Reliable Rescue Routing Optimization for Urban Emergency Logistics under Travel Time Uncertainty

The reliability of rescue routes is critical for urban emergency logistics during disasters. However, studies on reliable rescue routing under stochastic networks are still rare. This paper proposes a multiobjective rescue routing model for urban emergency logistics under travel time reliability. A hybrid metaheuristic integrating ant colony optimization (ACO) and tabu search (TS) was designed to solve the model. An experiment optimizing rescue routing plans under a real urban storm event, was carried out to validate the proposed model. The experimental results showed how our approach can improve rescue efficiency with high travel time reliability

UHRF1 predicts poor prognosis by triggering cell cycle in lung adenocarcinoma

Accumulating evidence suggests that ubiquitin-like with plant homeodomain and ring finger domains 1 (UHRF1) is overexpressed in non-small cell lung cancer (NSCLC); however, the expression and function of UHRF1 in the subtype of NSCLC are still unclear. Here, we investigate the expression and prognosis traits of UHRF1 in large NSCLC cohorts and explore the molecular characters during UHRF1 up-regulation. We find that UHRF1 is predominantly overexpressed in lung squamous cell carcinoma (SCC). Surprisingly, the up-regulated UHRF1 is only associated with the overall survival of lung adenocarcinoma (ADC) and knockdown of UHRF1 dramatically attenuates ADC tumorigenesis. Mechanically, we identify a hub gene that includes a total of 55 UHRF1-related genes, which are tightly associated with cell cycle pathway and yield to the poor clinical outcome in ADC patients. What's more, we observe knockdown of UHRF1 only affects ADC cells cycle and induces cell apoptosis. These results suggest that up-regulated UHRF1 only contributes to lung ADC survival by triggering cell cycle pathway, and it may be a prognostic biomarker for lung ADC patients.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Spatial variations in urban public ridership derived from GPS trajectories and smart card data

Understanding urban public ridership is essential for promoting public transportation. However, limited efforts have been made to reveal the spatial variations of multi-modal public ridership (such as buses, metro systems, and taxis) and the underlying controlling factors. This study explores multi-modal public ridership and compares the similarities and differences of the associated factors. Daily bus, metro, and taxi ridership patterns are first extracted from multiple sources of big transportation data, including vehicle (bus and taxi) GPS trajectories and smart card data. Multivariate regression analysis and geographically weighted regression analysis are used to reveal the associations between these data and demographic, land use, and transportation factors. An empirical study in Shenzhen, China, suggests that employment, mixed land use, and road density have significant effects on the ridership of each mode; however, some effects vary from negative to positive across the city. The results also indicate that road density, income, and metro accessibility do not have significant effects on metro, transit or bus ridership. These findings suggest that the effects of the associated factors vary depending on the mode of travel being considered and that the city should carefully consider which factors to emphasize in formulating future transport policy