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This article presents the Schumann resonance (SR) variations, obtained in the extreme low frequency (ELF) signal observatory of Sierra de Filabres belonging to the University of Almeria Spain, in operation since 2011. This station has two large and sensitive orthogonal sensors operating between 1 and 100 Hz. A 24 bit low-noise, high-resolution system processes the signal. The Sierra de Filabres ELF station is operating to offer valuable information on SRs’ activity in this area. This system allows us to have a broader range of comparison patterns to study correlations with other phenomena. The information presented in this work deals with the diurnal and seasonal variations of the first six modes of the SRs in 2016 and 2017. The graphs have been obtained through Lorentzian functions. We explain the diurnal variation based on the position and distance to the most important lightning hotspots and demonstrate its relation with the first mode captured in our observatory. We introduce a new yearly evolution method based on the resonance frequency through the most representative lightning activity hours in one year. This article is supported by the comparison between our signals and the global thunderstorm center’s activity.

Background: To analyze the long-term outcomes for advanced cancer patients admitted to an intermediate care unit (ImCU), an analysis of a do not resuscitate orders (DNR) subgroup was made. Methods: A retrospective observational study was conducted from 2006 to January 2019 in a single academic medical center of cancer patients with stage IV disease who suffered acute severe complications. The Simplified Acute Physiology Score 3 (SAPS 3) was used as a prognostic and severity score. In-hospital mortality, 30-day mortality and survival after hospital discharge were calculated. Results: Two hundred and forty patients with stage IV cancer who attended at an ImCU were included. In total, 47.5% of the cohort had DNR orders. The two most frequent reasons for admission were sepsis (32.1%) and acute respiratory failure (excluding sepsis) (38.7%). Mortality in the ImCU was 10.8%. The mean predicted in-hospital mortality according to SAPS 3 was 51.9%. The observed in-hospital mortality was 37.5% (standard mortality ratio of 0.72). Patients discharged from hospital had a median survival of 81 (30.75–391.25) days (patients with DNR orders 46 days (19.5–92.25), patients without DNR orders 162 days (39.5–632)). The observed mortality was higher in patients with DNR orders: 52.6% vs. 23.8%, p 0 < 0.001. By multivariate logistic regression, a worse ECOG performance status (3–4 vs. 0–2), a higher SAPS 3 Score and DNR orders were associated with a higher in-hospital mortality. By multivariate analysis, non-invasive mechanical ventilation, higher bilirubin levels and DNR orders were significantly associated with 30-day mortality. Conclusion: For patients with advanced cancer disease, even those with DNR orders, who suffer from acute complications or require continuous monitoring, an ImCU-centered multidisciplinary management shows encouraging results in terms of observed-to-expected mortality ratios.

Seven decades have passed since the Schumann resonances (SRs) were identified. Since then, their research interest has increased, currently being a topic of significance. Nonetheless, the papers that study their nature from the frequency perspective are in clear majority regarding those who focus on the time domain. To fill this gap in the literature and further characterize the SRs, a method to perform statistical analysis on the SRs signal in the time domain has been developed. For any given segment of data, the analysis performs a maximum likelihood estimation (MLE) of the statistical parameters from a group of previously selected distributions. After that, the best fit among the target distributions is selected through the application of Akaike information criterion (AIC). The method is tested by analyzing a month’s worth of data, showing the general analysis’ results and discussing the relationship between the chosen target distributions and common aspects between the time segments fitted to them. The reliability of the method’s results is also discussed by looking at different aspects of the analysis. Special emphasis is put over the results being correlated with lightning activity. This correlation highlights the usefulness of the method, given the well-established relationship between lightning and SRs.

