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This study aimed to facilitate a rapid evaluation of track service status and vehicle ride comfort based on car body acceleration. Consequently, a low-cost, data-driven approach was proposed for analyzing speed-related acceleration limits in metro systems.

A portable sensing terminal was developed to realize easy and efficient detection of car body acceleration. Further, field measurements were performed on a 51.95-km metro line. Data from 272 metro sections were tested as a case study, and a quantile regression method was proposed to fit the control limits of the car body acceleration at different speeds using the measured data.

First, the frequency statistics of the measured data in the speed-acceleration dimension indicated that the car body acceleration was primarily concentrated within the constant speed stage, particularly at speeds of 15.4, 18.3, and 20.9 m/s. Second, resampling was performed according to the probability density distribution of car body acceleration for different speed domains to achieve data balance. Finally, combined with the traditional linear relationship between speed and acceleration, the statistical relationships between the speed and car body acceleration under different quantiles were determined. We concluded the lateral/vertical quantiles of 0.8989/0.9895, 0.9942/0.997, and 0.9998/0.993 as being excellent, good, and qualified control limits, respectively, for the lateral and vertical acceleration of the car body. In addition, regression lines for the speed-related acceleration limits at other quantiles (0.5, 0.75, 2s, and 3s) were obtained.

The proposed method is expected to serve as a reference for further studies on speed-related acceleration limits in rail transit systems.

Counter-hedonic entertainment, characterized by evoking negative emotions in consumers, has emerged as one of the most popular and profitable forms of entertainment in Western countries. However, this entertainment type is not as popular in China as in Western countries. This study examines whether the free trial of counter-hedonic entertainment can promote the consumption of such entertainment and explores the moderating effect of contextual factors on this relationship.

This study investigates the promotional effectiveness of the free trial strategy on counter-hedonic entertainment through a quasi-experiment involving the broadcasting of a large-scale free counter-hedonic program. This study has analyzed the counter-hedonic consumption data from 31 major cities in China and employed econometric modeling to test the hypotheses.

Results show that the free trial of counter-hedonic entertainment can promote the sales of entertainment with similar categories. The follow-up moderation analyses show that the positive effect is amplified on days with less sunlight and for consumers with abundant (vs scarce) financial resources, thereby providing evidence to support the underlying drivers of the positive effect – the need for arousal and confidence frame. Meanwhile, the free trial of counter-hedonic entertainment also has a long-term effect after airing.

Based on existing research on consumers’ counter-hedonic consumption motivation, this study confirms the effectiveness and boundary conditions of the free trial strategy in promoting counter-hedonic entertainment consumption for the first time. The results also offer actionable insights for counter-hedonic entertainment marketers and practitioners.

The purpose of this study is to determine propeller damage based on acoustic recordings taken from unmanned aerial vehicle (UAV) propellers operated at different thrust conditions on a test bench. Propeller damage is especially critical for fixed-wing UAVs to sustain a safe flight. The acoustic characteristics of the propeller vary with different propeller damages.

For the research, feature extraction methods and machine learning techniques were used during damage detection from propeller acoustic data. First of all, sound recordings were obtained by operating five different damaged propellers and undamaged propellers under three different thrusts. Afterwards, the harmonic-to-noise ratio (HNR) feature extraction technique was applied to these audio recordings. Finally, model training and validation were performed by applying the Gaussian Naive Bayes machine learning technique to create a diagnostic approach.

A high recall value of 96.19% was obtained in the performance results of the model trained according to damaged and undamaged propeller acoustic data. The precision value was 73.92% as moderate. The overall accuracy value of the model, which can be considered as general performance, was obtained as 81.24%. The F1 score has been found as 83.76% which provides a balanced measure of the model’s precision and recall values.

This study include provides solid method to diagnose UAV propeller damage using acoustic data obtain from the microphone and allows identification of differently damaged propellers. Using that, the risk of in-flight failures can be reduced and maintenance costs can be lowered with addressing the occurred problems with UAV propeller before they worsen.

This study introduces a novel method to diagnose damaged UAV propellers using the HNR feature extraction technique and Gaussian Naive Bayes classification method. The study is a pioneer in the use of HNR and the Gaussian Naive Bayes and demonstrates its effectiveness in augmenting UAV safety by means of propeller damages. Furthermore, this approach contributes to UAV operational reliability by bridging the acoustic signal processing and machine learning.