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Recently, it has been found that e-commerce (EC) websites provide a large amount of useful information that exceed the human cognitive processing capacity. In order to help customers in comparing alternatives when buying a product, previous research authors have designed opinion summarization systems based on customer reviews. They ignored the template information provided by manufacturers, although its descriptive information has the most useful product characteristics and texts are linguistically correct, unlike reviews. Therefore, this paper proposes a methodology coined as SEOpinion (summarization and exploration of opinions) to summarize aspects and spot opinion(s) regarding them using a combination of template information with customer reviews in two main phases. First, the hierarchical aspect extraction (HAE) phase creates a hierarchy of aspects from the template. Subsequently, the hierarchical aspect-based opinion summarization (HAOS) phase enriches this hierarchy with customers’ opinions to be shown to other potential buyers. To test the feasibility of using deep learning-based BERT techniques with our approach, we created a corpus by gathering information from the top five EC websites for laptops. The experimental results showed that recurrent neural network (RNN) achieved better results (77.4% and 82.6% in terms of F1-measure for the first and second phases, respectively) than the convolutional neural network (CNN) and the support vector machine (SVM) technique.

This paper examines the impact of CEO characteristics on the International Entrepreneurship (IE) of listed island-based firms (IBFs) during the period 2009-2018. The research considers 164 companies from a sample of eight small islands with securities exchanges including more than one firm headquartered on the island. The selected islands are: Barbados, Cyprus, Fiji, Iceland, Jamaica, Malta, Mauritius, and Trinidad & Tobago. Framed on the upper echelons theory and social network theory, the influence on IE of CEO’s tenure, academic background and achievement, family allegiance, and international exposure is studied, taking into account the small island particularities. Through a binary probit model, it is concluded that CEOs’ family allegiance, tenure, and academic background (if the CEO majored in Business Administration, Finance, Accounting, or Economics) are negatively related with IE, while CEOs’ academic achievement and international exposure are positively associated with IE. Some of these results are atypical in the existing literature; nevertheless, islandness can explain these results. The conclusions attained suggest new theoretical and empirical lines of IE research for IBFs.

The Internet of Medical Things (IoMT) has dramatically benefited medical professionals that patients and physicians can access from all regions. Although the automatic detection and prediction of diseases such as melanoma and leukemia is still being investigated and studied in IoMT, existing approaches are not able to achieve a high degree of efficiency. Thus, with a new approach that provides better results, patients would access the adequate treatments earlier and the death rate would be reduced. Therefore, this paper introduces an IoMT proposal for medical images’ classification that may be used anywhere, i.e., it is an ubiquitous approach. It was designed in two stages: first, we employ a transfer learning (TL)-based method for feature extraction, which is carried out using MobileNetV3; second, we use the chaos game optimization (CGO) for feature selection, with the aim of excluding unnecessary features and improving the performance, which is key in IoMT. Our methodology was evaluated using ISIC-2016, PH2, and Blood-Cell datasets. The experimental results indicated that the proposed approach obtained an accuracy of 88.39% on ISIC-2016, 97.52% on PH2, and 88.79% on Blood-cell datsets. Moreover, our approach had successful performances for the metrics employed compared to other existing methods.

Iron is essential to the cell. Both iron deficiency and overload impinge negatively on cardiac health. Thus, effective iron homeostasis is important for cardiac function. Ferroportin (FPN), the only known mammalian iron-exporting protein, plays an essential role in iron homeostasis at the systemic level. It increases systemic iron availability by releasing iron from the cells of the duodenum, spleen, and liver, the sites of iron absorption, recycling, and storage respectively. However, FPN is also found in tissues with no known role in systemic iron handling, such as the heart, where its function remains unknown. To explore this function, we generated mice with a cardiomyocyte-specific deletion ofFpn. We show that these animals have severely impaired cardiac function, with a median survival of 22 wk, despite otherwise unaltered systemic iron status. We then compared their phenotype with that of ubiquitous hepcidin knockouts, a recognized model of the iron-loading disease hemochromatosis. The phenotype of the hepcidin knockouts was far milder, with normal survival up to 12 mo, despite far greater iron loading in the hearts. Histological examination demonstrated that, although cardiac iron accumulates within the cardiomyocytes ofFpnknockouts, it accumulates predominantly in other cell types in the hepcidin knockouts. We conclude, first, that cardiomyocyte FPN is essential for intracellular iron homeostasis and, second, that the site of deposition of iron within the heart determines the severity with which it affects cardiac function. Both findings have significant implications for the assessment and treatment of cardiac complications of iron dysregulation.

