LoRa Enabled Smart Inverters for Microgrid Scenarios with Widespread Elements

The introduction of low-power wide-area networks (LPWANs) has changed the image of smart systems, due to their wide coverage and low-power characteristics. This category of communication technologies is the perfect candidate to be integrated into smart inverter control architectures for remote microgrid (MG) applications. LoRaWAN is one of the leading LPWAN technologies, with some appealing features such as ease of implementation and the possibility of creating private networks. This study is devoted to analyze and evaluate the aforementioned integration. Initially, the characteristics of different LPWAN technologies are introduced, followed by an in-depth analysis of LoRa and LoRaWAN. Next, the role of communication in MGs with widespread elements is explained. A point-by-point LoRa architecture is proposed to be implemented in the grid-feeding control structure of smart inverters. This architecture is experimentally evaluated in terms of latency analysis and externally generated power setpoint, following smart inverters in different LoRa settings. The results demonstrate the effectiveness of the proposed LoRa architecture, while the settings are optimally configured. Finally, a hybrid communication system is proposed that can be effectively implemented for remote residential MG management

A multicenter multinational study of abdominal candidiasis: epidemiology, outcomes and predictors of mortality

Abstract Purpose: Clinical data on patients with intra-abdominal candidiasis (IAC) is still scarce. Methods: We collected data from 13 hospitals in Italy, Spain, Brazil, and Greece over a 3-year period (2011\u20132013) including patients from ICU, medical, and surgical wards. Results: A total of 481 patients were included in the study. Of these, 27 % were hospitalized in ICU. Mean age was 63 years and 57 % of patients were male. IAC mainly consisted of secondary peritonitis (41 %) and abdominal abscesses (30 %); 68 (14 %) cases were also candidemic and 331 (69 %) hadconcomitant bacterial infections. The most commonly isolated Candida species were C. albicans (n = 308 isolates, 64 %) and C. glabrata (n = 76, 16 %). Antifungal treatment included echinocandins (64 %), azoles (32 %), and amphotericin B (4 %). Septic shock was documented in 40.5 % of patients. Overall 30-day hospital mortality was 27 % with 38.9 % mortality in ICU. Multivariate logistic regression showed that age (OR 1.05, 95 % CI 1.03\u20131.07, P\0.001), increments in 1-point APACHE II scores (OR 1.05, 95 % CI 1.01\u20131.08, P = 0.028), secondary peritonitis (OR 1.72, 95 % CI 1.02\u20132.89, P = 0.019), septic shock (OR 3.29, 95 % CI 1.88\u20135.86, P\0.001), and absence of adequate abdominal source control (OR 3.35, 95 % CI 2.01\u20135.63, P\0.001) wereassociated with mortality. In patients with septic shock, absence of source control correlated with mortality rates above 60 % irrespective of administration of an adequate antifungal therapy. Conclusions: Low percentages of concomitant candidemia and high mortality rates are documented in IAC. In patients presenting with septic shock, source control is fundamental

Pro-vegetarian food patterns and cardiometabolic risk in the PREDIMED-Plus study: a cross-sectional baseline analysis

