Structure Evolution of Graphene Oxide during Thermally Driven Phase Transformation: Is the Oxygen Content Really Preserved?

A mild annealing procedure was recently proposed for the scalable enhancement of graphene oxide (GO) properties with the oxygen content preserved, which was demonstrated to be attributed to the thermally driven phase separation. In this work, the structure evolution of GO with mild annealing is closely investigated. It reveals that in addition to phase separation, the transformation of oxygen functionalities also occurs, which leads to the slight reduction of GO membranes and furthers the enhancement of GO properties. These results are further supported by the density functional theory based calculations. The results also show that the amount of chemically bonded oxygen atoms on graphene decreases gradually and we propose that the strongly physisorbed oxygen species constrained in the holes and vacancies on GO lattice might be responsible for the preserved oxygen content during the mild annealing procedure. The present experimental results and calculations indicate that both the diffusion and transformation of oxygen functional groups might play important roles in the scalable enhancement of GO properties

Faster Ray Tracing through Hierarchy Cut Code

We propose a novel ray reordering technique to accelerate the ray tracing process by encoding and sorting rays prior to traversal. Instead of spatial coordinates, our method encodes rays according to the cuts of the hierarchical acceleration structure, which is called the hierarchy cut code. This approach can better adapt to the acceleration structure and obtain a more reliable encoding result. We also propose a compression scheme to decrease the sorting overhead by a shorter sorting key. In addition, based on the phenomenon of boundary drift, we theoretically explain the reason why existing reordering methods cannot achieve better performance by using longer sorting keys. The experiment demonstrates that our method can accelerate secondary ray tracing by up to 1.81 times, outperforming the existing methods. Such result proves the effectiveness of hierarchy cut code, and indicate that the reordering technique can achieve greater performance improvement, which worth further research

Deep eutectic solvents enable the enhanced production of n-3 PUFA-enriched triacylglycerols

Efficient synthesis of n‐3 PUFA‐enriched triacylglycerol (TAG) by the esterification of glycerol with n‐3 PUFA in deep eutectic solvents (DES) is reported. There was a 1.2‐fold increase of TAG yield in DES compared with that in the solvent‐free system. Adsorption of the produced water by DES during esterification contributed to enhance the conversion efficiency by changing the reaction equilibrium. DES also served as an effective solvent for enriching the n‐3 PUFA of TAG in the upper layer of reaction media. A TAG yield of 55% was achieved under the optimal condition. Practical Applications: Enzymatic synthesis of n‐3 PUFA‐enriched triacylglycerol (TAG) is challenged by low yields. Here, deep eutectic solvents show great potential for enhancing the production of n‐3 PUFA‐enriched TAG

Western images of China : media representations of Chinese attempts to invest in Saab

The aim of this thesis is to describe Western images of China by focusing on media representations of Chinese attempts to invest in Saab. Theories of media representation, orientalism, racialization and stereotype are applied and used in the qualitative discourse analysis in order to find out if there are orientalist and racialized stereotypes in the material. The findings show that there are orientalist stereotypes and racialized stereotypes presented in the material. The analysis also sums up that China is a country whose people are represented to be adaptable and to have amazing productivity, since China has cheap labor power and lax labor law. Furthermore, China is represented as a country whose financial power is strong and solid, Western media characterizes China as a threat. Moreover, Chinese negotiators who went to Sweden to negotiate not only are represented as full of ambitions, but also they are seen as the saviors for Western companies which are on the verge of bankrupt. This thesis contributes to the literature by filling the gap about the Chinese attempts to invest in Saab, which is characterized by Western media

Analysis of stabilized finite volume method for poisson equation

In this work, we systematically analyze a stabilized finite volume method for the Poisson equation. On stating the convergence of this method, optimal error estimates in different norms are obtained by establishing the adequate connections between the finite element and finite volume methods. Furthermore, some super-convergence results are established by using L 2 -projection method on a coarse mesh based on some regularity assumptions for Poisson equation. Finally, some numerical experiments are presented to confirm the theoretical findings

Forecasting and Uncertainty Analysis of Day-Ahead Photovoltaic Power Based on WT-CNN-BiLSTM-AM-GMM

Accurate forecasting of photovoltaic (PV) power is of great significance for the safe, stable, and economical operation of power grids. Therefore, a day-ahead photovoltaic power forecasting (PPF) and uncertainty analysis method based on WT-CNN-BiLSTM-AM-GMM is proposed in this paper. Wavelet transform (WT) is used to decompose numerical weather prediction (NWP) data and photovoltaic power data into frequency data with time information, which eliminates the influence of randomness and volatility in the data information on the forecasting accuracy. A convolutional neural network (CNN) is used to deeply mine the seasonal characteristics of the input data and the correlation characteristics between the input data. The bidirectional long short-term memory network (BiLSTM) is used to deeply explore the temporal correlation of the input data series. To reflect the different influences of the input data sequence on the model forecasting accuracy, the weight of the calculated value of the BiLSTM model for each input data is adaptively adjusted using the attention mechanism (AM) algorithm according to the data sequence, which further improves the model forecasting accuracy. To accurately calculate the probability density distribution characteristics of photovoltaic forecasting errors, the Gaussian mixture model (GMM) method was used to calculate the probability density distribution of forecasting errors, and the confidence interval of the day-ahead PPF was calculated. Using a photovoltaic power station as the calculation object, the forecasting results of the WT-CNN-BiLSTM-AM, CNN-BiLSTM, WT-CNN-BiLSTM, long short-term memory network (LSTM), gate recurrent unit (GRU), and PSO-BP models were compared and analyzed. The calculation results show that the forecasting accuracy of the WT-CNN-BiLSTM-AM model is higher than that of the other models. The confidence interval coverage calculated from the GMM is greater than the given confidence level