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The efficient operation and interaction of autonomous robots play crucial roles in various fields, e.g., security, environmental monitoring, and disaster response. For these purposes, processing large volumes of sensor data and sharing data between robots is essential; however, processing such large data in an on-device environment for robots results in substantial computational resource demands, causing high battery consumption and heat issues. Thus, this study addresses challenges related to processing large volumes of sensor data and the lack of dynamic object information sharing among autonomous robots and other mobility systems. To this end, we propose an Edge-Driving Robotics Platform (EDRP) and a Local Dynamic Map Platform (LDMP) based on 5G mobile edge computing and Kubernetes. The proposed EDRP implements the functions of autonomous robots based on a microservice architecture and offloads these functions to an edge cloud computing environment. The LDMP collects and shares information about dynamic objects based on the ETSI TR 103 324 standard, ensuring cooperation among robots in a cluster and compatibility with various Cooperative-Intelligent Transport System (C-ITS) components. The feasibility of operating a large-scale autonomous robot offloading system was verified in experimental scenarios involving robot autonomy, dynamic object collection, and distribution by integrating real-world robots with an edge computing–based offloading platform. Experimental results confirmed the potential of dynamic object collection and dynamic object information sharing with C-ITS environment components based on LDMP. [ABSTRACT FROM AUTHOR]

Background: There is a growing concern that particulate matter (PM) such as PM2.5 and PM10 has contributed to exacerbating psychological disorders, particularly depression. However, little is known about the roles of these air pollutants on depression in elderly. Therefore, this study aimed to examine the association between PM2.5 and PM10, and depression in the elderly population in South Korea. Methods: We used panel survey data, the Korean Longitudinal Study of Aging (KLoSA), administered by the Labor Institute during the study period of 2016, 2018, and 2020 covering 217 districts in South Korea (n = 7674). Annual district-specific PM2.5 and PM10 concentrations were calculated for the study period from the monthly prediction concentrations produced by a machine-learning-based ensemble model (cross-validated R2: 0.87), then linked to the people matching with year and their residential district. We constructed a generalized estimating equation (GEE) model with a logit link to identify the associations between each of the long-term PM2.5 and PM10 exposures and depression (CES-D 10) after adjusting for individual and regional factors as confounders. Results: In single-pollutant models, we found that long-term 10 μ g / m 3 increments in PM2.5 (OR 1.36, 95% CI 1.20–1.56) and PM10 (OR 1.19, 95% CI 1.10–1.29) were associated with an increased risk of depression in the elderly. Associations were consistent after adjusting for other air pollutants (NO2 and O3) in two-pollutant models. In addition, the impacts substantially differed by regions grouped by the tertile of the population density, for which the risks of particulate matters on depression were substantial in the middle- or high-population-density areas in contrast to the low-population-density areas. Conclusions: Long-term exposure to PM2.5 and PM10 was associated with a higher risk of developing depression in elderly people. The impact was modified by the population density level of the region where they reside. [ABSTRACT FROM AUTHOR]

Background: Previous studies with large data have been widely reported that exposure to fine particulate matter (PM2.5) is associated with all-cause mortality; however, most of these studies adopted ecological time-series designs or have included limited study areas or individuals residing in well-monitored urban areas. However, nationwide cohort studies including cause-specific mortalities with different age groups were sparse. Therefore, this study examined the association between PM2.5 and cause-specific mortality in South Korea using the nationwide cohort., Methods: A longitudinal cohort with 187 917 National Health Insurance Service-National Sample Cohort participants aged 50-79 years in enrolment between 2002 and 2019 was used. Annual average PM2.5 was collected from a machine learning-based ensemble model (a test R2 = 0.87) as an exposure. We performed a time-varying Cox regression model to examine the association between long-term PM2.5 exposure and mortality. To reduce the potential estimation bias, we adopted generalized propensity score weighting method., Results: The association with long-term PM2.5 (2-year moving average) was prominent in mortalities related to diabetes mellitus [hazard ratio (HR): 1.03 (95% CI: 1.01, 1.06)], circulatory diseases [HR: 1.02 (95% CI: 1.00, 1.03)] and cancer [HR: 1.01 (95% CI: 1.00, 1.02)]. Meanwhile, circulatory-related mortalities were associated with a longer PM2.5 exposure period (1 or 2-year lags), whereas respiratory-related mortalities were associated with current-year PM2.5 exposure. In addition, the association with PM2.5 was more evident in people aged 50-64 years than in people aged 65-79 years, especially in heart failure-related deaths., Conclusions: This study identified the hypothesis that long-term exposure to PM2.5 is associated with mortality, and the association might be different by causes of death. Our result highlights a novel vulnerable population: the middle-aged population with risk factors related to heart failure., (© The Author(s) 2024; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)