Supergravity-matter actions in three dimensions and Chern-Simons terms

We study off-shell N-extended Yang-Mills multiplets coupled to conformal supergravity in three spacetime dimensions. Superform formulations are presented for the non-Abelian Chern-Simons actions in the cases N=1, 2, 3, and the corresponding component actions are explicitly worked out. Such a Chern-Simons action does not exist for N=4. In the latter case, a superform formulation is given for the BF term that describes the coupling of two Abelian vector multiplets with self-dual and anti-self-dual superfield strengths respectively. The superform results obtained are used to construct linear multiplet action principles in the cases N=2, 3, 4. The N=3 and N=4 actions are demonstrated to be universal in the sense that all known off-shell supergravity-matter systems (with the exception of pure conformal supergravity) may be described using such an action. Starting from the N=3 and N=4 Abelian vector multiplets, we also construct composite O(2) multiplets which are analogues of the four-dimensional construction of an N=2 reduced chiral scalar engineered from the improved tensor multiplet. Using these composites, we present the superfield equations of motion for N=3 and N=4 anti-de Sitter and topologically massive supergravity theories. We also sketch the construction of a large family of higher derivative couplings for N=3 and N=4 vector multiplets.Comment: 64 pages; V3: published versio

On curvature squared terms in N = 2 supergravity

We present the N = 2 supersymmetric completion of a scalar curvature squared term in a completely gauge independent form. We also elaborate on its component structure.Comment: 15 pages; V2: 17 pages, typos corrected, discussion comments and acknowledgement added; V3: published versio

The operational processing of wind estimates from cloud motions: Past, present and future

Current NESS winds operations provide approximately 1800 high quality wind estimates per day to about twenty domestic and foreign users. This marked improvement in NESS winds operations was the result of computer techniques development which began in 1969 to streamline and improve operational procedures. In addition, the launch of the SMS-1 satellite in 1974, the first in the second generation of geostationary spacecraft, provided an improved source of visible and infrared scanner data for the extraction of wind estimates. Currently, operational winds processing at NESS is accomplished by the automated and manual analyses of infrared data from two geostationary spacecraft. This system uses data from SMS-2 and GOES-1 to produce wind estimates valid for 00Z, 12Z and 18Z synoptic times

Higher derivative couplings and massive supergravity in three dimensions

We develop geometric superspace settings to construct arbitrary higher derivative couplings (including R^n terms) in three-dimensional supergravity theories with N=1,2,3 by realising them as conformal supergravity coupled to certain compensators. For all known off-shell supergravity formulations, we construct supersymmetric invariants with up to and including four derivatives. As a warming-up exercise, we first give a new and completely geometric derivation of such invariants in N=1 supergravity. Upon reduction to components, they agree with those given in arXiv:0907.4658 and arXiv:1005.3952. We then carry out a similar construction in the case of N=2 supergravity for which there exist two minimal formulations that differ by the choice of compensating multiplet: (i) a chiral scalar multipet; (ii) a vector multiplet. For these formulations all four derivative invariants are constructed in completely general and gauge independent form. For a general supergravity model (in the N=1 and minimal N=2 cases) with curvature-squared and lower order terms, we derive the superfield equations of motion, linearise them about maximally supersymmetric backgrounds and obtain restrictions on the parameters that lead to models for massive supergravity. We use the non-minimal formulation for N = 2 supergravity (which corresponds to a complex linear compensator) to construct a novel consistent theory of massive supergravity. In the case of N = 3 supergravity, we employ the off-shell formulation with a vector multiplet as compensator to construct for the first time various higher derivative invariants. These invariants may be used to derive models for N = 3 massive supergravity. As a bi-product of our analysis, we also present superfield equations for massive higher spin multiplets in (1,0), (1,1) and (2,0) anti-de Sitter superspaces.Comment: 84 pages; V3: references added, minor modifications, published versio

Chiral anomalies in six dimensions from harmonic superspace

We develop a superfield approach to compute chiral anomalies in general ${\cal N}=(1,0)$ supersymmetric gauge theories in six dimensions. Within the harmonic-superspace formulation for these gauge theories, the anomalous contributions to the effective action only come from matter and ghost hypermultiplets. By studying the short-distance behaviour of the propagator for the hypermultiplet coupled to a background vector multiplet, we compute the covariant and consistent chiral anomalies. We also provide a superform formulation for the non-abelian anomalous current multiplet in general ${\cal N}=(1,0)$ supersymmetric gauge theories.Comment: 33 page

