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A novel method for modelling permanent magnets is investigated based on numerical approximations with rational functions. This study aims to introduce the AAA algorithm and other recently developed, cutting-edge mathematical tools, which provide outstandingly fast and accurate numerical computation of potentials and vector fields.

First, the AAA algorithm is briefly introduced along with its main variants and other advanced mathematical tools involved in the modelling. Then, the analysis of a circular Halbach array with a one-pole pair is carried out by means of the AAA-least squares method, focusing on vector potential and flux density in the bore and validating results by means of classic finite element software. Finally, the investigation is completed by a finite difference analysis.

AAA methods for field analysis prove to be strikingly fast and accurate. Results are in excellent agreement with those provided by the finite element model, and the very good agreement with those from finite differences suggests future improvements. They are also easy programming; the MATLAB code is less than 200 lines. This indicates they can provide an effective tool for rapid analysis.

AAA methods in magnetostatics are novel, but their extension to analogous physical problems seems straightforward. Being a meshless method, it is unlikely that local non-linearities can be considered. An aspect of particular interest, left for future research, is the capability of handling inhomogeneous domains, i.e. solving general interface problems.

The authors use cutting-edge mathematical tools for the modelling of complex physical objects in magnetostatics.

This paper aims at assessing the role of authenticity of local food in contributing to tourism as an ecosystem service in a small mountain tourism destination, i.e. Alagna Valsesia (North-West of Italy).

A mix method approach has been adopted. The research has been carried out by using both CAWI (Computer Assisted Web Interview) and PAPI (Paper And Pen Interview) methodologies for surveys on the accommodation facilities managers and tourists. In addition, an analysis of electronic word-of-mouth platforms (TripAdvisor and Google) was implemented to understand more in-depth the local foodservice.

Findings show that Alagna Valsesia embodies its authenticity with the tradition of the local culture, characterised by natural heritage and Walser uses. Some tools are identified to improve the attractiveness of the tourist destination.

The main limitation of this study can be found in the study' application to a rather limited and geographically restricted area.

The results provided by this study can help local policy-makers in driving local policies and public funds in implementing ecotourism based practices.

Although the role of local food has been much studied in relation to the tourism sector, to the knowledge of the authors, this contribution represents a first attempt to analyse local food's role in terms of local food's crossover contribution to supporting cultural tourism.

Using a generic cheese as an anchor product, in this study consumers' preferences for different EU quality schemes have been investigated. Specifically, the study aims to understand whether “Protected Designation of Origin” (PDO), “Organic” and “Mountain Product” labels are independent or if there are some synergies existing between them, questioning – at the same time – whether this alleged exchange of value plays a positive or negative role in terms of consumers' willingness to pay.

A discrete choice experiment (DCE) was conducted on 600 Italian consumers performing a random parameter logit model. The respondents were representative of the Italian population in terms of age, gender and geographical distribution. Consumers' preferences for the presence of “Organic” and “Mountain product” labels were assessed in the DCE, together with the effect of price, for both PDO and generic cheeses.

Consumers are willing to pay a premium in price for “Organic” and “Mountain Product” per se, for cheese with and without the PDO denomination. Considering the interaction effects, results showed that the combined use of “Organic” and “Mountain Product” labels do not decrease consumers' intention to buy. However, when applied on a PDO product, these attributes generate a lower consumers' willingness to pay in comparison with the generic ones, highlighting a possible overlapping between them.

Despite the abundant literature on EU quality schemes in many food categories, this study represents one of the first attempts to measure the interaction effect between different EU quality schemes.

The circular economy (CE) has been endorsed as representing a model that is able to achieve environmental protection through decreased use of raw materials, together with changing economic values and social inclusion thanks to its demand for a wide variety of skill profiles. This has motivated many policy initiatives to support the implementation of the CE. The purpose of this study is to follow such policy initiatives in three geographically anchored industry-specific networks.

The study contributes to the research debate on the CE through a spatial approach with a focus on how the implementation of the CE is conditioned by spatial and regional contexts. The authors investigate three different networks in Sweden for CE with different locations and industrial profiles.

The findings reveal the difficulty that exist in relation to the implementation of the CE. The network and support functions in combination with private industry are vital. The risk of sustaining an uneven regional economic development is evident.

Although research on the development of the CE has proliferated, geographical approaches to this development are comparably rare to date. The authors seek to contextualise the strategy development and policy implementation of a CE policy.

Crowded urban regions pose a complex urban challenge that can adversely affect urban residents, encompassing aspects like mental and physical well-being, overall livability and quality of life. The complexity in determining the factors influencing the crowding perception, which encompass subjective and situational psychological factors alongside physical and environmental attributes, imparts ambiguity to planners' approach. This study aims to unravel the intricate interplay between crowding and the physical attributes inherent in the built environment.

This literature review analyses theories linking urban planning and environmental psychology to uncover gaps in the relationship between urban design principles and residents' perceptions of crowding. It also explores influential variables affecting crowding perception and diverse methodologies across contexts.

The study built upon a broad literature review which is expected to summarise and classify the variables of urban planning components and approaches according to their impacts on the psychological perception of crowding. Furthermore, highlighting a number of recommendations that can be considered a guide for planners and urban designers to enhance the urban experience and reduce the perception of crowding.

This study seeks to improve the overall experience of crowding in densely populated urban areas. It accomplishes this by identifying influential factors and comprehending the associated outcomes in such contexts. Furthermore, it bridges perspectives from various fields to examine relevant policies and strategies to mitigate crowding consequences.

Sarcasm is a linguistic expression that usually carries the opposite meaning of what is being said by words, thus making it difficult for machines to discover the actual meaning. It is mainly distinguished by the inflection with which it is spoken, with an undercurrent of irony, and is largely dependent on context, which makes it a difficult task for computational analysis. Moreover, sarcasm expresses negative sentiments using positive words, allowing it to easily confuse sentiment analysis models. This paper aims to demonstrate the task of sarcasm detection using the approach of machine and deep learning.

For the purpose of sarcasm detection, machine and deep learning models were used on a data set consisting of 1.3 million social media comments, including both sarcastic and non-sarcastic comments. The data set was pre-processed using natural language processing methods, and additional features were extracted and analysed. Several machine learning models, including logistic regression, ridge regression, linear support vector and support vector machines, along with two deep learning models based on bidirectional long short-term memory and one bidirectional encoder representations from transformers (BERT)-based model, were implemented, evaluated and compared.

The performance of machine and deep learning models was compared in the task of sarcasm detection, and possible ways of improvement were discussed. Deep learning models showed more promise, performance-wise, for this type of task. Specifically, a state-of-the-art model in natural language processing, namely, BERT-based model, outperformed other machine and deep learning models.

This study compared the performance of the various machine and deep learning models in the task of sarcasm detection using the data set of 1.3 million comments from social media.