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The rigid catenary system is widely used in tunnels to power electric trains via contact with a pantograph. Due to gravity, the contact wire normally has a sag that may affect the dynamic interaction performance with a pantograph. To reduce the contact wire sag, the most efficient measure is to improve the moment of inertia of the conductor rail, which is used to clamp the contact wire. Six new types of conductor rail with large moments of inertia are developed based on a conventional conductor rail. Then both the static and dynamic analyses are conducted to investigate the performance of the new types of conductor rail with a big moment of inertia. The conductor rail's 3D solid finite element model is built using a finite element approach. The vertical deflection and the stress distribution are comparatively analyzed among different types of conductor rail. The analysis results indicate that the vertical deflection and maximum stress are significantly reduced when using the conductor rail with a large moment of inertia. The best performance is observed when the conductor rail of case 1 is used. The maximum sag is reduced by 28.37%, and the maximum stress is decreased by 27.76% compared with the conventional conductor. Finally, a pantograph model is included to evaluate the dynamic performance of the conductor rail with large moments of inertia. The results indicate that contact force fluctuation is significantly reduced after the conductor rails with large moments of inertia are presented. The conductor rail of case 1 shows the best performance, which can reduce the contact force standard deviation by 32% and 27% at speeds of 160 km/h and 200 km/h. [ABSTRACT FROM AUTHOR]

A variety of Chinese textual operational text data has been recorded during the operation and maintenance of the high-speed railway catenary system. Such defect text records can facilitate defect detection and defect severity analysis if mined efficiently and accurately. Therefore, in this context, this paper focuses on a specific problem in defect text mining, which is to efficiently extract defect-relevant information from catenary defect text records and automatically identify catenary defect severity. The specific task is transformed into a machine learning problem for defect text classification. First, we summarize the characteristics of catenary defect texts and construct a text dataset. Second, we use BERT to learn defect texts and generate word embedding vectors with contextual features, fed into the classification model. Third, we developed a deep text categorization network (DTCN) to distinguish the catenary defect level, considering the contextualized semantic features. Finally, the effectiveness of our proposed method (BERT-DTCN) is validated using a catenary defect textual dataset collected from 2016 to 2018 in the China Railway Administration in Chengdu, Lanzhou, and Hengshui. Moreover, BERT-DTCN outperforms several competitive methods in terms of accuracy, precision, recall, and F 1 -score value. [ABSTRACT FROM AUTHOR]

Eddy-current analysis and losses estimation are significant components in the optimized design of the tractive transformer. The conventional analytical method forces to linearize the nonlinear magnetic property and as an important quantity in the calculation, the permeability is only acquired dependent on the empirical method. Furthermore, the computational precision is often difficult to assure in losses estimation when distorted excitation originated from nonlinear magnetized relation in electrical steel is neglected. In our study, the eddy-current field problem is delicately solved by analytical deduction, in which the nonlinear constitutive relation toward media and the harmonic component in the excitation source are sufficiently considered. Compared with the accuracy of the traditional formula, the proposed model achieves better accuracy on eddy-current losses with finite-element analysis, especially when the operating condition is closed to magnetic saturation. [ABSTRACT FROM AUTHOR]

Overhead contact lines (OCLs) are electric transmission lines that power railways, which are constantly threatened by external weather and environmental factors due to their outdoor location. Hence, for the long-term functioning of railway lines, a weather-driven risk predictor is an essential tool. Current prediction methods mainly adopt a single-point estimation system with fixed weights of neural networks and therefore cannot propagate the uncertainties within the data and model, resulting in unreliable predictions. To enhance safety-risk prevention capabilities, this paper proposes an uncertainty-aware trustworthy weather-driven failure-risk approach for OCLs, in a probabilistic deep multitask learning framework. Firstly, a deep Gaussian process is employed as the backbone model to deal with imbalanced weather-related failure datasets with limited fault samples. Moreover, a multi-task learning framework is embedded to simultaneously predict the multiple weather-driven failure risks under lightning, wind and haze. Finally, the predictive uncertainty is decomposed into epistemic and aleatory uncertainties, where epistemic and aleatory uncertainties account for the uncertainty within the model and data, respectively. Extensive experiments on actual OCLs are carried out to demonstrate the effectiveness of the proposed approach, which can effectively capture the predictive uncertainty and provide trustworthy predictive decisions of mitigating operational risk for railway operators. • Propose an uncertainty-aware trustworthy WDFR prediction approach in an MTL4DGP framework for OCLs. • Develop an MTL formulation for DGP in a two-layer structure. • Formulate an uncertainty decomposition scheme which investigates predictive uncertainty from epistemic and aleatory perspectives. [ABSTRACT FROM AUTHOR]

