Identification of Silver and Palladium in Irradiated TRISO Coated Particles of the AGR-1 Experiment

Evidence of the release of certain metallic fission products through intact tristructural isotropic (TRISO) particles has been seen for decades around the world, as well as in the recent AGR-1 experiment at the Idaho National Laboratory (INL). However, understanding the basic mechanism of transport is still lacking. This understanding is important because the TRISO coating is part of the high temperature gas-cooled reactor functional containment and critical for the safety strategy for licensing purposes. Our approach to identify fission products in irradiated AGR-1 TRISO fuel using scanning transmission electron microscopy (STEM), Electron Energy-Loss Spectroscopy (EELS) and Energy Filtered TEM (EFTEM), has led to first-of-a-kind data at the nano-scale indicating the presence of silver at triple-points and grain boundaries of the SiC layer in the TRISO particle. Cadmium was also found in the triple junctions. In this initial study, the silver was only identified in SiC grain boundaries and triple points on the edge of the SiC-IPyC interface up to a depth of approximately 0.5 μm. Palladium was identified as the main constituent of micron-sized precipitates present at the SiC grain boundaries. Additionally spherical nano-sized palladium rich precipitates were found inside the SiC grains. No silver was found in the center of the micron-sized fission product precipitates using these techniques, although silver was found on the outer edge of one of the Pd-U-Si containing precipitates which was facing the IPyC layer. Only Pd-U containing precipitates were identified in the IPyC layer and no silver was identified in the IPyC layer. The identification of silver alongside the SiC grain boundaries and the findings of Pd inside the SiC grains and alongside SiC grain boundaries provide important information needed to understand silver and palladium transport in TRISO fuel, which has been the topic of international research for the past forty years. The findings reported in this paper may support the postulations of recent research that Ag transport may be driven by grain boundary diffusion. However, more work is needed to fully understand the transport mechanisms. Additionally, the usefulness of the advanced electron microscopic techniques for TRISO coated particle research is demonstrated in this paper

Calibration of optimal execution of financial transactions in the presence of transient market impact

Trading large volumes of a financial asset in order driven markets requires the use of algorithmic execution dividing the volume in many transactions in order to minimize costs due to market impact. A proper design of an optimal execution strategy strongly depends on a careful modeling of market impact, i.e. how the price reacts to trades. In this paper we consider a recently introduced market impact model (Bouchaud et al., 2004), which has the property of describing both the volume and the temporal dependence of price change due to trading. We show how this model can be used to describe price impact also in aggregated trade time or in real time. We then solve analytically and calibrate with real data the optimal execution problem both for risk neutral and for risk averse investors and we derive an efficient frontier of optimal execution. When we include spread costs the problem must be solved numerically and we show that the introduction of such costs regularizes the solution.Comment: 31 pages, 8 figure

Coupling news sentiment with web browsing data improves prediction of intra-day price dynamics

The new digital revolution of big data is deeply changing our capability of understanding society and forecasting the outcome of many social and economic systems. Unfortunately, information can be very heterogeneous in the importance, relevance, and surprise it conveys, affecting severely the predictive power of semantic and statistical methods. Here we show that the aggregation of web users' behavior can be elicited to overcome this problem in a hard to predict complex system, namely the financial market. Specifically, our in-sample analysis shows that the combined use of sentiment analysis of news and browsing activity of users of Yahoo! Finance greatly helps forecasting intra-day and daily price changes of a set of 100 highly capitalized US stocks traded in the period 2012-2013. Sentiment analysis or browsing activity when taken alone have very small or no predictive power. Conversely, when considering a news signal where in a given time interval we compute the average sentiment of the clicked news, weighted by the number of clicks, we show that for nearly 50% of the companies such signal Granger-causes hourly price returns. Our result indicates a "wisdom-of-the-crowd" effect that allows to exploit users' activity to identify and weigh properly the relevant and surprising news, enhancing considerably the forecasting power of the news sentiment

New bioassays reveal susceptibility of stone-fruit rootstocks to capnodis tenebrionis larvae

Larvae of Capnodis tenebrionis (L.) (Coleoptera Buprestidae) feed and develop in roots of stone-fruit trees, thereby decreasing their efficiency, which can lead to plant death. The control of these larvae is critical, due to their localization in the root, and the management of this pest is focused on adults, mainly by using non-specific synthetic insecticides. Less susceptible Prunus rootstocks might be applied as a preventative management of larval infestation by this pest. The current research investigated the susceptibility to C. tenebrionis larvae of the most commonly used rootstocks by combining two bio-assays during two-year trials: development of larvae assayed on semi-artificial substrates containing rootstock bark flour; infestation by neonate larvae on rootstock twigs. The rearing assay on semi-artificial substrates made it possible to distinguish (1) a rootstock cluster (Montclar and GF677) in which larvae developed faster and heavier and produced larger adults, (2) a cluster (Adesoto, CAB6P, Colt and MaxMa60) in which larval growth was less efficient as well as adult size, and (3) a cluster (Garnem and Myrabolan 29C) with intermediate responses in larval development and adult size. The twig infestation assay by neonates showed the most infested (Colt) and least infested (Barrier, MaxMa60 and Marianna 26) rootstocks. When the results of both assays are combined, GF677 and Myrabolan 29C appear more susceptible, while Adesoto and MaxMa60 less susceptible to C. tenebrionis larvae, although Barrier and Marianna 26 require further investigation. The experimental model applied in the current trials can enable processing of a large number of tests on different rootstocks, thereby allowing the accumulation of a large quantity of data on the potential susceptibility of rootstocks. The possibility of rearing larvae on a substrate can allow comparison of additional compounds that could interact with larval growth

