A5075584709- Air Quality and Health Impacts
- Big Data and Business Intelligence
- Climate Change and Health Impacts
- Innovation and Knowledge Management
- Energy and Environment Impacts
- Ethics and Social Impacts of AI
- Open Source Software Innovations
- Neonatal Respiratory Health Research
- Impact of Light on Environment and Health
- Qualitative Comparative Analysis Research
- Urban Transport and Accessibility
- Child and Adolescent Psychosocial and Emotional Development
- Suicide and Self-Harm Studies
- Management and Organizational Studies
- Fault Detection and Control Systems
- Electronic Health Records Systems
- Noise Effects and Management
- Control Systems and Identification
- Digital Games and Media
- Health, Environment, Cognitive Aging
- Complex Systems and Decision Making
- Air Quality Monitoring and Forecasting
- Forecasting Techniques and Applications
- Digital Economy and Work Transformation
- Innovation, Sustainability, Human-Machine Systems
University of Washington
2025
Brigham Young University
2016-2025
University of Alabama System
2024
University of Alabama at Birmingham
2024
Midwestern University
2024
Johns Hopkins University Applied Physics Laboratory
2024
Harvard University Press
2023
Simon Fraser University
2015-2022
Harvard University
2018-2019
University Hospitals Sussex NHS Foundation Trust
2012
Exposure to ambient fine particulate matter (PM2.5) is a major global health concern. Quantitative estimates of attributable mortality are based on disease-specific hazard ratio models that incorporate risk information from multiple PM2.5 sources (outdoor and indoor air pollution use solid fuels secondhand active smoking), requiring assumptions about equivalent exposure toxicity. We relax these contentious by constructing PM2.5-mortality function only cohort studies outdoor covers the range....
Abstract Research Summary Supervised machine learning (ML) methods are a powerful toolkit for discovering robust patterns in quantitative data. The identified by ML could be used exploratory inductive or abductive research, post hoc analysis of regression results to detect that may have gone unnoticed. However, models should not treated as the result deductive causal test. To demonstrate application pattern discovery, we implement algorithms study employee turnover at large technology...
Past research offers mixed perspectives on whether domain experience helps or hurts algorithm-augmented worker performance. Reconciling these perspectives, we theorize that intermediate levels of are optimal for performance, due to the interplay between two countervailing forces—ability and aversion. Although can increase performance via increased ability complement algorithmic advice (e.g., identifying inaccurate predictions), it also decrease aversion accurate advice. Because developed...
Prior research on data-driven innovation, which assumes quantitative analysis as the default, suggests a tradeoff: Organizations that rely heavily tend to produce familiar, incremental innovations with moderate commercial potential, at expense of risky, novel breakthroughs or hit products. We argue this tradeoff does not hold when and qualitative are used together. substantially both types in new-product innovation process will benefit by triangulating quantifiably verifiable demand (which...
Abstract More and more companies are turning to emerging markets as sources of global innovation help transform business society. However, building capabilities in is still elusive for most companies. To understand how some successfully these capabilities, we examined workers within R&D units China across six foreign multinational corporations. In contrast with prior literature that emphasizes a structural view who the interacted innovate, our inductive analysis highlights behavioral...
Fine particulate air pollution (PM2.5) has been associated with many adverse health outcomes including school absences. Specifically, a previous study in the Utah Valley area, conducted during time relatively high exposure, found significant positive correlations between absences and pollution. We examined hypothesis that ambient PM2.5 exposures are elementary using quasi-natural experiment to help control for observed unobserved structural factors influence The Alpine, Provo, Salt Lake City...
Income, air pollution, obesity, and smoking are primary factors associated with human health longevity in population-based studies. These four may have countervailing impacts on longevity. This analysis investigates trade-offs between pollution income, explores how relative effects of income potentially influenced by accounting for obesity. County-level data from 2,996 U.S. counties were analyzed a cross-sectional to investigate relationships the interest: (mean 1999–2008 PM2.5), median...
Purpose: Currently, 1 in 5 college students struggle with suicidal ideation while 7% to 44% engage nonsuicidal self-injury. Illinois has one of the highest teenage and student suicide rates United States. This pilot study assessed self-harm behaviors at a public university. is first use standardized psychological instruments investigate these 2 crises freshmen who are all required reside dormitories. The main hypothesis was determine if independent effects students’ depression, Five-Factor...
