Integrated model of multiple kernel learning and differential evolution for EUR/USD trading.

Currency trading is an important area for individual investors, government policy decisions, and organization investments. In this study, we propose a hybrid approach referred to as MKL-DE, which combines multiple kernel learning (MKL) with differential evolution (DE) for trading a currency pair. MKL is used to learn a model that predicts changes in the target currency pair, whereas DE is used to generate the buy and sell signals for the target currency pair based on the relative strength index (RSI), while it is also combined with MKL as a trading signal. The new hybrid implementation is applied to EUR/USD trading, which is the most traded foreign exchange (FX) currency pair. MKL is essential for utilizing information from multiple information sources and DE is essential for formulating a trading rule based on a mixture of discrete structures and continuous parameters. Initially, the prediction model optimized by MKL predicts the returns based on a technical indicator called the moving average convergence and divergence. Next, a combined trading signal is optimized by DE using the inputs from the prediction model and technical indicator RSI obtained from multiple timeframes. The experimental results showed that trading using the prediction learned by MKL yielded consistent profits.

Stock price change rate prediction by utilizing social network activities.

Predicting stock price change rates for providing valuable information to investors is a challenging task. Individual participants may express their opinions in social network service (SNS) before or after their transactions in the market; we hypothesize that stock price change rate is better predicted by a function of social network service activities and technical indicators than by a function of just stock market activities. The hypothesis is tested by accuracy of predictions as well as performance of simulated trading because success or failure of prediction is better measured by profits or losses the investors gain or suffer. In this paper, we propose a hybrid model that combines multiple kernel learning (MKL) and genetic algorithm (GA). MKL is adopted to optimize the stock price change rate prediction models that are expressed in a multiple kernel linear function of different types of features extracted from different sources. GA is used to optimize the trading rules used in the simulated trading by fusing the return predictions and values of three well-known overbought and oversold technical indicators. Accumulated return and Sharpe ratio were used to test the goodness of performance of the simulated trading. Experimental results show that our proposed model performed better than other models including ones using state of the art techniques.

Zeolite-based catalytic membrane reactors for thermo-catalytic conversion of CO2.

Zeolite-based catalytic membrane reactors have been successfully applied in overcoming the thermodynamic limitations of CO2 hydrogenations and dry reforming of methane (DRM) reactions. This review summarizes the zeolites as membrane reactor components regarding the permeance, permselectivity, durability, conversion, selectivity, and stability by referring to the synergy of catalyst and membrane. Also, five operation parameters (temperature, pressure, feed ratio, sweeping gas flow rate, and gas hourly space velocity) are introduced regarding their impacts on the performance of membrane reactor. Besides, synthesis methods and conditions for zeolite membranes are critically illustrated in the category. Moreover, representative surface and structure properties of zeolite membranes are discussed by relating to the synthesis-structure-performance relationships. Finally, conclusive remarks are demonstrated and possible solutions to existing challenges are proposed. So far, this is the first time to discuss the applications of zeolite membrane reactors in the CO2 adsorption, separation, activation, and conversion in reforming and hydrogenation processes.

Construction of Ag-modified TiO2/ZnO heterojunction nanotree arrays with superior photocatalytic and photoelectrochemical properties.

The work involves the preparation of TiO2/ZnO heterojunction nanotree arrays by a three-step: hydrothermal, sol-gel, and secondary hydrothermal method, and then modification of Ag quantum dots (QDs). In the above process, the ZnO nanoparticles attached to the TiO2 surface were subjected to secondary growth by a hydrothermal method to form a unique nanotree structure with TiO2, followed by Ag quantum dot modification by quantum dot deposition. In summary, TiO2/ZnO nanotree arrays are cited for the first time. The prepared Ag-modified TiO2/ZnO heterojunction nanotree arrays were found to exhibit enhanced photoelectrochemical and photocatalytic properties. The photocurrent of the Ag-modified TiO2/ZnO heterojunction nanotree arrays is increased by 8-fold compared to the pure TiO2 nanorod arrays, the photocatalytic degradation rate within 180 minutes increased from 37% to 77% and the kinetic rate constants for the degradation of methyl orange were three times higher than the pure TiO2 nanorod arrays. The improved performance is partly due to the introduction of the TiO2/ZnO heterojunction nanotree arrays which provide Ag QDs with greater adhesion area. Localized surface plasmon resonance (LSPR) leads to an increase in the intensity of absorbed light due to the modification of Ag QDs. On the other hand the generation of the TiO2/ZnO heterojunction decreases the forbidden band width, resulting in the redshift of the light absorption edge. Therefore, TiO2/ZnO heterojunction nanotree arrays are expected to play an important role in solar cells and photocatalytic materials.