Prediction of grain structure in direct-chill cast Al–Zn–Mg–Cu billets using cellular automaton-finite element method

A cellular automaton-finite element (CA-FE) model was used to predict the solidification grain structure of permanent mold cast A7086 alloy. In this model, a Gaussian distribution of nucleation sites was adopted, and the Kurz-Giovanola-Trivedi model was extended to a multicomponent Al–Zn–Mg–Cu alloy system to determine the growth kinetics of the dendrite tip. For describing the Gaussian distribution of nucleation sites on the mold surface, an empirical relationship between the initial cooling rate of the melt and the nucleation density was proposed. Under various casting conditions, the calculated grain structures agreed well with the experimental results. Subsequently, the model was applied to the direct-chill (DC) cast billets, and the simulated grain structures reproduced well the experimental results. This confirmed that the current CA-FE model can be used practically and effectively in DC casting processes.     

The influence of transparency on budget forecast deviations in municipal governments

This paper analyzes the impact of transparency on fiscal performance. Our sample considers the 100 largest Spanish municipalities for the years 2008, 2009, 2010, 2012, and 2014. The results show that the level of municipal transparency influences budget forecast deviations in tax revenues and current expenditures. On the one hand, less transparent municipalities overestimate their revenues, allowing them to provide more public services without an immediate increase in taxes. On the other, these local governments, which are aware of the overestimation of their revenues, may spend less than they budgeted. More transparent municipalities, meanwhile, seem to be more prudent in their revenue estimations, since they underestimate their revenues, meaning they can spend more than projected. Our results also show that the behavior of politicians is influenced by the phase of the electoral cycle in which they find themselves, with politicians overestimating expenditures in the year before election.     

A new insight into combining forecasts for elections: The role of social media

This study is devoted to gain insight into a timely, accurate, and relevant combining forecast by considering social media (Facebook), opinion polls, and prediction markets. We transformed each type of raw data into the possibility of victory as a forecasting model. Besides the four single forecasts, namely Facebook fans, Facebook “people talking about this” (PTAT) statistics, opinion polls, and prediction markets, we generated three combined forecasts by associating various combinations of the four components. Then, we examined the predictive performance of each forecast on vote shares and the elected/non‐elected outcome across the election period. Our findings, based on the evidence of Taiwan's 2018 county and city elections, showed that incorporating the Facebook PTAT statistic with polls and prediction markets generates the most powerful forecast. Moreover, we recognized the matter of the time horizons where the best proposed model has better accuracy gains in prediction—in the “late of election,” but not in “approaching election”. The patterns of the trend of accuracy across time for each forecasting model also differ from one another. We also highlighted the complementarity of various types of data in the paper because each forecast makes important contributions to forecasting elections.     

Predicting opening spot prices using extended futures trading

Previous studies found that extended futures trading contains useful information in explaining subsequent overnight spot returns. This study therefore compares the performance of using the extended trading of the TAIFEX (Taiwan Futures Exchange) index futures and single‐stock futures to predict their opening underlying spot prices. Furthermore, according to the efficient market hypothesis, the share price fully reflects all the information available and should adjust to new information instantaneously. However, several studies have demonstrated that short‐sales restrictions delay the speed of price adjustment to negative information. The relevant question is whether short‐selling restrictions also slow down the speed at which the opening spot price adjusts to the new information revealed through extended futures trading, and thus reducing the price prediction function of extended futures trading. The empirical results find that using the opening futures price and the prediction method proposed in this study can more accurately predict the opening spot price on the same day. Furthermore, the performance of using the extended trading of index futures to predict the opening spot index price is superior to that of using the extended trading of single‐stock futures to predict the opening stock price. Finally, as found in previous studies, short‐selling restrictions also slow down the speed of stock price adjustment to the new information revealed through extended futures trading. Thus both the up‐tick rule and the short‐selling bans (especially the latter) negatively affect the price forecasting performance of extended futures trading.     

