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This study aims to realize a sensorless metal object detection (MOD) using machine learning, to prevent the wireless power transfer (WPT) system from the risks of electric discharge and fire accidents caused by foreign metal objects.

The data constructed by analyzing the input impedance using the finite element method are used in machine learning. From the loci of the input impedance of systems, the trained neural network (NN), support vector machine and naive Bayes classifier judge if a metal object exists. Then the proposed method is tested by experiments too.

In the test using simulated data, all of the three machine learning methods show high accuracy of over 80% for detecting an aluminum cylinder. And in the experimental verifications, the existence of an aluminum cylinder and empty can are successfully identified by a NN.

This work provides a new sensorless MOD method for WPT using three machine learning methods. And it shows that NNs obtain high accuracy than the others in both simulated and experimental verifications.

The prolonged training time of the neural network (NN) has sparked considerable debate regarding their application in the field of optimization. The purpose of this paper is to explore the beneficial assistance of NN-based alternative models in inductance design, with a particular focus on multi-objective optimization and uncertainty analysis processes.

Under Gaussian-distributed manufacturing errors, this study predicts error intervals for Pareto points and select robust solutions with minimal error margins. Furthermore, this study establishes correlations between manufacturing errors and inductance value discrepancies, offering a practical means of determining permissible manufacturing errors tailored to varying accuracy requirements.

The NN-assisted methods are demonstrated to offer a substantial time advantage in multi-objective optimization compared to conventional approaches, particularly in scenarios where the trained NN is repeatedly used. Also, NN models allow for extensive data-driven uncertainty quantification, which is challenging for traditional methods.

Three objectives including saturation current are considered in the multi-optimization, and the time advantages of the NN are thoroughly discussed by comparing scenarios involving single optimization, multiple optimizations, bi-objective optimization and tri-objective optimization. This study proposes direct error interval prediction on the Pareto front, using extensive data to predict the response of the Pareto front to random errors following a Gaussian distribution. This approach circumvents the compromises inherent in constrained robust optimization for inductance design and allows for a direct assessment of robustness that can be applied to account for manufacturing errors with complex distributions.

In tourism live streaming (TLS), streamers strive to capture viewers’ attention by responding quickly to viewers’ requests and providing tourism-related knowledge. However, the effectiveness of such practices in the TLS context remains unclear. Accordingly, based on flow theory, this study aims to uncover the effects of responsiveness and knowledge spillover on viewers’ travelling intentions.

The authors collected 319 valid questionnaires to examine the proposed model. Followingly, the authors used a partial least squares structural equation modelling approach using SmartPLS 4 to analyse the survey data.

The authors found that responsiveness could enhance viewers’ flow experience and destination attachment, fostering travelling intentions. The authors further found that knowledge spillover strengthened the relationship between responsiveness and travelling intentions and responsiveness and flow experience.

This study broadens the scope of extant tourism research by juxtaposing the effects of responsiveness and knowledge spillover on viewers’ travelling intentions in the TLS context. Practically, the findings provide valuable insights for streamers to conduct appropriate viewer–streamer interaction strategies by providing instant responses and tourism-related knowledge to viewers.

在旅游直播中, 主播常常通过快速响应观众并提供目的地知识, 以吸引观众注意。然而这种策略是否有效地提升了观众的旅游意愿仍需进一步验证。因此, 本研究基于心流理论验证了响应性及知识溢出效应对观众旅游意愿的影响。

我们通过评估319份有效问卷来检验所提出的模型, 采用了SmartPLS软件构建偏最小二乘结构方程模型（PLS-SEM）分析问卷数据。

我们发现, 响应性将增强观众的心流体验和目的地依恋感, 从而促进旅游意愿。此外, 知识溢出效应强化了响应性和旅游意愿及响应性和心流体验之间的关系。

本研究同时关注响应性及知识溢出在旅游直播情境下对观众旅游意愿的影响机制。从实践层面, 本研究为旅游主播提供了高效互动及目的地推广的策略。

En las retransmisiones turísticas en directo (TLS), los organizadores se esfuerzan por captar la atención de los espectadores respondiendo de forma rápida a sus peticiones y aportando conocimientos relacionados con el turismo. Sin embargo, la eficacia de estas prácticas en el contexto de la retransmisión turística en directo sigue sin estar clara. Por consiguiente, este estudio, basado en la teoría del flujo, trata de descubrir los efectos de la capacidad de respuesta y la difusión de conocimientos en la intención de viajar de los espectadores.

Se recogieron 319 cuestionarios válidos para examinar el modelo propuesto. Seguidamente, se aplicó la técnica de ecuaciones estructurales con mínimos cuadrados parciales (PLS-SEM) mediante el software SmartPLS para analizar los datos de la encuesta.

Se concluye que la capacidad de respuesta mejoraría la experiencia de flujo de los espectadores y el apego al destino, fomentando su intención de viajar. Además, se comprueba que la difusión de conocimientos fortalece la relación entre (1) la capacidad de respuesta y la intención de viajar y (2) la capacidad de respuesta y la experiencia de flujo.

La presente investigación amplía el enfoque de los estudios existentes en la investigación turística al aproximar los efectos de la capacidad de respuesta y la difusión de conocimientos sobre la intención de viajar de los espectadores en el contexto de retransmisiones turísticas en directo. Desde el punto de vista práctico, los resultados aportan ideas para que los streamers empleen estrategias de interacción apropiadas con los espectadores, proporcionándoles respuestas instantáneas y transmitiéndoles conocimientos relacionados con el turismo.