A fundamental aspect of well performing cities is successful public spaces. For centuries, understanding these places has been limited to sporadic observations and laborious data collection. This study proposes a novel methodology to analyze citywide, discrete urban spaces using highly accurate anonymized telecom data and machine learning algorithms. Through superposition of human dynamics and urban features, this work aims to expose clear correlations between the design of the city and the behavioral patterns of its users. Geolocated telecom data, obtained for the state of Andorra, were initially analyzed to identify "stay-points"-events in which cellular devices remain within a certain roaming distance for a given length of time. These stay-points were then further analyzed to find clusters of activity characterized in terms of their size, persistence, and diversity. Multivariate linear regression models were used to identify associations between the formation of these clusters and various urban features such as urban morphology or land-use within a 25-50 meters resolution. Some of the urban features that were found to be highly related to the creation of large, diverse and long-lasting clusters were the presence of service and entertainment amenities, natural water features, and the betweenness centrality of the road network; others, such as educational and park amenities were shown to have a negative impact. Ultimately, this study suggests a "reversed urbanism" methodology: an evidence-based approach to urban design, planning, and decision making, in which human behavioral patterns are instilled as a foundational design tool for inferring the success rates of highly performative urban places.

William Whyte, one of the most well-known urban planners, documented hundreds of hours of street life using videos, cameras, and interviews to develop social and physical policy recommendations for cities. Since then, studies of public life have primarily depended on human observation for data collection. Our research sets out to test whether Do-it-Yourself sensor technologies can automate this data collection process. To answer this question, our team embedded sensors in moveable benches and evaluated their performance according to the Gehl Method, a popular guideline that measures public life. During three field tests, we gathered information on public life via several sensors including image capture, location tracking, weight measurement, and other environmental sensing techniques. Ultimately, we determined that analysis derived from image processing was the most effective method for measuring public life. Our research demonstrates that it is possible to use sensors to automate the measurement of public life and highlights the value and precision of using video footage for collecting these data. Since image processing algorithms have become more accessible and can be applied to Do-it-Yourself projects, future work can build on this research to develop open access image processing tools to evaluate and advocate for urban design strategies.

Purpose: To determine the possible effects of fucosterol (FST) on non-alcoholic fatty liver disease (NAFLD), and the mechanisms involved. Methods: The NAFLD model was constructed using palmitic acid (PA) induction, and the expression of NF-E2-related factor 2 (Nrf2), lipocalin 13 (LCN13) and Keap1 were analyzed by immunoblot. The oxidative stress of hepatocytes was determined via ELISA assay. In addition, the role of FST on lipid content and metabolism were evaluated by Oil Red O staining and immunoblot, while the levels of p-AKT, p-IRS1, and p-PI3K were evaluated by immunoblot assay. Results: The data revealed that FST significantly increased the viability of PA-induced hepatocytes, and the expression levels of Nrf2 and LCN13 (p < 0.05). Fucosterol enhanced Keap1-Nrf2 mediated LCN13 expression, and alleviated PA-induced oxidative stress by contributing to Keap1-Nrf2-LCN13 axis. In addition, it significantly reduced (p < 0.05) lipid droplet formation, promoted lipid metabolism, and lowered insulin resistance by enhancing Keap1- Nrf2-LCN13 axis. Conclusion: Fucosterol regulates Keap1-Nrf2-mediated LCN13 to aid the ameliorate palmitic acid-induced oxidative stress, lipid droplet formation and insulin resistance in liver cells.

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