A non-destructive technique for the determination of mortar load capacityin situ

Materials and Structures - The determination of mortar load capacity in masonryin situ is not an established technique. An indirect method is described in this paper and, like the earlier one by...     

Dynamic analyses of information encoding in neural ensembles

Neural spike train decoding algorithms and techniques to compute Shannon mutual information are important methods for analyzing how neural systems represent biological signals. Decoding algorithms are also one of several strategies being used to design controls for brain-machine interfaces. Developing optimal strategies to design decoding algorithms and compute mutual information are therefore important problems in computational neuroscience. We present a general recursive filter decoding algorithm based on a point process model of individual neuron spiking activity and a linear stochastic state-space model of the biological signal. We derive from the algorithm new instantaneous estimates of the entropy, entropy rate, and the mutual information between the signal and the ensemble spiking activity. We assess the accuracy of the algorithm by computing, along with the decoding error, the true coverage probability of the approximate 0.95 confidence regions for the individual signal estimates. We illustrate the new algorithm by reanalyzing the position and ensemble neural spiking activity of CA1 hippocampal neurons from two rats foraging in an open circular environment. We compare the performance of this algorithm with a linear filter constructed by the widely used reverse correlation method. The median decoding error for Animal 1 (2) during 10 minutes of open foraging was 5.9 (5.5) cm, the median entropy was 6.9 (7.0) bits, the median information was 9.4 (9.4) bits, and the true coverage probability for 0.95 confidence regions was 0.67 (0.75) using 34 (32) neurons. These findings improve significantly on our previous results and suggest an integrated approach to dynamically reading neural codes, measuring their properties, and quantifying the accuracy with which encoded information is extracted.     

A Review of the U.S. Department of Energy's Inertial Fusion Energy Program

This is the final report of a panel set up by the U.S. Department of Energy (DOE) Fusion Energy Sciences Advisory Committee (FESAC) in response to a charge letter from Dr. Ray Orbach (Appendix A). In that letter, Dr. Orbach asked FESAC for an assessment of the present status of inertial fusion energy (IFE) research carried out in contributing programs. These programs include the heavy ion (HI) beam, the high average power laser (HAPL), and Z-Pinch drivers and associated technologies, including fast ignition (FI). This report, presented to FESAC on March 29, 2004, and subsequently approved by them (Appendix B), presents FESAC's response to that charge.     

General schema theory for genetic programming with subtree-swapping crossover: part I

This is the first part of a two-part paper which introduces a general schema theory for genetic programming (GP) with subtree-swapping crossover. The theory is based on a Cartesian node reference system which makes it possible to describe programs as functions over the space N(2) and allows one to model the process of selection of the crossover points of subtree-swapping crossovers as a probability distribution over N(4). In Part I, we present these notions and models and show how they can be used to calculate useful quantities. In Part II we will show how this machinery, when integrated with other definitions, such as that of variable-arity hyperschema, can be used to construct a general and exact schema theory for the most commonly used types of GP.     

The influence of hydrogen and nitrogen on the formation of Si nanoclusters embedded in sub-stoichiometric silicon oxide layers

This work reports the study concerning the influence of the preparation conditions on the structure of silicon rich oxide (SRO) deposited by PECVD method by which the structural properties of the film are strictly related. In particular we investigated the role of reactant gases N₂O and SiH₄ on the total Si concentration, Si excess concentration, Si clustered concentration and size of nanoclusters formed by high annealing temperature. We payed particular attention on the role of the hydrogen and nitrogen during the Si agglomeration.The presence of hydrogen atoms on the as-deposited specimen, confirmed by the Si–H bonds peak on the FTIR analysis, has been directly correlated to the silicon excess concentration in the layer. The silicon, oxygen and nitrogen atomic density has been calculated from RBS analysis. These information were coupled to the ones obtained using methodology based on electron energy loss spectroscopy combined with energy filtered images, which allowed us to quantify the clustered silicon concentration in annealed sub-stoichiometric silicon oxide layers (SiO_x). We have verified that the nitrogen dissolved in the layer inhibits the Si excess clustering so that the efficiency of silicon agglomeration process decreases as the nitrogen content increases.     

