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The authors will review the main concepts of graphs, present the implemented algorithm, as well as explain the different techniques applied to the graph, to achieve an efficient execution of the algorithm, both in terms of the use of multiple cores that the authors have available today, and the use of massive data parallelism through the parallelization of the algorithm, bringing the graph closer to the execution through CUDA on GPUs.

In this work, the authors approach the graphs isomorphism problem, approaching this problem from a point of view very little worked during all this time, the application of parallelism and the high-performance computing (HPC) techniques to the detection of isomorphism between graphs.

Results obtained give compelling reasons to ensure that more in-depth studies on the HPC techniques should be applied in these fields, since gains of up to 722x speedup are achieved in the most favorable scenarios, maintaining an average performance speedup of 454x.

The paper is new and original.

The purpose of this paper is to examine the Records in Contexts proposal of a conceptual model (RiC-CM) from the International Council on Archives’ (ICA) archival description and to propose an OWL ontology for its implementation in the semantic web.

The various elements of the model are studied and are related to earlier norms in order to understand their structure and the modeling of the ontology.

The analysis reveals the integrating nature of RiC-CM and the possibilities it offers for greater interoperability of data from archival descriptions. Two versions of an OWL ontology were developed to represent the conceptual model. The first makes a direct transposition of the conceptual model; the second optimizes the properties and relations in order to simplify the use and maintenance of the ontology.

The proposed ontology will follow the considerations of the final version of the ICA’s RiC-CM.

The analysis affords an understanding of the role of RiC-CM in publishing online archival data sets, while the ontology is an initial approach to the semantic web technologies involved.

This paper offers an overview of Records in Contexts with respect to the advantages in the field of semantic interoperability, and supposes the first proposal of an ontology based on the conceptual model.

The COVID-19 pandemic dramatically altered daily life for Gen Z. The purpose of this study was to examine parental perceptions of the pandemic’s effects on their children’s peer relationships. As children sought peer connection during the pandemic, technology usage soared. The second purpose of the current study was to assess how greater time on social media affected adjustment among Gen Z and whether this effect was mediated by experiences of cyber victimization.

In total, 250 U.S. parent-child dyads participated in the study. Parents reported on their children’s social media use and described how they believed the pandemic affected their children’s peer relationships. Child participants were transitioning to middle school and reported on cyber victimization and adjustment.

Thematic analysis of parental reflections revealed three themes: children spent more time online since the onset of the pandemic, there were negative implications of increased time online and there were positive and protective implications of being online. Analysis also indicated significant indirect effects of social media use on internalizing and externalizing problems through victimization.

Parents reported Gen Z continues to use electronic forms of communication and social media at high rates even after pandemic-related restrictions eased with some suggesting that their children prefer digital over face-to-face communication because they have become accustomed to this way of connecting and may find it easier than in-person interaction. Current findings highlight concerns about this increased time online as social media use negatively affected adjustment via cyber victimization.

This study aimed to provide user-centered evidence for health professionals to make optimal use of images for the effective dissemination of health information on Facebook (FB).

Using an eye-tracking experiment and a survey method, this study examined 42 participants' reading patterns as well as recall and recognition outcomes with 36 FB health information posts having various FB post features.

The findings demonstrated that FB posts with text-embedded images received more attention and resulted in the highest recall and recognition. Meanwhile, compared to text-embedded images, visual only images yielded less effective recall of information, but they caught the viewers' attention; graphics tended to attract more attention than photos. For effective communication, the text features in FB posts should align with the formats of the images.

The findings of this study provide practical implications for health information disseminators by suggesting that text-embedded images should be used for effective health communication.

This study provided evidence of users' different viewing patterns for FB health information posts and the relationship between FB post types and recall and recognition outcomes.

The purpose of this systematic review was to explore the nature of the marketing of higher education (HE) and universities in an international context. The objectives of the review were to: systematically collect, document, scrutinise and critically analyse the current research literature on supply‐side higher education marketing; establish the scope of higher education marketing; identify gaps in the research literature; and make recommendations for further research in this field.

The approach for this study entailed extensive searches of relevant business management and education databases. The intention was to ensure that, as far as possible, all literature in the field was identified – while keeping the focus on literature of greatest pertinence to the research questions.

The paper finds that potential benefits of applying marketing theories and concepts that have been effective in the business world are gradually being recognised by researchers in the field of HE marketing. However, the literature on HE marketing is incoherent, even inchoate, and lacks theoretical models that reflect upon the particular context of HE and the nature of their services.

The research field of HE marketing is still at a relatively pioneer stage with much research still to be carried out both from a problem identification and strategic perspective.

Despite the substantial literature on the marketisation of HE and consumer behaviour, scholarship to provide evidence of the marketing strategies that have been implemented by HE institutions on the supply‐side remains limited, and this is relatively uncharted territory. This paper reviews the literature in the field, focusing on marketing strategies in the rapidly developing HE international market.

It is well known in the mining industry that the increase in failures and breakdowns is due mainly to a poor maintenance policy for the equipment, in addition to the difficult access that specialized personnel have to combat the breakdown, which translates into more machine downtime. For this reason, this study aims to propose a remote assistance model for diagnosing and repairing critical breakdowns in mining industry trucks using augmented reality techniques and data analytics with a quality approach that considerably reduces response times, thus optimizing human resources.

In this work, the six-phase CRIPS-DM methodology is used. Initially, the problem of fault diagnosis in trucks used in the extraction of material in the mining industry is addressed. The authors then propose a model under study that seeks a real-time connection between a service technician attending the truck at the mine site and a specialist located at a remote location, considering the data transmission requirements and the machine's characterization.

It is considered that the theoretical results obtained in the development of this study are satisfactory from the business point of view since, in the first instance, it fulfills specific objectives related to the telecare process. On the other hand, from the data mining point of view, the results manage to comply with the theoretical aspects of the establishment of failure prediction models through the application of the CRISP-DM methodology. All of the above opens the possibility of developing prediction models through machine learning and establishing the best model for the objective of failure prediction.

The original contribution of this work is the proposal of the design of a remote assistance model for diagnosing and repairing critical failures in the mining industry, considering augmented reality and data analytics. Furthermore, the integration of remote assistance, the characterization of the CAEX, their maintenance information and the failure prediction models allow the establishment of a quality-based model since the database with which the learning machine will work is constantly updated.