• A two-layer energy management strategy based on fuzzy control for high-speed railway hybrid energy storage system is proposed. • Discharge threshold can be adaptively adjusted with power and lifespan of hybrid energy storage system. • The energy management system intelligently allocates power according to the state of charge of each subsystem and the traction power. • This strategy reduces the average energy extraction power and extends the life of the energy storage system. In order to extend the service life of the high-speed railway hybrid energy storage system and reduce the power shock impact of the traction network, an energy management strategy based on double-layer fuzzy logic control is proposed. This strategy can dynamically adjust the discharge threshold according to the external power and the remaining life of the energy storage system, and dynamically allocate power according to the real-time state of charge. At the same time, the strategy uses the rain flow counting method to complete the extraction of the depth of discharge and establishes an equivalent life evaluation model. The simulation results show that this energy management strategy can effectively reduce the frequency and depth of charging and discharging, and reduce the load impact of the traction network. Compared with the threshold-based energy management strategy, the life loss of this method can be reduced by more than 50%, which effectively improves the service life of the energy storage system. [ABSTRACT FROM AUTHOR]

Onboard cable is important electrical equipment of an electric locomotive. In order to effectively evaluate the aging state of the cable, this article proposes a method to evaluate the aging state of the cable based on a new method of dielectric spectrum data processing and a new structure model of recognition algorithm. First, the log-min-max gray-scale (LMMGS) method is proposed, which transforms the dielectric parameters based on the frequency-domain dielectric spectrum (FDS) test into the gray matrix. The correlation among different dielectric parameters is retained, and the interference of their order of magnitude difference on feature extraction is reduced. Then, the new structure of spatial pyramid pooling (SPP) is improved by rectangular mesh dividing named Rectangular-SPP, which increases the number of divisible layers of the spatial pyramid, and it is used to optimize convolutional neural network (CNN), so as to remove the limitation of recognition algorithm on the size of input data and expand the scale of feature extraction. Finally, a set of methods for evaluating the aging state of onboard cable is proposed. The experimental results show that this method can effectively distinguish the insulation aging state of onboard cable. Meanwhile, the proposed Rectangular-SPP-CNN algorithm has a better performance than the conventional machine learning algorithms. [ABSTRACT FROM AUTHOR]

As a key component of the high-speed railway catenary, the insulator plays an important role in supporting the catenary and maintaining the insulation between the catenary and earth. Regular inspection using computer vision technology is an effective way to detect insulator defects and improve the catenary operation safety. However, achieving full automation of insulator defects detection is still a difficult task due to the defective sample scarcity and subtle defect features. To overcome the problem, this article proposes defect detection GANs (DefGANs) that consist of a denoising autoencoder, a discriminator, and a classifier to detect defects, which can generate defective samples from pitted latent representations, thereby improving the reliability of defect detection classifier. The proposed DefGAN can pit the latent representations of normal samples to generate defective samples. Our proposal mainly consists of two stages. In the first stage, we use cascaded deep segmentation networks (CDSNets) to extract the insulator from the catenary images. Then, the insulator area is sampled into small patches. In the second stage, the patches are fed into the proposed DefGAN to detect defects. Moreover, the defect score is determined by the anomaly probability of the classifier and reconstruction error of the denoising autoencoder. The effectiveness of our work is measured across the catenary data sets. [ABSTRACT FROM AUTHOR]

With the rapid growth of China’s high-speed railway mileage, relevant safety technologies of railway operation are very urgent at present. One of the crucial parts is the pantograph–catenary system, which is directly related to the power supply safety of trains in high-speed railways. To ensure the safe operation of the pantograph–catenary system and eliminate the hidden danger during the power supply of trains, from 2011 in China, an automatic detection and monitoring system (called 6C system) of pantograph–catenary in high-speed railways was gradually established to timely discover and report all the potential defects and evaluate the performance of the pantograph–catenary system. The 6C system can measure the dynamic and static geometry parameters of the catenary and detect the defective working states of components and the operation conditions of the pantograph–catenary system. Considering the feedbacks from railway administrations, the 6C system can also realize a fine-grained detection and diagnosis of the pantograph–catenary and significantly improve the detection efficiency. In this article, the whole architecture of the 6C system is presented in detail, including six relatively independent parts for the detection and monitoring of pantograph–catenary systems and a data processing center with a three-level structure. Especially, some critical technologies for automatic detection and monitoring of pantograph–catenary systems are introduced. In the end, some new techniques of detection and monitoring are discussed, and suggestions are made for the future development of detection and monitoring systems of pantograph–catenary in high-speed railways. [ABSTRACT FROM AUTHOR]

