFN indicate that large diffusion coefficients, small apertures, and large fracture spacings reduce solute migration in fracture networks.

Meaning in life is important for human functioning. Both its sources and outcomes have received much scholarly attention. Based on self-determination theory and using four waves of longitudinal data, this study examines the bidirectional relationship between the satisfaction of basic psychological needs and meaning in life. The results of the cross-lagged model indicate that the satisfaction of three basic psychological needs, namely, autonomy, competence, and relatedness, significantly increased meaning in life and was informed by prior presence of meaning in life. In summary, our findings suggest that the relationship between basic psychological needs and meaning in life is bidirectional, and this provides theoretical and practical insights related to the psychological growth of individuals.

Disruptions due to either natural or anthropogenic hazards can significantly impact the operation of critical infrastructure networks (e.g., transportation systems) as they may instigate network-level (cascade) systemic risks, thus impacting the overall city resilience. Recent relevant studies demonstrated the need to quantify the resilience of city infrastructure networks following failures of one/some of their main components, considering both topological and operational network measures. Subsequently, focusing on robustness (a key resilience attribute) and on transit (a major critical infrastructure network), the current study develops a related quantification tool employing a hybrid approach that integrates complex network theoretic measures with data analytics, and specifically clustering and genetic algorithms. To demonstrate the practical utility of the developed tool, the robustness of the City of Minneapolis bus transit network is quantified under possible cascade failures represented by node (i.e., bus stop), link (i.e., route segment), and route failure scenarios. The robustness quantification of this transit network is facilitated by analyzing 43 topological and operational measures using a coupled map lattice model integrated with a direction-based passenger flow redistribution model. Absorptive capacity thresholds are subsequently identified under different passenger flow-to-route capacity ratios. Finally, the routes are categorized based on their influences on the network robustness using genetic algorithms coupled with K-means clustering. The developed approach aims at providing a better understanding of transit systems pre-and postdisruptions by identifying key components that control the network robustness and subsequently devising reliable systemic risk management strategies and recovery plans. Such strategies and plans are expected to facilitate city resilience planning through management of cascade failure risks attributed to natural and anthropogenic hazard events.

How does leadership emerge and function when multiple health care organizations come together to form a network? In this qualitative comparative case study, we draw on distributed leadership theory to examine the leadership practices that manifested during the implementation of three coordinated care networks. Thirty leaders and care providers participated in semistructured interviews. Interview data were inductively analyzed using thematic analysis. Although established in response to the same policy initiative, each case differed in its leadership approach and implementation strategy. We found that manifestation of distributed leadership was contingent on the presence of an individual leader who acted as a unifying force across their respective network. Our findings suggest that policies to encourage the development of interorganizational networks should include sufficient resources to support an individual leader who enables distributed leadership.

An exploratory study investigated the factors affecting digital literacy training offered by local community organizations, such as public libraries. Theory based on the educational assessment and information literacy instruction literatures, community informatics, and situated learning theory served as a lens of investigation. Case studies of two public libraries and five other local community organizations were carried out. Data collection comprised: one-on-one interviews with administrators, instructors, and community members who received training; analysis of training documents; observations of training sessions; and a survey administered to clients who participated in these training sessions. Data analysis yielded the generation of a holistic conceptual framework. The framework identifies salient factors of the learning environment and program components that affect learning outcomes arising from digital literacy training led by local community organizations. Theoretical propositions are made. Member checks confirmed the validity of the study's findings. Results are compared to prior theory. Recommendations for practice highlight the need to organize and train staff, acquire sustainable funding, reach marginalized populations, offer convenient training times to end-users, better market the training, share and adopt best practices, and better collect and analyze program performance measurement data. Implications for future research also are identified.

Launching crowdfunding campaigns for new products and ideas is popular, and quality signals, such as endorsement and past success, can indicate the likelihood of the success of future projects. However, as these signals are not always available, creators, especially new ones, must rely on other signals, such as narrative quality. Building on the emerging literature on rhetorical signaling, we investigate the moderating effect of narrative quality on the relationship between risk/reward rhetoric and crowdfunding success. We show that risk (reward) rhetoric negatively (positively) affects campaign success. Thereafter, we introduce two new signals (narrative structural quality and narrative wording quality). Using a sample of 331,060 Kickstarter campaigns from 2009 to 2018, we empirically show that the use of punctuation positively moderates the relationship between risk/reward rhetoric and success, whereas the use of informal language negatively moderates this relationship. Interestingly, this moderating effect is similar to that of traditional signals such as endorsement and past success.

