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This study aims to examine the use of the metaverse in tourism and hospitality to comprehend better how the technology might shape customer journey management, especially relative to information provision, experiences and customer benefits.

This explanatory research used a two-stage approach of media analysis and practitioner interviews to analyse the interactions among tourism information provision, customer experiences and customer benefits in the metaverse. It conceptualized and mapped the consumer journey of the emerging metaverse experience, focusing on the ideas and practices of metaverse design pioneers in tourism and hospitality.

Based on the media analysis and interviews with 27 designers, the metaverse – information – experiences – benefits (MIEB) model was proposed, containing three parts (information characteristics, customer experiences and customer benefits) and 31 supporting items grouped into nine components.

One of the unique contributions of this research is the MIEB model for applying the metaverse in customer journey management (pre-, during- and post-trip). The findings contribute to the current literature with this model based on the practical perspectives of metaverse designers and provide insights on how to incorporate the MIEB model in applying the metaverse in tourism and hospitality management. The findings also address existing literature gaps of insufficient research on metaverse management and design through all stages of the customer travel journey and by paying attention to stakeholders’ viewpoints, including the media and designers of metaverse applications. Engaging in semi-structured interviews with pioneers of the metaverse to gain insights into the design of tourism experiences was also different from other metaverse tourism research, although this is not claimed as a significant point of innovation.

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.