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With the growing ability to collect large volumes of volcano seismic data, the detection and labeling process of these records is increasingly challenging. Clearly, analyzing all available data through manual inspection is no longer a viable option. Supervised machine learning models might be considered to automatize the analysis of data acquired by in situ monitoring stations. However, the direct application of such algorithms is defiant, given the high complexity of waveforms and the scarce and often imbalanced amount of labeled data. In light of this and motivated by the wide success that generative adversarial networks (GANs) have seen at generating images, we present ESeismic-GAN, a GAN model to generate the magnitude frequency response of volcanic events. Our experiments demonstrate that ESeismic-GAN learns to generate the frequency components that characterize long-period and volcano-tectonic events from Cotopaxi volcano. We evaluate the performance of ESeismic-GAN during the training stage using Fréchet distance, and, later on, we reconstruct the signals into time-domain to be finally evaluated with Frechet inception distance.

This article proposes a new volcano seismic signal descriptor for improving the area under the receiver operating characteristic curve (AUC) in the classification of long-period (LP) and volcano-tectonic (VT) seismic events. It aims to describe a volcanic seismic event from a different and novel point of view that involves image processing techniques instead of classical seismic signal processing strategies, such as frequency or scale analysis. The proposed descriptor allows exploring the seismic signal space for obtaining the determination of the event patterns and, subsequently, the extraction of intensity-, shape-, and texture-based features into a numeric vectorial output for supplying a set of selected machine learning classifiers with different taxonomies. The descriptor was validated on a seismic signal database collected at the Cotopaxi volcano, containing a total of 637 events, including LP, VT, and other types of seismic events (e.g., rockfall or icequakes). An accuracy value of 96% was obtained in the determination of the event patterns using the signal database, while the values of 0.95 and 0.96 were obtained for the AUC when using a feedforward backpropagation artificial neural network classifier on two experimental data sets, containing feature vectors representing signal with and without event overlapping, respectively. The obtained results demonstrate that the proposed descriptor is capable of providing adequate seismic signal representations in a different feature space and that its output provides competitive results in the classification of volcanic seismic events.

Health systems are in constant change, either due to technological progress or due to the same need produced by external agents such as COVID-19, which have caused an analysis of all the information systems dedicated to the various procedures in the health area, from clinical information management, information from health insurers, image information management, drug supply management, medical supplies and supplies management, among other systems specific to each health institution. It is in this environment where a lot of vital information for decision making is produced that in most cases is impossible to manage, because it is found on different platforms and in most cases in different institutions. The present research presents a methodology for the exchange of information between systems based on the interoperability of systems and using the XML standard, very similar to what the HL7 protocol advises, the implementation of the methodology can be synchronous or asynchronous depending on the degree. of need that is required to use and depending on the different users who require to share their information. The results show the practicality of the implementation and that these can be implemented in the shortest time possible using communication between messages following the protocol presented, which will depend on the information required, the implementation can be done by reading the message and loading the case system Contrary to being able to enter the system directly, its application will depend on the ease of being able to update the information systems that are running. Breña

Despite the great advance of bone tissue engineering in the last few years, repair of bone defects remains a major problem. Low cell engraftment and dose‐dependent side effects linked to the concomitant administration of bone morphogenetic proteins (BMPs) are the main problems currently hindering the clinical use of mesenchymal stem cell (MSC)‐based therapies in this field. We have managed to bypass these drawbacks by combining the silencing the Smurf1 ubiquitin ligase in MSCs with the use of a scaffold that sustainably releases low doses of BMP‐2. In this system, Smurf1 silencing is achieved by using GapmeRs, a clinically safe method that avoids the use of viral vectors, facilitating its translation to the clinic. Here, we show that a single transient transfection with a small quantity of a Smurf1‐specific GapmeR is able to induce a significant level of silencing of the target gene, enough to prime MSCs for osteogenic differentiation. Smurf1 silencing highly increases MSCs responsiveness to BMP‐2, allowing a dramatic reduction of the dose needed to achieve the desired therapeutic effect. The combination of these primed cells with alginate scaffolds designed to sustainably and locally release low doses of BMP‐2 to the defect microenvironment is able to induce the formation of a mature bone matrix both in an osteoporotic rat calvaria system and in a mouse ectopic model. Importantly, this approach also enhances osteogenic differentiation in MSCs from osteoporotic patients, characterized by a reduced bone‐forming potential, even at low BMP doses, underscoring the regenerative potential of this system. stem cells translational medicine 2019;8:1306&1317, The BMP‐Smad signaling cascade is an effective therapeutic target to promote bone formation. Silencing of Smurf1, a known BMP signaling inhibitor, increases the responsiveness of Mesenchymal stem cells to BMP, allowing a dramatic reduction of the doses used in the clinic to promote bone formation and therefore, avoiding secondary effects associated to the use of these factors.

In health centers, hospital services are a useful source of information that cannot be interrupted, as they provide necessary clinical information such as results of clinical examinations, results of radiological examinations, search results for available drugs, among other necessary information. In the absence of commercial electrical energy, mechanisms should be considered to ensure the continuity of information services through the use of battery banks, this methodology presents a technique based on IoT techniques, through which the consumption of the package is monitored. of batteries found in the data centers of hospital establishments, to be able to analyze the level of charge and the state of the batteries at all times when the commercial electrical power is active, when the commercial power is cut, the battery bank power, so your tracking on the consumption of the battery Charging is of vital importance to be able to activate the alternative and energy mechanisms such as electric power generating motors, but these motors have an ignition and stabilization time, it is at this time that it is necessary to monitor the use and discharge of energy from the power bank batteries found in data centers, the mechanism to be used for the connection through IoT is based on an ATECC608A development device, which has an ATWINC1510 Wi-Fi connection with an interface developed in LabView, The results demonstrate the use and practicality as most power outages occur at night so data center workers are not at their workplace and remote monitoring of battery banks is very useful in these situations based on IoT technology.

This paper proposes an exhaustive evaluation of five different filter-based feature selection methods in combination with a Gaussian mixture model classifier for the classification of long-period (LP) and volcano-tectonic (VT) seismic events recorded at Cotopaxi volcano in Ecuador. The experimentation included both exploring and ranking search spaces of seismic-signal-based features, and selecting subsets of optimal features for event classification. The evaluation was carried out by using an experimental dataset formed by 587 LP and 81 VT feature vectors, each composed of 84 statistical, temporal, spectral, and scale-domain features extracted from the original seismic signals. The best result in accuracy, precision, recall, and processing time for LP seismic event classification was obtained by using the Chi2 discretization method with five features, achieving 95.62%, 99.08%, 95.94%, and 3.7 ms, respectively, whereas for VT seismic event classification, the uFilter method with five features reached the scores of 96.71%, 85.23%, 96.00%, and 4.1 ms, respectively. For the classification of both seismic events simultaneously, the uFilter method with five features yielded 96.70%, 97.77%, 96.7%, and 4.1 ms, respectively. According to the Wilcoxon statistical test, these classification schemes provide competitive seismic event classification, while reducing the required processing time.

