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This article proposes a unilateral Adaptive update strategy based Interval Prediction (AIP) model for short‐term load prediction, which is developed based on lower and upper bound estimation (LUBE) architecture. In traditional LUBE interval prediction model, the model training is usually trained by heuristic algorithms. In this article, the model training is formulated as a bi‐level optimization problem with the help of proposed unilateral adaptive update strategy and cost function. In lower‐level problem, a simplified biased convex cost function is developed to supervise the learning direction of basic prediction engines. The basic prediction engine utilizes Gated Recurrent Unit (GRU) to extract features and Full connected Neural Network (FNN) to generate interval boundary. In upper‐level problem, a unilateral adaptive update strategy with unilateral coverage rate is put forward. It iteratively tunes hyper‐parameters of cost function during training process. Comprehensive experiments based on residential load data are implemented and the proposed interval prediction model outperforms the tested state‐of‐the‐art algorithms, achieving a 15% reduction in prediction error and a 20% decrease in computational time. [ABSTRACT FROM AUTHOR]

Er3+ doped high density germanate glasses with compositions of 50GeO 2 -10BaO–10Al 2 O 3 -15Lu 2 O 3 -(15-x) La 2 O 3 -xEr 2 O 3 (x = 0, 0.1, 0.5, 1.0, 1.5, and 2.0, in mol%) have been prepared by the melt-quenching method. The optical and physical properties of the samples were characterized by differential scanning calorimetry (DSC), density, transmittance spectra, photoluminescence (PL) spectra, and X-ray excited luminescence (XEL) spectra. The densities were in the range from 5.90 to 6.07 g/cm3. The strong green emissions around 552 nm of Er3+ were observed upon the excitations of 378 nm light and the X-ray, and the lifetimes of 552 nm emission were in the range of 20.88–96.47 μs. The results show that Er3+ doped high density germanate glasses could be used as potential scintillators for X-ray CT. [ABSTRACT FROM AUTHOR]

By analyzing the data of Chinese industrial firms, we explore the association between governments' economic growth target management behaviour and the quality of local economic development. We find that the rise of economic growth pressure can decrease the total factor productivity (TFP) of local enterprises and has a negative impact on the quality of local economic development. Besides, the effect of economic growth pressure on local firms' productivity is more pronounced when firm is stated owned, when firm is located in cities with newly appointed officials, during the period of Communist Party of China National Congress (CPCNC), but would be attenuated if municipal party secretary has achieved a Ph.D. degree. Further mechanism analysis verifies that higher economic growth pressure could demolish the TFP of enterprises by distorting marginal revenue productivity of capital ( M R P K). [ABSTRACT FROM AUTHOR]