Alloy nanoparticles on oxide supports are widely used as heterogeneous catalysts in reactions involving oxygen. Here we discuss the oxidation behavior of Pd-Rh alloy nanoparticles on MgAl 2 O 4 (001) supports with a particle diameter from 6-11 nm. As an In situ tool, we employed high energy grazing incidence X-ray diffraction at a photon energy of 85 keV. We find that physical vapor deposited Pd-

We have investigated the atomic structure of graphene/Ir(111) supported platinum clusters with on average fewer than 40 atoms by means of surface x-ray diffraction (SXRD), grazing incidence small angle x-ray scattering (GISAXS), and normal incidence x-ray standing waves (NIXSW) measurements, in comparison with density functional theory calculations (DFT). GISAXS revealed that the clusters with 1.3

Studies of the structural dynamics of solids can improve our understanding of atomic motion in materials, and may thus help in the manufacture of new devices or the development of materials with novel structures and properties. Ultrashort laser pulses, a few tens of femtoseconds long, can deliver high energies (mJ–kJ). This energy is absorbed by the electrons in a solid material, leading to a rapi

A convenient mapping and an efficient algorithm for solving scheduling problems within the neural network paradigm is presented. It is based on a reduced encoding scheme and a mean field annealing prescription which was recently successfully applied to TSP.Most scheduling problems are characterized by a set of hard and soft constraints. The prime target of this work is the hard constraints. In thi

In a recent paper (Gislén et al. 1989) a convenient encoding and an efficient mean field algorithm for solving scheduling problems using a Potts neural network was developed and numerically explored on simplified and synthetic problems. In this work the approach is extended to realistic applications both with respect to problem complexity and size. This extension requires among other things the in

Bosonic strings can be discretized in terms of dynamically triangulatedrandom surfaces. We investigate the possibility of introducing fermionicdegrees of freedom on the surface in terms of Ising spins, which in twodimensions correspond to Majorana fermions. Critical properties of themodel are estimated using finite-size scaling methods.

Myocardial active relaxation and restoring forces are known determinants of left ventricular (LV) diastolic function. We hypothesize the existence of an additional mechanism involved in LV filling, namely, a hydraulic force contributing to the longitudinal motion of the atrioventricular (AV) plane. A prerequisite for the presence of a net hydraulic force during diastole is that the atrial short-ax

Rotor neurons are introduced to encode states living on the surface of a sphere in D dimensions. Such rotors can be regarded as continuous generalizations of binary (Ising) neurons. The corresponding mean field equations are derived, and phase transition properties based on linearized dynamics are given. The power of this approach is illustrated with an optimization problem—placing N identical cha

The Potts Neural Network approach to non-binary discrete optimizationproblems is described. It applies to problems that can be described asa set of elementary 'multiple choice' options. Instead of the conventionalbinary (Ising) neurons, mean field Potts neurons, having several availablestates, are used to describe the elementary degrees of freedom of suchproblems. The dynamics consists of iteratin

A general introduction to the use of feed-back artificial neural networks (ANN) for obtaining good approximate solutions to combinatorial optimization problems is given, assuming no previous knowledge in the field. In particular we emphasize a novel neural mapping technique which efficiently reduces the solution space. This approach maps the problems onto Potts glass rather than spin glass models.

We devise a feed-forward Artificial Neural Network (ANN) procedure for predicting utility loads and present the resulting predictions for two test problems given by ``The Great Energy Predictor Shootout - The First Building Data Analysis and Prediction Competition''. Key ingredients in our approach are a method ($\delta$ -test) for determiningrelevant inputs and the Multilayer Perceptron. These me

The generic relation between continuous polymers and zero-componentEuclidean field-theories is reviewed, and exemplified by polymers withcontact and Coulomb interactions. An analogous relation on thelattice is also discussed, relating the statistics of self-avoidingwalks to a zero-component spin-model.

The recurrent neural network approach to combinatorial optimization has during the last decade evolved into a competitive and versatile heuristic method, that can be used on a wide range of problem types. In the state-of-the-art neural approach the discrete elementary decisions (not necessarily binary) are represented by continuous Potts mean-field neurons, interpolating between the available disc

For combinatorial optimization problems that can be formulated as Ising or Potts spin systems, the Mean Field (MF) approximation yields a versatile and simple ANN heuristic, Deterministic Annealing. For problems involving assignments (or permutations), the situation is more complex -- the natural analog of the MF approximation lacks the simplicity present in the Potts and Ising cases. In this arti

The stress response is governed by automatic neurological and hormonal processes that occur before we become consciously aware of a dangerous situation. If stress ensues for 15-30 seconds, the hormonal processes may have progressed so far that recovery takes an hour or longer instead of minutes. Stress can affect our behavior and in certain professions, such as the police force and emergency servi