Realization of random-field dipolar Ising ferromagnetism in a molecular magnet

The longitudinal magnetic susceptibility of single crystals of the molecular magnet Mn$_{12}$-acetate obeys a Curie-Weiss law, indicating a transition to a ferromagnetic phase due to dipolar interactions. With increasing magnetic field applied transverse to the easy axis, the transition temperature decreases considerably more rapidly than predicted by mean field theory to a T=0 quantum critical point. Our results are consistent with an effective Hamiltonian for a random-field Ising ferromagnet in a transverse field, where the randomness is induced by an external field applied to Mn$_{12}$-acetate crystals that are known to have an intrinsic distribution of locally tilted magnetic easy axes.Comment: 4 pages, 4 figure

Final results from the EU project AVATAR: aerodynamic modelling of 10 MW wind turbines

This paper presents final results from the EU project AVATAR in which aerodynamic models are improved and validated for wind turbines on a scale of 10 MW and more. Special attention is paid to the improvement of low fidelity engineering (BEM based) models with higher fidelity (CFD) models but also with intermediate fidelity free vortex wake (FVW) models. The latter methods were found to be a good basis for improvement of induction modelling in engineering methods amongst others for the prediction of yawed cases, which in AVATAR was found to be one of the most challenging subjects to model. FVW methods also helped to improve the prediction of tip losses. Aero-elastic calculations with BEM based and FVW based models showed that fatigue loads for normal production cases were over predicted with approximately 15% or even more. It should then be realised that the outcome of BEM based models does not only depend on the choice of engineering add-ons (as is often assumed) but it is also heavily dependent on the way the induced velocities are solved. To this end an annulus and element approach are discussed which are assessed with the aid of FVW methods. For the prediction of fatigue loads the so-called element approach is recommended but the derived yaw models rely on an annulus approach which pleads for a generalised solution method for the induced velocities

Experimental determination of the Weiss temperature of Mn12_{12}-ac and Mn12_{12}-ac-MeOH

We report measurements of the susceptibility in the temperature range from $3.5$ K to $6.0$ K of a series of Mn$_{12}$-ac and Mn$_{12}$-ac-MeOH samples in the shape of rectangular prisms of length $l_c$ and square cross-section of side $l_a$. The susceptibility obeys a Curie-Weiss Law, $\chi=C/(T-\theta)$, where $\theta$ varies systematically with sample aspect ratio. Using published demagnetization factors, we obtain $\theta$ for an infinitely long sample corresponding to intrinsic ordering temperatures $T_c \approx 0.85$ K and $\approx 0.74$ K for Mn$_{12}$-ac and Mn$_{12}$-ac-MeOH, respectively. The difference in $T_c$ for two materials that have nearly identical unit cell volumes and lattice constant ratios suggests that, in addition to dipolar interactions, there is a non-dipolar (exchange) contribution to the Weiss temperature that differs in the two materials because of the difference in ligand molecules.Comment: 4.5 page

Dowels effectiveness investigation between ultra high performance fiber reinforced concrete and reinforced concrete

In the present study, the performance of Reinforced Concrete (RC) beams which were strengthened with Ultra High Performance Fiber Reinforced Concrete (UHPFRC) and dowels at the interface was investigated. RC beams with a length of 2.2 m strengthened with UHPFRC layers at the tensile side. Before the application of the layers, the interface between RC and UHPFRC was roughened. During the testing, the interface slips between UHPFRC and RC were recorded using Linear Variable Differential Transformers (LVDTs). The beams were tested under four-point flexural test. The results of the present study indicated that the dowels at the interface reduce the slips at the interface, delay the formation of cracks and result in higher load carrying capacity

A General Method for Targeted Quantitative Cross-Linking Mass Spectrometry

Chemical cross-linking mass spectrometry (XL-MS) provides protein structural information by identifying covalently linked proximal amino acid residues on protein surfaces. The information gained by this technique is complementary to other structural biology methods such as x-ray crystallography, NMR and cryo-electron microscopy[1]. The extension of traditional quantitative proteomics methods with chemical cross-linking can provide information on the structural dynamics of protein structures and protein complexes. The identification and quantitation of cross-linked peptides remains challenging for the general community, requiring specialized expertise ultimately limiting more widespread adoption of the technique. We describe a general method for targeted quantitative mass spectrometric analysis of cross-linked peptide pairs. We report the adaptation of the widely used, open source software package Skyline, for the analysis of quantitative XL-MS data as a means for data analysis and sharing of methods. We demonstrate the utility and robustness of the method with a cross-laboratory study and present data that is supported by and validates previously published data on quantified cross-linked peptide pairs. This advance provides an easy to use resource so that any lab with access to a LC-MS system capable of performing targeted quantitative analysis can quickly and accurately measure dynamic changes in protein structure and protein interactions

Measuring neural net robustness with constraints

Despite having high accuracy, neural nets have been shown to be susceptible to adversarial examples, where a small perturbation to an input can cause it to become mislabeled. We propose metrics for measuring the robustness of a neural net and devise a novel algorithm for approximating these metrics based on an encoding of robustness as a linear program. We show how our metrics can be used to evaluate the robustness of deep neural nets with experiments on the MNIST and CIFAR-10 datasets. Our algorithm generates more informative estimates of robustness metrics compared to estimates based on existing algorithms. Furthermore, we show how existing approaches to improving robustness "overfit" to adversarial examples generated using a specific algorithm. Finally, we show that our techniques can be used to additionally improve neural net robustness both according to the metrics that we propose, but also according to previously proposed metrics