A pantograph–catenary system (PCS) is an important part of a railway power supply system, which is an interface of the power supply system and the electric locomotive. The quality of a traction power supply depends on the stability of the contact between a pantograph and a catenary. Therefore, it is necessary to monitor the contact state by detecting the contact point (CPT) between the pantograph and the catenary. Recently, automatic CPT detection methods based on video monitoring have been introduced to improve the railway operation safety. However, the existing methods were still not stable enough in complex backgrounds. To improve the stability of CPT detection, we proposed a method combining a deep convolutional network with handcrafted features to detect the CPT. The proposed method consists of two stages. First, a deep pantograph network (DPN) was adopted to segment the pantograph strip. The DPN was mainly composed of a deep pantograph detection network (DPDN) and a deep pantograph segmentation network (DPSN). Then, the edge detection and the Hough transform were used to detect the contact line above the pantograph. Concretely, the CPT was obtained by finding the intersection of the contact line and the upper surface of the pantograph strip. The experimental results demonstrated the robustness and the accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

The exploration for estimation of eddy-current losses is of great significance to optimize the design of the transformer. This paper provides a more accurate formula to calculate the eddy-current losses in rectangle section of electrical steel by solving Maxwell’s equations rigorously, where the 3-D electromagnetic fields problem can be simplified to the 2-D problem because of the grain-oriented nature of steel sheet. Then an analytical model based on the single-layer trapezium steel sheet is established to validate the formula effectiveness through comparing with 3-D finite-element simulation, and the result shows a good approximation when the slope of trapezium steel is small enough. As for practical transformer core made up of multi-layers trapezium steel sheet, the various parameters of dimension in different layers are determined by geometrical analysis and the formula for power dissipation in wound core is proposed. Finally, the formula describing the eddy-current losses of wound core with the feature of semicircle section is obtained and compared with the experimental values after relevant parameter optimization about a nonlinear ferromagnetic material in the calculation, where the proposed methods are demonstrated to be applicable for engineering. [ABSTRACT FROM AUTHOR]

• We model the cascading failures of electrical CPS. • We construct the two graphs to analyze the vulnerability of electrical CPS. • We employ two graphs to construct the vulnerability indices. This paper aims to identify the vulnerability of electrical cyber-physical systems (CPSs) through fault propagation under cyber attacks. First, we propose a fault propagation model mainly considering the impact of interruptions on some nodes of the cyber network on the electrical physical systems. Secondly, two graphs, i.e. propagation graph and attack graph are proposed to reveal the physical fault propagation mechanisms and analyze the attack intensity of combinations of different communication nodes, respectively. Thirdly, a set of traditional vulnerable indices based on the propagation and attack graphs are employed to identify both the critical physical branches and communication nodes in the CPS. Finally, comparative analyses with and without considering the CPS on both IEEE 118- and 300- bus systems show that the fault propagation among are more sophisticated and the wrong decisions that the control center makes causes the higher vulnerability of the electrical network due to the interruption of the transmission information in the cyber system under cyber attacks. [ABSTRACT FROM AUTHOR]

The insulator is an important catenary component that maintains the insulation between the catenary and earth. Due to the long-term impact of railway vehicles and the environment, defects in the insulator are inevitable. Recently, automatic catenary inspection using computer vision and pattern recognition has been introduced to improve the safety of railway operation. However, achieving full automation of insulator defect detection is still very challenging due to the visual complexity of defects and the small number of defective insulators. To overcome these problems, this paper proposes a novel insulator surface defect detection system using a deep convolutional neural network (CNN). The proposed system consists of two stages. First, a Faster R-CNN network is adopted to localize the key catenary components, and the image areas that contain the insulators are obtained. Then, the classification score and anomaly score are determined from a deep multitask neural network that is composed of a deep material classifier and a deep denoising autoencoder. The defect state is determined by analyzing the classification score and anomaly score. Experiments of the catenary insulator defect detection along the Hefei–Fuzhou high-speed railway line indicate that the system can achieve high detection accuracy. [ABSTRACT FROM AUTHOR]

