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High number of magnetic poles in an electric machine allows reduction in radial thickness of stator and rotor yoke and thus heavy alloy. If frequency is allowed to increase with pole-count (constant speed), power level can be maintained. Thus, high frequency, together with high pole count, can improve power density of rotating electric machines. The proposed high frequency concept is applied to designing a 1 MW motor, with power density and efficiency goals of > 13kW/kg and > 96%, respectively .

Bacteriophage phi29 virus nanoparticles and its associated DNA packaging nanomotor can provide for novel possibilities towards the development of hybrid bio-nano structures. Towards the goal of interfacing the phi29 viruses and nanomotors with artificial micro and nano-structures, we fabricated nanoporous Anodic Aluminum Oxide (AAO) membranes with pore size of 70 nm and shrunk the pores to sub 40 nm diameter using atomic layer deposition (ALD) of Aluminum Oxide. We were able to capture and align particles in the anodized nanopores using two methods. Firstly, a functionalization and polishing process to chemically attach the particles in the inner surface of the pores was developed. Secondly, centrifugation of the particles was utilized to align them in the pores of the nanoporous membranes. In addition, when a mixture of empty capsids and packaged particles was centrifuged at specific speeds, it was found that the empty capsids deform and pass through 40 nm diameter pores whereas the particles packaged with DNA were mainly retained at the top surface of the nanoporous membranes. Fluorescence microscopy was used to verify the selective filtration of empty capsids through the nanoporous membranes.

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In an increasingly open electricity market environment, short-term load forecasting (STLF) can ensure the power grid to operate safely and stably, reduce resource waste, power dispatching, and provide technical support for demand-side response. Recently, with the rapid development of demand side response, accurate load forecasting can better provide demand side incentive for regional load of prosumer groups. Traditional machine learning prediction and time series prediction based on statistics failed to consider the non-linear relationship between various input features, resulting in the inability to accurately predict load changes. Recently, with the rapid development of deep learning, extensive research has been carried out in the field of load forecasting. On this basis, a feature selection algorithm based on random forest is first used in this paper to provide a basis for the selection of the input features of the load forecasting model. After the input features are selected, a hybrid neural network STLF algorithm based on multi-model fusion is proposed, of which the main structure of the hybrid neural network is composed of convolutional neural network and bidirectional gated recurrent unit (CNN-BiGRU). The input data is obtained by using long sliding time windows of different steps, then multiple CNN-BiGRU models are trained respectively. The forecasting results of multiple models are averaged to get the final forecasting load value. The load datasets come from a region in New Zealand and a region in Zhejiang, China, are used as load forecast examples. Finally, a variety of load forecasting algorithms are introduced for comparison. The experimental results show that our method has a higher accuracy than comparison models.

Vickers microhardness of (0001), (101̄0) and (112̄0) planes of ZrB2 single crystal prepared by the floating zone method has been investigated at various temperatures and loading times. As the temperature increases from 25°C to 1000°C, hardness drops from ∼20.9 GN m−2 of all planes to ∼7.85 GN m−2 for (0001) plane and ∼4.91 GN m−2 for (101̄0) and (112̄0) planes. The hardness of (101̄0) and (112̄0) planes exhibits almost same tendency and is always lower than that of (0001) plane by about 35%. The thermal softening coefficients of all three planes strongly depends on the temperature range with clear inflections at 400°C and 700°C. The loading time dependence of hardness is used to calculate the activation energy for creep. In addition, a relationship was found that shows the variation of hardness with temperature to be proportional to the variation with the loading time in a specific temperature range.