Active Sites Derived from Heteroatom Doping in Carbon Materials for Oxygen Reduction Reaction

The oxygen reduction reaction (ORR) is a key cathode reaction in fuel cells. Due to the sluggish kinetics of the ORR, various kinds of catalysts have been developed to compensate for the shortcomings of the cathode reaction. Carbon materials are considered ideal cathode catalysts. In particular, heteroatom doping is essential to achieve an excellent ORR activity. Interestingly, doping trace amounts of metals in carbon materials plays an important role in enhancing the electrocatalytic activities. This chapter describes the recent advancements with regard to heteroatom-doped carbons and discusses the active sites decorated in the carbon matrix in terms of their configurations and contents, as well as their effectiveness in boosting the ORR performance. Furthermore, trace metal residues and metal-free catalysts for the ORR are clarified

The Role of Sulfur-Related Species in Oxygen Reduction Reactions

Heteroatom (metal and nonmetal) doping is essential to achieve excellent oxygen reduction reaction (ORR) activity of carbon materials. Among the heteroatoms that have been studied to date, sulfur (S) doping, including metal sulfides and sulfur atoms, has attracted tremendous attention. Since S-doping can modify spin density distributions around the metal centers as well as the synergistic effect between S and other doped heteroatoms, the S-C bond and metal sulfides can function as important ORR active sites. Furthermore, the S-doped hybrid sample shows a small charge-transfer resistance. Therefore, S-doping contributes to the superior ORR performance. This chapter describes the recent advancements of S-doped carbon materials, and their development in the area of ORR with regard to components, structures, and their ORR activities of S-related species

MANSY: Generalizing Neural Adaptive Immersive Video Streaming With Ensemble and Representation Learning

The popularity of immersive videos has prompted extensive research into neural adaptive tile-based streaming to optimize video transmission over networks with limited bandwidth. However, the diversity of users' viewing patterns and Quality of Experience (QoE) preferences has not been fully addressed yet by existing neural adaptive approaches for viewport prediction and bitrate selection. Their performance can significantly deteriorate when users' actual viewing patterns and QoE preferences differ considerably from those observed during the training phase, resulting in poor generalization. In this paper, we propose MANSY, a novel streaming system that embraces user diversity to improve generalization. Specifically, to accommodate users' diverse viewing patterns, we design a Transformer-based viewport prediction model with an efficient multi-viewport trajectory input output architecture based on implicit ensemble learning. Besides, we for the first time combine the advanced representation learning and deep reinforcement learning to train the bitrate selection model to maximize diverse QoE objectives, enabling the model to generalize across users with diverse preferences. Extensive experiments demonstrate that MANSY outperforms state-of-the-art approaches in viewport prediction accuracy and QoE improvement on both trained and unseen viewing patterns and QoE preferences, achieving better generalization.Comment: This work has been submitted to the IEEE Transactions on Mobile Computing for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Neural Generalized Ordinary Differential Equations with Layer-varying Parameters

Deep residual networks (ResNets) have shown state-of-the-art performance in various real-world applications. Recently, the ResNets model was reparameterized and interpreted as solutions to a continuous ordinary differential equation or Neural-ODE model. In this study, we propose a neural generalized ordinary differential equation (Neural-GODE) model with layer-varying parameters to further extend the Neural-ODE to approximate the discrete ResNets. Specifically, we use nonparametric B-spline functions to parameterize the Neural-GODE so that the trade-off between the model complexity and computational efficiency can be easily balanced. It is demonstrated that ResNets and Neural-ODE models are special cases of the proposed Neural-GODE model. Based on two benchmark datasets, MNIST and CIFAR-10, we show that the layer-varying Neural-GODE is more flexible and general than the standard Neural-ODE. Furthermore, the Neural-GODE enjoys the computational and memory benefits while performing comparably to ResNets in prediction accuracy

An Expanded Spacing Advantage over Equal Spacing on Grammar Learning

Spacing effect is a robust phenomenon especially in cognitive psychology. Many studies proved that there was spacing effect in L2 words learning, but there are sparse studies about whether spacing effect, especially expanded spacing advantage, can be generalized to L2 grammar learning. The present study aims to examine spacing effect on L2 grammar learning. Ninety-seven subjects studied 3 tenses within one week in 3 groups. Group one studied 3 tenses in only one learning episode, namely massed group; group two on day 1, day 2, day 4 and day 7 (1-2-3), namely expanded spacing; group three on day 1, day 3 day 5 and day 7 (2-2-2), namely equal spacing. Results showed that there was spacing effect on L2 grammar learning; expanded spacing had an advantage over equal spacing not only in short-term memory but also in long-term memory. In other words, spacing effect, especially expanded spacing advantage in words learning, can be generalized to L2 grammar learning. Therefore, it is suggested that teachers teach students L2 grammar in an expanded schedule

An Expanded Spacing Advantage over Equal Spacing on Grammar Learning

Spacing effect is a robust phenomenon especially in cognitive psychology. Many studies proved that there was spacing effect in L2 words learning, but there are sparse studies about whether spacing effect, especially expanded spacing advantage, can be generalized to L2 grammar learning. The present study aims to examine spacing effect on L2 grammar learning. Ninety-seven subjects studied 3 tenses within one week in 3 groups. Group one studied 3 tenses in only one learning episode, namely massed group; group two on day 1, day 2, day 4 and day 7 (1-2-3), namely expanded spacing; group three on day 1, day 3 day 5 and day 7 (2-2-2), namely equal spacing. Results showed that there was spacing effect on L2 grammar learning; expanded spacing had an advantage over equal spacing not only in short-term memory but also in long-term memory. In other words, spacing effect, especially expanded spacing advantage in words learning, can be generalized to L2 grammar learning. Therefore, it is suggested that teachers teach students L2 grammar in an expanded schedule

Phenolic Characteristics and Antioxidant Activity of Merlot and Cabernet Sauvignon Wines Increase with Vineyard Altitude in a High-altitude Region

Altitude, as an important factor in the expression of terroir, may affect wine quality. We evaluated the effect of altitude and its related climatic conditions on the phenolic characteristics and antioxidant activity of red wines made from grapes originating from high-altitude areas. The content of total phenolic compounds, total flavonoids and total anthocyanins increased with altitude in Merlot (ME) and Cabernet Sauvignon (CS) wines.  Cabernet Sauvignon wines showed richer tannins with increasing altitude.  Merlot and CS wines from higher altitude vineyards, showed a greater antioxidant capacity. Salicylic acid, syringic acid, caffeic acid, (+)-catechin, (−)-epicatechin, and the sum of individual phenolic compounds in the winesincreased with altitude based on the results of HPLC. The scores of the sensory evaluation of ME wines increased with higher altitude. The highest score was determined for CS wine originating from 2 608 m.  A clear grouping of wines according to grape cultivar and vineyard altitude was observed by principal component analysis. Regression analysis showed that altitude, followed by sunshine hours, made the greatest contribution to differences in the phenolic characteristics and antioxidant activity of red wines at different sites in a high-altitude region