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Cleaning of a model food soil from horizontal plates by a moving vertical water jet
The removal of layers of a model food soil (dried Xanthan gum containing fluorescent ZnS particles) by a vertical water jet impinging normally on to the plate, generated by a solid stream nozzle which moves across the plate was reported by Köhler et al. (2014). Their experiments investigated nozzle pressures from 0.5-2.0 barg; nozzle diameters from 0.84-2.66 mm, nozzle-layer separation of 20 mm, and nozzle traverse speeds of 2.1-126 mm s-1. The flow parameters and separation are smaller than those typical of industrial jet cleaning operations.
The model developed by Wilson et al. (2014; Chem. Eng. Sci., 109, 183–196) for cleaning of similar layers by a stationary impinging jet was modified to describe the case of moving nozzle. This new model predicted the trends observed in the experiments, and analysis of the data yielded a similar cleaning rate constant to that obtained previously for cleaning of similar layers by stationary jets. The model predicted a non-circular cleaning front which matched that extracted from new experiments in which the flow was interrupted in order to capture this feature. The model allowed the cleaning performance indicators suggested by Köhler et al. (2014) to be expressed quantitatively: these indicated that higher nozzle traverse speeds give increased cleaning time, energy and liquid consumption performance.The work at TU Dresden was funded by the European Union and the Free State of Saxony as part of project SAB 080951793. A short vacation study grant for LC from Fitzwilliam College, Cambridge, is gratefully acknowledged.This is the accepted manuscript. The final version is available from Elsevier at http://www.sciencedirect.com/science/article/pii/S000925091400630
A Recurrent Neural Network Survival Model: Predicting Web User Return Time
The size of a website's active user base directly affects its value. Thus, it
is important to monitor and influence a user's likelihood to return to a site.
Essential to this is predicting when a user will return. Current state of the
art approaches to solve this problem come in two flavors: (1) Recurrent Neural
Network (RNN) based solutions and (2) survival analysis methods. We observe
that both techniques are severely limited when applied to this problem.
Survival models can only incorporate aggregate representations of users instead
of automatically learning a representation directly from a raw time series of
user actions. RNNs can automatically learn features, but can not be directly
trained with examples of non-returning users who have no target value for their
return time. We develop a novel RNN survival model that removes the limitations
of the state of the art methods. We demonstrate that this model can
successfully be applied to return time prediction on a large e-commerce dataset
with a superior ability to discriminate between returning and non-returning
users than either method applied in isolation.Comment: Accepted into ECML PKDD 2018; 8 figures and 1 tabl
The Rich Structure of Gauss-Bonnet Holographic Superconductors
We study fully backreacting, Gauss-Bonnet (GB) holographic superconductors in
5 bulk spacetime dimensions. We explore the system's dependence on the scalar
mass for both positive and negative GB coupling, . We find that when
the mass approaches the Breitenlohner-Freedman (BF) bound and
the effect of backreaction is to increase the
critical temperature, , of the system: the opposite of its effect in the
rest of parameter space. We also find that reducing below zero
increases and that the effect of backreaction is diminished. We study the
zero temperature limit, proving that this system does not permit regular
solutions for a non-trivial, tachyonic scalar field and constrain possible
solutions for fields with positive masses. We investigate singular, zero
temperature solutions in the Einstein limit but find them to be incompatible
with the concept of GB gravity being a perturbative expansion of Einstein
gravity. We study the conductivity of the system, finding that the inclusion of
backreaction hinders the development of poles in the conductivity that are
associated with quasi-normal modes approaching the real axis from elsewhere in
the complex plane.Comment: 26 pages, 11 figures, V3, Added discussion of non-tachyonic scalars,
alterations to figures and tex
Collided path replanning in dynamic environments using RRT and Cell decomposition algorithms
The motion planning is an important part of robots’ models. It is responsible for robot’s movements. In this work, the cell decomposition algorithm is used to find a spatial path on preliminary static workspaces, and then, the rapidly exploring random tree algorithm (RRT) is used to validate this path on the actual workspace. Two methods have been proposed to enhance the omnidirectional robot’s navigation on partially changed workspace. First, the planner creates a RRT tree and biases its growth toward the path’s points in ordered form. The planner reduces the probability of choosing the next point when a collision is detected, which in turn increases the RRT’s expansion on the free space. The second method uses a straight planner to connect path’s points. If a collision is detected, the planner places RRTs on