Conflict Adaptation in 5-Year-Old Preschool Children: Evidence From Emotional Contexts

This research investigated the individual behavioral and electrophysiological differences during emotional conflict adaptation processes in preschool children. Thirty children (16 girls, mean age 5.44 ± 0.28 years) completed an emotional Flanker task (stimulus-stimulus cognitive control, S-S) and an emotional Simon task (stimulus-response cognitive control, S-R). Behaviorally, the 5-year-old preschool children exhibited reliable congruency sequence effects (CSEs) in the emotional contexts, with faster response times (RTs) and lower error rates in the incongruent trials preceded by an incongruent trial (iI trial) than in the incongruent trials preceded by a congruent trial (cI trial). Regarding electrophysiology, the children demonstrated longer N2 and P3 latencies in the incongruent trials than in the congruent trials during emotional conflict control processes. Importantly, the boys showed a reliable CSE of N2 amplitude when faced with fearful target expression. Moreover, 5-year-old children showed better emotional CSEs in response to happy targets than to fearful targets as demonstrated by the magnitude of CSEs in terms of the RT, error rate, N2 amplitude and P3 latency. In addition, the results demonstrated that 5-year-old children processed S-S emotional conflicts and S-R emotional conflicts differently and performed better on S-S emotional conflicts than on S-R emotional conflicts according to the comparison of the RT-CSE and P3 latency-CSE values. The current study provides insight into how emotionally salient stimuli affect cognitive processes among preschool children

Life History of Lepidostoma hirtum in an Iberian Stream and its Role in Organic Matter Processing

Abstract The goal of this research was to determine the role of Lepidostoma hirtum Fabricius 1775 in the fragmentation of allochtonous organic material, in a segment of a mountain river in central Portugal. For this purpose, we measured leaf fragmentation and growth rates at four temperatures (9, 12, 15 and 18 C) and four leaf types (alder, Alnus glutinosa L.; oak, Quercus andegavensis Hy; poplar, Populus canadensis Moench; and chestnut, Castanea sativa Mill.). Growth rates ranged from 0.012 to 0.049 mg AFDW day-1 with no significant effect of temperature and leaf type. Fragmentation/consumption rates were significantly higher for alder (1.62 mg animal-1 day-1) than for other leaf types, and significantly lower at 9 C (0.70 mg animal-1 day-1) than at any other temperature (1.12 mg animal-1 day-1). In the studied stream, L. hirtum larvae had a univoltine life history, with an asynchronous development. Secondary production of L. hirtum ranged from 53.95 mg m-2 year-1 (pools) to 63.12 mg m-2 year-1 (riffles). Annual P/B ratios differ between habitats: they were 4.01 year-1 for pools and 4.49 year-1 for riffles. Considering the average density of this species in the study river and their consumption rates, this species has the potential to fragment 8.6 times the mean annual standing stock of organic matter, in the study location

Effective Multi-Graph Neural Networks for Illicit Account Detection on Cryptocurrency Transaction Networks

We study illicit account detection on transaction networks of cryptocurrencies that are increasi_testngly important in online financial markets. The surge of illicit activities on cryptocurrencies has resulted in billions of losses from normal users. Existing solutions either rely on tedious feature engineering to get handcrafted features, or are inadequate to fully utilize the rich semantics of cryptocurrency transaction data, and consequently, yield sub-optimal performance. In this paper, we formulate the illicit account detection problem as a classification task over directed multigraphs with edge attributes, and present DIAM, a novel multi-graph neural network model to effectively detect illicit accounts on large transaction networks. First, DIAM includes an Edge2Seq module that automatically learns effective node representations preserving intrinsic transaction patterns of parallel edges, by considering both edge attributes and directed edge sequence dependencies. Then utilizing the multigraph topology, DIAM employs a new Multigraph Discrepancy (MGD) module with a well-designed message passing mechanism to capture the discrepant features between normal and illicit nodes, supported by an attention mechanism. Assembling all techniques, DIAM is trained in an end-to-end manner. Extensive experiments, comparing against 14 existing solutions on 4 large cryptocurrency datasets of Bitcoin and Ethereum, demonstrate that DIAM consistently achieves the best performance to accurately detect illicit accounts, while being efficient. For instance, on a Bitcoin dataset with 20 million nodes and 203 million edges, DIAM achieves F1 score 96.55%, significantly higher than the F1 score 83.92% of the best competitor

