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This tourism study delves into the universal impact of authenticity on tourists' preferences. This study aims to enhance understanding and present a model delineating authenticity's intricate relationship with behavioral intention, involvement and nostalgia.

The research entails administering a sample survey to travelers located in the departure lounge of Macau International Airport. Ultimately, 499 fully completed questionnaires are obtained through convenience sampling. The analysis of the collected data incorporates an innovative method, using a quadratic model.

Results undeniably validate that a destination's perceived authenticity significantly influences tourists' intentions, engagement and nostalgic sentiments. Findings strongly support the assertion that this authenticity demonstrates a nonlinear pattern in its association with behavioral intention, involvement and nostalgia.

Through quantitative analysis and interpretation, this study has advanced the comprehension of the interplay between authenticity and tourism. It introduces a novel perspective that adds depth to the broader understanding in this domain. This foundational research is crucial for acknowledging the unique contributions made by this study to the fields of tourism and authenticity.

这项旅游研究深入探讨了真实性对游客偏好的普遍影响。它旨在增强理解并提出一个模型来描述真实性与行为意图、参与和怀旧之间的复杂关系。

该研究需要对位于澳门国际机场候机室的旅客进行抽样调查。最终通过便利抽样获得完整填写的问卷499份。对收集的数据的分析采用了一种创新方法, 利用二次方模型。

结果无可否认地证实, 目的地的感知真实性会显着影响游客的意图、参与度和怀旧情绪。研究结果有力地支持了这一主张, 即这种真实性在其与行为意图、参与和怀旧的关联中表现出一种非线性模式。

通过定量分析和解释, 这项研究增进了我们对真实性与旅游之间的回应的理解。它引入了一种新颖的视角, 加深了我们对这一领域更广泛的理解。这一基础研究为承认本研究对旅游和真实性领域做出了独特贡献。

Este estudio turístico profundiza en el impacto universal de la autenticidad en las preferencias de los turistas. Pretende mejorar la comprensión y presentar un modelo que delinee la intrincada relación de la autenticidad con la intención conductual, la implicación y la nostalgia.

La investigación consistió en administrar una encuesta por muestreo a los viajeros situados en la sala de embarque del Aeropuerto Internacional de Macao. Finalmente, se obtuvieron 499 cuestionarios totalmente cumplimentados mediante un muestreo de conveniencia. El análisis de los datos recogidos incorpora un método innovador, utilizando un modelo cuadrático.

Los resultados validan sin lugar a duda que la autenticidad percibida de un destino influye significativamente en las intenciones, la implicación y los sentimientos nostálgicos de los turistas. Los hallazgos apoyan firmemente la afirmación de que esta autenticidad demuestra un patrón no lineal en su asociación con la intención conductual, la implicación y la nostalgia.

Mediante el análisis cuantitativo y la interpretación, este estudio ha hecho avanzar nuestra comprensión de la interacción entre autenticidad y turismo. Introduce una perspectiva novedosa que añade profundidad a nuestra comprensión más amplia en este ámbito. Esta investigación fundacional es crucial para reconocer las contribuciones únicas de este estudio a los campos del turismo y la autenticidad.

In the reduction of food waste and the provision of food to the hungry, food banks play critical roles. However, as they are generally run by charitable organisations that are chronically short of human and other resources, their inbound logistics efforts commonly experience difficulties in two key areas: 1) how to organise stocks of donated food, and 2) how to assess the donated items quality and fitness for purpose. To address both these problems, the authors aimed to develop a novel artificial intelligence (AI)-based approach to food quality and warehousing management in food banks.

For diagnosing the quality of donated food items, the authors designed a convolutional neural network (CNN); and to ascertain how best to arrange such items within food banks' available space, reinforcement learning was used.

Testing of the proposed innovative CNN demonstrated its ability to provide consistent, accurate assessments of the quality of five species of donated fruit. The reinforcement-learning approach, as well as being capable of devising effective storage schemes for donated food, required fewer computational resources that some other approaches that have been proposed.

Viewed through the lens of expectation-confirmation theory, which the authors found useful as a framework for research of this kind, the proposed AI-based inbound-logistics techniques exceeded normal expectations and achieved positive disconfirmation.

As well as enabling machines to learn how inbound logistics are handed by human operators, this pioneering study showed that such machines could achieve excellent performance: i.e., that the consistency provided by AI operations could in future dramatically enhance such logistics' quality, in the specific case of food banks.

This paper’s AI-based inbound-logistics approach differs considerably from others, and was found able to effectively manage both food-quality assessments and food-storage decisions more rapidly than its counterparts.