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Due to its good mechanical and biocompatibility characteristics, nitinol SEMS is a popular endoprothesis used for relieving stricture problems in hollow organs due to carcinomas. Besides its mechanical application, SEMS can be regarded as well as potential electrode for performing RF ablation therapy on the tumor. The purpose of this work is to perform numerical and experimental analyses in order to characterize the lesion volume induced in biological tissue using this kind of tubular electrode.

Data concerning electrical conductivity and dimension of the damaged tissue after RF ablation procedure were obtained from ex vivo samples. Next, numerical models using 3D finite element method were obtained reassembling the conditions considered at experimentation setup and results were compared.

Numerical and experimental results show that a regular volume of damaged tissue can be obtained considering this type of electrode. Also, results obtained from numerical simulation are close to those obtained by experimentation.

SEMSs, commonly used as devices to minimize obstruction problems due to the growth of tumors, may still be considered as an active electrode for RF ablation procedures. A method considering this observation is presented in this paper. Also, numerical simulation can be regarded in this case as a tool for determining the lesion volume.

This research proposes to organise and distil this massive amount of data, making it easier to understand. Using data mining, machine learning techniques and visual approaches, researchers and managers can extract valuable insights (on guests' preferences) and convert them into strategic thinking based on exploration and predictive analysis. Consequently, this research aims to assist hotel managers in making informed decisions, thus improving the overall guest experience and increasing competitiveness.

This research employs natural language processing techniques, data visualisation proposals and machine learning methodologies to analyse unstructured guest service experience content. In particular, this research (1) applies data mining to evaluate the role and significance of critical terms and semantic structures in hotel assessments; (2) identifies salient tokens to depict guests' narratives based on term frequency and the information quantity they convey; and (3) tackles the challenge of managing extensive document repositories through automated identification of latent topics in reviews by using machine learning methods for semantic grouping and pattern visualisation.

This study’s findings (1) aim to identify critical features and topics that guests highlight during their hotel stays, (2) visually explore the relationships between these features and differences among diverse types of travellers through online hotel reviews and (3) determine predictive power. Their implications are crucial for the hospitality domain, as they provide real-time insights into guests' perceptions and business performance and are essential for making informed decisions and staying competitive.

This research seeks to minimise the cognitive processing costs of the enormous amount of content published by the user through a better organisation of hotel service reviews and their visualisation. Likewise, this research aims to propose a methodology and method available to tourism organisations to obtain truly useable knowledge in the design of the hotel offer and its value propositions.