An optimal strategy for building realistic geological models must combine different geophysical techniques, each with its advantages and limitations. However, dealing with multiple geophysical datasets to derive a geological interpretation is not straightforward since geophysical parameters are not always functionally related. In this work, we propose an innovative approach consisting of using machine learning techniques to jointly interpret three geophysical datasets (a pseudo-3D resistivity model, a 3D velocity model, and 4 well-logs). These datasets, among others, were acquired to characterize the suitability of an evaporitic sequence for hosting a temporary storage facility of hazardous radioactive waste, which was planned in Villar de Cañas (Spain). Our strategy consisted of integrating both models in a single 3D bi-parametric grid that nested the velocity and resistivity values in each node. We then used a supervised learning algorithm to lithologically classify each node according to a training set based on the borehole data, which acts as ground truth. The training set is composed of classifiers that lithologically label resistivity-velocity pairs. However, the very shallow nodes lack classifiers due to the poor well-log coverage at the top part of the evaporitic sequence. To fill this gap, we computed an unsupervised cluster analysis that provided new classes to complete the training set. Finally, the supervised classification was applied, providing a new 3D lithology model that is far more consistent with the geology than the models derived from each parameter independently. The 3D model also revealed geological features previously unknown, notably the existence of an inactive fault. The proposed method can be applied to integrate and jointly interpret any kind of multidisciplinary datasets in a wide range of geoscientific problems, including natural resource exploration, geological storage, environmental monitoring, civil engineering practice, and hazard assessment., This work has been supported by projects ref: CGL2014-56548-P, CGL2016-81964-REDE supported by the Spanish Ministry of Science and Innovation, 2017 SGR 1022 Generalitat de Catalunya and, by ENRESA, the latter also facilitated access to log and resistivity data acquired and processed by industry partners (thanks and acknowledgments to INYPSA, EPTISA, ByA-Peñarrolla, and AGS). The instrumentation for the seismic acquisition was provided by different sources: the seismic data recording system consisted of 10 GEODE (Geometrics) and was provided by the GIPP-GFZ Potsdam (Germany). The acoustic energy used as a source was generated by a 250 kg accelerated weight drop provided by the Instituto Tecnico Superior, Univ. Lisbon, (Portugal) and a 90 kg accelerated weight drop provided by the Univ. of Oviedo, (Spain). JA is funded by MICINN (Juan de la Cierva fellowship - IJC2018-036074-I).

We performed advanced opto-electrical simulations on thin-film BaSi2 solar cells. First, absorption spectra of BaSi2-pn homojunction solar cells on Si substrate were calculated based on flat and/or pyramidally-textured surfaces, wherein 20-nm-thick n+-BaSi2 was the topmost electron transport layer. By changing the front surface structure from flat to texture, the reflectance decreased in the wavelength (λ) range 700–1200 nm and the photocurrent density (Jph) delivered by the photogenerated carriers in the 500-nm thick p-BaSi2 layer increased by 1.2 mA/cm2. Simulations revealed that the key factor inhibiting light absorption in the p-BaSi2 layer was parasitic absorption in the n+-BaSi2 and in the c-Si substrate. To solve these optical issues, we propose a new device structure, Al-doped n+-ZnO (AZO, 50 nm)/i-ZnO (20 nm)/p-BaSi2 (500 nm) heterojunction solar cell (HJSC). In this device structure, the parasitic absorption reduced drastically, and Jph reached 30.23 mA/cm2. Furthermore, by replacing the Si substrate with a glass substrate, the light trapping worked more effectively, and the absorber layer thickness required for Jph to saturate was reduced to 1 μm, yielding 32.06 mA/cm2. Based on these simulation results, we manufactured n+-AZO/p-BaSi2 HJSC. The internal quantum efficiency exceeded 30% at λ = 600 nm, meaning that we demonstrated the operation of n+-AZO/p-BaSi2 HJSC for the first time. We investigated origins of small efficiencies compared to those simulated, and found that the passivation of defects in the p-BaSi2 layer and the reduction of carrier recombination at the i-ZnO/p-BaSi2 interface would significantly improve the solar cell performance.

REIPI-SEIMC COVID-19 group and COVID@HULP group., [Background] The clinical presentation of COVID-19 in patients admitted to hospital is heterogeneous. We aimed to determine whether clinical phenotypes of patients with COVID-19 can be derived from clinical data, to assess the reproducibility of these phenotypes and correlation with prognosis, and to derive and validate a simplified probabilistic model for phenotype assignment. Phenotype identification was not primarily intended as a predictive tool for mortality., [Methods] In this study, we used data from two cohorts: the COVID-19@Spain cohort, a retrospective cohort including 4035 consecutive adult patients admitted to 127 hospitals in Spain with COVID-19 between Feb 2 and March 17, 2020, and the COVID-19@HULP cohort, including 2226 consecutive adult patients admitted to a teaching hospital in Madrid between Feb 25 and April 19, 2020. The COVID-19@Spain cohort was divided into a derivation cohort, comprising 2667 randomly selected patients, and an internal validation cohort, comprising the remaining 1368 patients. The COVID-19@HULP cohort was used as an external validation cohort. A probabilistic model for phenotype assignment was derived in the derivation cohort using multinomial logistic regression and validated in the internal validation cohort. The model was also applied to the external validation cohort. 30-day mortality and other prognostic variables were assessed in the derived phenotypes and in the phenotypes assigned by the probabilistic model., [Findings] Three distinct phenotypes were derived in the derivation cohort (n=2667)—phenotype A (516 [19%] patients), phenotype B (1955 [73%]) and phenotype C (196 [7%])—and reproduced in the internal validation cohort (n=1368)—phenotype A (233 [17%] patients), phenotype B (1019 [74%]), and phenotype C (116 [8%]). Patients with phenotype A were younger, were less frequently male, had mild viral symptoms, and had normal inflammatory parameters. Patients with phenotype B included more patients with obesity, lymphocytopenia, and moderately elevated inflammatory parameters. Patients with phenotype C included older patients with more comorbidities and even higher inflammatory parameters than phenotype B. We developed a simplified probabilistic model (validated in the internal validation cohort) for phenotype assignment, including 16 variables. In the derivation cohort, 30-day mortality rates were 2·5% (95% CI 1·4–4·3) for patients with phenotype A, 30·5% (28·5–32·6) for patients with phenotype B, and 60·7% (53·7–67·2) for patients with phenotype C (log-rank test p, [Interpretation] Patients admitted to hospital with COVID-19 can be classified into three phenotypes that correlate with mortality. We developed and validated a simplified tool for the probabilistic assignment of patients into phenotypes. These results might help to better classify patients for clinical management, but the pathophysiological mechanisms of the phenotypes must be investigated., [Funding] Instituto de Salud Carlos III, Spanish Ministry of Science and Innovation, and Fundación SEIMC/GeSIDA., Funding: Instituto de Salud Carlos III, Spanish Ministry of Science and Innovation, and Fundación SEIMC/GeSIDA.

Background: Acute beetroot juice (BJ) intake has shown to enhance aerobic and anaerobic performance. However, no studies have evaluated the effects of BJ intake on CrossFit (CF) performance by linking hormonal, metabolic, and mechanical responses. The purpose of this study was to determine the causal physiological association between hormonal, metabolic and mechanical responses, and CF workouts performance after acute BJ intake. Methods: Twelve well-trained male practitioners undertook a CF workout after drinking 140 mL of BJ (~ 12.8 mmol NO3−) or placebo. The two experimental conditions (BJ or placebo) were administered using a randomized, double-blind, crossover design. The CF workout consisted of repeating the same exercise routine twice: Wall ball (WB) shots plus full back squat (FBS) with 3-min rest (1st routine) or without rest (2nd routine) between the two exercises. A 3-min rest was established between the two exercise routines. Results: An interaction effect was observed in the number of repetitions performed (p = 0.04). The Bonferroni test determined a higher number of repetitions after BJ than placebo intake when a 3-min rest between WB and FBS (1st routine) was established (p = 0.007). An interaction effect was detected in cortisol response (p = 0.04). Cortisol showed a higher increase after BJ compared to placebo intake (76% vs. 36%, respectively). No interaction effect was observed in the testosterone and testosterone/cortisol ratio (p > 0.05). A significant interaction effect was found in oxygen saturation (p = 0.01). A greater oxygen saturation drop was observed in BJ compared to placebo (p < 0.05). An interaction effect was verified in muscular fatigue (p = 0.03) with a higher muscular fatigue being observed with BJ than placebo (p = 0.02). Conclusions: BJ intake improved anaerobic performance only after the recovery time between exercises. This increase in performance in the first routine probably generated greater hypoxia in the muscle mass involved, possibly conditioning post-exercise performance. This was observed with a fall in oxygen saturation and in muscle fatigue measured at the end of the CF workout. The greatest perceived changes in cortisol levels after BJ intake could be attributed to the nitrate-nitrite-nitric oxide pathway