The advent and consolidation of the Massive Internet of Things (MIoT) comes with a need for new architectures to process the massive amount of generated information. A new approach, Mist Computing, entails a series of changes compared to previous computing paradigms, such as Cloud and Fog Computing, with regard to extremely low latency, local smart processing, high mobility, and massive deployment of heterogeneous devices. Hence, context awareness use cases will be enabled, which will vigorously promote the implementation of advantageous Internet of Things applications. Mist Computing is expected to reach existing fields, such as Industry 4.0, future 6G networks and Big Data problems, and it may be the answer for advanced applications where interaction with the environment is essential and lots of data are managed. Despite the low degree of maturity, it shows plenty of potential for IoT together with Cloud, Fog, and Edge Computing, but it is required to reach a general agreement about its foundations, scope, and fields of action according to the existing early works. In this paper, (i) an extensive review of proposals focused on Mist Computing is done to determine the application fields and network elements that must be developed for certain objectives, besides, (ii) a comparative assessment between Cloud, Fog, Edge, and Mist is completed and (iii) several research challenges are listed for future work. In addition, Mist Computing is the last piece to benefit from the resources of complete network infrastructures in the Fluid Computing paradigm.

Hepcidin is the master regulator of systemic iron homeostasis. Derived primarily from the liver, it inhibits the iron exporter ferroportin in the gut and spleen, the sites of iron absorption and recycling respectively. Recently, we demonstrated that ferroportin is also found in cardiomyocytes, and that its cardiac-specific deletion leads to fatal cardiac iron overload. Hepcidin is also expressed in cardiomyocytes, where its function remains unknown. To define the function of cardiomyocyte hepcidin, we generated mice with cardiomyocyte-specific deletion of hepcidin, or knock-in of hepcidin-resistant ferroportin. We find that while both models maintain normal systemic iron homeostasis, they nonetheless develop fatal contractile and metabolic dysfunction as a consequence of cardiomyocyte iron deficiency. These findings are the first demonstration of a cell-autonomous role for hepcidin in iron homeostasis. They raise the possibility that such function may also be important in other tissues that express both hepcidin and ferroportin, such as the kidney and the brain. Many proteins inside cells require iron to work properly, and so this mineral is an essential part of the diets of most mammals. However, because too much iron in the body is also bad for health, mammals possess several proteins whose role is to maintain the balance of iron. Two proteins in particular, called hepcidin and ferroportin, are thought to be important in this process. Some ferroportin is found in the cells that line the gut (where iron is absorbed into the body) and is required to release this iron into the bloodstream. It is also found in the spleen, which is where iron is removed from old red blood cells so that it can be recycled. The liver produces hepcidin to control when ferroportin is active in the gut and spleen. Both hepcidin and ferroportin are also found in heart cells. In 2015, a study reported that that heart ferroportin plays an important role in heart activity. However, it was not clear what role hepcidin plays in this organ. Now, Lakhal-Littleton et al. – including many of the researchers from the previous work – have genetically engineered mice such that they specifically lacked heart hepcidin, or had a version of ferroportin in their heart that does not respond to hepcidin. The experiments show that these changes caused fatal heart failure in the mice because ferroportin releases iron from heart cells in an uncontrolled manner. Lakhal-Littleton et al. were able to prevent heart failure by injecting the animals with iron directly into the bloodstream. These findings show that hepcidin produced outside the liver has a role in controlling the levels of iron in the body’s organs. Other organs such as the brain, kidney and placenta all have their own forms of hepcidin and ferroportin; further work could investigate the roles of these proteins. Finally, another challenge for the future will be to test whether new drugs that are being developed to block or mimic hepcidin from the liver have the potential to treat heart conditions in humans.

