No abstract available

[en] Highlights: • A novel hybrid modeling method is proposed for short-term wind speed forecasting. • Support vector regression model is constructed to formulate nonlinear state-space framework. • Unscented Kalman filter is adopted to recursively update states under random uncertainty. • The new SVR–UKF approach is compared to several conventional methods for short-term wind speed prediction. • The proposed method demonstrates higher prediction accuracy and reliability. - Abstract: Accurate wind speed forecasting is becoming increasingly important to improve and optimize renewable wind power generation. Particularly, reliable short-term wind speed prediction can enable model predictive control of wind turbines and real-time optimization of wind farm operation. However, this task remains challenging due to the strong stochastic nature and dynamic uncertainty of wind speed. In this study, unscented Kalman filter (UKF) is integrated with support vector regression (SVR) based state-space model in order to precisely update the short-term estimation of wind speed sequence. In the proposed SVR–UKF approach, support vector regression is first employed to formulate a nonlinear state-space model and then unscented Kalman filter is adopted to perform dynamic state estimation recursively on wind sequence with stochastic uncertainty. The novel SVR–UKF method is compared with artificial neural networks (ANNs), SVR, autoregressive (AR) and autoregressive integrated with Kalman filter (AR-Kalman) approaches for predicting short-term wind speed sequences collected from three sites in Massachusetts, USA. The forecasting results indicate that the proposed method has much better performance in both one-step-ahead and multi-step-ahead wind speed predictions than the other approaches across all the locations

[en] A facile method has been developed to synthesize Ti"3"+-doped TiO_2 nanoparticles as high-performance anode materials for Li-ion batteries. After reducing some Ti"4"+ ions to Ti"3"+ ions with zinc powders in TiCl_4 solution, Ti"3"+-doped TiO_2 was synthesized by solvothermal methode. The obtained Ti"3"+-doped TiO_2 nanoparticles are relatively uniform and better dispersed with an average size of 30 nm. Great improvement of the electrochemical performance was obtained by Ti"3"+-doping comparing with the pure TiO_2. The Ti"3"+-doped TiO_2 nanoparticles prepared at the Zn:Ti molar ratio of 4% are able to deliver a reversible capacity of 202.1 mAh g"−"1 at a current density of 100 mA g"−"1 and exhibit superior high-rate discharge/charge capability and cycling stability at the current density up to 3000 mA g"−"1 in a half cell configuration. The improved reversible capacity and rate capability could be ascribed to the presence of Ti"3"+, which improves the electrical conductivity and reduces the charge transfer resistance of the TiO_2 electrode

[en] W–1% La₂O₃ alloy is considered as the most promising material for plasma-facing components of fusion reactors. The microstructure of a commercial W–1% La₂O₃ alloy was investigated using optical and transmission electron microscopes. The microstructure of pure tungsten can be improved significantly by fabrication of W–1% La₂O₃ alloys. W–1% La₂O₃ alloys can be produced with no porosities and cracks, and with various oxide phases dispersed in alloy matrix. La₂O₃ with different crystal structures, La₆W₂O₁₅, WO₂, WO₃ and W₃O₈ phases were identified in as-forged W–1% La₂O₃ alloy. Long strip-like La₂O₃ has a very large size, whereas spherical La₆W₂O₁₅, navicular WO₃, hexagonal W₃O₈ and short rod-like La₂O₃ are smaller particles. Most identified phases have a heterogeneous distribution. Forging leads to a more dispersive distribution of large-sized La₂O₃ particles but not of fine WO₃ particles compared with rolling. The mechanical properties of the alloys are also discussed