The development of medical equipment is related to compliance with regulations, with the intention of safeguarding the physical integrity of patients, being able to ensure that a medical equipment complies with the regulations and can be registered with its respective authorization for use, it is a job long-term with the use of multiple resources. It is due to this characteristic that it is difficult for them to be able to register developments of medical equipment in most countries, either due to lack of infrastructure to carry out patient safety tests or due to the lack of regulations for the registration of medical equipment manufactured in their facilities of respective countries. In this research, a methodology is presented to be able to design medical equipment, considering the regulations and considerations regarding patient safety, as a result, a procedure based on the study of the market of similar equipment is presented in order to guarantee its possible registration and a matrix of technical information, to ensure compliance with regulations and patient safety. Breña

Information and communication technologies are revolutionizing the different areas of knowledge, and medicine is an area where the uses of new technologies are put into practice for the well-being of people. But these new ways of using technology produce a high risk when information is required to be transmitted, either due to the loss of information also due to the risk that the information may be altered for different reasons and due to the theft of information. It is for these reasons that it is necessary to have certain mechanisms to be able to achieve the maximum integrity of the information, in this work a methodology is presented to be able to safely transmit information from the different medical teams to medical information based on an organization of the information, in order to present in a practical way its possible implementation in the development of systems to develop TELESALUD, the protocol is based on the good practices provided by the HL7 standard for the management of clinical information as well as the interoperability between the systems of information and medical equipment that are operating remotely, as results a proposal is presented to be able to carry out rural TELESALUD, oriented to the measurement of patients using various equipment and their transmission to other hospital centers for their diagnosis. Breña

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., The automation of Network Services (NS) consisting of virtual functions connected through a multilayer packet-over-optical network requires predictable Quality of Service (QoS) performance, measured in terms of throughput and latency, to allow making proactive decisions. QoS is typically guaranteed by overprovisioning capacity dedicated to the NS, which increases costs for customers and network operators, especially when the traffic generated by the users and/or the virtual functions highly varies over the time. This article presents the PILOT methodology for modeling the performance of connectivity services during commissioning testing in terms of throughput and latency. Benefits are double: first, an accurate per-connection model allows operators to better operate their networks and reduce the need for overprovisioning; and second, customers can tune their applications to the performance characteristics of the connectivity. PILOT runs in a sandbox domain and constructs a scenario where an efficient traffic flow simulation environment, based on the CURSA-SQ model, is used to generate large amounts of data for Machine Learning (ML) model training and validation. The simulation scenario is tuned using real measurements of the connection (including throughput and latency) obtained from a set of active probes in the operator network. PILOT has been experimentally validated on a distributed testbed connecting UPC and Telefónica premises, This work was partially supported by the EC through the METRO-HAUL (G.A. nº 761727) project, from the Spanish MINECO/FEDER TWINS (TEC2017-90097-R) and TRÁFICA (TEC2015-69417-C2-1-R) projects and from the Catalan Institution for Research and Advanced Studies.

Microseism classification is primordial to easily identify what type of event we are facing at in a possibly dangerous situation. However, labeling events is a hard and time-consuming task since it requires expert volcanologists to do this work. To alleviate the need for abundant labeled data, we propose a semi-supervised approach using the self-training algorithm. First, we extract several relevant microseisms features from the registers on the provided database, then we apply PCA to reduce redundancy on the features and finally we classify them using an SVM classifier. As a result of this methodology we show that although the accuracy of using a supervised scheme is still better than a semi-supervised one, if we allow a 10% of false positive rate, our approach achieves similar performance to supervised techniques with only 50% of labeled data. This demonstrates the potential of semi-supervised schemes.

The proper identification of several types of volcanic seismic events can be related to the intrinsic behavior of a volcano, and it could be useful to provide an early alarm in the case of an impending eruption. Among different recorded seismic events, the long-period and volcano-tectonic stand as the most important events to track since their occurrence increase may help to forecast possible eruptions. Thus, the correct classification of both types of seismic events could increase the safety of peoples living around the volcano. In this sense, this paper proposed a comprehensive algorithm for building classification models to classify long-period and volcano-tectonic seismic events based on a suitable combination of five well-known machine learning classifiers that provide the highest performance over the area under receiver operating characteristic curve. The method explores different machine learning model combinations to find the most adequate in the context of long-period and volcano-tectonic seismic event classification. The proposed method was validated on an experimental dataset, containing 587 long-period and 81 volcano-tectonic seismic events recorded from the Cotopaxi Volcano in Ecuador. The random forest classifier with 100 tree based predictors demonstrated to be the best classification model. According to the Wilcoxon Statistical test, the proposed method was effective in providing competitive models for the classification of volcano seismic events.

The aim of this study is to review bacterial isolates from respiratory samples of patients with severe COVID-19 disease during the first two months of the first wave of in our hospital. A single-centre retrospective observational study in critically ill adult patients was performed. A total of 1251 respiratory samples from 1195 patients were processed. Samples from 66 patients (5.52%) were determined to be microbiologically significant by a semi-quantitative culture. All patients received broad spectrum antibiotherapy as an empirical treatment. The isolated bacteria were mainly Enterobacterales followed by Staphylococcus aureus and Pseudomonas aeruginosa. Bacterial co-infections in ICU stay could seem not dependant on the virus that has produced the viral pneumonia similarly as with other respiratory viruses such as Influenza virus.

Background: Tobacco and cannabis use is highly prevalent among patients with psychotic disorders. The smoking habit is the main risk factor for chronic respiratory diseases, such as COPD or asthma, this being among the main causes of premature mortality in psychosis. The study aims to evaluate the incidene of chronic respiratory pathology in a sample of patients with psychosis, during the first 10 years after the onset of the psychiatric disorder. Methodology: The respiratory function of a sample of patients with a first episode of psychosis (n=83) was evaluated after 10 years of debut of the disease. The functional tests consisted of spirometry enforced with bronchodilatation and diffusion of CO. Patients also answered dyspnea and quality of life tests. Results: 83 patients (47% women) were evaluated. 53.7% (n=44) had active tobacco consumption (of which 5 -6.4%- also used cannabis) and 8 patients (9.7%) “non-smokers” reported being “passive smokers”. 16 patients were “ex-smokers” (more than 6 months without tobacco and/or cannabis use). Patients presented low clinical respiratory severity: Dyspnea-12 scale (mean 4.85, SD 5.9) and mMRC (mean 0.4, SD 0.7). The baseline spirometry showed mean values of FVC of 4.2 liters (105% ref. value) and FEV1 of 3.2 liters (96.8% reference value). The CO diffusion test showed an average lung diffusion capacity of 71.4% (DLCO%, relative to reference values). Quality of life and health status average scores were 3.5 and 3.3 points out of 5, respectively (between “normal” and “fairly good”). Functional tests identified 5 new cases of COPD and 4 of asthma. Conclusions: The results showed the relevance of respiratory diseases in psychotic population.

