Spontaneous creation of Kibble-Zurek solitons in a Bose-Einstein condensate

When a system crosses a second-order phase transition on a finite timescale, spontaneous symmetry breaking can cause the development of domains with independent order parameters, which then grow and approach each other creating boundary defects. This is known as Kibble-Zurek mechanism. Originally introduced in cosmology, it applies both to classical and quantum phase transitions, in a wide variety of physical systems. Here we report on the spontaneous creation of solitons in Bose-Einstein condensates via the Kibble-Zurek mechanism. We measure the power-law dependence of defects number with the quench time, and provide a check of the Kibble-Zurek scaling with the sonic horizon. These results provide a promising test bed for the determination of critical exponents in Bose-Einstein condensates.Comment: 7 pages, 4 figure

Nonlinearization and waves in bounded media: old wine in a new bottle

We consider problems such as a standing wave in a closed straight tube, a self-sustained oscillation, damped resonance, evolution of resonance and resonance between concentric spheres. These nonlinear problems, and other similar ones, have been solved by a variety of techniques when it is seen that linear theory fails. The unifying approach given here is to initially set up the appropriate linear difference equation, where the difference is the linear travel time. When the linear travel time is replaced by a corrected nonlinear travel time, the nonlinear difference equation yields the required solution

Not in my backyard, but not far away from me: Local acceptance of wind power in China

Local acceptance of wind energy technology has become an important factor to consider when designing local and national wind energy technological innovation policies. Previous studies have investigated the factors that shape the local acceptance of wind power in high-income countries. However, to the best of our knowledge, these factors had not been investigated in China. Utilizing a survey and quantitative analysis, we have identified the factors that are correlated with local acceptance of wind power in China. We conducted our study in the city of Jiuquan, Gansu Province, which currently possesses the largest installed capacity for wind power generation in China. Two factors, namely, perceived economic benefits and perceived environmental costs, influence local acceptance of wind power in China most significantly. Local acceptance of wind power in China can be described as "not in my backyard, but not far away from me". In other words, the acceptance rate is lowest when the source of wind power is located in their village or community, highest when the project is located in their county and city and decreases for projects that are constructed further away

Transition from Order to Configurational Disorder for Surface Reconstructions on SrTiO3(111)

There is growing interest in ternary oxide surfaces due to their role in areas ranging from substrates for low power electronics to heterogeneous catalysis. Descriptions of these surfaces to date focus on lowtemperature explanations where enthalpy dominates, and less on the implications of configurational entropy at high temperatures. We report here the structure of three members of the n × n (2 ≤ n ≤ 4) reconstructions of the strontium titanate (111) surface using a combination of transmission electron diffraction, density functional theory modeling, and scanning tunneling microscopy. The surfaces contain a mixture of the tetrahedral TiO4 units found on the (110) surface sitting on top of octahedral TiO5½ (where [] is a vacant octahedral site), and TiO6 units in the second layer that are similar to those found on the (001) surface. We find clear evidence of a transition from the ordered enthalpy-dominated 3 × 3 and 4 × 4 structures to a configurational entropy-dominated 2 × 2 structure that is formed at higher temperatures. This changes many aspects of how oxide surfaces should be considered, with significant implications for oxide growth

Selection at a single locus leads to widespread expansion of toxoplasma gondii lineages that are virulent in mice

The determinants of virulence are rarely defined for eukaryotic parasites such as T. gondii, a widespread parasite of mammals that also infects humans, sometimes with serious consequences. Recent laboratory studies have established that variation in a single secreted protein, a serine/threonine kinase known as ROPO18, controls whether or not mice survive infection. Here, we establish the extent and nature of variation in ROP18among a collection of parasite strains from geographically diverse regions. Compared to other genes, ROP18 showed extremely high levels of diversification and changes in expression level, which correlated with severity of infection in mice. Comparison with an out-group demonstrated that changes in the upstream region that regulates expression of ROP18 led to an historical increase in the expression and exposed the protein to diversifying selective pressure. Surprisingly, only three atypically distinct protein variants exist despite marked genetic divergence elsewhere in the genome. These three forms of ROP18 are likely adaptations for different niches in nature, and they confer markedly different virulence to mice. The widespread distribution of a single mouse-virulent allele among geographically and genetically disparate parasites may have consequences for transmission and disease in other hosts, including humans

Effective Rheology of Bubbles Moving in a Capillary Tube

We calculate the average volumetric flux versus pressure drop of bubbles moving in a single capillary tube with varying diameter, finding a square-root relation from mapping the flow equations onto that of a driven overdamped pendulum. The calculation is based on a derivation of the equation of motion of a bubble train from considering the capillary forces and the entropy production associated with the viscous flow. We also calculate the configurational probability of the positions of the bubbles.Comment: 4 pages, 1 figur