both sides of the collided segment. The proposed methods are compared with the others approaches, and the simulation shows better results in term of efficiency and completeness.Plánování pohybu robota je důležitou součástí modelování funkcí robotů. Plán řídí pohyby robota. V této práci se algoritmus rozkladu na buňky používá k nalezení cesty pracovní plochou a algoritmus prozkoumání náhodného stromu (RRT) k ověření cesty skutečným prostorem. Byly navrženy dvě metody ke zlepšení navigace všesměrové pohyblivého robota částečně změněnou pracovní plochou. Za prvé, plánovač vytvoří RRT strom a vychyluje jeho růst směrem k bodu na cestě. Plánovač snižuje pravděpodobnost výběru dalšího bodu, když je detekována kolize, což zase zvyšuje expanzi RRT na volném prostoru. Druhá metoda používá shodný plánovač pro napojení bodů cesty. Pokud je detekována kolize, plánovač upravuje RRT na obou stranách kolizního segmentu. Navrhované metody jsou porovnávány s dalšími používanými přístupy, přečemž simulace ukazuje lepší výsledky z hlediska účinnosti a úplnosti plánování cesty.The motion planning is an important part of robots’ models. It is responsible for robot’s movements. In this work, the cell decomposition algorithm is used to find a spatial path on preliminary static workspaces, and then, the rapidly exploring random tree algorithm (RRT) is used to validate this path on the actual workspace. Two methods have been proposed to enhance the omnidirectional robot’s navigation on partially changed workspace. First, the planner creates a RRT tree and biases its growth toward the path’s points in ordered form. The planner reduces the probability of choosing the next point when a collision is detected, which in turn increases the RRT’s expansion on the free space. The second method uses a straight planner to connect path’s points. If a collision is detected, the planner places RRTs on both sides of the collided segment. The proposed methods are compared with the others approaches, and the simulation shows better results in term of efficiency and completeness
Holographic Entanglement Entropy in P-wave Superconductor Phase Transition
We investigate the behavior of entanglement entropy across the holographic
p-wave superconductor phase transition in an Einstein-Yang-Mills theory with a
negative cosmological constant. The holographic entanglement entropy is
calculated for a strip geometry at AdS boundary. It is found that the
entanglement entropy undergoes a dramatic change as we tune the ratio of the
gravitational constant to the Yang-Mills coupling, and that the entanglement
entropy does behave as the thermal entropy of the background black holes. That
is, the entanglement entropy will show the feature of the second order or first
order phase transition when the ratio is changed. It indicates that the
entanglement entropy is a good probe to investigate the properties of the
holographic phase transition.Comment: 19 pages,15 figures, extended discussion in Sec.5, references adde
Lifshitz black holes in string theory
We provide the first black hole solutions with Lifshitz asymptotics found in
string theory. These are expected to be dual to models enjoying anisotropic
scale invariance with dynamical exponent z=2 at finite temperature. We employ a
consistent truncation of type IIB supergravity to four dimensions with an
arbitrary 5-dimensional Einstein manifold times a circle as internal geometry.
New interesting features are found that significantly differ from previous
results in phenomenological models. In particular, small black holes are shown
to be thermodynamically unstable, analogously to the usual AdS-Schwarzschild
black holes, and extremality is never reached. This signals a possible
Hawking-Page like phase transition at low temperatures.Comment: 19 pages, 7 figures. v2 references adde
Holographic Superconductors from Einstein-Maxwell-Dilaton Gravity
We construct holographic superconductors from Einstein-Maxwell-dilaton
gravity in 3+1 dimensions with two adjustable couplings and the charge
carried by the scalar field. For the values of and we
consider, there is always a critical temperature at which a second order phase
transition occurs between a hairy black hole and the AdS RN black hole in the
canonical ensemble, which can be identified with the superconducting phase
transition of the dual field theory. We calculate the electric conductivity of
the dual superconductor and find that for the values of and where
is small the dual superconductor has similar properties to the
minimal model, while for the values of and where is
large enough, the electric conductivity of the dual superconductor exhibits
novel properties at low frequencies where it shows a "Drude Peak" in the real
part of the conductivity.Comment: 25 pages, 13 figures; v2, typos corrected; v3, refs added, to appear
in JHE
Assessing the Health of Richibucto Estuary with the Latent Health Factor Index
The ability to quantitatively assess the health of an ecosystem is often of
great interest to those tasked with monitoring and conserving ecosystems. For
decades, research in this area has relied upon multimetric indices of various
forms. Although indices may be numbers, many are constructed based on
procedures that are highly qualitative in nature, thus limiting the
quantitative rigour of the practical interpretations made from these indices.
