Mapping sidewalk accessibility with smartphone imagery and Visual AI: a participatory approach.

Evaluating sidewalk accessibility is conventionally a manual and time-consuming task that requires specialized personnel. While recent developments in Visual AI have paved the way for automating data analysis, the lack of sidewalk accessibility datasets remains a significant challenge. This study presents the design and validation of Sidewalk AI Scanner, a web app that enables quick, crowdsourced and low-cost sidewalk mapping.

Evaluating the Performance of Low-Cost PM Sensors in Mobile Settings.

Low-cost sensors (LCSs) for measuring air pollution are increasingly being deployed in mobile applications, but questions concerning the quality of the measurements remain unanswered. For example, what is the best way to correct LCS data in a mobile setting? Which factors most significantly contribute to differences between mobile LCS data and those of higher-quality instruments? Can data from LCSs be used to identify hotspots and generate generalizable pollutant concentration maps? To help address these questions, we deployed low-cost PM sensors (Alphasense OPC-N3) and a research-grade instrument (TSI DustTrak) in a mobile laboratory in Boston, MA, USA. We first collocated these instruments with stationary PM reference monitors (Teledyne T640) at nearby regulatory sites.

Hyperlocal environmental data with a mobile platform in urban environments.

Environmental data with a high spatio-temporal resolution is vital in informing actions toward tackling urban sustainability challenges. Yet, access to hyperlocal environmental data sources is limited due to the lack of monitoring infrastructure, consistent data quality, and data availability to the public. This paper reports environmental data (PM, NO, temperature, and relative humidity) collected from 2020 to 2022 and calibrated in four deployments in three global cities.

Key Themes, Trends, and Drivers of Mobile Ambient Air Quality Monitoring: A Systematic Review and Meta-Analysis.

Mobile ambient air quality monitoring is rapidly changing the current paradigm of air quality monitoring and growing as an important tool to address air quality and climate data gaps across the globe. This review seeks to provide a systematic understanding of the current landscape of advances and applications in this field. We observe a rapidly growing number of air quality studies employing mobile monitoring, with low-cost sensor usage drastically increasing in recent years.

Evaluating the Meteorological Effects on the Urban Form-Air Quality Relationship Using Mobile Monitoring.

Predictive models based on mobile measurements have been increasingly used to understand the spatiotemporal variations of intraurban air quality. However, the effects of meteorological factors, which significantly affect the dispersion of air pollution, on the urban-form-air-quality relationship have not been understood on a granular level. We attempt to fill this gap by developing predictive models of particulate matter (PM) in the Bronx (New York City) using meteorological and urban form parameters.

Can Open Source Hardware Mechanical Ventilator (OSH-MVs) initiatives help cope with the COVID-19 health crisis? Taxonomy and state of the art.

The field of Open Source Hardware Mechanical Ventilators (OSH-MVs) has seen a steep rise of contributions during the 2020 COVID-19 pandemic. As predictions showed that the number of patients would exceed current supply of hospital-grade ventilators, a number of formal (academia, the industry and governments) and informal (fablabs and startups) entities raced to develop cheap, easy-to-fabricate mechanical ventilators. The presence of actors with very diverse modus operandi as well as the speed at which the field has grown, led to a fragmented design space characterized by a lack of clear design patterns, projects not meeting the minimum functional requirements or showing little-to-no innovation; but also valid alternatives to hospital-grade devices.

Attachment and reflective functioning in children with somatic symptom disorders and disruptive behavior disorders.

Our goal in conducting this study was to examine whether children with somatic symptom disorders (SSD) and disruptive behavior disorders (DBD) have higher rates of insecure or disorganized attachment and difficulties in mentalizing (operationalized as reflective functioning) as compared to a control group. Participants were 131 children (8-15 years) spanning two groups-a clinical group (n = 85), comprised of children fitting the criteria of our target diagnostic classifications (SSD: N = 45; DBD: N = 40), as well as a comparison group of healthy control children (n = 46). Children completed the Child Attachment Interview, which was later coded by reliable raters for attachment security and reflective functioning (RF).