On‐Chip Magnetic Platform for Single‐Particle Manipulation with Integrated Electrical Feedback

Methods for the manipulation of single magnetic particles have become very interesting, in particular for in vitro biological studies. Most of these studies require an external microscope to provide the operator with feedback for controlling the particle motion, thus preventing the use of magnetic particles in high‐throughput experiments. In this paper, a simple and compact system with integrated electrical feedback is presented, implementing in the very same device both the manipulation and detection of the transit of single particles. The proposed platform is based on zig‐zag shaped magnetic nanostructures, where transverse magnetic domain walls are pinned at the corners and attract magnetic particles in suspension. By applying suitable external magnetic fields, the domain walls move to the nearest corner, thus causing the step by step displacement of the particles along the nanostructure. The very same structure is also employed for detecting the bead transit. Indeed, the presence of the magnetic particle in suspension over the domain wall affects the depinning field required for its displacement. This characteristic field can be monitored through anisotropic magnetoresistance measurements, thus implementing an integrated electrical feedback of the bead transit. In particular, the individual manipulation and detection of single 1‐μm sized beads is demonstrated.     

Simulated and measured X‐band radar reflectivity of land in mountainous terrain using a fan‐beam antenna

A computer program for meteorological radar siting, previously developed for pencil‐beam antenna, long‐range, C‐band radars, has been adapted for fan‐beam antenna, short‐range, X‐band radars. The simulator uses topographic information in the form of a raster digital elevation model and a surface backscattering cross‐section per unit area at grazing angles derived from the literature. It is independent of specific radar sites and radar systems. Its use will enhance optimized scanning strategies, antenna design tailored to mountainous terrain and knowledge of land‐clutter measurements at low grazing angles. The raster‐based approach used in implementing the program makes it compatible with any desired spatial resolution. Simplicity and short simulation times have been pursued as primary goals during the planning and implementation phases of the routines. A preliminary validation of the simulator using backscattering data acquired in the Alps is presented in this letter.     

Discrimination of vegetation types in alpine sites with ALOS PALSAR-, RADARSAT-2-, and lidar-derived information

Natural vegetation monitoring in the alpine mountain range is a priority in the European Union in view of climate change effects. Many potential monitoring tools, based on advanced remote sensing sensors, are still not fully integrated in operational activities, such as those exploiting very high-resolution synthetic aperture radar (SAR) or light detection and ranging (lidar) data. Their testing is important for possible incorporation in routine monitoring and to increase the quantity and quality of environmental information. In this study the potential of ALOS PALSAR and RADARSAT-2 SAR scenes' synergic use for discrimination of different vegetation types was tested in an alpine heterogeneous and fragmented landscape. The integration of a lidar-based canopy height model (CHM) with SAR data was also tested. A SPOT image was used as a benchmark to evaluate the results obtained with different input data. Discrimination of vegetation types was performed with maximum likelihood classification and neural networks. Six tested data combinations obtained more than 85% overall accuracy, and the most complex input which integrates the two SARs with lidar CHM outperformed the result based on SPOT. Neural network algorithms provided the best results. This study highlights the advantages of integrating SAR sensors with lidar CHM for vegetation monitoring in a changing environment.     

The New Mind: thinking beyond the head

Throughout much of the modern period, the human mind has been regarded as a property of the brain and therefore something confined to the inside of the head—a view commonly known as ‘internalism’. But recent works in cognitive science, philosophy, and anthropology, as well as certain trends in the development of technology, suggest an emerging view of the mind as a process not confined to the brain but spread through the body and world—an outlook covered by a family of views labelled ‘externalism’. In this paper, we will suggest there is now sufficient momentum in favour of externalism of various kinds to mark a historical shift in the way the mind is understood. We dub this emerging externalist tendency the ‘New Mind’. Key properties of the New Mind will be summarised and some of its implications considered in areas such as art and culture, technology, and the science of consciousness.