To estimate the prevalence of musculoskeletal disorders (MSK) and rheumatic diseases in an indigenous Wichi population in Argentina.This is a cross-sectional, community-based study using the Community-Oriented Program for the Control of Rheumatic Diseases (COPCORD) methodology in ≥ 18-year-old subjects. Validated surveys were conducted by trained interviewers. Subjects with MSK pain (positive cases) were evaluated by internists and rheumatologists for diagnosis and treatment.A total of 648 interviews were performed (90.4% of the census population). Mean age was 37.5 years (SD 14.8), and 379 (58.5%) were female. The mean years of education was 7.0 (SD 3.7); 552 subjects (85.2%) were covered by the public health care system. A total of 216 (33.3%) subjects had MSK pain in the last 7 days. Rheumatic disease prevalence was as follows: mechanical back pain (19.0%), rheumatic regional pain syndrome (5.2%), osteoarthritis (3.2%), rheumatoid arthritis (RA) (3.2%), inflammatory back pain (1.2%), undifferentiated arthritis (0.3%), Sjögren syndrome (0.15%), and fibromyalgia (0.15%). RA patients included 19 (90.5%) women and 9 (42.9%) with RA family history. One hundred percent were seropositive and 66.7% showed radiologic erosions. The mean of Disease Activity Score [DAS-28 (ESR)] at the time of diagnosis was 5.1 (SD 1.5) and the Health Assessment Questionnaire Disability Index (HAQ-DI) was 0.8 (SD 0.4).RA prevalence was 3.2%, one of the highest reported using the COPCORD methodology in indigenous and non-indigenous peoples in Latin America, with a high percentage of family cases. Pain and functional capacity were the variables allowing patients' early referral to a specialist. Key Points • The RA prevalence was 3.2%, one of the highest reported using COPCORD methodology in indigenous and non-indigenous peoples in Latin America. • The patients with RA had high percentage of familiar history of RA. • The pain and functional capacity were the variables associated with a diagnosis of any rheumatic disease and should be considered for early referral. • The mean of the delay in the diagnosis was 5.8 years. In this community, the lack of the "migration health" phenomenon may be a social determinant that negatively impacts their health.

Operators' network management continuously measures network health by collecting data from the deployed network devices; data is used mainly for performance reporting and diagnosing network problems after failures, as well as by human capacity planners to predict future traffic growth. Typically, these network management tools are generally reactive and require significant human effort and skills to operate effectively. As optical networks evolve to fulfil highly flexible connectivity and dynamicity requirements, and supporting ultra-low latency services, they must also provide reliable connectivity and increased network resource efficiency. Therefore, reactive human-based network measurement and management will be a limiting factor in the size and scale of these new networks. Future optical networks must support fully automated management, providing dynamic resource re-optimization to rapidly adapt network resources based on predicted conditions and events; identify service degradation conditions that will eventually impact connectivity and highlight critical devices and links for further inspection; and augment rapid protection schemes if a failure is predicted or detected, and facilitate resource optimization after restoration events. Applying automation techniques to network management requires both the collection of data from a variety of sources at various time frequencies, but it must also support the capability to extract knowledge and derive insight for performance monitoring, troubleshooting, and maintain network service continuity. Innovative analytics algorithms must be developed to derive meaningful input to the entities that orchestrate and control network resources; these control elements must also be capable of proactively programming the underlying optical infrastructure. In this article, we review the emerging requirements for optical network management automation, the capabilities of current optical systems, and the development and standardization status of data models and protocols to facilitate automated network monitoring. Finally, we propose an architecture to provide Monitoring and Data Analytics (MDA) capabilities, we present illustrative control loops for advanced network monitoring use cases, and the findings that validate the usefulness of MDA to provide automated optical network management.

This paper explores the problem of flow metering in 100 GbE links, presenting a flow exporter architecture based on a FPGA acceleration card using only on-chip memory. Peak performance without packet sampling even at the maximum packet rate is assured and means to avoid data loss are provided, since a low level of aggregation is achieved. This is the first approach in a series of architectures that are built upon the previous one, where the resources of the custom hardware are gradually increased, improving the aggregation level, while the required commodity hardware resources for subsequent stages are consequently lowered. We consider that FPGA-fabric offers adequate flexibility and performance for this task and is capable of reducing overall system cost. A functional prototype of the system has been implemented on the Xilinx VCU118 development board configured to export TCP sessions records. This achievement represents a cornerstone of a 100 GbE FPGA flow exporter design, that aims for supporting in the order of tens of millions concurrent flows.

End-to-end packet integrity in TCP/IP is ensured through checksums based on one’s complement addition. Once a negligible part of the overall cost of processing a packet, increasing network speeds have turned checksum computation into a bottleneck. Actually, supporting 100 Gbps bandwidth is a challenge partially due to the difficulties of performing checksums at line rate. As part of a larger effort to implement a 100 Gbps TCP/IP stack on an FPGA based NIC, in this paper we analyse the problem of checksum computation for 100+ Gbps TCP/IP links and describe an open-source solution for the 512-bit wide, 322 MHz buses being used in the 100 Gbps Ethernet interfaces of Xilinx UltraScale devices. The proposed architecture computes thirty-three 16-bit one’s complement additions in only 3.1 ns, more than enough to support 100 Gbps Ethernet links.

Adequate detection and classification of seismic events are crucial for understanding the internal status of a Volcano. Machine learning-based classifiers use different features from the time, frequency, and scale domains related to seismic events. Regarding power spectrum-based features, several methods can be used to compute such features. However, the more suitable method for analyzing volcanic activity is undetermined. This paper presents a study about the main frequency bands, which allows maximizing the performance metrics of an automated classifier for long-period (LP) and volcano-tectonic (VT) events based on parametric (Yule-Walker and Burg) and non-parametric (Welch and Multitaper) power spectrum density estimation methods. Feature selection using embedded (pruning) and wrapper (recursive feature elimination) methods was applied to select the main frequencies that maximize the balanced error rate of suitable classification algorithms, such as decision trees (DT) and support vector machines (SVM). Bootstrapping was used to estimate a confidence interval for the frequencies of the microearthquakes. An amplitude threshold difference of at least 3 dB was used to guarantee that possible frequency features that characterize each type of event do not overlap between classes. The method who achieved the worst overall performance was not considered by the voting strategy. A Dataset from Cotopaxi volcano was used to test the proposed classification schema. The best results show for DT classifier a total of 10 key frequencies, while for SVM classifier 39 key frequencies grouped in three main frequency bands, as main features to distinguish LP events from VT earthquakes. The best classification results were achieved by the Welch method with the DT and by the Multitaper method with the SVM classifiers. Furthermore, the study confirms that there is a frequency band above 40 Hz, which seems like a critical feature for the detection and classification of stages.