The anomalous current multiplet in 6D minimal supersymmetry

For supersymmetric gauge theories with eight supercharges in four, five and six dimensions, a conserved current belongs to the linear multiplet. In the case of six-dimensional $\cal N=(1,0)$ Poincar\'e supersymmetry, we present a consistent deformation of the linear multiplet which describes chiral anomalies. This is achieved by developing a superform formulation for the deformed linear multiplet. In the abelian case, we compute a nonlocal effective action generating the gauge anomaly.Comment: 27 pages; V2: published versio

The Impacts of Triclosan on Anaerobic Community Structures, Function, and Antimicrobial Resistance

Triclosan is a widespread antimicrobial agent that accumulates in anaerobic digesters used to treat the residual solids generated at municipal wastewater treatment plants; there is very little information, however, about how triclosan impacts microbial communities in anaerobic digesters. We investigated how triclosan impacts the community structure, function and antimicrobial resistance genes in lab-scale anaerobic digesters. Previously exposed (to triclosan) communities were amended with 5, 50, and 500 mg/kg of triclosan, corresponding to the median, 95th percentile, and 4-fold higher than maximum triclosan concentration that has been detected in U.S. biosolids. Triclosan amendment caused all of the Bacteria and Archaea communities to structurally diverge from that of the control cultures (based on ARISA). At the end of the experiment, all triclosan-amended Archaea communities had diverged from the control communities, regardless of the triclosan concentration added. In contrast, over time the Bacteria communities that were amended with lower concentrations of triclosan (5 mg/kg and 50 mg/kg) initially diverged and then reconverged with the control community structure. Methane production at 500 mg/kg was nearly half the methane production in control cultures. At 50 mg/kg, a large variability in methane production was observed, suggesting that 50 mg/kg may be a tipping point where function begins to fail in some communities. When previously unexposed communities were exposed to 500 mg triclosan/kg, function was maintained, but the abundance of a gene encoding for triclosan resistance (mexB) increased. This research suggests that triclosan could inhibit methane production in anaerobic digesters if concentrations were to increase and may also select for resistant Bacteria. In both cases, microbial community composition and exposure history alter the influence of triclosan

The Impacts of Triclosan on Anaerobic Community Structures, Function, and Antimicrobial Resistance

Triclosan is a widespread antimicrobial agent that accumulates in anaerobic digesters used to treat the residual solids generated at municipal wastewater treatment plants; there is very little information, however, about how triclosan impacts microbial communities in anaerobic digesters. We investigated how triclosan impacts the community structure, function and antimicrobial resistance genes in lab-scale anaerobic digesters. Previously exposed (to triclosan) communities were amended with 5, 50, and 500 mg/kg of triclosan, corresponding to the median, 95th percentile, and 4-fold higher than maximum triclosan concentration that has been detected in U.S. biosolids. Triclosan amendment caused all of the Bacteria and Archaea communities to structurally diverge from that of the control cultures (based on ARISA). At the end of the experiment, all triclosan-amended Archaea communities had diverged from the control communities, regardless of the triclosan concentration added. In contrast, over time the Bacteria communities that were amended with lower concentrations of triclosan (5 mg/kg and 50 mg/kg) initially diverged and then reconverged with the control community structure. Methane production at 500 mg/kg was nearly half the methane production in control cultures. At 50 mg/kg, a large variability in methane production was observed, suggesting that 50 mg/kg may be a tipping point where function begins to fail in some communities. When previously unexposed communities were exposed to 500 mg triclosan/kg, function was maintained, but the abundance of a gene encoding for triclosan resistance (mexB) increased. This research suggests that triclosan could inhibit methane production in anaerobic digesters if concentrations were to increase and may also select for resistant Bacteria. In both cases, microbial community composition and exposure history alter the influence of triclosan

Identifying structural changes with unsupervised machine learning methods

Unsupervised machine learning methods are used to identify structural changes using the melting point transition in classical molecular dynamics simulations as an example application of the approach. Dimensionality reduction and clustering methods are applied to instantaneous radial distributions of atomic configurations from classical molecular dynamics simulations of metallic systems over a large temperature range. Principal component analysis is used to dramatically reduce the dimensionality of the feature space across the samples using an orthogonal linear transformation that preserves the statistical variance of the data under the condition that the new feature space is linearly independent. From there, k-means clustering is used to partition the samples into solid and liquid phases through a criterion motivated by the geometry of the reduced feature space of the samples, allowing for an estimation of the melting point transition. This pattern criterion is conceptually similar to how humans interpret the data but with far greater throughput, as the shapes of the radial distributions are different for each phase and easily distinguishable by humans. The transition temperature estimates derived from this machine learning approach produce comparable results to other methods on similarly small system sizes. These results show that machine learning approaches can be applied to structural changes in physical systems