(Tables 1-3) Pb and Sr isotopic data for sediments and rocks from DSDP holes in the Mariana island-arc system

Analyses of the isotopic composition of Pb in (1) western Pacific Ocean sediments [Jurassic(?) to Pleistocene in age, including clays and biogenic oozes], (2) Pacific Ocean basaltic rocks, (3) Mariana frontal arc volcanic rocks (Eocene to Miocene), and (4) Mariana active arc volcanic rocks [Pliocene (?) to Holocene] indicate that Pacific Ocean sediments could not have been a significant component of the source material for the Mariana arc volcanic rocks. Calculations involving the average concentrations and isotopic compositions of Pb in oceanic sediments, sea-floor basaltic rocks, and the Mariana arc volcanic rocks suggest that the sediment component must have been less than 1 percent of this source material. The Pb isotopic compositions of the Mariana arc volcanic rocks lie, within experimental error, along the trend of available Pacific Ocean basalt analyses in versus 207Pb/204Pb versus 206Pb/204Pb and 208Pb/204Pb versus 206Pb/204Pb diagrams. Isotopic analyses of Pb in Pacific Ocean sediments do not lie along this trend; they have higher 207Pb/204Pb and 208Pb/204Pb values for comparable 206Pb/204Pb ratios. Clayey sediments generally have higher 208Pb/204Pb and 207Pb/204Pb ratios than biogenic oozes regardless of the age of the sediment. Comparison of combined Sr and Pb isotopic analyses for (1) mantle-derived materials erupted through oceanic crust, (2) altered ocean-floor basaltic rocks, and (3) volcanic rocks from oceanic island arcs suggests that the Mariana arc volcanic rocks were derived, at least in part, from altered Pacific lithosphere subducted beneath the Mariana arc. Unaltered basalts from the Mariana inter-arc basin (Mariana Trough) have Pb and Sr isotopic compositions that are very similar to those reported for some Hawaiian volcanic rocks but distinct from Mariana active and frontal arc compositions. These observations, in addition to existing major-and trace-element data, support a mantle origin for the interarc basin volcanic rocks. Dacites dredged from the Mariana remnant arc (South Honshu Ridge) have Pb isotopic compositions that are within experimental error of the active-arc analyses, consistent with a genetic relation.

Self-learning of fault diagnosis identification

A good and early fault detection and isolation system along with efficient alarm management and fine sensor validation systems are very important in today¿s complex process plants, specially in terms of safety enhancement and costs reduction. This paper presents a methodology for fault characterization. This is a self-learning approach developed in two phases. An initial, learning phase, where the simulation of process units, without and with different faults, will let the system (in an automated way) to detect the key variables that characterize the faults. This will be used in a second (on line) phase, where these key variables will be monitored in order to diagnose possible faults. Using this scheme the faults will be diagnosed and isolated in an early stage where the fault still has not turned into a failure.

Distributed Fault Diagnosis Using Bayesian Reasoning in MAGNETO

Many of the emerging telecom services make use of Outer Edge Networks, in particular Home Area Networks. The configuration and maintenance of such services may not be under full control of the telecom operator which still needs to guarantee the service quality experienced by the consumer. Diagnosing service faults in these scenarios becomes especially difficult since there may be not full visibility between different domains. This paper describes the fault diagnosis solution developed in the MAGNETO project, based on the application of Bayesian Inference to deal with the uncertainty. It also takes advantage of a distributed framework to deploy diagnosis components in the different domains and network elements involved, spanning both the telecom operator and the Outer Edge networks. In addition, MAGNETO features self-learning capabilities to automatically improve diagnosis knowledge over time and a partition mechanism that allows breaking down the overall diagnosis knowledge into smaller subsets. The MAGNETO solution has been prototyped and adapted to a particular outer edge scenario, and has been further validated on a real testbed. Evaluation of the results shows the potential of our approach to deal with fault management of outer edge networks.

The use of arc-erosion as a patterning technique for transparent conductive materials

Within the framework of cost-effective patterning processes a novel technique that saves photolithographic processing steps, easily scalable to wide area production, is proposed. It consists of a tip-probe, which is biased with respect to a conductive substrate and slides on it, keeping contact with the material. The sliding tip leaves an insulating path (which currently is as narrow as 30 μm) across the material, which enables the drawing of tracks and pads electrically insulated from the surroundings. This ablation method, called arc-erosion, requires an experimental set up that had to be customized for this purpose and is described. Upon instrumental monitoring, a brief proposal of the physics below this process is also presented. As a result an optimal control of the patterning process has been acquired. The system has been used on different substrates, including indium tin oxide either on glass or on polyethylene terephtalate, as well as alloys like Au/Cr, and Al. The influence of conditions such as tip speed and applied voltage is discussed

Study of the evolution of the seismic cycle of stress and strain associated to the El Salvador Fault Zone

• Central America: – Regional studies in Central America (Seismic Hazard). – El Salvador Fault Zone (ESFZ). – Aguacaliente‐Navarro Fault Zone (ANFZ), Central Valley of Costa Rica. – Haiti (seismic hazard) • Spain: – Regional‐Nacional studies of seismic hazards (applications to building codes, eurocode, emergency plans, etc.) – Betic range zone, south of Spain. – Ibero‐Maghrebi region (collision zone)

Fault Tolerance Analysis and Self-Healing Strategy of Autonomous, Evolvable Hardware Systems

This paper presents an analysis of the fault tolerance achieved by an autonomous, fully embedded evolvable hardware system, which uses a combination of partial dynamic reconfiguration and an evolutionary algorithm (EA). It demonstrates that the system may self-recover from both transient and cumulative permanent faults. This self-adaptive system, based on a 2D array of 16 (4×4) Processing Elements (PEs), is tested with an image filtering application. Results show that it may properly recover from faults in up to 3 PEs, that is, more than 18% cumulative permanent faults. Two fault models are used for testing purposes, at PE and CLB levels. Two self-healing strategies are also introduced, depending on whether fault diagnosis is available or not. They are based on scrubbing, fitness evaluation, dynamic partial reconfiguration and in-system evolutionary adaptation. Since most of these adaptability features are already available on the system for its normal operation, resource cost for self-healing is very low (only some code additions in the internal microprocessor core)