Barium disilicide (BaSi2) shows great promise as a new material for thin film solar cells (Fig. 1) [1]. It has a suitable bandgap of 1.3 eV, a large optical absorption coefficient of 3×104 cm−1 for a photon energy of 1.5 eV, and a large minority-carrier diffusion length of about 10 μm. Furthermore, it is composed of only earth abundant elements and highly stable. Therefore, BaSi2 can be used for future terawatt-class power generation. We have achieved the operation of BaSi2 homojunction solar cells (Fig. 2) [2]. In this structure, an open-circuit voltage V OC beyond 0.8 V and a conversion efficiency (η) beyond 25% are expected [3]. However, the achieved η was very small. In this study, three-dimensional (3D) optical simulation using a 3D pyramid texture that is effective in preventing reflection was performed on a BaSi2 solar cells in order to increase the photocurrent. Futhermore, we suggested a new device structure using Al-doped ZnO (AZO) electron transfer layer (ETL). In this study, we performed a 3D optical simulation using the GenPro4 model developed at TU Delft [4]. In this simulation, the absorptance of each layer is calculated as a function of wavelength λ. We introduced the 3D-texture structure on the front and back surfaces of the Si substrate to investigate the anti-reflection and light trapping effects (Fig. 3(a)). The thickness and complex refractive index n + ik of every layer are given as input (Fig. 3 (b,c)). In addition, to increase light absorption in BaSi2 light absorber layers, we investigated n+-AZO/p-BaSi2 heterojunction solar cells in which the ETL was changed from n+-BaSi2 to n+-AZO. The absorption spectra of the BaSi2 homojunction solar cells are shown Fig. 5. By changing the front surface structure from flat to texture, the reflectance in the λ range 700 – 1200 nm reduced and the J ph in p-BaSi2 increased by 1.2 mA/cm2. On the other hand, the light trapping effect by the texture structure on the back surface could not be observed well. This is probably because almost all the transmitted light was absorbed by the Si substrate before reaching the back surface. Photons with λ between 800 and 1200 nm are absorbed in the Si substrate. In addition, it was found that the largest factor inhibiting light absorption in the p-BaSi2 layer was parasitic absorption in the ETL n+-BaSi2. This parasitic absorption is unavoidable when BaSi2 is used for ETL, because BaSi2 has a very large light absorption coefficient. Other thin film solar cells, e.g. CIGS, Perovskite, are a kind of heterojunction solar cells which consist of p-type absorbers and n-type window layers. The wide bandgap of the window layer allows more photons to reach the absorber layer. Another advantage of a heterojunction is that the recombination in the wide bandgap window layer is quite low in comparison to that in homojunctions. Now based on these basics, we proposed n+-AZO/p-BaSi2 heterojunction solar cell in which the ETL was changed from n+-BaSi2 to n+-AZO (Fig. 6). Because the E g of AZO is very large (~3.3 eV), much light can be transfer to BaSi2 light absorber layer. The absorption spectra of the n+-AZO/p-BaSi2 heterojunction solar cells were shown in Fig. 7. The parasitic absorption in the ETL was significantly reduced. The J ph in the p-BaSi2 layer increased by more than 10 mA/cm2 compared with that of the homojunction solar cells and reached a maximum of 30.23 mA/cm2 using the front textured surface. Based on these simulated results, we tried to form n+-AZO/p-BaSi2 heterojunction solar cells experimentally. p-BaSi2 and p+-BaSi2 films were grown epitaxially on a Cz-p+-Si(111) substrate by molecular beam epitaxy, and ZnO and AZO were deposited by sputtering. The J-V characteristics under AM1.5 illumination and the internal quantum efficiency (IQE) spectrum for an n+-AZO/p-BaSi2 heterojunction solar cell are shown in Fig. 8. It shows η = 0.04%, a short-circuit current density J SC of 3.7 mA/cm2, and an V OC of 50 mV. IQE exceeded 30% at λ = 600 nm. This efficiency is almost the same value as that obtained for BaSi2-pn homojunction solar cells. Therefore, we can state that we succeeded the demonstration of n+-AZO/p-BaSi2 heterojunction solar cells for the first time. [1] T. Suemasu and N. Usami, J. Phys. D: Appl. Phys. 50, 023001 (2017). [2] K. Kodama et al., Appl. Phys. Express 12, 041005 (2019). [3] T. Suemasu, Jpn. J. Appl. Phys. 54, 07JA01 (2015). [4] R. Santbergen et al., IEEE J. Photovoltaics, 7, no. 3, 919–926 (2017). Figure 1

Volcano Early Warning Systems (VEWS) have become a research topic in order to preserve human lives and material losses. In this setting, event detection criteria based on classification using machine learning techniques have proven useful, and a number of systems have been proposed in the literature. However, to the best of our knowledge, no comprehensive and principled study has been conducted to compare the influence of the many different sets of possible features that have been used as input spaces in previous works. We present an automatic recognition system of volcano seismicity, by considering feature extraction, event classification, and subsequent event detection, in order to reduce the processing time as a first step towards a high reliability automatic detection system in real-time. We compiled and extracted a comprehensive set of temporal, moving average, spectral, and scale-domain features, for separating long period seismic events from background noise. We benchmarked two usual kinds of feature selection techniques, namely, filter (mutual information and statistical dependence) and embedded (cross-validation and pruning), each of them by using suitable and appropriate classification algorithms such as k Nearest Neighbors ( k -NN) and Decision Trees (DT). We applied this approach to the seismicity presented at Cotopaxi Volcano in Ecuador during 2009 and 2010. The best results were obtained by using a 15 s segmentation window, feature matrix in the frequency domain, and DT classifier, yielding 99% of detection accuracy and sensitivity. Selected features and their interpretation were consistent among different input spaces, in simple terms of amplitude and spectral content. Our study provides the framework for an event detection system with high accuracy and reduced computational requirements.

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. M. Ruiz, J. Ramos, G. Sutter, J. E. Lopez de Vergara, S. Lopez-Buedo and J. Aracil, "Accurate and affordable packet-train testing systems for multi-gigabit-per-second networks," in IEEE Communications Magazine, vol. 54, no. 3, pp. 80-87, March 2016. doi: 10.1109/MCOM.2016.7432152, Communication networks these days face a relentless increase in traffic load. Multi-gigabit-per-second links are becoming widespread, and network devices are under continuous stress, so testing whether they guarantee the specified throughput or delay is a must. Software-based solutions, such as packet-train traffic injection, were adequate for lower speeds, but they have become inaccurate in the current scenario. Hardware-based solutions have proved to be very accurate, but usually at the expense of much higher development and acquisition costs. Fortunately, new affordable FPGA SoC devices, as well as high-level synthesis tools, can very efficiently reduce these costs. In this article we show the advantages of hardware-based solutions in terms of accuracy, comparing the results obtained in an FPGA SoC development platform and in NetFPGA-10G to those of software. Results show that a hardware-based solution is significantly better, especially at 10 Gb/s. By leveraging high-level synthesis and open source platforms, prototypes were quickly developed. Noticeable advantages of our proposal are high accuracy, competitive cost with respect to the software counterpart, which runs in high-end off-the-shelf workstations, and the capability to easily evolve to upcoming 40 Gb/s and 100 Gb/s networks., This work was partially supported by the Spanish Ministry of Economy and Competitiveness under the project PackTrack (TEC2012-33754) and by the European Union through the Integrated Project (IP) IDEALIST under grant agreement FP7-317999

Resumen Objetivo Determinar la prevalencia de vacunacion antigripal en una muestra poblacional de pacientes EPOC y la efectividad de la vacunacion. Metodologia Estudio de cohortes retrospectivo. Se identificaron los antecedentes de vacunacion antigripal (campana 2011-2012) en 899 pacientes con EPOC confirmada obtenidos mediante muestreo aleatorio simple a partir de todos los EPOC identificados a 31 de diciembre de 2011 en Cantabria. Las agudizaciones graves (ingresos por agudizacion EPOC) y la mortalidad por todas las causas durante el ano 2012 fueron tratadas como variables dependientes, calculandose odds ratios ajustadas (ORa) como medida de asociacion y fracciones de prevencion ajustadas en los expuestos (PFe-ajustada) como medida de impacto. Resultados La prevalencia global de vacunacion fue del 62,7%. Esta prevalencia fue menor en EPOC muy grave en base al FEV1 (52,0%). La vacunacion antigripal mostro un efecto protector estadisticamente significativo sobre el riesgo de agudizaciones graves al ano siguiente: ORa: 0,54 (IC 95%: 0,35-0,84); PFe-ajustada: 0,46 (IC 95%: 0,16-0,65). El riesgo de mortalidad fue menor, pero sin alcanzar significacion estadistica: Ora: 0,76 (IC 95%: 0,41-1,40). Al estratificar en funcion de la gravedad de la EPOC, el efecto protector para el riesgo de ingreso por agudizacion fue mayor en EPOC mas graves: Ora: 0,23 (IC 95%: 0,11-0,48); PFe-ajustada: 0,77 (IC 95%: 0,52-0,89). Conclusiones Nuestros resultados apoyan el efecto protector de la vacunacion antigripal, disminuyendo el riesgo de ingreso por agudizacion. A pesar de nuestros resultados protectores, la prevalencia global de vacunacion antigripal fue suboptima, especialmente en los EPOC con un estadio mas grave.