Over the last 15 years, there has been a trend in Canada to centralise the provision of health services that were previously administratively and fiscally decentralised. Canadian policy rhetoric on centralisation often identifies improved innovation as an anticipated outcome. This paper challenges the assumed relationship between centralisation and innovation. We incorporate evidence from the management literature into the debate on the structure of health systems to explore the effects that centralisation is likely to have on innovation in health systems. The findings of this paper will be of interest to international policymakers, who are currently grappling with the prospect of maintaining a decentralised approach or adopting a more centralised health system structure in the future.

Meaning in life is a summative cognition of valuable goals, life purpose, and relationships among things and people. A central feature of meaning in life is the broad consideration of more than oneself. We extend this logic to suggest that people higher in meaning in life will engage in more prosocial behaviors, compared to others. Further extending this idea, we hypothesized and longitudinally tested the assertion that one of the potentials, yet overlooked and important mechanisms that mediates the association between current meaning in life and prosocial behavior among university students six-months later is psychological capital (PsyCap). A total of 913 Chinese university students (25.6% males; 70.3% females; Mage = 19.63, SDage = 1.04) completed a Meaning in Life Questionnaire (MLQ), a Prosocial Tendencies Measure (PTM), and a Positive Psychological Capital Questionnaire (PPQ) at three different times. The results showed that the association between T1 meaning in life and T3 prosocial behavior was significant before adding the mediator variables into the model (? = 0.10, p < 0.001). T2 PsyCap significantly mediated the influence of T1 meaning in life on T3 prosocial behavior (indirect effect = 0.10; 95% CI [0.06, 0.14]). We concluded that meaning in life in university students can influence subsequent prosocial behaviors, directly, as well as through PsyCap.

Functional Dependencies define attribute relationships based on syntactic equality, and when used in data cleaning, they erroneously label syntactically different but semantically equivalent values as errors. We explore dependency-based data cleaning with Ontology Functional Dependencies (OFDs), which express semantic attribute relationships such as synonyms defined by an ontology. We study the theoretical foundations of OFDs, including sound and complete axioms and a linear-time inference procedure. We then propose an algorithm for discovering OFDs (exact ones and ones that hold with some exceptions) from data that uses the axioms to prune the search space. Toward enabling OFDs as data quality rules in practice, we study the problem of finding minimal repairs to a relation and ontology with respect to a set of OFDs. We demonstrate the effectiveness of our techniques on real datasets and show that OFDs can significantly reduce the number of false positive errors in data cleaning techniques that rely on traditional Functional Dependencies.

We studied 14 universities across Canada and Australia to examine how the COVID-19 crisis, mediated through management strategies and conflict over financial control in higher education, influenced workers’ job security and affective outcomes like stress and happiness. The countries differed in their institutional frameworks, their union density, their embeddedness in neoliberalism and their negotiation patterns. Management strategies also differed between universities. Employee outcomes were influenced by differences in union involvement. Labour cost reductions negotiated with unions could improve financial outcomes, but, even in a crisis, management might not be willing to forego absolute control over finance, and it was not the depth of the crisis that shaped management decisions.

The authors investigated children's automatic imitation in the context of observed shyness by adapting the widely used automatic imitation task (AIT). AIT performance in 6-year-old children (N = 38; 22 female; 71% White) and young adults (17–22 years; N = 122; 99 female; 32% White) was first examined as a proof of concept and to assess age-related differences in responses to the task (Experiment 1). Although error rate measures of automatic imitation were comparable between children and adults, children displayed less reaction time interference than adults. Children's shyness coded from direct behavioral observations was then examined in relation to AIT scores (Experiment 2). Observed shyness at 5 years old predicted higher automatic imitation one year later. We discuss the latter findings in the context of an adaptive strategy. We argue that shy children may possess a heightened sensitivity to others’ motor cues and therefore are more likely to implicitly imitate social partners’ actions. This tendency may serve as a strategy to signal appeasement and affiliation, allowing for shy children to blend in and feel less inhibited in a social environment.