Auto‐transformer (AT) power traction networks with a parallel connection are the unique mode on high‐speed electric railways in China. The up line and down line of power traction networks are connected at substation (SS), AT post (ATP) and sectioning post (SP) through busbars, circuit breakers and disconnectors. Currently, when a fault occurs on power traction networks, the feeder line protection devices at the up line and down line in SS trip simultaneously. After that, the nonfault line and instantaneous fault line recover by means of autoreclosing. This tripping mode enlarges the power supply‐interrupted region. Based on the analysis of circuit networks of power traction networks on high‐speed electric railways, the interrelations of currents in SS and ATP and in SS and SP are derived. When a fault occurs on one side of the power traction networks, the feeder currents in ATP and SP flow from the nonfault side to the fault side. The relation between feeder currents in SS and ATP and between feeder currents in SS and SP reflect this characteristic. A criterion for discriminating the fault side and selective‐tripping scheme of breakers is proposed based on the correlation analysis of feeder current fault components in multisite. The simulation results show that this scheme can discriminate fault side and make a selective trip rapidly and correctly. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

• Propose three weather-driven failure probability prediction techniques. • Develop a failure probability model that integrates internal triggers and external weather-driven hazard factors. • Establish a risk propagation network for OCLs to capture the dependencies between risk factors and risk consequences. • Propose a data-driven integrated predictive probabilistic risk analytic in a dynamic Bayesian network framework for OCLs. Due to completely working under open-air conditions without backup equipment, the overhead contact lines (OCLs) are suffering from external extreme weather conditions, except for the long-term dynamic vibrations of catenary-pantograph system. These risk factors are prone to cause failures of OCL components and power outages, which may further result in transportation interruptions, enormous economic losses, serious social impacts, and even catastrophic safety accidents. To comprehensively investigate the associated risks in OCLs, a data-driven integrated predictive probabilistic risk analytics framework based on dynamic Bayesian network is proposed to identify the significant risk factors and analyse the time-dependant failure patterns in dynamic risk propagation network of OCLs. After exploiting the weather-driven analytics for failure probability prediction, an integrated failure probability modelling for OCL components is developed, simultaneously incorporating internal, and weather-driven hazard factors of OCLs. A predictive risk metric is suggested based on the newly established risk propagation network of OCLs, which can account for weather hazards, system failures, financial costs, and social trust losses, in response to external weather conditions over time. Numerical studies conducted on the actual OCLs demonstrate that the proposed framework can dynamically model and evaluate risks of failure patterns that expose to OCLs. [ABSTRACT FROM AUTHOR]

• Propose a probabilistic lightning model, depicting uncertain features in occurrence and intensity of lightning strike. • Propose a spatiotemporal fragility model, investigating spatiotemporal dependencies between LI and failure probability of OCLs. • Develop a data-driven BN approach for predicting lightning-related failure risk of OCLs. Lightning-related failures are of great concerns for the reliable performance of overhead contact lines (OCLs) of high-speed railway. Predicting lightning-related failure probability is valuable to capture the recurrence of OCL failures due to lightning strike and enable predictive maintenance decision-making. In this paper, a data-driven Bayesian network (BN) approach with spatiotemporal fragility model is developed to investigate the dependencies between lightning strike and OCL failures, and predict lightning-related failure risk of OCLs. It consists of three critical components, (1) a probabilistic lightning model that integrates multiple key lightning parameters is proposed to capture the uncertainty in the occurrence and intensity of lightning strike; (2) a spatiotemporal fragility model of OCL corridor is presented to examine the impacts of lightning strike on OCL failure probability; (3) furthermore, the Bayesian network is embedded with above-mentioned two models to predict lightning-related failure risk of OCLs, improving its robustness. Compared with other advanced prediction methods, the proposed approach achieves better prediction performance with high accuracy over imbalanced dataset. In addition, it can still work acceptably on noisy lightning data with a signal-to-noise ratio of 15dB or higher. [ABSTRACT FROM AUTHOR]