Barrier Inhomogeneity of Schottky Diode on Nonpolar AlN Grown by Physical Vapor Transport

An aluminum nitride (AlN) Schottky barrier diode (SBD) was fabricated on a nonpolar AlN crystal grown on tungsten substrate by physical vapor transport. The Ni/Au-AlN SBD features a low ideality factor n of 3.3 and an effective Schottky barrier height (SBH) of 1.05 eV at room temperature. The ideality factor n decreases and the effective SBH increases at high temperatures. The temperature dependences of n and SBH were explained using an inhomogeneous model. A mean SBH of 2.105 eV was obtained for the Ni-AlN Schottky junction from the inhomogeneity analysis of the current-voltage characteristics. An equation in which the parameters have explicit physical meanings in thermionic emission theory is proposed to describe the current-voltage characteristics of inhomogeneous SBDs.Comment: 6 pages, 6 figure

Negative Magnetoresistance in Dirac Semimetal Cd3As2

A large negative magnetoresistance is anticipated in topological semimetals in the parallel magnetic and electric field configuration as a consequence of the nontrivial topological properties. The negative magnetoresistance is believed to demonstrate the chiral anomaly, a long-sought high-energy physics effect, in solid-state systems. Recent experiments reveal that Cd3As2, a Dirac topological semimetal, has the record-high mobility and exhibits positive linear magnetoresistance in the orthogonal magnetic and electric field configuration. However, the negative magnetoresistance in the parallel magnetic and electric field configuration remains unveiled. Here, we report the observation of the negative magnetoresistance in Cd3As2 microribbons in the parallel magnetic and electric field configuration as large as 66% at 50 K and even visible at room temperatures. The observed negative magnetoresistance is sensitive to the angle between magnetic and electrical field, robust against temperature, and dependent on the carrier density. We have found that carrier densities of our Cd3As2 samples obey an Arrhenius's law, decreasing from 3.0x10^17 cm^-3 at 300 K to 2.2x10^16 cm^-3 below 50 K. The low carrier densities result in the large values of the negative magnetoresistance. We therefore attribute the observed negative magnetoresistance to the chiral anomaly. Furthermore, in the perpendicular magnetic and electric field configuration a positive non-saturating linear magnetoresistance up to 1670% at 14 T and 2 K is also observed. This work demonstrates potential applications of topological semimetals in magnetic devices

Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities

With the increasing amount of spatial-temporal~(ST) ocean data, numerous spatial-temporal data mining (STDM) studies have been conducted to address various oceanic issues, e.g., climate forecasting and disaster warning. Compared with typical ST data (e.g., traffic data), ST ocean data is more complicated with some unique characteristics, e.g., diverse regionality and high sparsity. These characteristics make it difficult to design and train STDM models. Unfortunately, an overview of these studies is still missing, hindering computer scientists to identify the research issues in ocean while discouraging researchers in ocean science from applying advanced STDM techniques. To remedy this situation, we provide a comprehensive survey to summarize existing STDM studies in ocean. Concretely, we first summarize the widely-used ST ocean datasets and identify their unique characteristics. Then, typical ST ocean data quality enhancement techniques are discussed. Next, we classify existing STDM studies for ocean into four types of tasks, i.e., prediction, event detection, pattern mining, and anomaly detection, and elaborate the techniques for these tasks. Finally, promising research opportunities are highlighted. This survey will help scientists from the fields of both computer science and ocean science have a better understanding of the fundamental concepts, key techniques, and open challenges of STDM in ocean