[Purpose]: We explored the cross-sectional association between the adherence to three different provegetarian (PVG) food patterns defined as general (gPVG), healthful (hPVG) and unhealthful (uPVG), and the cardiometabolic risk in adults with metabolic syndrome (MetS) of the PREDIMED-Plus randomized intervention study. [Methods]: We performed a cross-sectional analysis of baseline data from 6439 participants of the PREDIMED-Plus randomized intervention study. The gPVG food pattern was built by positively scoring plant foods (vegetables/fruits/legumes/grains/potatoes/nuts/olive oil) and negatively scoring, animal foods (meat and meat products/animal fats/eggs/fish and seafood/dairy products). The hPVG and uPVG were generated from the gPVG by adding four new food groups (tea and coffee/fruit juices/sugar-sweetened beverages/sweets and desserts), splitting grains and potatoes and scoring them differently. Multivariable-adjusted robust linear regression using MM-type estimator was used to assess the association between PVG food patterns and the standardized Metabolic Syndrome score (MetS z-score), a composed index that has been previously used to ascertain the cardiometabolic risk, adjusting for potential confounders. [Results]: A higher adherence to the gPVG and hPVG was associated with lower cardiometabolic risk in multivariable models. The regression coefficients for 5th vs. 1st quintile were − 0.16 (95% CI: − 0.33 to 0.01) for gPVG (p trend: 0.015), and − 0.23 (95% CI: − 0.41 to − 0.05) for hPVG (p trend: 0.016). In contrast, a higher adherence to the uPVG was associated with higher cardiometabolic risk, 0.21 (95% CI: 0.04 to 0.38) (p trend: 0.019). [Conclusion]: Higher adherence to gPVG and hPVG food patterns was generally associated with lower cardiovascular risk, whereas higher adherence to uPVG was associated to higher cardiovascular risk.This work was supported by the official Spanish Institutions for funding scientific biomedical research, CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN) and Instituto de Salud Carlos III (ISCIII), through the Fondo de Investigación para la Salud (FIS), which is co-funded by the European Regional Development Fund (six coordinated FIS projects leaded by JS-S and JVi, including the following projects: PI13/00673, PI13/00492, PI13/00272, PI13/01123, PI13/00462, PI13/00233, PI13/02184, PI13/00728, PI13/01090, PI13/01056, PI14/01722, PI14/00636, PI14/00618, PI14/00696, PI14/01206, PI14/01919, PI14/00853, PI14/01374, PI14/00972, PI14/00728, PI14/01471, PI16/00473, PI16/00662, PI16/01873, PI16/01094, PI16/00501, PI16/00533, PI16/00381, PI16/00366, PI16/01522, PI16/01120, PI17/00764, PI17/01183, PI17/00855, PI17/01347, PI17/00525, PI17/01827, PI17/00532, PI17/00215, PI17/01441, PI17/00508, PI17/01732, PI17/00926, PI19/00957, PI19/00386, PI19/00309, PI19/01032, PI19/00576, PI19/00017, PI19/01226, PI19/00781, PI19/01560, PI19/01332, PI20/01802, PI20/00138, PI20/01532, PI20/00456, PI20/00339, PI20/00557, PI20/00886, PI20/01158); the Especial Action Project entitled: Implementación y evaluación de una intervención intensiva sobre la actividad física Cohorte PREDIMED-Plus grant to JS-S; the European Research Council (Advanced Research Grant 2014–2019; agreement #340918) granted to MÁM-G.; the Recercaixa (number 2013ACUP00194) grant to JS-S; grants from the Consejería de Salud de la Junta de Andalucía (PI0458/2013, PS0358/2016, PI0137/2018); the PROMETEO/2017/017 grant from the Generalitat Valenciana; the SEMERGEN grant; None of the funding sources took part in the design, collection, analysis, interpretation of the data, or writing the report, or in the decision to submit the manuscript for publication

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Cabbage and fermented vegetables : From death rate heterogeneity in countries to candidates for mitigation strategies of severe COVID-19

Large differences in COVID-19 death rates exist between countries and between regions of the same country. Some very low death rate countries such as Eastern Asia, Central Europe, or the Balkans have a common feature of eating large quantities of fermented foods. Although biases exist when examining ecological studies, fermented vegetables or cabbage have been associated with low death rates in European countries. SARS-CoV-2 binds to its receptor, the angiotensin-converting enzyme 2 (ACE2). As a result of SARS-CoV-2 binding, ACE2 downregulation enhances the angiotensin II receptor type 1 (AT(1)R) axis associated with oxidative stress. This leads to insulin resistance as well as lung and endothelial damage, two severe outcomes of COVID-19. The nuclear factor (erythroid-derived 2)-like 2 (Nrf2) is the most potent antioxidant in humans and can block in particular the AT(1)R axis. Cabbage contains precursors of sulforaphane, the most active natural activator of Nrf2. Fermented vegetables contain many lactobacilli, which are also potent Nrf2 activators. Three examples are: kimchi in Korea, westernized foods, and the slum paradox. It is proposed that fermented cabbage is a proof-of-concept of dietary manipulations that may enhance Nrf2-associated antioxidant effects, helpful in mitigating COVID-19 severity.Peer reviewe