Dramatic variability of the carbonate system at a temperate coastal ocean site (Beaufort, North Carolina, USA) is regulated by physical and biogeochemical processes on multiple timescales.

Increasing atmospheric carbon dioxide (CO2) from anthropogenic sources is acidifying marine environments resulting in potentially dramatic consequences for the physical, chemical and biological functioning of these ecosystems. If current trends continue, mean ocean pH is expected to decrease by ~0.2 units over the next ~50 years. Yet, there is also substantial temporal variability in pH and other carbon system parameters in the ocean resulting in regions that already experience change that exceeds long-term projected trends in pH. This points to short-term dynamics as an important layer of complexity on top of long-term trends. Thus, in order to predict future climate change impacts, there is a critical need to characterize the natural range and dynamics of the marine carbonate system and the mechanisms responsible for observed variability. Here, we present pH and dissolved inorganic carbon (DIC) at time intervals spanning 1 hour to >1 year from a dynamic, coastal, temperate marine system (Beaufort Inlet, Beaufort NC USA) to characterize the carbonate system at multiple time scales. Daily and seasonal variation of the carbonate system is largely driven by temperature, alkalinity and the balance between primary production and respiration, but high frequency change (hours to days) is further influenced by water mass movement (e.g. tides) and stochastic events (e.g. storms). Both annual (~0.3 units) and diurnal (~0.1 units) variability in coastal ocean acidity are similar in magnitude to 50 year projections of ocean acidity associated with increasing atmospheric CO2. The environmental variables driving these changes highlight the importance of characterizing the complete carbonate system rather than just pH. Short-term dynamics of ocean carbon parameters may already exert significant pressure on some coastal marine ecosystems with implications for ecology, biogeochemistry and evolution and this shorter term variability layers additive effects and complexity, including extreme values, on top of long-term trends in ocean acidification.

Biological and physical interactions on a tropical island coral reef: Transport and retention processes on moorea, French Polynesia

The Moorea Coral Reef Long Term Ecological Research project funded by the US National Science Foundation includes multidisciplinary studies of physical processes driving ecological dynamics across the fringing reef, back reef, and fore reef habitats of Moorea, French Polynesia. A network of oceanographic moorings and a variety of other approaches have been used to investigate the biological and biogeochemical aspects of water transport and retention processes in this system. There is evidence to support the hypothesis that a low-frequency counterclockwise flow around the island is superimposed on the relatively strong alongshore currents on each side of the island. Despite the rapid flow and flushing of the back reef, waters over the reef display chemical and biological characteristics distinct from those offshore. The patterns include higher nutrient and lower dissolved organic carbon concentrations, distinct microbial community compositions among habitats, and reef assemblages of zooplankton that exhibit migration behavior, suggesting multigenerational residence on the reef. Zooplankton consumption by planktivorous fish on the reef reflects both retention of reef-associated taxa and capture by the reef community of resources originating offshore. Coral recruitment and population genetics of reef fishes point to retention of larvae within the system and high recruitment levels from local adult populations. The combined results suggest that a broad suite of physical and biological processes contribute to high retention of externally derived and locally produced organic materials within this island coral reef system. © 2013 by The Oceanography Society. All rights reserved.

Assessing segmentation processes by click detection: online measure of statistical learning, or simple interference?

Statistical learning can be used to extract the words from continuous speech. Gómez, Bion, and Mehler (Language and Cognitive Processes, 26, 212–223, 2011) proposed an online measure of statistical learning: They superimposed auditory clicks on a continuous artificial speech stream made up of a random succession of trisyllabic nonwords. Participants were instructed to detect these clicks, which could be located either within or between words. The results showed that, over the length of exposure, reaction times (RTs) increased more for within-word than for between-word clicks. This result has been accounted for by means of statistical learning of the between-word boundaries. However, even though statistical learning occurs without an intention to learn, it nevertheless requires attentional resources. Therefore, this process could be affected by a concurrent task such as click detection. In the present study, we evaluated the extent to which the click detection task indeed reflects successful statistical learning. Our results suggest that the emergence of RT differences between within- and between-word click detection is neither systematic nor related to the successful segmentation of the artificial language. Therefore, instead of being an online measure of learning, the click detection task seems to interfere with the extraction of statistical regularities.

