This paper presents a new methodology that provides the analysis of surface texture changes in areas adjacent to the volcano and its impact product of volcanic activity. To do this, algorithms from digital image processing such as the co-occurrence matrix and the wavelet transform are used. These methods are working on images taken by the Landsat satellite platform sensor 5 TM and Landsat 7 ETM + sensor, and implemented with the purpose of evaluating superficial changes that can warn of surface movements of the volcano. The results were evaluated by similarity metrics for grayscale images, and validated in two different scenarios that have the same type of eruption, but differ, essentially, in climate and vegetation. Finally, the proposed algorithm is presented, setting the parameters and constraints for implementation and use.

The fast and reliable processing of medical images is of paramount importance to adequately generate data to feed machine learning algorithms that can prevent and diagnose health issues. Here we benchmark different compressed sensing techniques applied to magnetic resonance imaging as a means to reduce the acquisition time spent in the collection of data and signals that form the image. We show that by using these techniques, it is possible to reduce the number of signals needed and, therefore, substantially decrease the time to acquire the measurements. To this end, we have considered and compared different algorithms: the Iterative Re-Weighted Least Squares, the Iterative Soft Thresholding Algorithm, the Iterative Hard Thresholding Algorithm, the Primal Dual Algorithm and the Log-Barrier Algorithm. We have implemented such algorithms in different analysis programs that have been used to perform the reconstruction of the images and found that the Iterative Soft Thresholding Algorithm gives the optimal results. We found that the images obtained with this algorithm have less quality than the original ones, but the quality is good enough to distinguish each body structure and detect any health problems.

(1) Background: This study identifies and analyzes those variables that may influence sick leave due to mental illness, based on a retrospective descriptive study of a mutual insurance company in the industrialized region of southern Europe (Catalonia). (2) Methods: All workers who were on sick leave due to mental illness during the period 2009-2019 were included in the study. The relationships between sick leave due to mental illness and social/employment-related and economical and demographic factors were analyzed using multivariate logistic regression and Cox regression model. (3) Results: The period studied included 34,764 workers. Anxious-depressive disorders account for 83.3% of the diagnosed mental disorders. The age cohorts between 30 and 50 years represent 60% of the affected workers. Highest income and high population density regions are the most affected. The levels of mental illness are higher in spring and summer. Professions related to manufacturing industry, automobile mechanics companies, the hospitality industry, education and healthcare and social service companies was more heavily affected. (4) Conclusions: Population density and GDP per capita, the age cohort, the season of the year, the type of payment, the type of contract, and the worker’s business and profession can predict the appearance of sick leave due to mental illness. Mutual insurance companies should plan interventions to minimize these factors and avoid the socioeconomic consequences.

The commonly accepted definition of sustainability considers the availability of relevant resources to make an activity feasible and durable while also recognizing users' support as essential part of the social side of sustainability. IoT represents a disruption in the general scenario of computing for both users and professionals, The real expansion and integration of applications based on IoT depend on our capacity of exploring the necessary skills and professional profiles that are essential for implementation of IoT projects, but also on the perception of relevant aspects for users, e.g., on privacy, legal, IPR and security issues. Our participation in several EU-funded projects with focus in this area has enabled the collection of information on both sides of IoT sustainability through surveys but also collecting data from a variety of sources. Thanks to these varied and complementary sources of information, this article will explore the user and professional aspects for sustainability of Internet of Things in practice.

The spectra currently emerging from modern ground- and space-based astronomical instruments are of exceptionally high quality and resolution. To meaningfully analyse these spectra researchers utilise complex modelling codes to replicate the observations. The main inputs to these codes are atomic data such as excitation and photoionisation cross sections as well as radiative transition probabilities, energy levels and line strengths. In this publication the current capabilities of the numerical methods and computer packages used in the generation of these data are discussed. Particular emphasis is given to Fe-peak species and the heavy systems of tungsten and molybdenum. Some of the results presented to highlight certain issues and/or advances have already been published in the literature, while other sections present, for the first time, new recently evaluated atomic data.

Artificial intelligence methods may help in unveliling information hidden in high-dimensional oncological data. Flow cytometry studies of haematological malignancies provide quantitative data with the potential to be used for the construction of response biomarkers. Many computational methods from the bioinformatics toolbox can be applied to these data but have not been exploited in their full potential in leukaemias, specifically for the case of childhood B-cell acute lymphoblastic leukemia. In this paper we analysed flow cytometry data obtained on diagnosis from 54 paediatric B-cell acute lymphoblastic leukemia patients from two local institutions. We constructed classifiers based on the Fisher’s Ratio to quantify differences in expression levels of immunophenotypical markers between patients with relapsing and non-relapsing disease. The distribution of the marker CD38 was found and validated to have a strong discriminating power between both patient cohorts, thus providing a classifier.

Vascular calcification (VC), is a common complication in patients with chronic kidney disease and increases mortality. Although oxidative stress is involved in the onset and progression of this disorder, the specific role of some main redox regulators such as catalase, the main scavenger of H2O2, remains unclear. In the present study, epigastric arteries of kidney transplant recipients, an in vivo model of VC and an in vitro model of VC exhibiting catalase overexpression (Cts) were analyzed. Peri-calcification areas of human epigastric arteries have increased levels of catalase and cytoplasmic rather than nuclear RUNX2. In the in vivo model, advanced aortic VC concurs with lower levels of the H2O2 scavenger, glutathione peroxidase 3 compared to controls. In an early model of calcification using vascular smooth muscle cells (VSMCs), Cts VSMCs showed the expected increase in RUNX2 total levels. However, Cts VMSC also exhibited lower percentage of the nucleus stained for RUNX2 in response to calcifying media. At this early model of VC we did not observe dysregulation of mitochondrial redox state, an increase in general redox state was observed in the cytoplasm. These in vivo and in vitro results highlight the complex role of antioxidant enzymes as catalase in the process of VC by regulation of RUNX2 location.