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Due to the low communication overhead and robustness to failures, distributed energy management is of paramount importance in smart grids, especially in microgrids, which feature distributed generation (DG). Distributed economic dispatch for a microgrid with renewable penetration and demand-side management operating in the grid-connected mode is considered in this paper. To address the challenge of intrinsically stochastic availability of renewable energy sources (RES), a novel power scheduling approach involving the actual renewable energy as well as the energy traded with the main grid is introduced, effecting the supply-demand balance. Its optimality amounts to minimizing the microgrid net cost, which includes conventional DG cost as well as worst-case transaction cost stemming from the uncertainty in RES. Leveraging the dual decomposition, the optimization problem formulated is solved in a distributed fashion. Numerical results are reported to corroborate the effectiveness of the novel approach.IEEE SmartGridComm 2012 Symposium -Support for Storage, Renewable Sources, and MicroGrid 978-1-4673-0911-0/12/$31.00 ©2012 IEEE

This paper develops a novel graph convolutional network (GCN) framework for fault location in power distribution networks. The proposed approach integrates multiple measurements at different buses while taking system topology into account. The effectiveness of the GCN model is corroborated by the IEEE 123 bus benchmark system. Simulation results show that the GCN model significantly outperforms other widely-used machine learning schemes with very high fault location accuracy. In addition, the proposed approach is robust to measurement noise and data loss errors. Data visualization results of two competing neural networks are presented to explore the mechanism of GCNs superior performance. A data augmentation procedure is proposed to increase the robustness of the model under various levels of noise and data loss errors. Further experiments show that the model can adapt to topology changes of distribution networks and perform well with a limited number of measured buses. research on metal oxide varistors and high voltage polymeric metal oxide surge arresters. From 2014 to 2015, he was a Visiting Professor with the

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Abstract-Network Function Virtualization (NFV) enables mobile operators to virtualize their network entities as Virtualized Network Functions (VNFs), offering fine-grained on-demand network capabilities. VNFs can be dynamically scale-in/out to meet the performance desire and other dynamic behaviors. However, designing the auto-scaling algorithm for desired characteristics with low operation cost and low latency, while considering the existing capacity of legacy network equipment, is not a trivial task. In this paper, we propose a VNF Dynamic Auto Scaling Algorithm (DASA) considering the tradeoff between performance and operation cost. We develop an analytical model to quantify the tradeoff and validate the analysis through extensive simulations. The results show that the DASA can significantly reduce operation cost given the latency upper-bound. Moreover, the models provide a quick way to evaluate the cost-performance tradeoff and system design without wide deployment, which can save cost and time.

The development of reference values of trace elements is recognized as a fundamental prerequisite for the assessment of trace element nutritional status and health risks. In this study, a total of 1400 pregnant women aged 27.0 ± 4.5 years were randomly selected from the China Nutrition and Health Survey 2010–2012 (CNHS 2010–2012). The concentrations of 14 serum trace elements were determined by high-resolution inductively coupled plasma mass spectrometry. Reference values were calculated covering the central 95% reference intervals (P2.5–P97.5) after excluding outliers by Dixon’s test. The overall reference values of serum trace elements were 131.5 (55.8-265.0 μg/dL for iron (Fe), 195.5 (107.0–362.4) μg/dL for copper (Cu), 74.0 (51.8–111.3) μg/dL for zinc (Zn), 22.3 (14.0–62.0) μg/dL for rubidium (Rb), 72.2 (39.9–111.6) μg/L for selenium (Se), 45.9 (23.8-104.3) μg/L for strontium (Sr), 1.8 (1.2–3.6) μg/L for molybdenum (Mo), 2.4 (1.2–8.4) μg/L for manganese (Mn), 1.9 (0.6–9.0) ng/L for lead (Pb), 1.1 (0.3-5.6) ng/L for arsenic (As), 835.6 (219.8–4287.7) ng/L for chromium (Cr), 337.9 (57.0–1130.0) ng/L for cobalt (Co), 193.2 (23.6–2323.1) ng/L for vanadium (V), and 133.7 (72.1–595.1) ng/L for cadmium (Cd). Furthermore, some significant differences in serum trace element reference values were observed between different groupings of age intervals, residences, anthropometric status, and duration of pregnancy. We found that serum Fe, Zn, and Se concentrations significantly decreased, whereas serum Cu, Sr, and Co concentrations elevated progressively compared with reference values of 14 serum trace elements in pregnant Chinese women. The reference values of serum trace elements established could play a key role in the following nutritional status and health risk assessment.

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