This study focuses on developing a comprehensive model of a rigid overhead system, which includes essential components such as the suspension structure, positioning clamp, and expansion joint. The modelling approach utilizes finite element theory and beam elements to accurately represent the displacement, stiffness, and mass characteristics of the system. The models also incorporate the suspension structure and positioning line clamp, which play crucial roles in suspending and positioning the busbar. Various suspension structures and positioning line clamps are evaluated based on their dynamic characteristics. The expansion joint, responsible for connecting different anchor sections of the rigid overhead system, undergoes a detailed analysis. Different assembly scenarios, including ideal and deflected assembly conditions, are considered. To simulate the dynamic behaviour of the expansion joint, additional beams are introduced into the system model. The primary finding of the analysis is that the maximum stresses observed in the constructed expansion joint model, under different temperature conditions and normal/deflected assembly conditions, remain within the permissible stress limits of the material. This indicates a high level of safety. However, certain areas exhibit stress concentration, particularly at the sliding block B and sliding rod A positions. This stress concentration is primarily attributed to the unique assembly form of the expansion joint. To improve stress distribution and enhance service reliability, the analysis suggests optimizing the installation deflection angle and geometric design of the expansion joint. Furthermore, the concentrated mass at the expansion joint significantly impacts the current collection quality of the pantograph-overhead system. Mitigating this negative impact can be achieved by reducing the mass of the expansion joint. [ABSTRACT FROM AUTHOR]

In many hub traction power-supply systems (TPSSs), the 27.5 kV busbars in the traction substation (TSS) have supplied several railway lines in a local area for reducing the number of TSSs. However, the multiple railway lines connected to the busbar may affect the resonance behaviors of the entire system. It is therefore necessary to investigate the resonance behaviors under such hub TPSS conditions since the harmonic current collection of all railway lines will aggravate the resonance problem. Thus, resonance-mode assessment and its sensitivity method is adopted for investigating the resonance behaviors of hub TPSS affected by different numbers of connected lines, lengths of supply lines, system capacities, etc. The numerical results of modal sensitivity indices are compared to investigate and quantify the harmonic resonance issue caused by multiple electrical railway lines. In addition, the resonance frequency-shift technique and modal impedance sensitivity index-based method are also employed to mitigate the critical harmonic resonances. The results illustrate that it is effective to analyze resonance issues by adopting the harmonic model and modal methods in this paper. The two types of resonance behaviors (i.e., primary resonance and resonance band) caused from the hub TPSS have been fully investigated, and the resulting harmonic distortion of hub TPSS is suppressed. [ABSTRACT FROM PUBLISHER]

Harmonic pollution of the traction power-supply system (TPSS) has become a vital concern for its adverse impacts on the tractive drive system of the train and power-quality issues of TPSS and connected power system. Harmonic modeling is the precondition to evaluate the harmonic pollution for TPSS. This paper systematically discusses the main harmonic sources in TPSS as well as their characteristics based on accurate field measurements. Moreover, the background harmonic voltage of the connected utility system and the harmonic of the train are compartmentalized and modeled with consideration of the stochastic and statistical features. This paper studies the background harmonic voltage with time slicing and probability statistics technique. Meanwhile, the harmonic of the train is described with the Norton equivalent model, which considers the interaction between harmonic voltage and harmonic current. Moreover, the influence of the working conditions and power output on the harmonic distribution is mentioned in the low-order harmonic modeling of the train. The results suggests the proposed methods can effectively restore the distribution and characteristics of the harmonics in TPSS. [ABSTRACT FROM PUBLISHER]

• Select optimal candidates based on spatiotemporal similarity check. • Develop a probabilistic wind model, accounting for the uncertainty in wind variables. • Design an environment sensitive parameter for floater intrusion. • Propose a wind-caused floater intrusion risk prediction approach for OCLs. Wind-caused floater intrusion has posed enormous threats to the safety and resilience of overhead contact lines (OCLs) of electrified railway. In this paper, a Bayesian neural network (BNN) based prediction model is proposed, incorporating spatiotemporal correlations, uncertainty of extreme wind speed and direction, characteristic parameters of OCLs, environmental information and human factors into floater intrusion risk prediction. To select optimal candidates, the spatial-temporal correlation among wind data with respect to different OCL corridors are examined. Then, the probabilistic wind model is developed to capture the stochastic nature of wind events and account for the uncertainty in wind speed and direction. The spatiotemporal correlation-constrained environment sensitive parameter is formulated to investigate the impacts of wind, characteristic parameters of OCLs, environmental information and human factors on floater intrusion of OCLs. A BNN model is implemented into predicting wind-caused floater intrusion risk. Finally, the remarkable effectiveness and robustness of the proposed model are compared with some other advanced prediction methods. The experimental results demonstrate that the proposed model not only has the capability of uncertainty estimation, but also provides the confidence interval of floater intrusion risk prediction, which can play a significant role in preventive operational flexibility and resilience against weather-related risks. [ABSTRACT FROM AUTHOR]

