Reliable estimation of rail useful lifetime can provide valuable information for predictive maintenance in railway systems. However, in most cases, lifetime data is incomplete because not all pieces of rail experience failure by the end of the study horizon, a problem known as censoring. Ignoring or otherwise mistreating the censored cases might lead to false conclusions. Survival approach is particularly designed to handle censored data for analysing the expected duration of time until one event occurs, which is rail failure in this paper. This paper proposes a deep Bayesian survival approach named BNN-Surv to properly handle censored data for rail useful lifetime modelling. The proposed BNN-Surv model applies the deep neural network in the survival approach to capture the non-linear relationship between covariates and rail useful lifetime. To consider and quantify uncertainty in the model, Monte Carlo dropout, regarded as the approximate Bayesian inference, is incorporated into the deep neural network to provide the confidence interval of the estimated lifetime. The proposed approach is implemented on a four-year dataset including track geometry monitoring data, track characteristics data, various types of defect data, and maintenance and replacement (M&R) data collected from a section of railway tracks in Australia. Through extensive evaluation, including Concordance index (C-index) and root mean square error (RMSE) for evaluating model performance, as well as a proposed CW-index for evaluating uncertainty estimations, the effectiveness of the proposed approach is confirmed. The results show that, compared with other commonly used models, the proposed approach can achieve the best concordance index (C-index) of 0.80, and the estimated rail useful lifetimes are closer to real lifetimes. In addition, the proposed approach can provide the confidence interval of the estimated lifetime, with a correct coverage of 81% of the actual lifetime when the confidence interval is 1.38, which is more useful than point estimates in decision-making and maintenance planning of railroad systems.

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The rapid urbanization in China urges scholars to investigate the impacts of built environment on the level of travel satisfaction of rural residents to improve their quality of life and make planning exercises more human-centric. This study samples six villages out of the 25 top rural areas in Chengdu, Sichuan, China, as the research object and constructs a structural equation model to explore the direct and indirect impacts of the built environment on daily travel satisfaction of rural residents. The research finds that building density (0.609), road density (0.569), the number of accessible markets (0.314), and private car ownership (0.02) have significant positive impacts on travel satisfaction. Public transport (−0.063) has a direct negative impact on travel satisfaction. Consequently, in order to further improve travel satisfaction, construction departments and rural planners should improve the building and road densities of new rural areas and increase the number of accessible markets. The convenience of rural public transport services also needs improvement.@en

Xe is only produced by cryogenic distillation of air, and its availability is limited by the extremely low abundance. Therefore, Xe recovery after usage is the only way to guarantee sufficient supply and broad application. Herein we demonstrate DD3R zeolite as a benchmark membrane material for CO₂/Xe separation. The CO₂ permeance after an optimized membrane synthesis is one order magnitude higher than for conventional membranes and is less susceptible to water vapour. The overall membrane performance is dominated by diffusivity selectivity of CO₂ over Xe in DD3R zeolite membranes, whereby rigidity of the zeolite structure plays a key role. For relevant anaesthetic composition (<5 % CO₂) and condition (humid), CO₂ permeance and CO₂/Xe selectivity stabilized at 2.0×10⁻⁸ mol m⁻² s⁻¹ Pa⁻¹ and 67, respectively, during long-term operation (>320 h). This endows DD3R zeolite membranes great potential for on-stream CO₂ removal from the Xe-based closed-circuit anesthesia system. The large cost reduction of up to 4 orders of magnitude by membrane Xe-recycling (>99+%) allows the use of the precious Xe as anaesthetics gas a viable general option in surgery.

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The 'joint assignment' is a creative bachelor education project for Biomedical Engineering (BME) in Shanghai University (SHU), China. The objective of this project is to improve students' capabilities in design thinking and teamwork through practices in the process of the design and development of complex medical product. As the first step, a pilot project 'design and development of intelligent nursing bed' was set up in May 2015. This paper describes details of how project organization and management, various teaching methods and scientific evaluation approaches were achieved in this pilot project. For example, a method containing one main line and four branches is taken to manage the project and 'prototyping model' was used as the main research approach. As a result a multi-win situation was achieved. The results showed, firstly, 62 bachelor students including 16 BME students were well trained. They improved themselves in use of practical tools, communication skills and scientific writing; Secondly, commercial companies received a nice product design on intelligent nursing bed, and have been working on industrializing it; Thirdly, the university and associated schools obtained an excellent practical education experience to supplement traditional class education; Fourthly and most importantly, requirements from end-users will be met. The results also showed that the 'joint assignment' task could become a significant component in BME bachelor education.

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