SEM studies of stressed and irradiated Climax Stock quartz monzonite

In an effort to find the mechanism by which gamma irradiation weakens the unconfined compressive strength of Climax Stock quartz monzonite (CSQM), sections of rock which had been irradiated and loaded to near failure were studied by scanning electron microscopy and compared to sections of rock which had been loaded but not irradiated. The quantities measured and compared were numbers and lengths of microfractures in the rock. We found that the crack parameters depended neither on irradiation treatment nor even on stress history, except in one sample which actually failed. By comparison to cracks counted in other granites by other workers, the crack statistics on CSQM are much noisier and much less indicative of stress history. CSQM is structurally more heterogeneous than the other granites, which is probably the cause of the greater noise level. 12 references, 3 figures, 5 tables

Physical and chemical changes to rock near electrically heated boreholes at Spent Fuel Test-Climax

Sections of Climax Stock quartz monzonite taken from the vicinity of two electrically heated boreholes at Spent Fuel Test-Climax (SFT-C) have been studied by scanning electron microscopy and optical microscopy for signs of changes in crack structure and in mineralogy resulting from operations at SFT-C. The crack structure, as measured by density of cracks and average crack lengths was found not to have changed as a result of heating, regardless of distance from the heater hole. However, rock near the heater borehole sampled in the north heater drift was found to be more cracked than rock near the borehole sampled in the south heater drift. Mineralogically, the post-test samples are identical to the pre-test samples. No new phases have been formed as a result of the test. 10 refs., 6 figs., 8 tabs

Bacteria solve the problem of crowding by moving slowly

Bacteria commonly live attached to surfaces in dense collectives containing billions of cells1. While it is known that motility allows these groups to expand en masse into new territory2,3,4,5, how bacteria collectively move across surfaces under such tightly packed conditions remains poorly understood. Here we combine experiments, cell tracking and individual-based modelling to study the pathogen Pseudomonas aeruginosa as it collectively migrates across surfaces using grappling-hook-like pili3,6,7. We show that the fast-moving cells of a hyperpilated mutant are overtaken and outcompeted by the slower-moving wild type at high cell densities. Using theory developed to study liquid crystals8,9,10,11,12,13, we demonstrate that this effect is mediated by the physics of topological defects, points where cells with different orientations meet one another. Our analyses reveal that when defects with topological charge +1/2 collide with one another, the fast-moving mutant cells rotate to point vertically and become trapped. By moving more slowly, wild-type cells avoid this trapping mechanism and generate collective behaviour that results in faster migration. In this way, the physics of liquid crystals explains how slow bacteria can outcompete faster cells in the race for new territory

Design, Construction, Operation and Performance of a Hadron Blind Detector for the PHENIX Experiment

A Hadron Blind Detector (HBD) has been developed, constructed and successfully operated within the PHENIX detector at RHIC. The HBD is a Cherenkov detector operated with pure CF4. It has a 50 cm long radiator directly coupled in a window- less configuration to a readout element consisting of a triple GEM stack, with a CsI photocathode evaporated on the top surface of the top GEM and pad readout at the bottom of the stack. This paper gives a comprehensive account of the construction, operation and in-beam performance of the detector.Comment: 51 pages, 39 Figures, submitted to Nuclear Instruments and Method

Simulation and validation studies of a large drift tube Muon Tracker

Cosmic ray muons are massive, charged particles created from high energy cosmic rays colliding with atomic nuclei in Earth’s atmosphere. Because of their high momenta and weak interaction, these muons can penetrate through large thicknesses of dense material before being absorbed, making them ideal for nondestructive imaging of objects composed of high-Z elements. A Giant Muon Tracker with two horizontal 8 × 6 in.2 and two vertical 6 × 6 in.2 modules of drift tubes was used to measure muon tracks passing through samples placed inside the detector volume. The experimental results were used to validate a Monte Carlo simulation of the Giant Muon Tracker. The imaging results of simulated samples were reconstructed and compared with those from the experiment, which showed excellent agreement

