Experimental study of the effects of culture and location on single-file fundamental diagrams.

Empirical observation, controlled experiments, and pedestrian dynamics models are used to research pedestrian movement. These studies rely on single-file fundamental diagrams. Experiments were conducted in Ghana, and African students in China and Germany undertook experiments (Seyfried et al., 2005) [1]. Different groups of pedestrians were tested, and then told the entrance group conducted three corridor rotations. A t-test and z-test were employed to compare all measurement findings statistically. The study found significant spatial and cultural implications on single-file pedestrian travel. African pupils in China have an R2 of 0.63 (63%), while Ghanaians have an R2 of 0.77 (77%). Both groups are African, suggesting that location influences single-file pedestrian principles. According to a comparable study, Indian and German pedestrian fundamental diagrams [2,3], German and Brazil [4,5] show considerable variances. This research examines whether locations and culture affect single-file pedestrian travel.

Data-driven test strategy for COVID-19 using machine learning: A study in Lahore, Pakistan.

AIMS: We aimed at giving a preliminary analysis of the weakness of a current test strategy, and proposing a data-driven strategy that was self-adaptive to the dynamic change of pandemic. The effect of driven-data selection over time and space was also within the deep concern. METHODS: A mathematical definition of the test strategy were given. With the real COVID-19 test data from March to July collected in Lahore, a significance analysis of the possible features was conducted. A machine learning method based on logistic regression and priority ranking were proposed for the data-driven test strategy. With performance assessed by the area under the receiver operating characteristic curve (AUC), time series analysis and spatial cross-test were conducted. RESULTS: The transition of risk factors accounted for the failure of the current test strategy. The proposed data-driven strategy could enhance the positive detection rate from 2.54% to 28.18%, and the recall rate from 8.05% to 89.35% under strictly limited test capacity. Much more optimal utilization of test resources could be realized where 89.35% of total positive cases could be detected with merely 48.17% of the original test amount. The strategy showed self-adaptability with the development of pandemic, while the strategy driven by local data was proved to be optimal. CONCLUSIONS: We recommended a generalization of such a data-driven test strategy for a better response to the global developing pandemic. Besides, the construction of the COVID-19 data system should be more refined on space for local applications.