This paper explores the use of six different clustering-based methods to classify long-period and volcano-tectonic seismic events and to find possible overlapping signals of non-volcanic origin that could occur at the same time or immediately after the occurrence of volcano-seismic events. According to the explored classifiers space, the BIRCH method with k = 2 was chosen as the best model in the classification of both pure seismic events, reaching a weighted balanced accuracy and accuracy scores of 0.81 and 0.88, respectively. The accuracy result represents a satisfactory and competitive classification performance when compared to the state of the art methods. Besides, the spectral-clustering method with k = 3 was able to classify seismic events with and without overlapped signals of non-volcanic origin, attaining a weighted balanced accuracy score of 0.51. This result was at least 0.18 units higher than the other classifiers. Additionally, the obtained true positive rates of 0.94 corroborated the excellent performance of this classifier to detect seismic events with overlapping. According to the obtained results, it is possible to state that the proposed clustering-based exploration was effective in providing competitive models for both the classification of uncontaminated seismic events as well as for the detection of seismic events with overlapped signals.

In this work, we present the development, description, and performance evaluation of two volcano seismological datasets: one containing raw seismic signals (MicSigV1) and another containing a set of 84 distinctive attributes or features for bench-marking (SeisBenchV1), which was extracted from the MicSigV1 dataset, recorded at Cotopaxi volcano in Ecuador. These datasets represent the first public Ecuadorian volcano seismicity repository that will update, publish, and support the release of data to the scientific community on a continual basis. Each dataset contains samples of mostly long-period and volcano-tectonic event classes. Other types of events, such as regional, hybrid, and icequakes, were also included despite their minor presence in the datasets. These datasets will serve to provide an important resource for research in signal processing, feature calculation, feature selection, feature space reduction, seismic events detection, and classification areas. The datasets can be downloaded through a copyright agreement that supports the correct use of the released datasets.

In this paper, we present an efficient hardwareimplementation of a video encoder optimized for ultra low-latency, using the Logarithmic Hop Encoding algorithm. This design provides the following features: (i) A maximum marginal output latency of 23 clock cycles, (ii) small area requirements, (iii) proven rate up to 95 Millions of pixels per second in a low-end FPGA (i.e. FHD video can be streamed), (iv) on-the-fly configuration, (v) scalable architecture. The proposed design has been tested in a real video transmission scenario, where the video transmitter prototype is implemented using a ZynqBerry board, leveraging all SoC capabilities.

International audience; The continuous Global Positioning System (cGPS) network operating in the northern Andes (Ecuador and Colombia) for about a decade has the main objectives of quantifying interseismic coupling along the subduction interface, detecting occurrence of transient aseismic episodic slip, detailing the rupture kinematics of large earthquakes, recording long‐term movements along crustal faults, as well as recording swelling or deflation on the flanks of volcanoes. An opportunity to test the network’s timely registry of surface coseismic offsets was provided by the 16 April 2016 Mw 7.8 Pedernales, Ecuador, earthquake whose epicenter was along the western margin of central Ecuador, South America. This large earthquake was the biggest to occur in the northern Andes since 1979 and produced static surface offsets that were recorded by the cGPS stations operating at distances out to ∼400 km from source. Near‐field stations, operating along the Ecuadorian littoral recorded static horizontal surface displacements up to 80 cm and high‐rate GPS (HRGPS) stations recorded dynamic peak‐to‐peak displacements reaching 2 m. These measurements, together with seismic data, revealed the southward propagation of the seismic rupture, its spatial extent, and the successive breaking of two main asperities (Nocquet et al., 2017). Here, we provide the complete data set of static coseismic displacements recorded from Ecuador to southern Colombia out to 400 km from the rupture. North of the Pedernales earthquake’s foci, in the adjoining Esmeraldas‐Nariño segment, some patches show high interseismic coupling and rapid strain accumulation is ongoing. In the 200‐km‐long Esmeraldas‐Nariño segment, the seismic potential is particularly high. cGPS data suggest that the Esmeraldas‐Nariño segment is likely a zone of future rupture.

Network traffic monitoring usually faces the problem of packet duplication, which arises when port mirroring is being used. That is, when traffic is copied from the ports of a switch or a router that are being monitored, to a mirror port where a monitoring probe is attached. Thus, a packet can be copied twice, both at the ingress and egress ports, therefore generating duplicates. Information redundancy caused by packet duplication not only leads to increased workloads at the monitoring probes, but also calls for more disk space to store the network traces. Actually, packet duplication may increase 100% the monitoring load. There are different sorts of packet duplication; in this paper we focus on switching duplication, because it is the most common in a network monitoring scenario, where the network probe is attached to a core switch. We present a high performance FPGA architecture that is able to detect and remove duplicated packets in 100 Gbit/s networks. It is based on a 64-bit key and a BRAM-based shift register that allows us to build an element-based sliding window of size up to 79,872 elements. The design targets the Xilinx Virtex UltraScale family, using the integrated 100G Ethernet Subsystem available in such devices, and it has been tested on a VCU108 evaluation kit.

In network traffic monitoring, a very important analysis is to find heavy hitters. That is, finding those flows that use most resources in a given network link. This information can be very useful for security or traffic management purposes. Though this analysis might seem easy to implement, since it is essentially based on counting, the fact is that doing it at 100 Gbit/s rates is far from trivial. In 100 Gbit/s Ethernet (100 GbE), up to 148 million packets per second can be received, thus making it very difficult to parse packets and maintain counters at such rate. In this paper, we leverage the integrated 100G Ethernet Subsystem available in Xilinx UltraScale devices to implement a heavy hitter detector for 100 GbE in a VCU108 evaluation kit. Thanks to the integration of the Count Sketch algorithm with a priority list and a network packet parser, the proposed architecture is able to work at line rate for average packet sizes bigger than 215 bytes. The work presents a theoretical analysis of the error, as well as the technical details of the proposed solution. The implementation has been validated using real-world traces, obtaining an average error of 1.29%.

Urban seismology has become an active research field in the recent years, both with seismological objectives, as obtaining better microzonation maps in highly populated areas, and with engineering objectives, as the monitoring of traffic or the surveying of historical buildings. We analyze here the seismic records obtained by a broad-band seismic station installed in the ICTJA-CSIC institute, located near the center of Barcelona city. Although this station was installed to introduce visitors to earth science during science fairs and other dissemination events, the analysis of the data has allowed to infer results of interest for the scientific community. The main results include the evidence that urban seismometers can be used as a easy-to-use, robust monitoring tool for road traffic and subway activity inside the city. Seismic signals generated by different cultural activities, including rock concerts, fireworks or football games, can be detected and discriminated from its seismic properties. Beside the interest to understand the propagation of seismic waves generated by those rather particular sources, those earth shaking records provide a powerful tool to gain visibility in the mass media and hence have the opportunity to present earth sciences to a wider audience., This work was funded by the Spanish Ministry of Economy and Competitiveness Project MISTERIOS (CGL2013-48601-C2-2-R), with additional support from the Generalitat de Catalunya grant 2009SGR1595.

En 106 mujeres atendidas en la consulta externa ginecológica encontramos que la actividad y deseo sexual disminuye significativamente luego de la menopausia, siendo la causa principal la disminución del deseo sexual. El ó0% no sabe la causa de la disminución del deseo sexual. Las pacientes con antecedente de cirugía pélvica tiene menos relaciones sexuales satisfactorias. La iniciativa sexual de la mujer peruana postmenopáusica es mínima.

