Segmenting tourists by expenditure patterns: An instrument for enhancing tourism economic benefits on a Portuguese World Heritage site

Tourism is an activity with high potential for stimulating the development of local economies, with different types of visitors having distinct environmental, social and economic effects on destinations (Lundie, Dwyer, & Forsyth, 2007; Nickerson, Jorgenson, & Boley, 2016). Notwithstanding, there is a research gap on strategies aimed at maximizing the economic relevance of tourism for local tourism destinations using market segmentation, based on visitors’ daily expenditure level at the destination, as a starting point (Lima, Eusébio, & Kastenholz, 2012). This gap becomes more evident when we refer to World Heritage Sites (WHS) destinations (Amir, Osman, Bachok, & Ibrahim, 2016). This study presents the results of the tourist market segmentation of a Portuguese municipality - Évora, a UNESCO’s WHS, located in Alentejo. The study aims to identify the homogenous groups of visitors that prevail in a WHS destination based on expenditure patterns and furthermore at contributing to the development of marketing strategies to enhance the economic development of this cultural destination.This research is funded with a grant from the FCT – National Funding Agency for Science, Research and Technology, COMPETE, FEDER, Portugal 2020 under the project UID/HIS/00057/ 2013 (POCI-01-0145-FEDER-007702) – CIDEHUS

Estratégia de sensibilização dos alunos universitários para o tema do turismo acessível: O caso da licenciatura em Turismo da Universidade de Évora

Vive-se atualmente um contexto de reflexão internacional por parte de diversos investigadores que reconhecem a necessidade de reinventar os planos de estudos e as suas dinâmicas de formação, de modo a que os alunos fiquem mais aptos a enfrentar os desafios do mercado de trabalho (e.g. Ayikoru, Tribe & Airey, 2009; Fidgeon, 2010; Hoidn & Kärkkäinen, 2014; OECD, 2016; Stergiou, Airey & Riley, 2008). São valorizadas iniciativas que envolvam dinâmicas educativas inovadoras e que sejam capazes de incutir nos alunos as qualidades humanas necessárias para desenvolver um sentido mais crítico sobre os problemas sociais que realmente afetam a nossa sociedade e, em consequência, possam ter efeitos positivos no nível de competitividade das empresas. Neste contexto, o presente trabalho tem como objetivo apresentar o contexto no qual a licenciatura em Turismo da Universidade de Évora promoveu uma iniciativa, de carácter voluntário e inovador, para comemorar o Dia Mundial de Turismo, em 27 de setembro de 2016, subordinado ao tema “Tourism For All – Promoting Universal Accessibility”. Em anos anteriores, o Dia Mundial de Turismo foi comemorado com a dinamização de iniciativas que visavam sensibilizar a população local para a existência deste acontecimento. No entanto, este ano optou-se por uma estratégia educativa que pudesse reforçar as competências dos alunos sobre este tema de relevante atualidade e importância.Este trabalho é financiado por fundos nacionais através da Fundação para a Ciência e a Tecnologia e pelo Fundo Europeu de Desenvolvimento Regional (FEDER) através do COMPETE 2020 – Programa Operacional Competitividade e Internacionalização (POCI) e PT2020, no âmbito do projeto UID/HIS/00057 – POCI-01-0145-FEDER-00770

MARTA: A high-energy cosmic-ray detector concept with high-accuracy muon measurement

A new concept for the direct measurement of muons in air showers is presented. The concept is based on resistive plate chambers (RPCs), which can directly measure muons with very good space and time resolution. The muon detector is shielded by placing it under another detector able to absorb and measure the electromagnetic component of the showers such as a water-Cherenkov detector, commonly used in air shower arrays. The combination of the two detectors in a single, compact detector unit provides a unique measurement that opens rich possibilities in the study of air showers.Comment: 11 page

Semantically Aware Text Categorisation for Metadata Annotation

In this paper we illustrate a system aimed at solving a longstanding and challenging problem: acquiring a classifier to automatically annotate bibliographic records by starting from a huge set of unbalanced and unlabelled data. We illustrate the main features of the dataset, the learning algorithm adopted, and how it was used to discriminate philosophical documents from documents of other disciplines. One strength of our approach lies in the novel combination of a standard learning approach with a semantic one: the results of the acquired classifier are improved by accessing a semantic network containing conceptual information. We illustrate the experimentation by describing the construction rationale of training and test set, we report and discuss the obtained results and conclude by drawing future work.</p

Gastronomy and Wine in the Alentejo Portuguese Region: Motivation and Satisfaction of Turists from Évora

Food and winemaking are a recognized tangible and intangible culturalheritage of Portugal. From the relationshipbetween these twocomponents, astrategic product emerged with a considerable potential for tourism industry, which is notignored bymany of tourism organizations. This chapter intends to analyze food and winemaking from atourism demand perspective. Particularly, this study describes visitors’ profi le, including, their motivations, their knowledgeabout theenological and gastronomicresourcesand the degreeof satisfaction. A total of 308 questionnaires were collected between February and May of 2012, from the visitors that visited the historic center of Évora (Alentejo-Portugal). Results reveal a visitor profi le associated with regional cuisine and wine products from Portugal. Moreover, visitors’ evidenced a high level of knowledge regarding the Portuguese cuisine and regional wines; although this not matches with their primary motivation for visit the city of Évora

Machine Learning based tool for CMS RPC currents quality monitoring

The muon system of the CERN Compact Muon Solenoid (CMS) experiment includes more than a thousand Resistive Plate Chambers (RPC). They are gaseous detectors operated in the hostile environment of the CMS underground cavern on the Large Hadron Collider where pp luminosities of up to $2\times 10^{34}$ $\text{cm}^{-2}\text{s}^{-1}$ are routinely achieved. The CMS RPC system performance is constantly monitored and the detector is regularly maintained to ensure stable operation. The main monitorable characteristics are dark current, efficiency for muon detection, noise rate etc. Herein we describe an automated tool for CMS RPC current monitoring which uses Machine Learning techniques. We further elaborate on the dedicated generalized linear model proposed already and add autoencoder models for self-consistent predictions as well as hybrid models to allow for RPC current predictions in a distant future

Effects of the electronic threshold on the performance of the RPC system of the CMS experiment

Resistive Plate Chambers have a very important role for muon triggering both in the barrel and in the endcap regions of the CMS experiment at the Large Hadron Collider (LHC). In order to optimize their performance, it is of primary importance to tune the electronic threshold of the front-end boards reading the signals from these detectors. In this paper we present the results of a study aimed to evaluate the effects on the RPC efficiency, cluster size and detector intrinsic noise rate, of variations of the electronics threshold voltage