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Sub-optical-cycle dynamics of dense electron bunches in relativistic-intensity laser–solid interactions lead to the emission of high-order harmonics and attosecond light pulses. The capacity of particle-in-cell simulations to accurately model these dynamics is essential for the prediction of emission properties because the attosecond pulse intensity depends on the electron density distribution at the time of emission and on the temporal distribution of individual electron Lorentz-factors in an emitting electron bunch. Here, we show that in one-dimensional collisionless simulations, the peak density of the emitting electron bunch increases with the increase in the spatial resolution of the simulation grid. When collisions are added to the model, the peak electron density becomes independent of the spatial resolution. Collisions are shown to increase the spread of the peaks of Lorentz-factors of emitting electrons in time, especially in the regimes far from optimum generation conditions, thus leading to lower intensities of attosecond pulses as compared to those obtained in collisionless simulations. 

Abstract In studies of the unicellular eukaryote Dictyostelium discoideum , many have anecdotally observed that cell dilution below a certain ‘threshold density’ causes cells to undergo a period of slow growth (lag). However, little is documented about the slow growth phase and the reason for different growth dynamics below and above this threshold density. In this paper, we extend and correct our earlier work to report an extensive set of experiments, including the use of new cell counting technology, that set this slow-to-fast growth transition on a much firmer biological basis. We show that dilution below a certain density (around 10 4 cells ml −1 ) causes cells to grow slower on average and exhibit a large degree of variability: sometimes a sample does not lag at all, while sometimes it takes many moderate density cell cycle times to recover back to fast growth. We perform conditioned media experiments to demonstrate that a chemical signal mediates this endogenous phenomenon. Finally, we argue that while simple models involving fluid transport of signal molecules or cluster-based signaling explain typical behavior, they do not capture the high degree of variability between samples but nevertheless favor an intra-cluster mechanism. 

With increasing biodiversity loss occurring worldwide, there is a need to understand how these losses will affect ecosystem structure and function. Biodiversity loss leads to changes in species interactions and alters the trophic complexity of food webs. These alterations to trophic complexity can be described by changes to the diversity of food resources and the diversity of trophic levels. To understand how biodiversity affects trophic complexity of food webs, we used 10 islands across the Aleutian Archipelago to compare the alternate state communities found in kelp forest ecosystems (kelp forest and urchin barren communities) and then compared these to natural reference communities without local benthic production (their associated offshore communities). We constructed food webs for each community across the Aleutian Archipelago using primary producer and consumer carbon (δ¹³C, a proxy for food sources to a consumer) and nitrogen (δ¹⁵N, a proxy for consumer trophic level) stable isotope values. Our findings suggest that biodiversity loss (i.e., phase change from kelp forest to urchin barren) leads to reductions in trophic complexity, which was similar to naturally occurring communities with low local resource biodiversity. This was expressed by lower consumer isotopic dietary niche areas, especially omnivores and herbivores, and lower omnivore and carnivore trophic levels within the urchin barren communities. We clarify how biodiversity promotes food resources and increases trophic levels and complexity through critical trophic conduits.

 

Since the very first detection of gravitational waves from the coalescence of two black holes in 2015, Bayesian statistical methods have been routinely applied by LIGO and Virgo to extract the signal out of noisy interferometric measurements, obtain point estimates of the physical parameters responsible for producing the signal, and rigorously quantify their uncertainties. Different computational techniques have been devised depending on the source of the gravitational radiation and the gravitational waveform model used. Prominent sources of gravitational waves are binary black hole or neutron star mergers, the only objects that have been observed by detectors to date. But also gravitational waves from core‐collapse supernovae, rapidly rotating neutron stars, and the stochastic gravitational‐wave background are in the sensitivity band of the ground‐based interferometers and expected to be observable in future observation runs. As nonlinearities of the complex waveforms and the high‐dimensional parameter spaces preclude analytic evaluation of the posterior distribution, posterior inference for all these sources relies on computer‐intensive simulation techniques such as Markov chain Monte Carlo methods. A review of state‐of‐the‐art Bayesian statistical parameter estimation methods will be given for researchers in this cross‐disciplinary area of gravitational wave data analysis.

This article is categorized under:

Applications of Computational Statistics > Signal and Image Processing and Coding

Statistical and Graphical Methods of Data Analysis > Markov Chain Monte Carlo (MCMC)

Statistical Models > Time Series Models