Abstract Background Experiential learning through simulation allows students to apply didactic knowledge real-world situations. Tabletop for the exploration of a variety topics, including cybersecurity in health care. Due its low frequency, yet high-risk nature, is perfect educational modality practice responding attack. As such, authors designed and executed tabletop consisting prebriefing, four rounds injects detailing potential breaches that must address, structured debriefings included...
We describe how to employ machine learning (ML) methods in theory development. Compared traditional causal inference methods, ML make far fewer a priori assumptions about the functional form of underlying model that best represents data. Given this, researchers could use such explore novel and robust patterns data lead inductive building. strengths include replicable identification Additionally, address several concerns (such as 'p-hacking' confounding local effects for global effects)...
Previous work has examined how audiences evaluate category-spanning organizations, but little is known about their entrance affects evaluations of other, proximate organizations. We posit that the emergence entrants signals advent an altered future state—and seeds doubt incumbents’ prospects in a reordered industry-categorization scheme. test this hypothesis by treating announcements funding for startups as information shock to investors evaluating incumbent financial service providers...
With the current growing availability of datasets coming from multiple sources and domains, systems onboard our military assets have an immediate need being functional in handling large amounts data, implementing fast appropriate analyses for these datasets. However, often very limited computational resources upon which to process tasks. Generalized additive models (GAMs), are statistical model that better able account non-linear relationships between independent dependent variables, been...
Supervised machine learning (ML) methods are a powerful toolkit for discovering robust patterns in quantitative data. The identified by ML could be used exploratory inductive or abductive research, post-hoc analysis of regression results to detect that may have gone unnoticed. However, models should not treated as the result deductive causal test. To demonstrate application pattern discovery, we implement algorithms study employee turnover at large technology company. We interpret...
In the context of strategic action, human capital represents a static view value creation process. contrast, problem and solution search approach dynamic view. The purpose this paper is to uncover process that links organizational learning employee knowledge behaviors. We do by first discussing how two different types (exploratory exploitative) must both be present create new value. draw upon behavioral event analysis over 60 interviews across 15 multinational corporations build elaborate...
Epigenetic markers of early life exposures in the Canadian Healthy Infant Longitudinal Development (CHILD) birth cohort INTRODUCTION: The majority asthmatics trace onset symptoms to childhood, frequently including development atopy and with a decline lung function by school-age. aims determine role wide range environmental genetic factors asthma allergy. This paper will discuss findings from epigenetic analyses. METHODS: From 2008-2012, 3,624 pregnant women were recruited multiple urban...
This study investigates experimentation-driven product innovation in user communities. Prior research has largely focused on the benefits of leveraging users and communities as an experimentation resource. In this paper, we posit that reaping innovation-related is contingent upon degree to which community represents demand broader market. However, at same time, failing incorporate feedback from engaged may lead losing support community. Jointly, suggest firms face a engagement dilemma:...
ISEE-0803 Background and Objective: Epidemiological studies of outdoor air pollution continue to be impacted by errors in estimating personal exposure. Differences the infiltration between homes over time contribute exposure errors, but very few epidemiologic have considered because it is not feasible measure large numbers published literature on modeling still scarce. This study sought estimate efficiencies PM2.5 detached residential Toronto identify housing characteristics that could used...
<h3>Aims</h3> Wheeze during infancy may be related to development of asthma in childhood. Most studies that have explored this question been retrospective and not assessed wheeze at 1 year age. We established a genotyped birth cohort (GO-CHILD) investigate the influence genetic environmental factors on childhood atopy. The aim preliminary analysis data is identify risk for age year. <h3>Methods</h3> Participants were recruited antenatally. Following Ethics approval informed consent,...
We ask how expertise of individuals influences the quality decisions when using machine learning (ML) based decision support tools—an increasingly important question as algorithmic predictions become an integral part a variety work processes in organizations. The answer to this is unclear due two opposing forces identified literature. On one hand, we would expect workers with greater domain have higher productivity new tool because accumulated experience contributes absorptive capacity,...
BACKGROUND: We previously developed and tested Washington Passive Samplers (WPS), an ultra-low-cost passive sampling method for long-term average level of light-absorbing carbon (LAC) air pollution. Briefly, we passively expose filter-paper in the WPS, measure before/after change reflectance via analyzing filter images. Tests a laboratory Seattle polluted homes Bangalore, India, (Clark et al., 2020) indicated robust reproducibility (duplicate samplers R2 = 0.98) but did not test accuracy....