Crowd monitoring was an essential measure to deal with over-tourism problems in urban destinations in the pre-COVID era. It will play a crucial role in the pandemic scenario when restarting tourism and making destinations safer. Notably, a Destination Management Organisation (DMO) of a smart destination needs to deploy a technological layer for crowd monitoring that allows data gathering in order to count visitors and distinguish them from residents. The correct identification of visitors versus residents by a DMO, while privacy rights (e.g., Regulation EU 2016/679, also known as GDPR) are ensured, is an ongoing problem that has not been fully solved. In this paper, we describe a novel approach to gathering crowd data by processing (i) massive scanning of WiFi access points of the smart destination to find SSIDs (Service Set Identifier), as well as (ii) the exposed Preferred Network List (PNL) containing the SSIDs of WiFi access points to which WiFi-enabled mobile devices are likely to connect. These data enable us to provide the number of visitors and residents of a crowd at a given point of interest of a tourism destination. A pilot study has been conducted in the city of Alcoi (Spain), comparing data from our approach with data provided by manually filled surveys from the Alcoi Tourist Info office, with an average accuracy of 83%, thus showing the feasibility of our policy to enrich the information system of a smart destination. This research was carried out within the research Project Alcoi Tourist Lab framework, co-funded by the Alcoi City Council & the Valencian Innovation Agency. The research was also partially funded by project UAPOSTCOVID19-10 from the University of Alicante. Finally, this research was partly supported by the EU CEF project GreenMov, CARM HORECOV-21 project (https://horecovid.com/ (accessed on 12 January 2022)). is financed through the Call for Public Aid destined to finance the Strategic projects contemplated in the Research and Innovation Strategy for Smart Specialization - RIS3MUR Strategy by the Autonomous Community of the Region of Murcia, through the Ministry of Economic Development, Tourism and Employment within the framework of the FEDER Region of Murcia Operational Program 2014–2020 within the framework Thematic Objective 1. Strengthen research, technological development and innovation by 80% and with CARM’s own funds in 20%, and finally the EU project H2020 NIoVE (833742).

International audience; Background: Streptococcus equi subspecies equi (S.equi) is the cause of the highly contagious equine respiratory disease ‘strangles’. Approximately 10% of recovered animals can persistently carry the bacteria and transmit it to naïve animals. The global movement of horses is an ideal mechanism for widespread transmission to geographically distant locations.Objectives: Utilise whole-genome sequence data to disentangle the transmission of S. equi and subsequent outbreaks of strangles.Study design: In vitro analysis of micro-organisms.Methods: Isolates (n = 670) of S. equi were recovered from clinical samples submitted to multiple collaborating clinics and institutions globally. Following species confirmation, isolates underwent whole-genome sequencing using Illumina technology. Sequence reads passing quality control measures were assembled and uploaded to Pathogenwatch, which assigned a phylogeny based upon sequences of core genome alleles. Population structure was inferred using the population mixture analysis in BAPS.Results: BAPS clustered the isolates into six different clusters (BAPS 1-6) and showed dominant lineages in different geographical areas but also global transmission within the clusters. Sub-groups within the clusters highlighted multiple outbreaks at local, national and international scales and highlighted population structures and transmission dynamics within single locations. For example, four different strains collected over just seven months were identified in a single location. Sequence data also identified a statistically significant decline in BAPS-5 since 2010.Main limitations: Pathogenwatch has shown its utility in investigating S. equi transmission and population structure. However, it is based upon a curated set of 1286 core genome loci. Further investigations will need to be conducted using the full spectrum of data available from whole-genome sequencing.Conclusions: Pathogenwatch was used as a tool to rapidly identify and visualise the whole-genome sequence data of a large S. equi dataset. The data demonstrate widespread transmission of multiple S. equi lineages and provide strong evidence that asymptomatic carrier horses are perpetuating this dissemination.

Sustainability is becoming of vital importance in project management, and a life cycle assessment (LCA) can ensure a body of knowledge to bear in mind the environmental burdens the project involves. In this study, two different ways of supplying energy to a sensor network are analyzed. Firstly, we analyze the environmental impact of the devices connected to the public grid. Secondly, we analyze the completely full off-grid system, with the sensor connected to a photovoltaic (PV) panel. Our findings show that the off-grid option has a greater number of benefits than the grid-connected option in terms of environmental impacts, although it is less economically advantageous. In a detailed analysis of the off-grid scenario, it can be observed that the battery is the component with the highest impact, so actions to try to reduce consumption, and, therefore, the battery size and its negative impact are taken. After reducing the battery size, the break-even point was reached, providing a net economic benefit of EUR 0.23 sensor/year. However, this analysis refers to a single sensor, and although the environmental and economic benefits seem low, in an economy of scale, this could result in large savings if these types of sensors are massively installed.