BackgroundIn the weeks following the first imported case of Ebola in the U. S. on September 29, 2014, coverage of the very limited outbreak dominated the news media, in a manner quite disproportionate to the actual threat to national public health; by the end of October, 2014, there were only four laboratory confirmed cases of Ebola in the entire nation. Public interest in these events was high, as reflected in the millions of Ebola-related Internet searches and tweets performed in the month following the first confirmed case. Use of trending Internet searches and tweets has been proposed in the past for real-time prediction of outbreaks (a field referred to as “digital epidemiology”), but accounting for the biases of public panic has been problematic. In the case of the limited U. S. Ebola outbreak, we know that the Ebola-related searches and tweets originating the U. S. during the outbreak were due only to public interest or panic, providing an unprecedented means to determine how these dynamics affect such data, and how news media may be driving these trends.MethodologyWe examine daily Ebola-related Internet search and Twitter data in the U. S. during the six week period ending Oct 31, 2014. TV news coverage data were obtained from the daily number of Ebola-related news videos appearing on two major news networks. We fit the parameters of a mathematical contagion model to the data to determine if the news coverage was a significant factor in the temporal patterns in Ebola-related Internet and Twitter data.ConclusionsWe find significant evidence of contagion, with each Ebola-related news video inspiring tens of thousands of Ebola-related tweets and Internet searches. Between 65% to 76% of the variance in all samples is described by the news media contagion model.

Distributed power generation systems (DPGSs) based on inverters require reliable islanding detection algorithms (passive or active) in order to determine the electrical grid status and operate the grid-connected inverter properly. These methods are based on the analysis of the DPGS voltage, current, and power in time or frequency domain. This paper proposes a time-frequency detection algorithm based on monitoring the DPGS output power considering the influence of the pulsewidth modulation, the output LCL filter, and the employed current controller. Wavelet analysis is applied to obtain time localization of the islanding condition. Simulation and experimental results show the performance of the proposed detection algorithm in comparison with existing methods.

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A common single nucleotide polymorphism in the ACTN3 gene might result in the complete deficiency of α-actinin-3 (i.e., XX genotype). It has been found that ACTN3 XX individuals have several traits related to lessened muscle performance. This study aimed to determine the influence, if any, of ACTN3 genotypes on injury incidence of marathoners during the year preceding to participating in a competitive marathon race. Using a cross-sectional experimental design, the type and conditions of sports injuries were documented for one year in a group of 139 marathoners. Injuries were recorded following a consensus statement on injuries in Athletics. Afterward, ACTN3 genotyping was performed, and injury epidemiology was compared among RR, RX, and XX genotypes. The distribution of the RR/RX/XX genotypes was 28.8/42.8/23.5%, respectively. A total of 67 injuries were recorded. The frequency of marathoners that reported any injury during the previous year was not different across the genotypes (55.0/38.8/40.6%, P = 0.241). Although the overall injury incidence was not different among genotypes (2.78/1.65/1.94 injuries/1000 h of running, P = 0.084), the likelihood of suffering an injury was higher in RR than in RX (OR = 1.93: 95%CI = 0.87-4.30), and higher than in XX (OR = 1.79: 0.70-4.58). There was no difference in the conditions, severity, body location, time of year, or leading cause of injury among genotypes. However, XX presented a higher frequency of sudden-onset injuries (P = 0.024), and the OR for muscle-type injuries was 2.0 (0.51-7.79) times higher compared to RR runners. Although XX marathoners did not have a higher overall incidence of injury, the OR in these runners for muscle-type injuries was superior to RR and RX runners. The likelihood of suffering a muscle injury, especially with a sudden-onset, was twice in XX than in RR endurance runners.

Abstract-Grid-connected photovoltaic (PV) inverters employ an islanding detection functionality in order to determine the status of the electrical grid. In fact the inverter must be stopped once the islanding operation mode is detected according to the standards and grid-code limits.Diverse islanding detection algorithms have been proposed in literature to cope with this safety requirement. Among them active methods, based on the deliberate perturbation of the inverter behavior, can minimize the so called non detection zone (NDZ) that is the range of conditions in which the inverter does not recognize that is operating in undesired island. In most cases, the performances of these methods have been analyzed considering an highly dispersed generation scheme, where only one distributed generation power system (DGPS) is connected to the local electrical power system (EPS). However in some studies it has been highlighted that if two or more PV inverters are connected to the same local EPS, their anti-islanding algorithms do not behave ideally and can fail in detecting the islanding condition. However there is no systematic study that has investigated the overall capability of different anti-islanding methods employed on several inverters connected to the same EPS to detect islanding condition. This paper is a first attempt to carry out a systematic study of the performances of the most common active detection methods in case of two inverters connected to the same EPS. In order to evaluate the global capability of the two systems to detect islanding condition a new performance index is introduced and applied also to the case when the two inverters employ different anti-islanding algorithms.