Rational design of carbon shell-encapsulated cobalt nanospheres to enhance microwave absorption performance

Co@C core–shell nanospheres highly dispersed on carbon supports were rationally designed to improve the microwave absorbing property of the composite material, and fabricated by one pot thermal decomposition and simple annealing process. The Co nanospheres were completely encapsulated with thin carbon shells, which can effectively prevent the oxidation of the Co surface. Additionally, the particle size of Co nanospheres were properly controlled to optimize the electromagnetic property of the composite material. As a result, the lightweight Co@C/C composites with the particle size of 20 nm exhibited much enhanced microwave absorption properties. The improved microwave absorption performance is attributed mainly to the enhanced isotropic ratio and impedance matching of magnetic composites via tuning the Co particle size. Therefore, the welldesigned core-shell Co@C composite structure will provide a new insight for the development of high performance microwave absorbers.     

Can We Predict Exchange Rate Movements at Short Horizons?

This paper explains the unpredictability of exchange rate movements at short horizons and provides a plausible answer on the exchange rate disconnect puzzle. By generalizing Chaboud and Wright's (Journal of International Economics 2005; 66 : 349–362) work, it is shown that exchange rates follow a martingale process at short horizons but over long horizons may contain some predictable structure. The empirical results applied to several major currencies of the US dollar support our hypothesis. This evidence is not coincided with the explanation of the inefficient market hypothesis under which exchange rate movements can be predictable in both short and long horizons. Copyright © 2011 John Wiley & Sons, Ltd.     

Seed dispersal in hybrid Salsola

The study population is a group of hybrids of Salsola iberica × S. paulsenii . The plants show great variation in phenotypic characteristics, and the character complexes of the parent species seem to have largely disappeared. Seeds are dispersed both locally, from intact plants, and by tumbling. Tumbling is the dominant form of dispersal in most individuals studied. Most locally dispersed seeds fall between .5 and 1.5 m from the parent plant. Despite the dominance of tumbling, large numbers of seeds are dispersed locally and probably serve to maintain the population while those dispersed by tumbling introduce the plants into new areas.     

Effects of salinity on establishment of Populus fremontii (cottonwood) and Tamarix ramosissima (saltcedar) in southwestern United States

The exotic shrub Tamarix ramosissima (saltcedar) has replaced the native Populus fremontii (cottonwood) along many streams in southwestern United States. We used a controlled outdoor experiment to examine the influence of river salinity on germination and first year survival of P. fremontii var. wislizenii (Rio Grande cottonwood) and T. ramosissima on freshly deposited alluvial bars. We grew both species from seed in planters of sand subjected to a declining water table and solutions containing 0, 1, 3, and 5 times the concentrations of major ions in the Rio Grande at San Marcia, NM (1.2, 10.0, 25.7 and 37.4 meq 1 -1 ; 0.11, 0.97, 2.37, and 3.45 dS m -1 ). Germination of P. fremontii declined by 35% with increasing salinity ( P = .008). Germination of T. ramosissima was not affected. There were no significant effects of salinity on morality or above- and belowground growth of either species. In laboratory tests the same salinities had no effect on P. fremontii germination. P. fremontii germination is more sensitive to salinity outdoors than in covered petri dishes, probably because water scarcity resulting from eavaportion intensifies the low soil water potential associated with high salinity. River salinity appears to play only a minor role in determining relative numbers of P. fremontii and T. ramosissima seedlings on freshly deposited sandbars. However, over many years salt becomes concentrated on floodplains as a result of evaporation and salt extrusion from saltcedar leaves. T. ramosissima is known to be more tolerant of the resulting extreme salinities than P. fremontii . Therefore, increases in river salinities could indirectly contribute to decline of P. fremontii forests by exacerbating salt accumulation on floodplains.     

Bayesian structure selection for vector autoregression model

A vector autoregression (VAR) model is powerful for analyzing economic data as it can be used to simultaneously handle multiple time series from different sources. However, in the VAR model, we need to address the problem of substantial coefficient dimensionality, which would cause some computational problems for coefficient inference. To reduce the dimensionality, one could take model structures into account based on prior knowledge. In this paper, group structures of the coefficient matrices are considered. Because of the different types of VAR structures, corresponding Markov chain Monte Carlo algorithms are proposed to generate posterior samples for performing inference of the structure selection. Simulation studies and a real example are used to demonstrate the performances of the proposed Bayesian approaches.