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Recently, wireless sensor networks (WSNs) have been extensively deployed to monitor environments. Sensor nodes are susceptible to fault generation due to hardware and software failures in harsh environments. Anomaly detection for the time-series streaming data of sensor nodes is a challenging but critical fault diagnosis task, particularly in large-scale WSNs. The data-driven approach is becoming essential for the goal of improving the reliability and stability of WSNs. We propose a data-driven anomaly detection approach in this paper, named median filter (MF)-stacked long short-term memory-exponentially weighted moving average (LSTM-EWMA), for time-series status data, including the operating voltage and panel temperature recorded by a sensor node deployed in the field. These status data can be used to diagnose device anomalies. First, a median filter (MF) is introduced as a preprocessor to preprocess obvious anomalies in input data. Then, stacked long short-term memory (LSTM) is employed for prediction. Finally, the exponentially weighted moving average (EWMA) control chart is employed as a detector for recognizing anomalies. We evaluate the proposed approach for the panel temperature and operating voltage of time-series streaming data recorded by wireless node devices deployed in harsh field conditions for environmental monitoring. Extensive experiments were conducted on real time-series status data. The results demonstrate that compared to other approaches, the MF-stacked LSTM-EWMA approach can significantly improve the detection rate (DR) and false rate (FR). The average DR and FR values with the proposed approach are 95.46% and 4.42%, respectively. MF-stacked LSTM-EWMA anomaly detection also achieves a better F2 score than that achieved by other methods. The proposed approach provides valuable insights for anomaly detection in WSNs by detecting anomalies in the time-series status data recorded by wireless sensor nodes.

The intelligent environment monitoring network, as the foundation of ecosystem research, has rapidly developed with the ever-growing Internet of Things (IoT). IoT-networked sensors deployed to monitor ecosystems generate copious sensor data characterized by nonstationarity and nonlinearity such that outlier detection remains a source of concern. Most outlier detection models involve hypothesis tests based on setting outlier threshold values. However, signal decomposition describes stationary and nonstationary relationships sensor data. Therefore, this paper proposes a three-level hybrid model based on the median filter (MF), empirical mode decomposition (EMD), classification and regression tree (CART), autoregression (AR) and exponential weighted moving average (EWMA) methods called MF-EMD-CART-AR-EWMA to detect outliers in sensor data. The first-level performance is compared to that of the Butterworth filter, FIR filter, moving average filter, wavelet filter and Wiener filter. The second-level prediction performance is compared to support vector regression (SVR), K-nearest neighbor (KNN), CART, complementary ensemble EEMD with CART and AR (EEMD-CART-AR) and ensemble CEEMD with CART and AR (CEEMD-CART-AR) methods. Finally, EWMA is compared to Cumulative Sum Control Chart (CUSUM) and Shewhart control charts. The proposed hybrid model was evaluated with a real dataset from the hydrometeorological observation network in the Heihe River Basin, yielding experimental results with better generalization ability and higher accuracy than the compared models, and providing extremely effective detection of minor outliers in predicted values. This paper provides valuable insight and a promising reference for outlier detection involving sensor data and presents a new perspective for detecting outliers.

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BackgroundGrain protein concentration (GPC) is a major determinant of quality in barley (Hordeum vulgare L.). Breeding barley cultivars with high GPC has practical value for feed and food properties. The aim of the present study was to identify quantitative trait loci (QTLs) for GPC that could be detected under multiple environments.ResultsA population of 190 recombinant inbred lines (RILs) deriving from a cross between Chinese landrace ZGMLEL with high GPC (> 20%) and Australian cultivar Schooner was used for linkage and QTL analyses. The genetic linkage map spanned 2353.48 cM in length with an average locus interval of 2.33 cM. GPC was evaluated under six environments for the RIL population and the two parental lines. In total, six environmentally stable QTLs for GPC were detected on chromosomes 2H (1), 4H (1), 6H (1), and 7H (3) and the increasing alleles were derived from ZGMLEL. Notably, the three QTLs on chromosome 7H (QGpc.ZiSc-7H.1, QGpc.ZiSc-7H.2, and QGpc.ZiSc-7H.3) that linked in coupling phase were firstly identified. Moreover, the genetic effects of stable QTLs on chromosomes 2H, 6H and 7H were validated using near isogenic lines (NILs).ConclusionsCollectively, the identified QTLs expanded our knowledge about the genetic basis of GPC in barley and could be selected to develop cultivars with high grain protein concentration.Electronic supplementary materialThe online version of this article (doi:10.1186/s12870-017-1067-6) contains supplementary material, which is available to authorized users.

In the building domain, the non-renewable resource of sand is widely used to produce concrete and mortar. The sand production has been estimated to be more than 10 billion tons with a total of 1.2 billion tons used in concrete in the last decade, which causes the gradual reduction of available building materials and impacts the environment. Since there are abundant desert sand resources in northwestern China, it would be viable to utilize desert sand as an alternative material for concrete production. In this study, an investigation of dynamic mechanical behaviors of desert sand concrete (DSC) was conducted. Various desert sand replacement ratios (0–100%) were used to replace the equivalent hill sand as fine aggregate. Experimental results showed that strain rate had a strong effect on the dynamic mechanical behaviors of DSC. The compressive strength (at room temperature) and flexural strength (after elevated temperature) increased with desert sand replacement ratio (DSRR) with the optimum replacement ratio of 40%, which was because the increase of DSRR improved the compaction of DSC. However, the effect of the low strength of desert sand was higher than that of the compaction when the DSSR exceeded 40%, so both strength values generally decreased with the increase of DSRR. Moreover, the dynamic constitutive model of DSC at room temperature was established on the basis of a nonlinear visco–elastic constitutive model (ZWT model), which can predict the stress–strain curves of DSC.

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Stem solidness is an important agronomic trait for increasing the ability of wheat to resist lodging. In this study, four new synthetic hexaploid wheat with solid stems were developed from natural chromosome doubling of F1 hybrids between a solid-stemmed durum wheat (Triticum turgidum ssp. durum, 2n = 4x = 28, AABB) and four Aegilops tauschii (2n = 2x = 14, DD) accessions. The solid expression of the second internode at the base of the stem was stable for two synthetic hexalpoid wheat Syn-SAU-117 and Syn-SAU-119 grown in both the greenhouse and field. The lodging resistance of four synthetic solid-stem wheats is stronger than that of CS, and Syn-SAU-116 has the strongest lodging resistance, followed by Syn-SAU-119. The paraffin sections of the second internode showed that four synthetic wheat lines had large outer diameters, well-developed mechanical tissues, large number of vascular bundles, and similar anatomical characteristics with solid-stemmed durum wheat. The chromosomal composition of four synthetic hexaploid wheat was identified by FISH (fluorescence in situ hybridization) using Oligo-pSc119.2-1 and Oligo-pTa535-1. At adult stage, all four synthetic hexaploid wheat showed high resistance to mixed physiological races of stripe rust pathogen (CYR31, CYR32, CYR33, CYR34). These synthetic hexaploid wheat lines provide new materials for the improvement of common wheat.