The COVID-19 pandemic has renewed interest in the importance of indoor air quality for health. The spread of respiratory aerosols is the primary mechanism for COVID-19 transmission, making it crucial to understand the role of effective ventilation in managing the risk of disease transmission. The concentration of exhaled carbon dioxide (CO2) in indoor spaces can be used as a proxy measure of ventilation efficiency. Poor indoor air quality has been associated with a range of acute and chronic health problems, including respiratory issues, cardiovascular disease, and cancer. Poor air quality may also impair cognitive performance and productivity. Social and economic inequalities exacerbate the impact of indoor air quality issues, making it crucial to address these problems in an equitable manner. Public libraries have been identified as an effective intermediary for providing education and free air quality monitoring technology to communities, with the ultimate goal of promoting awareness and increasing access to tools to promote accountability for maintaining high indoor air quality standards. The primary objectives of this initiative are to: 1) develop a citizen science toolkit for assessing indoor air quality in public spaces and deploy the toolkit to public libraries in Arizona; and 2) to conduct a program evaluation to determine whether this kit can be effectively deployed through public libraries to promote citizen science efforts and engage community members in promoting healthier indoor air quality, identify areas where improvements can be made, and prepare the program to be scaled to a larger audience.

The relation between flux and fluctuation is fundamental to complex physical systems that support and transport flows. A recently obtained law predicts monotonous enhancement of fluctuation as the average flux is increased, which in principle is valid but only for large systems. For realistic complex systems of small sizes, this law breaks down when both the average flux and fluctuation become large. Here we demonstrate the failure of this law in small systems using real data and model complex networked systems, derive analytically a modified flux-fluctuation law, and validate it through computations of a large number of complex networked systems. Our law is more general in that its predictions agree with numerics and it reduces naturally to the previous law in the limit of large system size, leading to new insights into the flow dynamics in small-size complex systems with significant implications for the statistical and scaling behaviors of small systems, a topic of great recent interest.

Mild Cognitive Impairment (MCI) is a transitional stage between normal aging and dementia and people with MCI are at high risk of progression to dementia. MCI is attracting increasing attention, as it offers an opportunity to target the disease process during an early symptomatic stage. Structural magnetic resonance imaging (MRI) measures have been the mainstay of Alzheimer's disease (AD) imaging research, however, ventricular morphometry analysis remains challenging because of its complicated topological structure. Here we describe a novel ventricular morphometry system based on the hyperbolic Ricci flow method and tensor-based morphometry (TBM) statistics. Unlike prior ventricular surface parameterization methods, hyperbolic conformal parameterization is angle-preserving and does not have any singularities. Our system generates a one-to-one diffeomorphic mapping between ventricular surfaces with consistent boundary matching conditions. The TBM statistics encode a great deal of surface deformation information that could be inaccessible or overlooked by other methods. We applied our system to the baseline MRI scans of a set of MCI subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI: 71 MCI converters vs. 62 MCI stable). Although the combined ventricular area and volume features did not differ between the two groups, our fine-grained surface analysis revealed significant differences in the ventricular regions close to the temporal lobe and posterior cingulate, structures that are affected early in AD. Significant correlations were also detected between ventricular morphometry, neuropsychological measures, and a previously described imaging index based on fluorodeoxyglucose positron emission tomography (FDG-PET) scans. This novel ventricular morphometry method may offer a new and more sensitive approach to study preclinical and early symptomatic stage AD.