The temporal dynamics of metacognition: Dissociating task-related activity from later metacognitive processes

In recent years, neuroscience research spent much effort in revealing brain activity related to metacognition. Despite this endeavor, it remains unclear exactly when metacognitive experiences develop during task performance. To investigate this, the current study used EEG to temporally and spatially dissociate task-related activity from metacognitive activity. In a masked priming paradigm, metacognitive experiences of difficulty were induced by manipulating congruency between prime and target. As expected, participants more frequently rated incongruent trials as difficult and congruent trials as easy, while being completely unable to perceive the masked primes. Results showed that both the N2 and the P3 ERP components were modulated by congruency, but that only the P3 modulation interacted with metacognitive experiences. Single-trial analysis additionally showed that the magnitude of the P3 modulation by congruency accurately predicted the metacognitive response. Source localization indicated that the N2 task-related activity originated in the ACC, whereas the P3-interplay between task-related activation and metacognitive experiences originated from the precuneus. We conclude that task-related activity can be dissociated from later metacognitive processing.

Temporal modelling of short-term rainfall using Cox processes

We study two marked point process models based on the Cox process. These models are used to describe the probabilistic structure of the rainfall intensity process. Mathematical formulation of the models is described and some second-moment characteristics of the rainfall depth, and aggregated processes are considered. The derived second-order properties of the accumulated rainfall amounts at different levels of aggregation are used in order to examine the model fit. A brief data analysis is presented. Copyright © 1998 John Wiley & Sons, Ltd.

Computational issues in the modelling of materials-based manufacturing processes

The manufacture of materials products involves the control of a range of interacting physical phenomena. The material to be used is synthesised and then manipulated into some component form. The structure and properties of the final component are influenced by both interactions of continuum-scale phenomena and those at an atomistic-scale level. Moreover, during the processing phase there are some properties that cannot be measured (typically the liquid-solid phase change). However, it seems there is a potential to derive properties and other features from atomistic-scale simulations that are of key importance at the continuum scale. Some of the issues that need to be resolved in this context focus upon computational techniques and software tools facilitating: (i) the multiphysics modeling at continuum scale; (ii) the interaction and appropriate degrees of coupling between the atomistic through microstructure to continuum scale; and (iii) the exploitation of high-performance parallel computing power delivering simulation results in a practical time period. This paper discusses some of the attempts to address each of the above issues, particularly in the context of materials processing for manufacture.

Computational modelling of multi-physics processes on high performance parallel computer systems

The demands of the process of engineering design, particularly for structural integrity, have exploited computational modelling techniques and software tools for decades. Frequently, the shape of structural components or assemblies is determined to optimise the flow distribution or heat transfer characteristics, and to ensure that the structural performance in service is adequate. From the perspective of computational modelling these activities are typically separated into: • fluid flow and the associated heat transfer analysis (possibly with chemical reactions), based upon Computational Fluid Dynamics (CFD) technology • structural analysis again possibly with heat transfer, based upon finite element analysis (FEA) techniques.

Modelling meso-scale diffusion processes in stochastic fluid bio-membranes

The space–time dynamics of rigid inhomogeneities (inclusions) free to move in a randomly fluctuating fluid bio-membrane is derived and numerically simulated as a function of the membrane shape changes. Both vertically placed (embedded) inclusions and horizontally placed (surface) inclusions are considered. The energetics of the membrane, as a two-dimensional (2D) meso-scale continuum sheet, is described by the Canham–Helfrich Hamiltonian, with the membrane height function treated as a stochastic process. The diffusion parameter of this process acts as the link coupling the membrane shape fluctuations to the kinematics of the inclusions. The latter is described via Ito stochastic differential equation. In addition to stochastic forces, the inclusions also experience membrane-induced deterministic forces. Our aim is to simulate the diffusion-driven aggregation of inclusions and show how the external inclusions arrive at the sites of the embedded inclusions. The model has potential use in such emerging fields as designing a targeted drug delivery system.