A number of studies have been initiated to explore how to improve the soundscape quality in urban parks. However, good soundscape quality in parks cannot be provided without a thorough understanding of the complex relationships among sound, environment, and individuals. As acoustic comfort is considered to be an important outcome of soundscape quality, this study investigates the relative impacts of the factors influencing acoustic comfort evaluation by formulating a multivariate ordered logit model. This study also explores the inter-relationships among acoustic comfort evaluation, acceptability of the environment, and preference to stay in a park using a path model. A total of 595 valid responses were obtained from interview surveys administered in four parks in Hong Kong while objective sound measurements were carried out at the survey spots concurrently. The findings unveil that acoustic comfort evaluation, besides visual comfort evaluation of landscape, also plays an important role on users' acceptability of the urban park environment. Compared with all the studied acoustic related factors, acoustic comfort evaluation serves as a better proxy for park users' preference to stay in urban parks. Hearing the breeze will significantly increase the likelihood of individuals in giving high acoustic comfort evaluation. Conversely, hearing the sounds from heavy vehicles or sounds from bikes will significantly reduce the likelihood in giving a high acoustic evaluation.

To accurately reveal rolling bearing operating status, multi-feature entropy distance method was proposed for the process character analysis and diagnosis of rolling bearing faults by the integration of four information entropies in time domain, frequency domain and time–frequency domain and two kinds of signals including vibration signals and acoustic emission signals. The multi-feature entropy distance method was investigated and the basic thought of rolling bearing fault diagnosis with multi-feature entropy distance method was given. Through rotor simulation test rig, the vibration and acoustic emission signals of six rolling bearing faults (ball fault, inner race fault, outer race fault, inner ball faults, inner–outer faults and normal) are gained under different rotational speeds. In the view of the multi-feature entropy distance method, the process diagnosis of rolling bearing faults was implemented. The analytical results show that multi-feature entropy distance fully reflects the process feature of rolling bearing faults with the change of rotating speed; the multi-feature entropy distance with vibration and acoustic emission signals better reports signal features than single type of signal (vibration or acoustic emission signal) in rolling bearing fault diagnosis; the proposed multi-feature entropy distance method holds high diagnostic precision and strong robustness (anti-noise capacity). This study provides a novel and useful methodology for the process feature extraction and fault diagnosis of rolling element bearings and other rotating machinery.

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