Sistema Regional de Información
en línea para Revistas Científicas de América Latina,
el Caribe, España y Portugal

Economía Digital Sostenible es una monografía escrita por la Profesora Carmen Pastor Sempere, Profesora Titular de Derecho Mercantil de la Universidad de Alicante y Directora de BAES Blockchain Lab., Grupo de Investigación del Instituto de Economía Internacional de la Universidad de Alicante, formado por un conjunto multidisciplinar de investigadores/as con el objetivo de desarrollar la tecnología Blockchain en el ámbito de la Administración Pública y de la empresa privada y alcanzar un estándar de Blockchain europeo, y en cuyos resultados se enmarca esta obra. Obra que, además, está asociada a la Revista Aranzadi de Derecho y Nuevas Tecnologías.Así, dividida en dos partes principales, “Economía Digital Sostenible como nuevo fenómeno del siglo XXI” y “Nuevos operadores y acceso a la financiación en la Economía Digital Sostenible”, y con un total de seis capítulos, la monografía parte de ser una invitación a reconstruir el sistema de mercado tras la crisis COVID-19 haciendo uso de las nuevas tecnologías disponibles en el diseño de instrumentos para la financiación y restructuración de deuda, y proporciona un completo estudio de ello con propuestas para lograr un modelo de actuación empresarial acorde con el desarrollo sostenible.

La obra que procedemos a recensionar, coordinada por Dña. Olivia Fontela, elaborada y redactada por Dña. Diana Martín, Dña. Daria Wencell y Dña. María Atienza, nos presenta una interesante y oportuna publicación en torno al análisis del perfil de competencias de las mujeres en las Empresas de Economía Social y Solidaria, que pone en cuestión el liderazgo masculino predominante y el techo de cristal, resaltando la importancia de la formación y la visibilización femenina en este tipo de entidades.

En el presente artículo se analizan los diferentes antecedentes normativos que se redactaron en España previos a la ley de cooperativas de 1931. El punto de partida se encuentra en el proyecto de Joaquín Díaz de Rábago a fines del siglo XIX, con el que quiso superar las deficiencias de que adolecía sobre esta materia tanto el Código de Comercio de 1885 como la ley de asociaciones de 1887. Proyecto que no cristalizó, como tampoco el anteproyecto que se elaboró por una comisión nombrada en 1925 y en la que desempeñó un papel fundamental Antonio Gascón y Miramón. Tras este nuevo fracaso, se sucedieron otros intentos con igual resultado negativo hasta que poco después de proclamarse la Segunda República española se promulgó, a instancias del ministro de Trabajo Largo Caballero, el decreto de 4 de julio de 1931, que el 9 de septiembre se elevó a la categoría de ley.

La presente obra aborda uno de los temas de mayor actualidad en el ámbito societario, tanto a nivel profesional como académico, la transformación digital y la incorporación del uso de herramientas digitales en la gobernanza corporativa. En este caso, el estudio profundiza específicamente en la digitalización de las sociedades laborales y cooperativas, convirtiéndose en una de las obras más completas existentes a día de hoy sobre este asunto: digitalización societaria en las entidades de economía social.

Word embeddings are used in natural language processing to group semantically similarwords. In this paper, we create word embeddings for Internet Domain Names (DNS)from corpora of anonymized DNS queries from an Internet Service Provider. We use eachembedding as a layer of a recurrent neural network (RNN) that works as a LanguageModel for the DNS queries generated by the users. We use these RNNs to predict thenext DNS query in two different cases. A first case tries to predict the next domain queryfrom the DNS server’s point of view so the corpus is close to the original log data. Asecond case tries to predict the next domain queried by a user from the user’s point ofview. Here the corpus has larger preprocessing.We show that this procedure has good accuracy for the DNS server-side problem, butlow accuracy for the user-side problem. Moreover, we show that training the same RNNwithout using the pre-trained embedding takes more time and is substantially less accu-rate. These results have practical applications for the service’s latency reduction, cacheoptimization in recursive DNS servers, automatic filtering of inappropriate domains, anddetecting anomalies.