On the criticality of inferred models

Advanced inference techniques allow one to reconstruct the pattern of interaction from high dimensional data sets. We focus here on the statistical properties of inferred models and argue that inference procedures are likely to yield models which are close to a phase transition. On one side, we show that the reparameterization invariant metrics in the space of probability distributions of these models (the Fisher Information) is directly related to the model's susceptibility. As a result, distinguishable models tend to accumulate close to critical points, where the susceptibility diverges in infinite systems. On the other, this region is the one where the estimate of inferred parameters is most stable. In order to illustrate these points, we discuss inference of interacting point processes with application to financial data and show that sensible choices of observation time-scales naturally yield models which are close to criticality.Comment: 6 pages, 2 figures, version to appear in JSTA

Micro/Nano-Structural Examination and Fission Product Identification in Neutron Irradiated AGR-1 TRISO Fuel

Advanced electron microscopic and micro-analysis techniques were developed and applied to study irradiation effects and fission-product behavior in selected low-enriched uranium-oxide/uranium-carbide tristructural-isotropic (TRISO)-coated particles from fuel compacts in four capsules irradiated to burnups of 11.2 to 19.6% fissions per initial metal atom (FIMA) consisting of Baseline, Variant 1, and Variant 3 fuel types. Trend analysis shows precipitates were mostly random in their distribution along the perimeter of the inner pyrolytic carbon-silicon carbide (IPyC-SiC) interlayer with only weak association with kernel protrusion and buffer fractures. Pd is dominantly found in most precipitates in both intra and intergranular locations. Nano-sized Ag is predominantly found in grain boundaries and triple points with only two findings of Ag inside a SiC grain in two different compacts (Baseline and Variant 3 fueled compacts). Generally, more element combinations exist for precipitates from particles with relatively low Ag retention compared to particles with relatively high Ag-retention irrespective of fuel type. This study shows the presence of Cs in particles from all compacts evaluated. From this work, no single fission product mechanism hypothesis can be reported. The complexity of mechanisms is further highlighted by the multiple variations of elemental combinations found in the more than 700 fission product precipitates examined. It seems that movement of Ag is not assisted by a specific element in all cases. Therefore, it is not necessarily true that a chemical-assisted transport mechanism is dominant. The presence of Ag predominantly on grain boundaries suggests that a grain boundary transport mechanism may be prominent. Studies to determine the effect of neutron damage are recommended for future work

Quantifying trading behavior in financial markets using Google Trends

Crises in financial markets affect humans worldwide. Detailed market data on trading decisions reflect some of the complex human behavior that has led to these crises. We suggest that massive new data sources resulting from human interaction with the Internet may offer a new perspective on the behavior of market participants in periods of large market movements. By analyzing changes in Google query volumes for search terms related to finance, we find patterns that may be interpreted as “early warning signs” of stock market moves. Our results illustrate the potential that combining extensive behavioral data sets offers for a better understanding of collective human behavior

The Effects of Twitter Sentiment on Stock Price Returns

Social media are increasingly reflecting and influencing behavior of other complex systems. In this paper we investigate the relations between a well-know micro-blogging platform Twitter and financial markets. In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We find a relatively low Pearson correlation and Granger causality between the corresponding time series over the entire time period. However, we find a significant dependence between the Twitter sentiment and abnormal returns during the peaks of Twitter volume. This is valid not only for the expected Twitter volume peaks (e.g., quarterly announcements), but also for peaks corresponding to less obvious events. We formalize the procedure by adapting the well-known "event study" from economics and finance to the analysis of Twitter data. The procedure allows to automatically identify events as Twitter volume peaks, to compute the prevailing sentiment (positive or negative) expressed in tweets at these peaks, and finally to apply the "event study" methodology to relate them to stock returns. We show that sentiment polarity of Twitter peaks implies the direction of cumulative abnormal returns. The amount of cumulative abnormal returns is relatively low (about 1-2%), but the dependence is statistically significant for several days after the events

Functional consequences of the variable stoichiometry of the Kv1.3-KCNE4 complex

The voltage-gated potassium channel Kv1.3 plays a crucial role during the immune response. The channel forms oligomeric complexes by associating with several modulatory subunits. KCNE4, one of the five members of the KCNE family, binds to Kv1.3, altering channel activity and membrane expression. The association of KCNEs with Kv channels is the subject of numerous studies, and the stoichiometry of such associations has led to an ongoing debate. The number of KCNE4 subunits that can interact and modulate Kv1.3 is unknown. KCNE4 transfers important elements to the Kv1.3 channelosome that negatively regulate channel function, thereby fine-tuning leukocyte physiology. The aim of this study was to determine the stoichiometry of the functional Kv1.3-KCNE4 complex. We demonstrate that as many as four KCNE4 subunits can bind to the same Kv1.3 channel, indicating a variable Kv1.3-KCNE4 stoichiometry. While increasing the number of KCNE4 subunits steadily slowed the activation of the channel and decreased the abundance of Kv1.3 at the cell surface, the presence of a single KCNE4 peptide was suffcient for the cooperative enhancement of the inactivating function of the channel. This variable architecture, which depends on KCNE4 availability, differentially affects Kv1.3 function. Therefore, our data indicate that the physiological remodeling of KCNE4 triggers functional consequences for Kv1.3, thus affecting cell physiology