A Hydrofracturing-Triggered Earthquake Occurred Three Years after the Stimulation

Hydrofracturing, used for shale gas exploitation, may induce felt, even damaging earthquakes. On 15 June 2019, an Mw2.8 earthquake occurred, spatially correlated with the location of earlier exploratory hydrofracturing operations for shale gas in Wysin in Poland. However, this earthquake was atypical. Hydrofracturing-triggered seismicity mainly occurs during stimulation; occasionally, it continues a few months after completion of the stimulation. In Wysin, there were only two weaker events during two-month hydrofracturing and then 35 months of seismic silence until the mentioned earthquake occurred. The Wysin site is in Gdańsk Pomerania broader region, located on the very weakly seismically active Precambrian Platform. The historical documents, covering 1000 years, report no natural earthquakes in Gdańsk Pomerania. We conclude, therefore, that despite the never observed before that long lag time after stimulation, the Mw2.8 earthquake was triggered by hydrofracturing. It is possible that its unusually late occurrence in relation to the time of its triggering technological activity was caused by changes in stresses due to time-dependent deformation of reservoir shales. The Wysin earthquake determines a new time horizon for the effect of HF on the stress state, which can lead to triggering earthquakes. Time-dependent deformation and its induced stress changes should be considered in shall gas reservoir exploitation plans.

Estimating the Local Burden of Disease During the First Wave of the COVID-19 Epidemic in England, Using Different Data Sources from Changing Surveillance Practices

Background The COVID-19 epidemic has differentially impacted communities across England, with regional variation in rates of confirmed cases, hospitalisations and deaths. Measurement of this burden changed substantially over the first months, as surveillance was expanded to accommodate the escalating epidemic. Laboratory confirmation was initially restricted to clinical need (“pillar 1”) before expanding to community-wide symptomatics (“pillar 2”). This study aimed to ascertain whether inconsistent measurement of case data resulting from varying testing coverage could be reconciled by drawing inference from COVID-19-related deaths. MethodsWe fit a Bayesian spatio-temporal model to weekly COVID-19-related deaths per local authority (LTLA) throughout the first wave (1 January - 30 June 2020), adjusting for the local epidemic timing and the age, deprivation and ethnic composition of its population. We combined predictions from this model with case data under community-wide, symptomatic testing and infection prevalence estimates from the ONS infection survey, to infer the likely trajectory of infections implied by the deaths in each LTLA.ResultsA model including temporally- and spatially-correlated random effects was found to best accommodate the observed variation in COVID-19-related deaths, after accounting for local population characteristics. Predicted case counts under community-wide symptomatic testing suggest a total of 275,000-420,000 cases over the first wave - a median of over 100,000 additional to the total confirmed in practice under varying testing coverage. This translates to a peak incidence of around 200,000 total infections per week across England. The extent to which estimated total infections are reflected in confirmed case counts was found to vary substantially across LTLAs, ranging from 7% in Leicester to 96% in Gloucester with a median of 23%. ConclusionsLimitations in testing capacity biased the observed trajectory of COVID-19 infections throughout the first wave. Basing inference on COVID-19-related mortality and higher-coverage testing later in the time period, we could explore the extent of this bias more explicitly. Evidence points towards substantial under-representation of initial growth and peak magnitude of infections nationally, to which different parts of the country contribute unequally.

Error handling in GPS data processing

We are aware that the processing of GPS data through GAMIT processing software is not free from errors. Some of them are generated due to different modules involved in processing. The data quality depends so many factors, like quality of met-instrument, which supplies the meteorological data, algorithm of processing which based on the network homogeneity or heterogeneity and location of the site, whether it is free from multi-path etc. The root mean square errors for New Delhi, Mumbai, Kolkata, Guwahati and Chennai GPS stations are spatially correlated and observations are weighted according to the satellite elevation angle. Diurnal variability of Integrated Precipitable Water Vapour (IPWV) has been shown its range from 45 mm to 65 mm for New Delhi during the monsoon season, 2008.

