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A power grid is a complex system connecting electric power generators to consumers through power transmission and distribution networks across a large geographical area. System monitoring is necessary to ensure the reliable operation of power grids, and state estimation is used in system monitoring to best estimate the power grid state through analysis of meter measurements and power system models. Various techniques have been developed to detect and identify bad measurements, including interacting bad measurements introduced by arbitrary, nonrandom causes. At first glance, it seems that these techniques can also defeat malicious measurements injected by attackers. In this article, we expose an unknown vulnerability of existing bad measurement detection algorithms by presenting and analyzing a new class of attacks, called false data injection attacks , against state estimation in electric power grids. Under the assumption that the attacker can access the current power system configuration information and manipulate the measurements of meters at physically protected locations such as substations, such attacks can introduce arbitrary errors into certain state variables without being detected by existing algorithms. Moreover, we look at two scenarios, where the attacker is either constrained to specific meters or limited in the resources required to compromise meters. We show that the attacker can systematically and efficiently construct attack vectors in both scenarios to change the results of state estimation in arbitrary ways. We also extend these attacks to generalized false data injection attacks , which can further increase the impact by exploiting measurement errors typically tolerated in state estimation. We demonstrate the success of these attacks through simulation using IEEE test systems, and also discuss the practicality of these attacks and the real-world constraints that limit their effectiveness.

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Tooth agenesis is one of the most common developmental anomalies in humans. Oligodontia, a severe form of tooth agenesis, is genetically and phenotypically a heterogeneous condition. Although significant efforts have been made, the genetic etiology of dental agenesis remains largely unknown. In the present study, we performed whole-exome sequencing to identify the causative mutations in Chinese families in whom oligodontia segregates with dominant inheritance. We detected a heterozygous missense mutation (c.632G>A [p.Arg211Gln]) in WNT10B in all affected family members. By Sanger sequencing a cohort of 145 unrelated individuals with non-syndromic oligodontia, we identified three additional mutations (c.569C>G [p.Pro190Arg], c.786G>A [p.Trp262(∗)], and c.851T>G [p.Phe284Cys]). Interestingly, analysis of genotype-phenotype correlations revealed that mutations in WNT10B affect the development of permanent dentition, particularly the lateral incisors. Furthermore, a functional assay demonstrated that each of these mutants could not normally enhance the canonical Wnt signaling in HEPG2 epithelial cells, in which activity of the TOPFlash luciferase reporter was measured. Notably, these mutant WNT10B ligands could not efficiently induce endothelial differentiation of dental pulp stem cells. Our findings provide the identification of autosomal-dominant WNT10B mutations in individuals with oligodontia, which increases the spectrum of congenital tooth agenesis and suggests attenuated Wnt signaling in endothelial differentiation of dental pulp stem cells.

A wind power short-term forecasting method based on discrete wavelet transform and long short-term memory networks (DWT_LSTM) is proposed. The LSTM network is designed to effectively exhibit the dynamic behavior of the wind power time series. The discrete wavelet transform is introduced to decompose the non-stationary wind power time series into several components which have more stationarity and are easier to predict. Each component is dug by an independent LSTM. The forecasting results of the wind power are obtained by synthesizing the prediction values of all components. The prediction accuracy has been improved by the proposed method, which is validated by the MAE (mean absolute error), MAPE (mean absolute percentage error), and RMSE (root mean square error) of experimental results of three wind farms as the benchmarks. Wind power forecasting based on the proposed method provides an alternative way to improve the security and stability of the electric power network with the high penetration of wind power.