Blocked-micropores,surface functionalized,bio-compatible and silica-coated iron oxide nanocomposites as advanced MRI contrast agent

Quasi-critical fluctuations occur close to critical points or close to continuous phase transitions. In three-dimensional systems, precision tuning is required to access the fluctuation regime. Lowering the dimensionality enhances the parameter space for quasi-critical fluctuations considerably. This enables one to make use of novel properties emerging in fluctuating systems, such as giant susceptibilities, Casimir forces or novel quasi-particle interactions. Examples are discussed ranging from simple metal–adsorbate systems to unconventional superconductivity in iron-based superconductors.     

From a Point Cloud to a Simulation Model—Bayesian Segmentation and Entropy Based Uncertainty Estimation for 3D Modelling

The 3D modelling of indoor environments and the generation of process simulations play an important role in factory and assembly planning. In brownfield planning cases, existing data are often outdated and incomplete especially for older plants, which were mostly planned in 2D. Thus, current environment models cannot be generated directly on the basis of existing data and a holistic approach on how to build such a factory model in a highly automated fashion is mostly non-existent. Major steps in generating an environment model of a production plant include data collection, data pre-processing and object identification as well as pose estimation. In this work, we elaborate on a methodical modelling approach, which starts with the digitalization of large-scale indoor environments and ends with the generation of a static environment or simulation model. The object identification step is realized using a Bayesian neural network capable of point cloud segmentation. We elaborate on the impact of the uncertainty information estimated by a Bayesian segmentation framework on the accuracy of the generated environment model. The steps of data collection and point cloud segmentation as well as the resulting model accuracy are evaluated on a real-world data set collected at the assembly line of a large-scale automotive production plant. The Bayesian segmentation network clearly surpasses the performance of the frequentist baseline and allows us to considerably increase the accuracy of the model placement in a simulation scene.     

On gradient field theories: gradient magnetostatics and gradient elasticity

In this work, the fundamentals of gradient field theories are presented and reviewed. In particular, the theories of gradient magnetostatics and gradient elasticity are investigated and compared. For gradient magnetostatics, non-singular expressions for the magnetic vector gauge potential, the Biot–Savart law, the Lorentz force and the mutual interaction energy of two electric current loops are derived and discussed. For gradient elasticity, non-singular forms of all dislocation key formulas (Burgers equation, Mura equation, Peach–Koehler stress equation, Peach–Koehler force equation, and mutual interaction energy of two dislocation loops) are presented. In addition, similarities between an electric current loop and a dislocation loop are pointed out. The obtained fields for both gradient theories are non-singular due to a straightforward and self-consistent regularization.     

Nanosatellites for quantum science and technology

Bringing quantum science and technology to the space frontier offers exciting prospects for both fundamental physics and applications such as long-range secure communication and space-borne quantum probes for inertial sensing with enhanced accuracy and sensitivity. But despite important terrestrial pathfinding precursors on common microgravity platforms and promising proposals to exploit the significant advantages of space quantum missions, large-scale quantum test beds in space are yet to be realised due to the high costs and lead times of traditional ‘Big Space’ satellite development. But the ‘small space’ revolution, spearheaded by the rise of nanosatellites such as CubeSats, is an opportunity to greatly accelerate the progress of quantum space missions by providing easy and affordable access to space and encouraging agile development. We review space quantum science and technology, CubeSats and their rapidly developing capabilities and how they can be used to advance quantum satellite systems.     

Condensate density and superfluid mass density of a dilute Bose-Einstein condensate near the condensation transition

We derive via diagrammatic perturbation theory the scaling behavior of the condensate and superfluid mass density of a dilute Bose gas just below the condensation temperature, T(c). Sufficiently below T(c) particle excitations are described by mean field (Bogoliubov). Near T(c), however, mean field fails, and the system undergoes a second order phase transition, rather than first order as predicted by Bogoliubov theory. Both condensation and superfluidity occur at the same T(c), and have similar scaling functions below T(c), but different finite size scaling at T(c) to leading order in the system size. A self-consistent two-loop calculation yields the condensate fraction critical exponent, 2beta approximately 0.66.     

Optimized absorption imaging of mesoscopic atomic clouds

We report on the optimization of high-intensity absorption imaging for small Bose–Einstein condensates. The imaging calibration exploits the linear scaling of the quantum projection noise with the mean number of atoms for a coherent spin state. After optimization for atomic clouds containing up to 300 atoms, we find an atom number resolution of $\varDelta_{\rm det}= 3.7$ atoms, mainly limited by photon shot noise and radiation pressure.     

Metastability and Low Lying Spectra¶in Reversible Markov Chains

We study a large class of reversible Markov chains with discrete state space and transition matrix P _N . We define the notion of a set of metastable points as a subset of the state space Γ _N such that (i) this set is reached from any point x∈Γ _N without return to x with probability at least b _N , while (ii) for any two points x, y in the metastable set, the probability T ^{− 1} _{x , y} to reach y from x without return to x is smaller than a _{N − 1}< b _N . Under some additional non-degeneracy assumption, we show that in such a situation: (i) To each metastable point corresponds a metastable state, whose mean exit time can be computed precisely. (ii) To each metastable point corresponds one simple eigenvalue of 1 −P _N which is essentially equal to the inverse mean exit time from this state. Moreover, these results imply very sharp uniform control of the deviation of the probability distribution of metastable exit times from the exponential distribution. Received: 1 August 2000 / Accepted: 19 November 2001