No abstract available

[en] Optimizing wind power generation and controlling the operation of wind turbines to efficiently harness the renewable wind energy is a challenging task due to the intermittency and unpredictable nature of wind speed, which has significant influence on wind power production. A new approach for long-term wind speed forecasting is developed in this study by integrating GMCM (Gaussian mixture copula model) and localized GPR (Gaussian process regression). The time series of wind speed is first classified into multiple non-Gaussian components through the Gaussian mixture copula model and then Bayesian inference strategy is employed to incorporate the various non-Gaussian components using the posterior probabilities. Further, the localized Gaussian process regression models corresponding to different non-Gaussian components are built to characterize the stochastic uncertainty and non-stationary seasonality of the wind speed data. The various localized GPR models are integrated through the posterior probabilities as the weightings so that a global predictive model is developed for the prediction of wind speed. The proposed GMCM–GPR approach is demonstrated using wind speed data from various wind farm locations and compared against the GMCM-based ARIMA (auto-regressive integrated moving average) and SVR (support vector regression) methods. In contrast to GMCM–ARIMA and GMCM–SVR methods, the proposed GMCM–GPR model is able to well characterize the multi-seasonality and uncertainty of wind speed series for accurate long-term prediction. - Highlights: • A novel predictive modeling method is proposed for long-term wind speed forecasting. • Gaussian mixture copula model is estimated to characterize the multi-seasonality. • Localized Gaussian process regression models can deal with the random uncertainty. • Multiple GPR models are integrated through Bayesian inference strategy. • The proposed approach shows higher prediction accuracy and reliability

[en] This Paper compared the performance of CRDM using metal bobbin coils with those using the traditional non-metal bobbin. From the test result, CRDMs with both kinds of coils can carry out their function correctly and all parameters are well within the specification. But metal bobbin has many advantages over non-metal bobbin, such as higher temperature endurance, radiation resistant, aseismatic performance, mechanical strength and machinability, and lower cost. Using the metal bobbin, coils with temperature index of 420℃ or above can be built and the goal to eliminate the cooling system on top of the reactor can be achieved. (authors)

[en] Isotropically conductive TiC/C hybrid nanofibers (TCCNFs) have been prepared by electrospinning for supercapacitor application for the first time. By changing the atmosphere of stabilization process, the TCCNFs with different TiC contents were successfully synthesized with uniform morphology and average diameter of about 100 nm. The TCCNFs stabilized in Ar contain much more TiC than those stabilized in air. The specific capacitance of the TCCNFs stabilized in Ar and air were measured to be 77.8 F g"−"1 and 130.0 F g"−"1 at the current density of 0.1 A g"−"1, respectively, which were much higher than the pure TiC nanoparticles and reported carbon materials with similar specific surface area. The charge storage mechanisms were discussed by analyzing the capacitive and diffusion-controlled contributions to the total capacitance. Reversible valance change of Ti atoms was observed during charge/discharge process, indicative of the occurrence of pseudoreaction. The experimental results support that the higher specific capacitance of the TCCNFs may be caused by a synergistic enhancing effect between TiC and carbon. The capacitance retention reaches 98.9% and 93.0% for the TCCNFs stabilized in Ar and air after 25 000 cycles, respectively, showing excellent stability. The present work provides a novel conductive electrode material for supercapacitors with possible pseudocapacitance, worthy of further investigation

No abstract available

[en] Peroxisome proliferator-activated receptor gamma (PPARγ), a multiple functional transcription factor, has been reported to have anti-tumor effects through inhibition of cells proliferation. However, its effects on cardiac myxoma (CM) cells and the underlying signaling mechanism is unclear. In the present study, we demonstrated that the level of PPARγ is inversely correlated with that of myocyte enhancer factor 2D (MEF2D), a biomarker of CM. We found that activation of PPARγ inhibit MEF2D expression via upregulation of miR-122, which can target the 3′-UTR of MEF2D and inhibit MEF2D expression, by directly binding to the PPRE in the miR-122 promoter region. Functional experiments further showed that miR-122-dependent downregulation of MEF2D by PPARγ suppress the proliferation of CM cells. These results suggest that PPARγ may exert its antiproliferative effects by negatively regulating the MEF2D in CM cells, which through upregulation of miR-122, and PPARγ/miR-122/MEF2D signaling pathway may be a novel target for treatment of CM. -- Highlights: •PPARγ expression is inversely correlated with MEF2D expression in CM tissues. •PPARγ downregulates MEF2D expression in CM cells. •PPARγ inhibits MEF2D expression via upregulation of miR-122. •miR-122-dependent downregulation of MEF2D by PPARγ suppresses the proliferation of CM cells.