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Evaluation and improvement of a slow traffic system in a cold regional campus based on travel satisfaction

Zhang Jiaxin, Sun Xiaohui, Liu Jincheng, Guliziba Aimaiti

Transport Problems

2025

T. 20, z. 1

99--110

This study establishes a Bayesian-structural equation model based on the travel satisfaction survey at the Boda Campus of Xinjiang University to construct and optimize the slow traffic system of campus in cold regions. Moreover, relevant indicators are selected to construct the evaluation system of the campus’s slow traffic system in cold regions. Then, strategies are proposed to optimize the campus’s slow traffic system according to the key elements highlighted by the evaluation system. The results show that subjective emotion and perceived time have a great influence on travel satisfaction. The connectivity and density of the walking and cycling network, anti-skid performance of the road surface, canopy amount, and parking/pick-up convenience of shared bicycle sites substantially influence the construction of campuses’ slow traffic systems in cold regions. The score of the optimized campus’s slow traffic system increased by 72.10% compared with that of the pre-optimized system.

Quantitative evaluation of the effect of Circle of Willis structures on cerebral hyperperfusion: A multi-scale model analysis

Huang Suqin, Li Bao, Liu Jincheng, Zhang Liyuan, Sun Hao, Guo Huanmei, Zhang Yanping, Liang Fuyou, Gong Yanjun, Liu Youjun

Biocybernetics and Biomedical Engineering

2024

Vol. 44, no. 4

782--793

Cerebral hyperperfusion occurs in some patients after superficial temporal artery-middle cerebral artery bypass surgery. However, there is uncertainty about cerebral hyperperfusion after bypass for patients with different Circle of Willis (CoW) structures. This study established a lumped-parameter model coupled with one-dimensional model (0-1D), whilst a deep learning model for predicting pressure drop (DLM-PD) caused by stenosis and a cerebral autoregulation model (CAM) were introduced into the model. Based on this model, 9 CoW structural models before and after bypass were constructed, to investigate the effects of different CoW structures on cerebral hyperperfusion after bypass. The model and the results were further validated by clinical data. The MSE of mean flow rates from 0-1D model calculation and from clinical measurement was 1.4%. The patients exhibited hyperperfusion in three CoW structures after bypass: missing right anterior segment of the anterior cerebral artery (mRACA1) (13.96% hyperperfusion); mRACA1 and foetal-type right anterior segment of posterior cerebral artery (12.81%), and missing anterior communicating artery and missing left posterior communicating artery (112.41%). The error between the average flow ratio from the model calculations and from clinical measurements was less than 5%. This study demonstrated that the CoW structure had a significant impact on hyperperfusion after bypass. The general 0-1D model coupled with DLM-PD and CAM proposed in this study, could accurately simulate the hemodynamic environment of different CoW structures before and after bypass, which might help physicians identify high-risk patients with hyperperfusion before surgery, and promote the development of non-invasive diagnosis and treatment of cerebrovascular diseases.