J Environ Manage
January 2025
The integration of crowdsourced data has become central to contemporary built environment studies, driven by the rapid growth in digital technologies and participatory approaches that characterize modern urbanism. Despite its potential, a systematic framework for its analysis remains underdeveloped. This review, conducted in accordance with the PRISMA protocol, examines the use of crowdsourced data in shaping the built environment, scrutinizing its applications, crowdsourcing techniques, methodologies, and comparison with other big data forms.
View Article and Find Full Text PDFBackground: Fear of COVID-19 leads to stress and may result in various kinds of mental health problems. Many factors are associated with an individual’s perception of stress, including neuroticism and perceived social support. This study aimed to examine the role of neuroticism and perceived social support as mediators of fear of COVID-19 on perceived stress.
View Article and Find Full Text PDFA method to measure the superficial velocity of the water phase in gas-water flow using an electromagnetic flowmeter (EMF) and rotating electric field conductance sensors (REFCSs) is introduced in this paper. An electromagnetic flowmeter instrument factor model is built and the correlation between electromagnetic flowmeter output and gas holdup in different flow patterns are explored through vertical upward gas-water flow dynamic experiments in a pipe with an inner diameter (ID) of 20 mm. Water superficial velocity is predicted based on pattern identification among bubble, churn, and slug flows.
View Article and Find Full Text PDFIn the process of production logging to evaluate fluid flow inside pipe, logging tools that force all flow to pass through a small measuring pipe are commonly utilized for measuring mixture density. For these logging tools, studying the fluid flow phenomenon inside the small diameter pipe and improving the prediction accuracy of pressure drop are beneficial to accurately measure mixture density. In this paper, a pressure drop prediction system is designed based on a combination of an eight-electrode rotating electric field conductance sensor (REFCS), plug-in cross-correlation conductance sensor, and differential pressure sensor.
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