Resumen Introduccion : La fibromialgia (FM) se considera la causa mas frecuente de dolor osteomuscular cronico en Espana y su prevalencia se estima en un 2,4%. Esta enfermedad genera importantes perjuicios en la calidad de vida de los pacientes, ademas de un alto consumo de recursos sanitarios. El manejo alopatico de esta enfermedad dista de conseguir unos resultados satisfactorios. Objetivos Valorar la respuesta clinica de pacientes diagnosticados de FM mediante un enfoque homeopatico. Metodos Se selecciono a pacientes con diagnostico de FM en consulta. En la primera visita se anade a su tratamiento basal, sin efecto ciego: Rhus toxicodendron 9 CH e Ignatia amara 15 CH ambas una dosis diaria. Se realizo seguimiento evolutivo a 1, 3 y 6 meses despues de la primera consulta. Como variables respuesta, se utilizaron: puntuacion en el FIQ (Fibromyalgia Impact Questionnaire); dias con dolor que limitaron la actividad funcional y numero de dosis de farmacos de rescate que se usaron durante las semanas previas a las visitas. Resultados Un total de 3 pacientes completaron el estudio. Eran mujeres entre 38 y 61 anos. La media de puntuacion obtenida en la primera visita fue de 79 (limitacion grave) y descendio un 38,8% a los 6 meses. Los dias con limitacion funcional y el uso de farmacos de rescate en la ultima semana se redujeron de media un 50,0 y un 37,1% respectivamente. Conclusiones Asumiendo las limitaciones de esta serie de casos, pero con el soporte de la revision bibliografica, se puede concluir que un adecuado planteamiento homeopatico podria aportar beneficios dentro de un enfoque multidisciplinar.

Bacteraemia caused by anaerobic bacteria is rare in the hospital setting. The Clostridium genus is the second most common cause of these infections, particularly Clostridium perfringens, which has a high mortality rate. However, reviews in the literature of these infections are scarce. The aim of this study was to retrospectively document the incidence, clinical characteristics and risk factors involved in the acquisition of bacteraemia caused by C. perfringens among patients treated at our hospital over a 10-year period. Twenty-eight patients with C. perfringens bacteraemia were included in the study. We evaluated pre-existing comorbidities, the source of bacteraemia, clinical features, the antimicrobial treatment administered and patient outcome. C. perfringens bacteraemia occurs rarely in our setting, but with a very high mortality rate. This rate is associated with old age and pre-existing, largely gastrointestinal malignancies. It presents with few specific symptoms but requires rapid and appropriate diagnosis and treatment to reduce the high mortality of this infection.

OBJECTIVE: Our objective was to study predictors of frequent exacerbations in mild grade COPD patients in comparison with more severe COPD patients. METHODS: We conducted a retrospective population-based cohort study including 723 patients with confirmed COPD and FEV1 data available.Exacerbation frequency was observed for the previous year (2011) and the following year (2012). Patients were defined as 9Frequent Exacerbator9 (FE) phenotype if they suffered ≥ 2 exacerbations in a year, and were categorized according to severity of COPD (GOLD Grades 1-4). Odds Ratios and their 95% confidence intervals were estimated by logistic regression adjusting for Age, Gender and Smoking status. RESULTS: 75 out of the 723 patients (10.4%) were classified as GOLD 1; 433 (59.9%) were GOLD 2; 190 (26.3%) GOLD 3 and 25 patients (3.5%) GOLD 4. The main predictor of being FE among all grades of COPD severity, was a history of frequent exacerbations the previous year. However, this association was higher in Mild-GOLD 1 patients: adjusted OR 15.90, in comparison with the rest of patients: adjusted OR GOLD 2 patients 4.20; OR GOLD 3 patients 6.58; OR GOLD 4 patients 4.24 . CONCLUSIONS: The role of the FE phenotype on the risk of future exacerbations seem to be higher in patients with milder disease (GOLD 1), suggesting a higher susceptibility in these patients.

Networks are currently essential for computing: It is therefore essential to guarantee the quality of network links in order to ensure a proper operation of computing systems. However, a widespread deployment of network monitoring devices might not be economically feasible. In this paper, we propose the use of Programmable System-on-Chip FPGAs (PSoCs) for enabling a comprehensive testing of networks. Software-only solutions are no longer valid, because the timescales of current networks call for custom-hardware solutions. Thus, we show that PSoCs are a perfect fit for network quality monitoring devices, by mapping the required measurements to the capabilities of such devices. In order to demonstrate the benefits of PSoCs to monitor the quality of network links, we have developed a prototype based on Xilinx Zynq that is capable of measuring the key performance indicators of Gigabit Ethernet networks, namely: available bandwidth, packet loss, delay and jitter. The monitoring probe features GPS-based timestamping, thus enabling the construction of network delay maps. We present the benefits of the proposed approach in terms of cost and simplicity, and we also show how it could be expanded to multi-Gb/s networks.

RESUMEN: En pacientes con trauma encéfalo-craneano (TEC) la presión arterial en el momento de la admisión por urgencias tiene una relevancia especial por ser la base de la presión de perfusión cerebral. El propósito de esta investigación fue estimar la asociación entre los valores de presión arterial al ingreso a urgencias y la mortalidad hospitalaria en dichos pacientes. Métodos: cohorte retrospectiva en pacientes mayores de 18 años con diagnóstico de TEC admitidos al Hospital Pablo Tobón Uribe entre enero de 2012 y enero de 2014. Se hizo una regresión logística multivariable para estimar el efecto independiente de los valores de presión arterial en la mortalidad. Resultados: en 582 pacientes la mediana de edad fue 36 años (rango intercuartílico = 25-59) y el 77,1 % (n = 449) eran hombres. La mortalidad según los valores de presión arterial sistólica al ingreso en las categorías de 150 mm Hg fue 34,6 % (18/52), 13,3 % (56/421) y 29,4 % (32/109), respectivamente. En el análisis univariable tanto la presión menor de 100 como la mayor de 150 mm Hg se asociaron con mortalidad, pero dicha asociación perdió magnitud y significado estadístico (OR = 1,81; IC95 % = 0,94-3,48 y OR = 1,91; IC95 % = 0,86-4,54, respectivamente) al ajustar por la escala de coma de Glasgow, la saturación de oxígeno y la presencia de edema cerebral. Conclusiones: no se demostró asociación estadísticamente significativa entre los valores de presión arterial al ingreso a urgencias y la mortalidad en los pacientes con TEC. ABSTRACT: Blood pressure is of special relevance in patients with traumatic brain injury (TBI) at admission to emergency services, since it is the basis of cerebral perfusion pressure. The purpose of this research was to estimate the association between blood pressure values measured on admission and hospital mortality in patients with TBI. Methods: Retrospective cohort study in patients older than 18 years admitted with TBI to Hospital Pablo Tobón Uribe, in Medellín (Colombia) between January 2012 and January 2014. A multivariate logistic regression model was performed to estimate the independent effect of blood pressure values on mortality. Results: 582 patients with a median age of 36 years (IQR = 25-59) 77.1 % of them males (n = 449) were evaluated. Mortality according to categories of systolic blood pressure at admission (150 mmHg) was as follows: 34.6 % (18/52), 13.3 % (56/421) and 29.4 % (32/109), respectively. Univariate analysis showed that blood pressure less than 100 or greater than 150 mm Hg were associated with hospital mortality, but that association lost magnitude and statistical significance (OR = 1.81; 95 % CI = 0.94-3.48 and OR = 1.91; 95 % CI = 0.86-4.54, respectively) after adjustment by Glasgow coma scale, oxygen saturation and cerebral edema. Conclusions: We did not demonstrate a statistically significant association between blood pressure values at admission to the emergency service and mortality in patients with TBI.

