The tracking of tourist movements is an essential aspect in the management of sustainable tourist destinations. The current information and communication technologies provide innovative ways of collecting data on tourist movements, but it is still necessary to evaluate tools and methods of study for this challenge. At this point, mobile technologies are the best candidate for this task. Given the relevance of the topic, this paper proposes a mapping science analysis of publications on “movement of tourists” and “traceability.” It has been carried out in the two main sources WOS and SCOPUS. The term “traceability” is brought from industry and technology areas to be applied to the tourist movement/mobility tracking and management. The methodological scheme is based on a selection of search criteria with combinations of terms. The sources of specialized information in applied social sciences and technology were then selected. From there, the searches have been executed for their subsequent analysis in three stages—(I) relevance analysis filtering the results to obtain the most pertinent; (II) analysis of articles with similarity thematic, authors, journals or citations; (III) analysis of selected papers as input for the mapping analysis using Citespace. The automatic naming of clusters under the selected processing confirms that the analysis of movements is a valid scientific trend but research-oriented from the perspective of traceability is non-existent, so this approach is novel and complementary to existing ones and a potential contribution to knowledge about tourist movements. Finally, a set of methodological considerations and a classification of information capture tools are proposed. In this classification, mobile technology is the best option to enable tourist movement analysis.

Breast cancer is the most commonly occurring cancer in women worldwide. While mammography remains the gold standard in breast cancer screening, ultrasound is an important imaging modality for both screening and cancer diagnosis. This paper presents a novel method for the detection of breast lesions in ultrasound images using texton filter banks, local configuration pattern features, and classification, without employing any segmentation technique. The developed method was able to accurately detect and classify breast lesions and achieved an accuracy, sensitivity, specificity, and positive predictive value of 96.1%, 96.5%, 95.3%, and 97.9%, respectively. The paradigm that we describe may, therefore, be useful as an effective tool to detect breast nodules during screening and in whole breast imaging, enabling clinicians to focus on images where a lesion is already known to be present. The developed method may also serve as a component for automatic breast nodule detection, and, when found, for the subsequent classification between lesion type benign versus malignant.

The collection of physiological data from people has been facilitated due to the mass use of cheap wearable devices. Although the accuracy is low compared to specialized healthcare devices, these can be widely applied in other contexts. This study proposes the architecture for a tourist experiences recommender system (TERS) based on the user’s emotional states who wear these devices. The issue lies in detecting emotion from Heart Rate (HR) measurements obtained from these wearables. Unlike most state-of-the-art studies, which have elicited emotions in controlled experiments and with high-accuracy sensors, this research’s challenge consisted of emotion recognition (ER) in the daily life context of users based on the gathering of HR data. Furthermore, an objective was to generate the tourist recommendation considering the emotional state of the device wearer. The method used comprises three main phases: The first was the collection of HR measurements and labeling emotions through mobile applications. The second was emotional detection using deep learning algorithms. The final phase was the design and validation of the TERS-ER. In this way, a dataset of HR measurements labeled with emotions was obtained as results. Among the different algorithms tested for ER, the hybrid model of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks had promising results. Moreover, concerning TERS, Collaborative Filtering (CF) using CNN showed better performance.

In recent decades the research of heavy vehicles has taken great importance for the development of multiple industries at a commercial level. From the scientific point of view, this led to the need for various studies on this field. This paper analyses the field of heavy vehicles from 1912 to the first quarter of 2017 identifying the patterns from the publications, including the most important authors and institutions; high-frequency categories and keywords; journal and author contributions; most important research topics and evolution of the field based on cluster analysis. A scientometric approach based on bibliometric analysis is employed. Records from WOS and Scopus have been gathered using two bibliometric tools CiteSpace and SciMAT.

This article presents the design experience of the “Folk dance as cultural heritage” course, which is offered to undergraduate students from University of Cauca the “massive online course” modality. It shows the main challenges faced in the exercise of the integration of two in-person courses, one practical and one theoretical, and its process of transformation into a MOOC. The course was designed for two academic credits with six hours of dedication per week and is offered as an elective (non-mandatory) class of the Integral Social and Human Formation (FISH) component, through a space in the MOOC Open edX platform. The integration process of the two in-person courses and their transformation into a MOOC, brought with it several challenges related to the adjustment of contents, academic activities and evaluation. This article presents how these challenges were faced in this experience. It is worth mentioning that one of the results obtained in this research is associated with the contribution of the research to the first folk dance course in MOOC modality in Latin America, what makes it an innovative educational proposal that allows the rescue of culture through the adaptation of traditional educational contents.