Satellite‐derived data provide the temporal means and seasonal and nonseasonal variability of four physical and biological parameters off Oregon and Washington (41°–48.5°N). Eight years of data (1998–2005) are available for surface chlorophyll concentrations, sea surface temperature (SST), and sea surface height, while six years of data (2000–2005) are available for surface wind stress. Strong cross‐shelf and alongshore variability is apparent in the temporal mean and seasonal climatology of all four variables. Two latitudinal regions are identified and separated at 44°–46°N, where the coastal ocean experiences a change in the direction of the mean alongshore wind stress, is influenced by topographic features, and has differing exposure to the Columbia River Plume. All these factors may play a part in defining the distinct regimes in the northern and southern regions. Nonseasonal signals account for ∼60–75% of the dynamical variables. An empirical orthogonal function analysis shows stronger intra‐annual variability for alongshore wind, coastal SST, and surface chlorophyll, with stronger interannual variability for surface height. Interannual variability can be caused by distant forcing from equatorial and basin‐scale changes in circulation, or by more localized changes in regional winds, all of which can be found in the time series. Correlations are mostly as expected for upwelling systems on intra‐annual timescales. Correlations of the interannual timescales are complicated by residual quasi‐annual signals created by changes in the timing and strength of the seasonal cycles. Examination of the interannual time series, however, provides a convincing picture of the covariability of chlorophyll, surface temperature, and surface height, with some evidence of regional wind forcing.

Surface drifters released in the Gulf of California between June 2004 and August 2006 are used to describe the surface circulation in late spring and summer. In the June to September mean, there was a poleward coastal current on the shelf and slope of the mainland side of the Gulf, with mean speed 0.3 m/s; it reached the northern Gulf and joined the cyclonic circulation typical of this zone in summer. In the western half of the southern Gulf, the drifters presented recirculating currents that are due to mesoscale eddies that appear to dominate the surface circulation in summer. In June 2004, the coastal current presented an enhancement event with mean speed around 0.60 m/s and maximum 0.80 m/s. It took 20 days for a particular drifter to travel the 1000 km from the Gulf entrance to the head. This strengthening of the coastal current was apparent in chlorophyll a and SST satellite images, the drifters following closely the intrusion of warm, chlorophyll-poor surface water from outside the Gulf. The drifters and the satellite images suggest that the current-enhancement event lasted less than a month. This mesoscale event was linked with a mesoscale remote forcing in the tropical Pacific coast and with a mesoscale local forcing of the wind. These events seem to occur every year, and are probably important in carrying organisms and properties from the entrance to the whole length of the Gulf.

Recent studies in structural health monitoring have shown that damage detection algorithms based on statistical pattern recognition techniques for ambient vibrations can be used to successfully detect damage in simulated models. However, these algorithms have not been tested on full-scale civil structures, because such data are not readily available. A unique opportunity for examining the effectiveness of these algorithms was presented when data were systematically collected from a progressive damage field test on the Z24 bridge in Switzerland. This paper presents the analysis of these data using an autoregressive algorithm for damage detection, localization, and quantification. Although analyses of previously obtained experimental or numerically simulated data have provided consistently positive diagnosis results, field data from the Z24 bridge show that damage is consistently detected, however not well localized or quantified, with the current diagnostic methods. Difficulties with data collection in the field are also revealed, pointing to the need for careful signal conditioning prior to algorithm application. Furthermore, interpretation of the final results is made difficult due to the lack of detailed documentation on the testing procedure.

In this paper we develop an extension to the Signal Detection Theory (SDT) framework to separately estimate internal noise arising from representational and decision processes. Our approach constrains SDT models with decision noise by combining a multi-pass external noise paradigm with confidence rating responses. In a simulation study we present evidence that representation and decision noise can be separately estimated over a range of representative underlying representational and decision noise level configurations. These results also hold across a number of decision rules and show resilience to rule miss-specification. The new theoretical framework is applied to a visual detection confidence-rating task with three and five response categories. This study compliments and extends the recent efforts of researchers (Benjamin, Diaz, & Wee, 2009; Mueller & Weidemann, 2008; Rosner & Kochanski, 2009, Kellen, Klauer, & Singmann, 2012) to separate and quantify underlying sources of response variability in signal detection tasks.

Introducción. El COVID-19 representa la crisis de salud global que define nuestro tiempo. El 11 de marzo de 2020 se confirmaron los dos primeros casos en territorio hondureño, luego de que la Organización Mundial de la Salud declaraba al COVID-19 como una pandemia. Este estudio exploró los eventos adversos de las vacunas anticovid en individuos del departamento de Atlántida, Honduras. Métodos. El estudio fue transversal, incluyendo a personas mayores de 18 años, con más de 24 horas de haber sido vacunadas. Se utilizó una encuesta en línea previamente validada por el Observatorio de COVID-19 de UNITEC y el Consorcio de Investigadores COVID-19, Honduras, difundiéndola por la aplicación de mensajería de WhatsApp, a través de la dinámica bola de nieve. Resultados. Participaron 212 personas durante los primeros seis meses de 2021. La vacuna AstraZeneca/Oxford fue la más frecuente (88%) entre los encuestados. El 72% de los vacunados con AstraZeneca, presentó entre 1 a 4 síntomas y el 28% restante presentó más de 4 síntomas como efectos secundarios. La mayoría de los efectos secundarios surgieron el día siguiente a la inoculación, siendo los más frecuentes el dolor en el brazo inyectado, fiebre y dolor muscular generalizado. El 42% de los participantes necesitaron analgésicos o antiinflamatorios para tratar los síntomas posvacuna y 8% afirmó haber tomado algún medicamento previo a la vacunación para reducir la posibilidad de eventos adversos.  Conclusión. La vacuna de mayor disponibilidad durante el estudio fue AstraZeneca, asociada a efectos secundarios transitorios en menor grado que las otras vacunas.