The advent of new computing and communication trends that link pervasive data sources and consumers, such as Edge Computing, 5G and IIoT, has led to the development of the Cloud-to-Edge Continuum in order to take advantage of the resources available in massive IoT scenarios and to conduct data analysis to leverage intelligence at all levels. This paper outlines the challenging requirements of this novel IoT context and presents an innovative IoT framework to develop dataflow applications for data-centric environments. The proposed design takes advantage of decentralized Pub/Sub communication and serverless nanoservice architecture, using novel technologies such as Zenoh and WebAssembly, respectively, to implement lightweight services along the Cloud-to-Edge infrastructure. We also describe some use cases to illustrate the benefits and concerns of the coming IoT generation, giving a communication performance comparison of Zenoh over brokered MQTT strategies. Graphical abstract

Collaboration among scholars and institutions is progressively becoming essential to the success of research grant procurement and to allow the emergence and evolution of scientific disciplines. Our work focuses on analysing if the volume of collaborations of one author together with the relevance of his collaborators is somewhat related to his research performance over time. In order to prove this relation we collected the temporal distributions of scholars' publications and citations from the Google Scholar platform and the co-authorship network (of Computer Scientists) underlying the well-known DBLP bibliographic database. By the application of time series clustering, social network analysis and non-parametric statistics, we observe that scholars with similar publications (citations) patterns also tend to have a similar centrality in the co-authorship network. To our knowledge, this is the first work that considers success evolution with respect to co-authorship.

Santa Lucía peloid is a sediment used in pelotherapy in Cuban primary health care services. Therefore, in addition to physicochemical regulated parameters, other analyses are required to complement their physicochemical characterization and understand potential element mobility, radiological risk, and toxicity as well as likely bioactive compounds present in Santa Lucía peloid. For these purposes, inorganic and organic elements and compounds were considered to evaluate Santa Lucía peloid’s quality. This was accomplished through an integral approach that included (1) determination of physicochemical parameters (pH, electrical conductivity, oxidation–reduction potential, temperature, dissolved oxygen, elemental C, H, and N analyses, organic matter, and hexane removable substances content); (2) determination of total concentration of elements with biological and toxicological importance (i.e., Na, K, Ca, Mg, Fe, Mn, Cr, Cu, Ni, Pb, and Zn), as well as their distribution in operationally defined solid phases, mineralogy, particle size distribution, and total content of radionuclides and radiological dose calculations; and (3) its organic characterization. Results from this study showed that Santa Lucía peloid was non-contaminated and showed low metal mobility and acceptable radiological dose levels, being safe for therapeutic uses. Additionally, these results contribute to the understanding of the organic composition of peloides, provide strong evidences to scientifically explain the therapeutic action of peloids in the treatment of inflammatory diseases, and set a new frame to improve peloid guidelines in Cuba and other countries.

Pneumonia is a life-threatening lung infection resulting from several different viral infections. Identifying and treating pneumonia on chest X-ray images can be difficult due to its similarity to other pulmonary diseases. Thus, the existing methods for predicting pneumonia cannot attain substantial levels of accuracy. This paper presents a computer-aided classification of pneumonia, coined Ensemble Learning (EL), to simplify the diagnosis process on chest X-ray images. Our proposal is based on Convolutional Neural Network (CNN) models, which are pretrained CNN models that have been recently employed to enhance the performance of many medical tasks instead of training CNN models from scratch. We propose to use three well-known CNNs (DenseNet169, MobileNetV2, and Vision Transformer) pretrained using the ImageNet database. These models are trained on the chest X-ray data set using fine-tuning. Finally, the results are obtained by combining the extracted features from these three models during the experimental phase. The proposed EL approach outperforms other existing state-of-the-art methods and obtains an accuracy of 93.91% and a F1-score of 93.88% on the testing phase.