Bacterial Nanotubes as Intercellular Linkages in Marine Assemblages

Several types of bacterial appendages, e.g., pili and fimbriae, are known for their role in promoting interactions and aggregation with particles and bacteria in the ocean. First discovered in Bacillus subtilis and Escherichia coli, but novel to marine bacteria, bacterial nanotubes are hollow tubular structures connecting cell pairs that allow for the internal transport of cytoplasmic metabolites across the connecting structure. While the significance of nanotubes in exchange of cytoplasmic content has been established in non-marine bacteria, their occurrence and potential ecological significance in marine bacteria has not been reported. Using multiple high-resolution microscopy methods (atomic force microscopy, scanning, and transmission electron microscopy), we have determined that marine bacteria isolates and natural assemblages from nearshore upper ocean waters can express bacterial nanotubes. In marine isolates Pseudoalteromonas sp. TW7 and Alteromonas sp. ALTSIO, individual bacterial nanotubes measured 50–160 nm in width and extended 100–600 nm between connected cells. The spatial coupling of different cells via nanotubes can last for at least 90 min, extending the duration of interaction events between marine bacteria within natural assemblages. The nanomechanical properties of bacterial nanotubes vary in adhesion and dissipation properties, which has implication for structural and functional variability of these structures in their ability to stick to surfaces and respond to mechanical forces. Nanotube frequency is low among cells in enriched natural assemblages, where nanotubes form short, intimate connections, <200 nm, between certain neighboring cells. Bacterial nanotubes can form the structural basis for a bacterial ensemble and function as a conduit for cytoplasmic exchange (not explicitly studied here) between members for multicellular coordination and expression. The structural measurements and nanomechanical analyses in this study also extends knowledge about the physical properties of bacterial nanotubes and their consequences for marine microenvironments. The discovery of nanotube expression in marine bacteria has significant potential implications regarding intimate bacterial interactions in spatially correlated marine microbial communities.

Modelling the probability of capture for New Zealand's longfin eels ('Anguilla dieffenbachii') and shortfin eels ('Anguilla australis')

<p>Longfin eel and shortfin eel probability of capture models can be used to build probability of capture maps. These maps can help identify eel encounter hotspots in New Zealand and are useful for managing and conserving the species. This research models longfin eel and shortfin eel presence/absence data using regularized random forest (RRF) models, vectorautoregressive spatial-temporal (VAST) models and Bayesian Gaussian random field (GRaF) models. Probability of capture maps built under VAST and GRaF remain approximately consistent with the maps built under RRF models. That is, longfin eels have high probabilities of capture around the coast of New Zealand’s North Island and have low probabilities of capture throughout the centre of New Zealand’s South Island. Shortfin eels have high probabilities of capture in small isolated regions of New Zealand’s North Island and have very low probabilities of capture throughout most of New Zealand’s South Island. Cross validation and spatial cross validation was used to compare the models. Cross validation results show that, compared to RRF models, VAST models improve predictive accuracy for the longfin eel and shortfin eel. Whereas, GRaF only improves predictive performance for the longfin eel. However, spatial cross validation shows no significant difference between VAST and RRF models. Hence, VAST models have higher predictive accuracy than RRF models for the longfin eel and shortfin eel when the training set is spatially correlated to the test set.</p>

Modelling the probability of capture for New Zealand's longfin eels ('Anguilla dieffenbachii') and shortfin eels ('Anguilla australis')

<p>Longfin eel and shortfin eel probability of capture models can be used to build probability of capture maps. These maps can help identify eel encounter hotspots in New Zealand and are useful for managing and conserving the species. This research models longfin eel and shortfin eel presence/absence data using regularized random forest (RRF) models, vectorautoregressive spatial-temporal (VAST) models and Bayesian Gaussian random field (GRaF) models. Probability of capture maps built under VAST and GRaF remain approximately consistent with the maps built under RRF models. That is, longfin eels have high probabilities of capture around the coast of New Zealand’s North Island and have low probabilities of capture throughout the centre of New Zealand’s South Island. Shortfin eels have high probabilities of capture in small isolated regions of New Zealand’s North Island and have very low probabilities of capture throughout most of New Zealand’s South Island. Cross validation and spatial cross validation was used to compare the models. Cross validation results show that, compared to RRF models, VAST models improve predictive accuracy for the longfin eel and shortfin eel. Whereas, GRaF only improves predictive performance for the longfin eel. However, spatial cross validation shows no significant difference between VAST and RRF models. Hence, VAST models have higher predictive accuracy than RRF models for the longfin eel and shortfin eel when the training set is spatially correlated to the test set.</p>

A survey on rendering homogeneous participating media

Participating media are frequent in real-world scenes, whether they contain milk, fruit juice, oil, or muddy water in a river or the ocean. Incoming light interacts with these participating media in complex ways: refraction at boundaries and scattering and absorption inside volumes. The radiative transfer equation is the key to solving this problem. There are several categories of rendering methods which are all based on this equation, but using different solutions. In this paper, we introduce these groups, which include volume density estimation based approaches, virtual point/ray/beam lights, point based approaches, Monte Carlo based approaches, acceleration techniques, accurate single scattering methods, neural network based methods, and spatially-correlated participating media related methods. As well as discussing these methods, we consider the challenges and open problems in this research area.