Piecewise Approximate Bayesian Computation: fast inference for discretely observed Markov models using a factorised posterior distribution

Many modern statistical applications involve inference for complicated stochastic models for which the likelihood function is difficult or even impossible to calculate, and hence conventional likelihood-based inferential techniques cannot be used. In such settings, Bayesian inference can be performed using Approximate Bayesian Computation (ABC). However, in spite of many recent developments to ABC methodology, in many applications the computational cost of ABC necessitates the choice of summary statistics and tolerances that can potentially severely bias the estimate of the posterior. We propose a new “piecewise” ABC approach suitable for discretely observed Markov models that involves writing the posterior density of the parameters as a product of factors, each a function of only a subset of the data, and then using ABC within each factor. The approach has the advantage of side-stepping the need to choose a summary statistic and it enables a stringent tolerance to be set, making the posterior “less approximate”. We investigate two methods for estimating the posterior density based on ABC samples for each of the factors: the first is to use a Gaussian approximation for each factor, and the second is to use a kernel density estimate. Both methods have their merits. The Gaussian approximation is simple, fast, and probably adequate for many applications. On the other hand, using instead a kernel density estimate has the benefit of consistently estimating the true piecewise ABC posterior as the number of ABC samples tends to infinity. We illustrate the piecewise ABC approach with four examples; in each case, the approach offers fast and accurate inference

Comparison of MAGIC and Diatom paleolimnological model hindcasts of lakewater acidification in the Adirondack region of New York

Thirty-three lakes that had been statistically selected as part of the US Environmental Protection Agency's Eastern Lake Survey and Direct Delayed Response Project (DDRP) were used to compare the MAGIC (watershed) and Diatom (paleolimnological) models. The study lakes represented a well-defined group of Adirondack lakes, each larger than 4 ha in area and having acid-neutralizing capacity (ANC) <400 {mu}eq L{sup {minus}1}. The study first compared current and pre-industrial (before 1850) pH and ANC estimates from Diatom and MAGIC as they were calibrated in the preceding Paleocological Investigation of Recent Lake Acidification (PIRLA) and DDRP studies, respectively. Initially, the comparison of hindcasts of pre-industrial chemistry was confounded by seasonal and methodological differences in lake chemistry data used in calibration of the model. Although certain differences proved to be of little significance for comparison, MAGIC did predict significantly higher pre-industrial ANC and pH values than did Diatom, using calibrations in the preceding studies. Both models suggest acidification of low ANC Adirondack region lakes since preindustrial times, but differ primarily in that MAGIC inferred greater acidification and that acidification has occurred in all lakes in the comparison, whereas Diatom inferred that acidification has been restricted to low ANC lakes (<about 50 {mu}eq L{sup {minus}1}). 133 refs., 30 figs., 16 tabs

Steering self-organisation through confinement

Self-organisation is the spontaneous emergence of spatio-temporal structures and patterns from the interaction of smaller individual units. Examples are found across many scales in very different systems and scientific disciplines, from physics, materials science and robotics to biology, geophysics and astronomy. Recent research has highlighted how self-organisation can be both mediated and controlled by confinement. Confinement is an action over a system that limits its units’ translational and rotational degrees of freedom, thus also influencing the system's phase space probability density; it can function as either a catalyst or inhibitor of self-organisation. Confinement can then become a means to actively steer the emergence or suppression of collective phenomena in space and time. Here, to provide a common framework and perspective for future research, we examine the role of confinement in the self-organisation of soft-matter systems and identify overarching scientific challenges that need to be addressed to harness its full scientific and technological potential in soft matter and related fields. By drawing analogies with other disciplines, this framework will accelerate a common deeper understanding of self-organisation and trigger the development of innovative strategies to steer it using confinement, with impact on, e.g., the design of smarter materials, tissue engineering for biomedicine and in guiding active matter