Effect of Thickener Particle Geometry and Concentration on the Grease EHL Film Thickness at Medium Speeds

The aim of this paper was to understand the parameters influencing the grease film thickness in a rolling elastohydrodynamically lubricated contact under fully flooded conditions at medium speeds. Film thickness measurements were taken under pure rolling for six commercial greases and their bled oils. The grease film thickness was found to be higher than corresponding bled oil, suggesting the presence of thickener in the contact. No rheological properties (characterized by steady and dynamic shear) showed any direct relation to the film thickness of the studied greases. AFM measurements of the thickener microstructure, from which the dimensional properties of the thickener particles (fibers/platelets/spheres) were estimated, showed that the relative increase in the film thickness due to entrainment of the thickener was proportional to the ratio of thickener volume fraction to the size of the fibers/platelets/spheres. Hence, with the same concentration, smaller thickener particles lead to the generation of thicker films than larger thickener particles. Next, this relation was used to establish the percentage of the thickener particles passing through the contact. Depending on the grease type, between about 1 and 70 % of the thickener particles were found to travel through the contact

Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks

Deep neural networks have emerged as a widely used and effective means for tackling complex, real-world problems. However, a major obstacle in applying them to safety-critical systems is the great difficulty in providing formal guarantees about their behavior. We present a novel, scalable, and efficient technique for verifying properties of deep neural networks (or providing counter-examples). The technique is based on the simplex method, extended to handle the non-convex Rectified Linear Unit (ReLU) activation function, which is a crucial ingredient in many modern neural networks. The verification procedure tackles neural networks as a whole, without making any simplifying assumptions. We evaluated our technique on a prototype deep neural network implementation of the next-generation airborne collision avoidance system for unmanned aircraft (ACAS Xu). Results show that our technique can successfully prove properties of networks that are an order of magnitude larger than the largest networks verified using existing methods.Comment: This is the extended version of a paper with the same title that appeared at CAV 201

Application of semidefinite programming to maximize the spectral gap produced by node removal

The smallest positive eigenvalue of the Laplacian of a network is called the spectral gap and characterizes various dynamics on networks. We propose mathematical programming methods to maximize the spectral gap of a given network by removing a fixed number of nodes. We formulate relaxed versions of the original problem using semidefinite programming and apply them to example networks.Comment: 1 figure. Short paper presented in CompleNet, Berlin, March 13-15 (2013

“The impact of European Neuromuscular Centre (ENMC) workshops on the neuromuscular field; 25 years on …”

Merit, Expertise and Measuremen

Unbiased Global Optimization of Lennard-Jones Clusters for N <= 201 by Conformational Space Annealing Method

We apply the conformational space annealing (CSA) method to the Lennard-Jones clusters and find all known lowest energy configurations up to 201 atoms, without using extra information of the problem such as the structures of the known global energy minima. In addition, the robustness of the algorithm with respect to the randomness of initial conditions of the problem is demonstrated by ten successful independent runs up to 183 atoms. Our results indicate that the CSA method is a general and yet efficient global optimization algorithm applicable to many systems.Comment: revtex, 4 pages, 2 figures. Physical Review Letters, in pres

Early onset as a marker for disease severity in facioscapulohumeral muscular dystrophy

Contains fulltext : 202651.pdf (publisher's version ) (Open Access)OBJECTIVE: To assess the relation between age at onset and disease severity in facioscapulohumeral muscular dystrophy (FSHD). METHODS: In this prospective cross-sectional study, we matched adult patients with FSHD with an early disease onset with 2 sex-matched FSHD control groups with a classic onset; the first group was age matched, and the second group was disease duration matched. Genetic characteristics, muscle performance, respiratory functioning, hearing loss, vision loss, epilepsy, educational level, and work status were compared with the 2 control groups. RESULTS: Twenty-eight patients with early-onset FSHD were age (n = 28) or duration (n = 27) matched with classic-onset patients. Patients with early-onset FSHD had more severe muscle weakness (mean FSHD clinical score 11 vs 5 in the age-matched and 9 in the duration-matched group, p < 0.05) and a higher frequency of wheelchair dependency (57%, 0%, and 30%, respectively, p < 0.05). In addition, systemic features were more frequent in early-onset FSHD, most important, hearing loss, decreased respiratory function and spinal deformities. There was no difference in work status. Genetically, the shortest D4Z4 repeat arrays (2-3 units) were found exclusively in the early-onset group, and the largest repeat arrays (8-9 units) were found only in the classic-onset groups. De novo mutations were more frequent in early-onset patients (46% vs 4%). CONCLUSIONS: Patients with early-onset FSHD more often have severe muscle weakness and systemic features. The disease severity is greater than in patients with classic-onset FSHD who are matched for disease duration, suggesting that the progression is faster in early-onset patients

Effects of bifrontal transcranial direct current stimulation on brain glutamate levels and resting state connectivity: multimodal MRI data for the cathodal stimulation site

Transcranial direct current stimulation (tDCS) over prefrontal cortex (PFC) regions is currently proposed as therapeutic intervention for major depression and other psychiatric disorders. The in-depth mechanistic understanding of this bipolar and non-focal stimulation technique is still incomplete. In a pilot study, we investigated the effects of bifrontal stimulation on brain metabolite levels and resting state connectivity under the cathode using multiparametric MRI techniques and computational tDCS modeling. Within a double-blind cross-over design, 20 subjects (12 women, 23.7 ± 2~years) were randomized to active tDCS with standard bifrontal montage with the anode over the left dorsolateral prefrontal cortex (DLPFC) and the cathode over the right DLPFC. Magnetic resonance spectroscopy (MRS) was acquired before, during, and after prefrontal tDCS to quantify glutamate (Glu), Glu + glutamine (Glx) and gamma aminobutyric acid (GABA) concentration in these areas. Resting-state functional connectivity MRI (rsfcMRI) was acquired before and after the stimulation. The individual distribution of tDCS induced electric fields (efields) within the MRS voxel was computationally modelled using SimNIBS 2.0. There were no significant changes of Glu, Glx and GABA levels across conditions but marked differences in the course of Glu levels between female and male participants~were observed. Further investigation yielded a significantly stronger Glu reduction after active compared to sham stimulation~in female participants, but not in male participants. For rsfcMRI neither significant changes nor correlations with MRS data were observed. Exploratory analyses of the effect of efield intensity distribution on Glu changes showed distinct effects in different efield groups. Our findings are limited by the small sample size, but correspond to previously published results of cathodal tDCS. Future studies should address gender and efield intensity as moderators of tDCS induced effects

Lagrangian chaos in an ABC--forced nonlinear dynamo

The Lagrangian properties of the velocity field in a magnetized fluid are studied using three-dimensional simulations of a helical magnetohydrodynamic dynamo. We compute the attracting and repelling Lagrangian coherent structures, which are dynamic lines and surfaces in the velocity field that delineate particle transport in flows with chaotic streamlines and act as transport barriers. Two dynamo regimes are explored, one with a robust coherent mean magnetic field and one with intermittent bursts of magnetic energy. The Lagrangian coherent structures and the statistics of finite--time Lyapunov exponents indicate that the stirring/mixing properties of the velocity field decay as a linear function of the magnetic energy. The relevance of this study for the solar dynamo problem is discussed