The challenges of optimizing logistics operations in all links of the supply chain have led to the development of new dynamics around the revolution 4.0 and the response of operational efficiency linked to environmental sustainability. Smart ports are born as a strategy to meet customer needs from a technological evolution that generates quality logistical and operational responses. The objective of this review is to identify and to analyze the research perspectives related to smart ports. The implemented methodology considered a scientific mapping to determine the most relevant publications in terms of authors, journals, and countries with the greatest scientific participation in the subject and a network analysis based on the implementation of the tree metaphor of the graph theory. The R-studio software and the Bibliometrix plugin were used to process the information. The review considered 204 documents from the Scopus and Web of Science databases, identifying a growing trend in the number of enhanced publications as of 2019, with China being the country with the largest number of papers. In relation to research trends, the adaptation of ports to industry 4.0, maritime ports and technological security, and green and smart ports are the perspectives on the subject of study. Finally, an agenda for future research is presented.

The operations of international trade drive global economies, with maritime transport serving as a key pillar in market connectivity and acceleration. The opportunities presented by Industry 4.0 to connect supply chain actors through integrated logistics pose a challenge that fuels the number of studies in this area, with a particular interest in the transportation stage due to its responsibilities for efficiency. This study conducts a systematic literature review by analyzing scientometric aspects and identifying research trends related to maritime transportation logistics. The PRISMA methodology, graph theory, and bibliometric indicators such as indegree, betweenness, and outdegree were employed to process the information related to maritime transportation logistics from the Scopus and Web of Science databases spanning from 2000 to 2022. The gathered information and results were processed and analyzed using R-studio and Bibliometrix tools. The documents were classified as original, structural, and research trends, with the latter focused on topics related to the environment, port competitiveness, logistics optimization, and economics. Finally, an agenda for future research in the field is presented.

Proteins are the key elements on the path from genetic information to the development of life. The roles played by the different proteins are difficult to uncover experimentally as this process involves complex procedures such as genetic modifications, injection of fluorescent proteins, gene knock-out methods and others. The knowledge learned from each protein is usually annotated in databases through different methods such as the proposed by The Gene Ontology (GO) consortium. Different methods have been proposed in order to predict GO terms from primary structure information, but very few are available for large-scale functional annotation of plants, and reported success rates are much less than the reported by other non-plant predictors. This paper explores the predictability of GO annotations on proteins belonging to the Embryophyta group from a set of features extracted solely from their primary amino acid sequence.

This paper presents a nonlinear approach for time-frequency representations (TFR) data analysis, based on a statistical learning methodology - support vector regression (SVR), that being a nonlinear framework, matches recent findings on the underlying dynamics of cardiac mechanic activity and phonocardiographic (PCG) recordings. The proposed methodology aims to model the estimated TFRs, and extract relevant features to perform classification between normal and pathologic PCG recordings (with murmur). Modeling of TFR is done by means of SVR, and the distance between regressions is calculated through dissimilarity measures based on dot product. Finally, a k-nn classifier is used for the classification stage, obtaining a validation performance of 97.85%.