Background and Aim Exacerbations of chronic obstructive pulmonary disease (COPD) carry significant consequences for patients and are responsible for considerable health-care costs—particularly if hospitalization is required. Despite the importance of hospitalized exacerbations, relatively little is known about their determinants. This study aimed to analyze predictors of hospitalized exacerbations and mortality in COPD patients. Methods This was a retrospective population-based cohort study. We selected 900 patients with confirmed COPD aged ≥35 years by simple random sampling among all COPD patients in Cantabria (northern Spain) on December 31, 2011. We defined moderate exacerbations as events that led a care provider to prescribe antibiotics or corticosteroids and severe exacerbations as exacerbations requiring hospital admission. We observed exacerbation frequency over the previous year (2011) and following year (2012). We categorized patients according to COPD severity based on forced expiratory volume in 1 second (Global Initiative for Chronic Obstructive Lung Disease [GOLD] grades 1–4). We estimated the odds ratios (ORs) by logistic regression, adjusting for age, sex, smoking status, COPD severity, and frequent exacerbator phenotype the previous year. Results Of the patients, 16.4% had ≥1 severe exacerbations, varying from 9.3% in mild GOLD grade 1 to 44% in very severe COPD patients. A history of at least two prior severe exacerbations was positively associated with new severe exacerbations (adjusted OR, 6.73; 95% confidence interval [CI], 3.53–12.83) and mortality (adjusted OR, 7.63; 95%CI, 3.41–17.05). Older age and several comorbidities, such as heart failure and diabetes, were similarly associated. Conclusions Hospitalized exacerbations occurred with all grades of airflow limitation. A history of severe exacerbations was associated with new hospitalized exacerbations and mortality.

Previous studies have demonstrated that human subjects update the location of visual targets for saccades after head and body movements and in the absence of visual feedback. This phenomenon is known as spatial updating. Here we investigated whether a similar mechanism exists for the perception of motion direction. We recorded eye positions in three dimensions and behavioral responses in seven subjects during a motion task in two different conditions: when the subject's head remained stationary and when subjects rotated their heads around an anteroposterior axis (head tilt). We demonstrated that after head-tilt subjects updated the direction of saccades made in the perceived stimulus direction (direction of motion updating), the amount of updating varied across subjects and stimulus directions, the amount of motion direction updating was highly correlated with the amount of spatial updating during a memory-guided saccade task, subjects updated the stimulus direction during a two-alternative forced-choice direction discrimination task in the absence of saccadic eye movements (perceptual updating), perceptual updating was more accurate than motion direction updating involving saccades, and subjects updated motion direction similarly during active and passive head rotation. These results demonstrate the existence of an updating mechanism for the perception of motion direction in the human brain that operates during active and passive head rotations and that resembles the one of spatial updating. Such a mechanism operates during different tasks involving different motor and perceptual skills (saccade and motion direction discrimination) with different degrees of accuracy.

Background: Despite the importance of exacerbations of chronic obstructive pulmonary disease (COPD), we know little about their determinants. Objectives: To analyze whether there is a frequent-exacerbation phenotype independent of COPD severity, and the independent effect of COPD severity on the risk of exacerbations. Methods: Retrospective population-based cohort study. 900 patients with confirmed COPD ≥35 years, including non-smokers and mild stages, were selected by simple random sampling from all COPD patients in Cantabria-Spain at 31.12.2011. Exacerbation was defined as in the ECLIPSE study (Hurst JR. et al. N Engl J Med 2010:363:1128-38). We defined 9Frequent Exacerbations the Following Year (FEFY)9 as ≥2 exacerbations in 2012. Severity of COPD was categorized according to GOLD stages 1-4. Odds Ratios (OR) were estimated by logistic regression adjusting for Age, Gender, Smoking, COPD severity and exacerbations. Results: The main predictor of FEFY, independent of COPD severity, was a history of frequent exacerbations the previous year. Severe exacerbations predicted better the risk of severe exacerbations: adjusted OR (ORa) 6.73; 95%CI 3.53-18.83, and overall exacerbations (moderate + severe) predicted better the overall risk of FEFY. COPD severity, was associated with a higher risk of FEFY: Crude OR GOLD Stage 4: 3.86; 95%CI 1.50–9.93. However, this association diminished after adjusting for frequency of exacerbations the previous year: ORa 2.08; 95%CI 0.75–5.82. Conclusions: Our results support a frequent-exacerbation phenotype independent of COPD severity. COPD severity was associated with a higher risk of exacerbations, but this effect would be dependent of the exacerbations the previous year.

Background. A previous history of moderate and severe exacerbations, and 9chronic obstructive pulmonary disease (COPD)9 severity, are the most consistent predictors of Exacerbations Objective. To estimate the predictive accuracy of a functional index, based on the three main predictors mentioned above. Methods: Retrospective population-based cohort study. 900 patients with confirmed COPD ≥ 35 years, including non-smokers and mild-stages of FEV1, were selected by simple random sampling from all COPD patients in Cantabria (Northern Spain) at 31 December 2011. Frequency of exacerbations, and overall mortality during the following year (2012) were treated as dependent variables. Our functional index was computed in a quantitative scale (range 0-6), and then ordinally categorized into mild/moderate/severe/very severe. Mild category was treated as reference, and Odds Ratios (OR) were estimated by unconditional logistic regression, adjusting for age and gender. Results: Regarding the risk of ≥ 2 exacerbations in 2012, predictive accuracy of our index was superior to each individual predictor: OR moderate 2.57; OR severe 5.20; OR very severe 12.80. Predictive accuracy of COPD hospitalization and of overall mortality was also greater. When we added heart failure and diabetes to our index, predictive accuracy of COPD hospitalization: OR very severe 40.55, and overall mortality: OR 20.05, was clearly increased. Conclusions. Our composite score based on moderate, severe exacerbations and COPD severity, obtained a high predictive accuracy. Adding comorbidities increased specially predictive accuracy of COPD hospitalization and overall mortality.

Objective To determine the prevalence of influenza vaccination in chronic obstructive pulmonary disease (COPD) patients, and the effectiveness of the procedure. Methods Retrospective population-based cohort study. On 31 December 2011, influenza vaccination history was retrieved from 899 patients with confirmed COPD selected by simple random sampling from all COPD patients in Cantabria (northern Spain). Severe exacerbations (hospitalization due to COPD exacerbation) and overall mortality during 2012 were treated as dependent variables. Odds ratios (OR) were estimated by logistic regression, adjusting for age, sex, smoking status, severity of COPD, and frequency of exacerbations during the previous year. Prevented fraction among the exposed (PFe-adjusted) was determined as a measure of impact. Results Overall prevalence of influenza vaccination was 62.7%, but this rate fell in patients classified as more severe according to FEV1 (52.0%). Influenza vaccination showed a statistically significant protective effect against severe exacerbations in the following year: aOR: 0.54 (95% CI: 0.35–0.84); FPe-adjusted: 0.46 (95% CI: 0.16–0.65). A non-significant protective effect for overall mortality was observed: aOR: 0.76 (95% CI: 0.41–1.40). When stratified according to COPD severity (FEV1), the protective effect against risk of hospitalization was higher in more severe COPD patients: aOR: 0.23 (95% CI: 0.11–0.48); FPe-adjusted: 0.77 (95% CI: 0.52–0.89). Conclusions We found that influenza vaccination has a protective effect and reduces the risk of hospitalization due to exacerbations in the following year. Despite the evidence for protection, prevalence of vaccination was not optimal, especially in more severe COPD patients.

Augmenting heavy and power-hungry data collection equipment with lighten smaller wireless sensor network nodes leads to faster, larger deployments. Arrays comprising dozens of wireless sensor nodes are now possible, allowing scientific studies that aren't feasible with traditional instrumentation. Designing sensor networks to support volcanic studies requires addressing the high data rates and high data fidelity these studies demand. The authors' sensor-network application for volcanic data collection relies on triggered event detection and reliable data retrieval to meet bandwidth and data-quality demands.