The daily operation of organizations leaves a trail of the execution of business processes (BPs) including activities, events and decisions taken by participants. %, as a basis for process improvement. Compliance requirements add specific control elements to process execution, e.g. domain and/or country regulations to be fulfilled, enforcing order of interaction messages or activities, or security checks on roles and permissions. As the amount of available data in organizations grows everyday, using execution data to detect compliance violations and its causes, can help organizations to take corrective actions for improving their processes and comply to applying rules. Compliance requirements violations can be detected at runtime to prevent further execution, or in a post mortem way using Process Mining to evaluate process execution data against the specified compliance requirements for the process. In this paper we present a BP compliance Requirements Model (BPCRM) defining generic compliance controls that can be used to specify specific compliance requirements over BPs, that are used as input to assess compliance violations with process mining. This model can be seen as a catalogue that includes a set of predefined compliance rules or patterns in one place, helping organizations to specify and evaluate the compliance of their processes.

Since the beginning of 2020, COVID-19 has had a strong impact on the health of the world population. The mostly used approach to stop the epidemic is the application of controls of a classic epidemic such as case isolation, contact monitoring, and quarantine, as well as physical distancing and hygienic measures. Tracing the contacts of infected people is one of the main strategies for controlling the pandemic. Manual contact tracing is a slow, error-prone (by omission or forgotten) process, and vulnerable in terms of security and privacy. Furthermore, it needs to be carried out by specially trained personnel and it is not effective in identifying contacts with strangers (for example in public transport, supermarkets, etc). Given the high rates of contagion, which makes difficult an effective manual contact tracing, multiple initiatives arose for developing digital proximity tracing technologies. In this paper, we discuss in depth the security and personal data protection requirements that these technologies must satisfy, and we present an exhaustive and detailed list of the various applications that have been deployed globally, as well as the underlying infrastructure models and technologies they used. In particular, we identify potential threats that could undermine the satisfaction of the analyzed requirements, violating hegemonic personal data protection regulations.

In the context of the project "WeNet: Internet of us" we are studying the role of diversity in relation to Internet-mediated social interactions. In this paper, in particular, we analyze a possible relationship between personality aspects and social interaction mediated by digital platforms. More specifically, we rely on the five personality traits (Extraversion, Agreeableness, Conscientiousness, Emotional Stability and Openness to Experience), commonly referred to as "Big-five", and associate them to automatically extracted behavioral characteristics derived from the experience of using a Chatbot for a closed community of students at the Universidad Católica "Nuestra Señora de la Asunci´ón" (UC). The personality data comes from a self-report made by the users through questionnaires. According to a survey to the participants, overall the results show very positive appraisals about the use of the Chatbot in terms of user experience and its main functionalities, which is very encouraging for future pilots. As for the role of personality in relation to the main use of the Chatbot, although further experience is required to confirm trends, the results suggest that the Big-five personality traits are to some extent correlated with: the active participation (Agreeableness and Openess); the type of contribution in term of length of questions/requests for help and answers (Agreeableness, Neuroticism and Openness); and, the network of interactions evolution over time (Openness and Neuroticism).

Knowing the structure of a protein is of enormous importance but presents great theoretical and technological challenges. The COVID-19 pandemic showed how important it was to be able to determine the structure and form of SARS-COV-2 to better understand its functioning and to be able to develop vaccines and combat drugs. This article presents mathematical-computational and physical-chemical aspects involved in the reconstruction and validation of the three-dimensional molecular conformations of SARS-CoV-2 virus proteins, including the variant discovered in patients from Brazil  in 2021, the lineage B.1.1.28/P.1. The methodology used is based on the sequencing of the virus protein through the incorporation of new in silico mutations in already known structures, the result is then submitted to computational reconstruction using an enumerative feasibility algorithm validated by the Ramachandran diagram and alignment structural. After the structural reconstruction of the virus, a stability study is performed with the protein generated through classical molecular dynamics.