Carbonate aggregate mineralogy and stable isotopes at DSDP Site 87-583

Crystalline aggregates composed of calcium carbonate were recovered in the uppermost 50 m of Nankai Trough sediments during DSDP Leg 87A. These aggregates decomposed with time to masses of sandy calcite as determined by X-ray diffraction analysis. Petrographic and scanning electron microscopy revealed textures suggestive of a precursor phrase prior to calcite, and this precursor has been tentatively identified as the mineral ikaite, CaCO3*6H2O. Stable isotope data suggest a large component of terrigenous organic matter as the carbon source, consistent with the appearance of these aggregates in highly reducing pyritic sediments containing abundant plant remains. We propose that these nodules formed in euxinic basins on the upper part of the Trough slope under normal seafloor conditions of pressure and temperature. Calculated temperatures of formation of this phase are not unusually low. The specimens from Site 583 are the first reported occurrences of ikaite in active margin sediments.

Hydrostatic pressure affects aggregate transformation and organic carbon transport: in and beyond the twilight zone: Experiment December 2010

This work aimed to explore evaluated the effects of the increased of hydrostatic pressure on a defined bacterial community on aggregates formed from an axenic culture of marine diatoms by simulating sedimentation to the deep sea by increase of hydrostatic pressure up to 30 bar (equivalent to 3000 m water depth) against control at ambient surface pressure. Our hypothesis was that microbial colonization and community composition and thus microbial OM turnover is greatly affected by changes in hydrostatic pressure during sinking to the deep ocean.

Disruption of abscisic acid signaling constitutively activates Arabidopsis resistance to the necrotrophic fungus Plectosphaerella cucumerina.

Plant resistance to necrotrophic fungi is regulated by a complex set of signaling pathways that includes those mediated by the hormones salicylic acid (SA), ethylene (ET), jasmonic acid (JA), and abscisic acid (ABA). The role of ABA in plant resistance remains controversial, as positive and negative regulatory functions have been described depending on the plant-pathogen interaction analyzed. Here, we show that ABA signaling negatively regulates Arabidopsis (Arabidopsis thaliana) resistance to the necrotrophic fungus Plectosphaerella cucumerina. Arabidopsis plants impaired in ABA biosynthesis, such as the aba1-6 mutant, or in ABA signaling, like the quadruple pyr/pyl mutant (pyr1pyl1pyl2pyl4), were more resistant to P. cucumerina than wild-type plants. In contrast, the hab1-1abi1-2abi2-2 mutant impaired in three phosphatases that negatively regulate ABA signaling displayed an enhanced susceptibility phenotype to this fungus. Comparative transcriptomic analyses of aba1-6 and wild-type plants revealed that the ABA pathway negatively regulates defense genes, many of which are controlled by the SA, JA, or ET pathway. In line with these data, we found that aba1-6 resistance to P. cucumerina was partially compromised when the SA, JA, or ET pathway was disrupted in this mutant. Additionally, in the aba1-6 plants, some genes encoding cell wall-related proteins were misregulated. Fourier transform infrared spectroscopy and biochemical analyses of cell walls from aba1-6 and wild-type plants revealed significant differences in their Fourier transform infrared spectratypes and uronic acid and cellulose contents. All these data suggest that ABA signaling has a complex function in Arabidopsis basal resistance, negatively regulating SA/JA/ET-mediated resistance to necrotrophic fungi.

Simulator of the JET real-time disruption predictor

A disruption predictor based on support vector machines (SVM) has been developed to be used in JET. The training process uses thousands of discharges and, therefore, high performance computing has been necessary to obtain the models. To this respect, several models have been generated with data from different JET campaigns. In addition, various kernels (mainly linear and RBF) and parameters have been tested. The main objective of this work has been the implementation of the predictor model under real-time constraints. A “C-code” software application has been developed to simulate the real-time behavior of the predictor. The application reads the signals from the JET database and simulates the real-time data processing, in particular, the specific data hold method to be developed when reading data from the JET ATM real time network. The simulator is fully configurable by means of text files to select models, signal thresholds, sampling rates, etc. Results with data between campaigns C23and C28 will be shown.

Results of the JET real-time disruption predictor in the ITER-like wall campaigns

The impact of disruptions in JET became even more important with the replacement of the previous Carbon Fiber Composite (CFC) wall with a more fragile full metal ITER-like wall (ILW). The development of robust disruption mitigation systems is crucial for JET (and also for ITER). Moreover, a reliable real-time (RT) disruption predictor is a pre-requisite to any mitigation method. The Advance Predictor Of DISruptions (APODIS) has been installed in the JET Real-Time Data Network (RTDN) for the RT recognition of disruptions. The predictor operates with the new ILW but it has been trained only with discharges belonging to campaigns with the CFC wall. 7 realtime signals are used to characterize the plasma status (disruptive or non-disruptive) at regular intervals of 1 ms. After the first 3 JET ILW campaigns (991 discharges), the success rate of the predictor is 98.36% (alarms are triggered in average 426 ms before the disruptions). The false alarm and missed alarm rates are 0.92% and 1.64%.

Latest developments in data analysis tools for disruption prediction and for the exploration of multimachine operational spaces.

In the last years significant efforts have been devoted to the development of advanced data analysis tools to both predict the occurrence of disruptions and to investigate the operational spaces of devices, with the long term goal of advancing the understanding of the physics of these events and to prepare for ITER. On JET the latest generation of the disruption predictor called APODIS has been deployed in the real time network during the last campaigns with the new metallic wall. Even if it was trained only with discharges with the carbon wall, it has reached very good performance, with both missed alarms and false alarms in the order of a few percent (and strategies to improve the performance have already been identified). Since for the optimisation of the mitigation measures, predicting also the type of disruption is considered to be also very important, a new clustering method, based on the geodesic distance on a probabilistic manifold, has been developed. This technique allows automatic classification of an incoming disruption with a success rate of better than 85%. Various other manifold learning tools, particularly Principal Component Analysis and Self Organised Maps, are also producing very interesting results in the comparative analysis of JET and ASDEX Upgrade (AUG) operational spaces, on the route to developing predictors capable of extrapolating from one device to another.

Implementation of the disruption predictor APODIS in JET Real Time Network using the MARTe framework

Disruptions in tokamaks devices are unavoidable, and they can have a significant impact on machine integrity. So it is very important have mechanisms to predict this phenomenon. Disruption prediction is a very complex task, not only because it is a multi-dimensional problem, but also because in order to be effective, it has to detect well in advance the actual disruptive event, in order to be able to use successful mitigation strategies. With these constraints in mind a real-time disruption predictor has been developed to be used in JET tokamak. The predictor has been designed to run in the Multithreaded Application Real-Time executor (MARTe) framework. The predictor ?Advanced Predictor Of DISruptions? (APODIS) is based on Support Vector Machine (SVM).

Variable Speed Limits. Review and development of an aggregate indicator based on floating car data.

Changing factors (mainly traffic intensity and weather conditions) affecting road conditions require a suitable optimal speed at any time. To solve this problem, variable speed limit systems (VSL) ? as opposed to fixed limits ? have been developed in recent decades. This term has included a number of speed management systems, most notably dynamic speed limits (DSL). In order to avoid the indiscriminate use of both terms in the literature, this paper proposes a simple classification and offers a review of some experiences, how their effects are evaluated and their results This study also presents a key indicator, which measures the speed homogeneity and a methodology to obtain the data based on floating cars and GPS technology applying it to a case study on a section of the M30 urban motorway in Madrid (Spain).

Temperature-depnedent disruption of circadian oscillations in woody plants-A mathematical approach

Poster