The statistical modelling approach to construct the latent health factor index
(LHFI) was recently developed to express ecological data, collected to
construct conventional multimetric health indices, in a rigorous quantitative
model that integrates qualitative features of ecosystem health and preconceived
ecological relationships among such features. This hierarchical modelling
approach allows (a) statistical inference of health for observed sites and (b)
prediction of health for unobserved sites, all accompanied by formal
uncertainty statements. Thus far, the LHFI approach has been demonstrated and
validated on freshwater ecosystems. The goal of this paper is to adapt this
approach to modelling estuarine ecosystem health, particularly that of the
previously unassessed system in Richibucto in New Brunswick, Canada. Field data
correspond to biotic health metrics that constitute the AZTI marine biotic
index (AMBI) and abiotic predictors preconceived to influence biota. We also
briefly discuss related LHFI research involving additional metrics that form
the infaunal trophic index (ITI). Our paper is the first to construct a
scientifically sensible model to rigorously identify the collective explanatory
capacity of salinity, distance downstream, channel depth, and silt-clay content
--- all regarded a priori as qualitatively important abiotic drivers ---
towards site health in the Richibucto ecosystem.Comment: On 2013-05-01, a revised version of this article was accepted for
publication in PLoS One. See Journal reference and DOI belo
Holography of Charged Dilaton Black Holes
We study charged dilaton black branes in . Our system involves a
dilaton coupled to a Maxwell field with dilaton-dependent
gauge coupling, . First, we find the solutions for
extremal and near extremal branes through a combination of analytical and
numerical techniques. The near horizon geometries in the simplest cases, where
, are Lifshitz-like, with a dynamical exponent
determined by . The black hole thermodynamics varies in an interesting
way with , but in all cases the entropy is vanishing and the specific
heat is positive for the near extremal solutions. We then compute conductivity
in these backgrounds. We find that somewhat surprisingly, the AC conductivity
vanishes like at T=0 independent of . We also explore the
charged black brane physics of several other classes of gauge-coupling
functions . In addition to possible applications in AdS/CMT, the
extremal black branes are of interest from the point of view of the attractor
mechanism. The near horizon geometries for these branes are universal,
independent of the asymptotic values of the moduli, and describe generic
classes of endpoints for attractor flows which are different from .Comment: 33 pages, 3 figures, LaTex; v2, references added; v3, more refs
added; v4, refs added, minor correction
Neutralisation of SARS-CoV-2 by monoclonal antibody through dual targeting powder formulation
Neutralising monoclonal antibody (mAb) is an important weapon in our arsenal for combating respiratory viral infections. However, the effectiveness of neutralising mAb has been impeded by the rapid emergence of mutant variants. Early administration of broad-spectrum mAb with improved delivery efficiency can potentially enhance efficacy and patient outcomes. WKS13 is a humanised mAb which was previously demonstrated to exhibit broad-spectrum activity against SARS-CoV-2 variants. In this study, a dual targeting formulation strategy was designed to deliver WKS13 to both the nasal cavity and lower airways, the two critical sites of infection caused by SARS-CoV-2. Dry powders of WKS13 were first prepared by spray drying, with cyclodextrin used as stabiliser excipient. Two-fluid nozzle (TFN) was used to produce particles below 5 μm for lung deposition (C-TFN formulation) and ultrasonic nozzle (USN) was used to produce particles above 10 μm for nasal deposition (C-USN formulation). Gel electrophoresis and size exclusion chromatography studies showed that the structural integrity of mAb was successfully preserved with no sign of aggregation after spray drying. To achieve dual targeting property, C-TFN and C-USN were mixed at various ratios. The aerosolisation property of the mixed formulations dispersed from a nasal powder device was examined using a Next Generation Impactor (NGI) coupled with a glass expansion chamber. When the ratio of C-TFN in the mixed formulation increased, the fraction of particles deposited in the lung increased proportionally while the fraction of particles deposited in the nasal cavity decreased correspondingly. A customisable aerosol deposition profile could therefore be achieved by manipulating the mixing ratio between C-TFN and C-USN. Dual administration of C-TFN and C-USN powders to the lung and nasal cavity of hamsters, respectively, was effective in offering prophylactic protection against SARS-CoV-2 Delta variant. Viral loads in both the lung tissues and nasal wash were significantly reduced, and the efficacy was comparable to systemic administration of unformulated WKS13. Overall, dual targeting powder formulation of neutralising mAb is a promising approach for prophylaxis of respiratory viral infections. The ease and non-invasive administration of dual targeting nasal powder may facilitate the widespread distribution of neutralising mAb during the early stage of unpredictable outbreaks
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