Magnetoelectric interaction and transport behaviours in magnetic nanocomposite thermoelectric materials

How to suppress the performance deterioration of thermoelectric materials in the intrinsic excitation region remains a key challenge. The magnetic transition of permanent magnet nanoparticles from ferromagnetism to paramagnetism provides an effective approach to finding the solution to this challenge. Here, we have designed and prepared magnetic nanocomposite thermoelectric materials consisting of BaFe12O19 nanoparticles and Ba0.3In0.3Co4Sb12 matrix. It was found that the electrical transport behaviours of the nanocomposites are controlled by the magnetic transition of BaFe12O19 nanoparticles from ferromagnetism to paramagnetism. BaFe12O19 nanoparticles trap electrons below the Curie temperature (TC) and release the trapped electrons above the TC, playing an ‘electron repository’ role in maintaining high figure of merit ZT. BaFe12O19 nanoparticles produce two types of magnetoelectric effect—electron spiral motion and magnon-drag thermopower—as well as enhancing phonon scattering. Our work demonstrates that the performance deterioration of thermoelectric materials in the intrinsic excitation region can be suppressed through the magnetic transition of permanent magnet nanoparticles

Search for new phenomena in final states with an energetic jet and large missing transverse momentum in pp collisions at √ s = 8 TeV with the ATLAS detector

Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses 20.3 fb−1 of √ s = 8 TeV data collected in 2012 with the ATLAS detector at the LHC. Events are required to have at least one jet with pT > 120 GeV and no leptons. Nine signal regions are considered with increasing missing transverse momentum requirements between Emiss T > 150 GeV and Emiss T > 700 GeV. Good agreement is observed between the number of events in data and Standard Model expectations. The results are translated into exclusion limits on models with either large extra spatial dimensions, pair production of weakly interacting dark matter candidates, or production of very light gravitinos in a gauge-mediated supersymmetric model. In addition, limits on the production of an invisibly decaying Higgs-like boson leading to similar topologies in the final state are presente

Tensor based multichannel reconstruction for breast tumours identification from DCE-MRIs

A new methodology based on tensor algebra that uses a higher order singular value decomposition to perform three-dimensional voxel reconstruction from a series of temporal images obtained using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is proposed. Principal component analysis (PCA) is used to robustly extract the spatial and temporal image features and simultaneously de-noise the datasets. Tumour segmentation on enhanced scaled (ES) images performed using a fuzzy C-means (FCM) cluster algorithm is compared with that achieved using the proposed tensorial framework. The proposed algorithm explores the correlations between spatial and temporal features in the tumours. The multi-channel reconstruction enables improved breast tumour identification through enhanced de-noising and improved intensity consistency. The reconstructed tumours have clear and continuous boundaries; furthermore the reconstruction shows better voxel clustering in tumour regions of interest. A more homogenous intensity distribution is also observed, enabling improved image contrast between tumours and background, especially in places where fatty tissue is imaged. The fidelity of reconstruction is further evaluated on the basis of five new qualitative metrics. Results confirm the superiority of the tensorial approach. The proposed reconstruction metrics should also find future applications in the assessment of other reconstruction algorithms

Generic, network schema agnostic sparse tensor factorization for single-pass clustering of heterogeneous information networks

Heterogeneous information networks (e.g. bibliographic networks and social media networks) that consist of multiple interconnected objects are ubiquitous. Clustering analysis is an effective method to understand the semantic information and interpretable structure of the heterogeneous information networks, and it has attracted the attention of many researchers in recent years. However, most studies assume that heterogeneous information networks usually follow some simple schemas, such as bi-typed networks or star network schema, and they can only cluster one type of object in the network each time. In this paper, a novel clustering framework is proposed based on sparse tensor factorization for heterogeneous information networks, which can cluster multiple types of objects simultaneously in a single pass without any network schema information. The types of objects and the relations between them in the heterogeneous information networks are modeled as a sparse tensor. The clustering issue is modeled as an optimization problem, which is similar to the well-known Tucker decomposition. Then, an Alternating Least Squares (ALS) algorithm and a feasible initialization method are proposed to solve the optimization problem. Based on the tensor factorization, we simultaneously partition different types of objects into different clusters. The experimental results on both synthetic and real-world datasets have demonstrated that our proposed clustering framework, STFClus, can model heterogeneous information networks efficiently and can outperform state-of-the-art clustering algorithms as a generally applicable single-pass clustering method for heterogeneous network which is network schema agnostic