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Covid-19 pandemic, war, climate emergency and other recent challenges are inflicting tremendous stress to youth. However, death and tragedy are nowadays considered taboo, as there is generally no standardized nor naturalized discussion on the subject, especially with young people. The current multi-crisis scenario is intensifying the need to incorporate an education on tragedy and resilience in our learning systems. In this context, it is necessary to find suitable teaching resources for this educational challenge that are attractive, entertaining and suitable for children and youth. A resource that meets all these requirements are children’s folk songs (CFSs). Apart from the intrinsic educational potential of music, folk songs have a simplicity and musicality that make them an ideal teaching resource. Considering their oral historical transmission, their survival confirms the attraction that this type of composition causes on children. However, to consider CFSs as an adequate resource to carry out an education for death and tragedy, it is necessary to study whether they present a non-negligible proportion of tragic passages and with enough variety of themes. This paper aims to address the study of the presence of explicit tragic content in Spanish CFSs and thus could be considered a cultural resource with transformative educational potential to develop resilience capabilities on the face of tragedy.

An analysis of lyrics of 2,558 Spanish CFSs is presented, using a manual content analysis as well as a computerized content analysis with the aim of identifying the tragic component of these songs and, thereby, assessing their pedagogical potential as a transformative educational resource.

The results obtained show a considerable presence of death and tragedy (19.78%) and a variety of tragedy dimensions. CFSs have been transmitted orally not only as a ludic resource, but also to prepare children for life (and death). The results show the complementarity of both analyses to avoid subjectivity while considering the underlying meanings of the songs.

This task had previously not been approached in an automated manner in the literature, nor there had been a similar study with a sample of this magnitude. The outcomes obtained show the considerable presence of tragedy in Spanish CFSs and emphasize the interest of this currently undervalued didactic resource.

The twofold aim of this theory-building article is to raise questions about the ability of queer cinema to transform market culture and ideologies around gender and sexuality. First, the authors examine how the very capitalization of queer signifiers may compromise the dominant order from within. Second, the authors address how brands possibly can draw on these signifiers to project authenticity.

Through visual methods of film criticism and the semiotic analysis of three films (Moonlight, Call Me By Your Name and Portrait of a Lady on Fire), the authors outline some profound narrative tensions addressed by movie makers seeking to give an authentic voice to queer lives.

Brands can tap into these narrative attempts at “seeing the invisible” to signify authenticity. False sublation, i.e. the “catch-22” of commodifying the queer imaginaries one seeks to represent, follows from a Marcusean analysis.

In more practical terms, “seeing the invisible” is proposed as a cultural branding technique. To be felicitous, one has to circumvent three narrative traditions: pathologization, rationalization and trivialization.

In contrast to Marcuse's pessimist view emphasizing its affirmative aspects, the authors conclude that such commodification in the long term may have transformative effects on the dominant ideology. This is because even if something is banished to the realm of imagination, e.g. through aesthetic semblance, it can still be enacted in real life.

Short-term rentals (STRs) (like Airbnb) are reshaping social behaviour, notably in gastronomy, altering how people dine while travelling. This study delves into revenue management, examining the impact of seasonality and dining options near guests’ Airbnb. Machine Learning analysis of Airbnb data suggests owners enhance revenue strategies by adjusting prices seasonally, taking nearby food amenities into account.

This study analysed 220 Airbnb establishments from Madrid, Spain, using consistent monthly price data from Seetransparent and environment variables from MapInfo GIS. The Machine Learning algorithm calculated average prices, determined seasonal prices, applied factor analysis to categorise months and used cluster analysis to identify tourism-dwelling typologies with similar seasonal behaviour, considering nearby supermarkets/restaurants by factors such as proximity and availability of food options.

The findings reveal seasonal variations in three groups, using Machine Learning to improve revenue management: Group 1 has strong autumn-winter patterns and fewer restaurants; Group 2 shows higher spring seasonality, likely catering to tourists, and has more restaurants, while Group 3 has year-round stability, fewer supermarkets and active shops, potentially affecting local restaurant dynamics. Food establishments in these groups may need to adapt their strategies accordingly to capitalise on these seasonal trends.

Current literature lacks information on how seasonality, rental housing and proximity to amenities are interconnected. The originality of this study is to fill this gap by enhancing the STR price predictive model through a Machine Learning study. By examining seasonal trends, rental housing dynamics, and the proximity of supermarkets and restaurants to STR properties, the research enhances our understanding and predictions of STR price fluctuations, particularly in relation to the availability and demand for food options.

The purpose of this paper is to review the current literature in the field of tourism demand forecasting.

Published papers in the high quality journals are studied and categorized based their used forecasting method.

There is no forecasting method which can develop the best forecasts for all of the problems. Combined forecasting methods are providing better forecasts in comparison to the traditional forecasting methods.

This paper reviews the available literature from 2007 to 2017. There is not such a review available in the literature.

An examination is made of developments in port dynamics since 1965. Initially, this task is addressed by studying changes in past port patterns using a simple descriptive model to accommodate shifts induced by containerization. Over time these changes have led to the reversal of the concentration and centralization of port activities. Then consideration is given to the behavior of stakeholders active in the contemporary port scene by elaborating a bipolar global-local analytical framework through an invocation of the hybrid concepts of glocalization and loglobalization. This analysis leads to an examination of emerging economies to gauge future trends in port dynamics following the dramatic emergence of China. Finally, there is a discussion of the need to go beyond inter-port competition to comprehend global production-distribution networks by exploring synergies between the supply chain and the total transport network to bring out parallels in the hub-and-spoke structure not only underpinning maritime activities but also air transport and telecommunications.