This paper presents a novel modular recurrent neural network based on the pipelined architecture (PRWNN) to reduce the computational complexity and improve the performance of the recurrent wavelet neural network (RWNN). The PRWNN inherits the modular architectures of the pipelined recurrent neural network proposed by Haykin and Li and is made up of a number of RWNN modules that are interconnected in a chained form. Since those modules of the PRWNN can be simultaneously performed in a pipelined parallelism fashion, this would lead to a crucial improvement of computational efficiency. Furthermore, owing to the cascade interconnection of dynamic modules, the performance of the PRWNN can be further enhanced. An adaptive gradient algorithm based on the real-time recurrent learning is derived to suit for the modular PRWNN. Simulation examples are given to evaluate the effectiveness of the PRWNN model on the identification of nonlinear dynamic systems and analysis of sunspot number time series. According to simulation results, it is clearly shown that the PRWNN provides impressive better performance in comparison with the single RWNN model. [ABSTRACT FROM PUBLISHER]

Association mapping is a powerful approach for exploring the molecular basis of phenotypic variations in plants. A maize ( Zea mays L.) association mapping panel including 527 inbred lines with tropical, subtropical and temperate backgrounds, representing the global maize diversity, was genotyped using 1,536 single nucleotide polymorphisms (SNPs). In total, 926 SNPs with minor allele frequencies of ≥0.1 were used to estimate the pattern of genetic diversity and relatedness among individuals. The analysis revealed broad phenotypic diversity and complex genetic relatedness in the maize panel. Two different Bayesian approaches identified three specific subpopulations, which were then reconfirmed by principal component analysis (PCA) and tree-based analyses. Marker-trait associations were performed to assess the suitability of different models for false-positive correction by population structure (Q matrix/PCA) and familial kinship (K matrix) alone or in combination in this panel. The K, Q + K and PCA + K models could reduce the false positives, and the Q + K model performed slightly better for flowering time, ear height and ear diameter. Our findings suggest that this maize panel is suitable for association mapping in order to understand the relationship between genotypic and phenotypic variations for agriculturally complex quantitative traits using optimal statistical methods. [ABSTRACT FROM AUTHOR]

Leaf collection from the field, labeling and tracking back to the source plants after genotyping are rate limiting steps in leaf DNA-based genotyping. In this study, an optimized genotyping method using endosperm DNA sampled from single maize seeds was developed, which can be used to replace leaf DNA-based genotyping for both genetic studies and breeding applications. A similar approach is likely to be suitable for all plants with relatively large seeds. Part of the endosperm was excised from imbibed maize seeds and DNA extracted in 96-tube plates using individuals from eight F2 populations and seven inbreds. The quality of the resultant DNA was functionally comparable to DNA extracted from leaf tissue. Extraction from 30 mg of endosperm yields 3–10 μg DNA, which is sufficient for analysis of 200–400 agarose-gel PCR-based markers, with the potential for several million chip-based SNP marker analyses. By comparing endosperm DNA and leaf DNA for individuals from an F2 population, genotyping errors caused by pericarp contamination and hetero-fertilization were found to average 3.8 and 0.6%, respectively. Endosperm sampling did not affect germination rates under controlled conditions, although under normal field conditions the germination rate, seedling establishment, and growth vigor were significantly lower than that of non-sampled controls for some genotypes. However, careful field management can compensate for these effects. Seed DNA-based genotyping lowered costs by 24.6% compared to leaf DNA-based genotyping due to reduced field plantings and labor costs. A substantial advantage of this approach is that it can be used to select desirable genotypes before planting. As such it provides an opportunity for dramatic improvements in the efficiency and selective gain of breeding systems based on optimum combinations of marker-assisted selection and phenotypic selection within and between generations. [ABSTRACT FROM AUTHOR]

To reveal the mechanism of fault propagation and temporal information between electrical network branches intuitively and vividly, we have proposed a fault chain-based cascading fault graph (CFG) that considers the topological, physical, and fault operational features from an overload mechanism perspective. The proposed CFG is used to construct metrics to identify vulnerable branches of an electrical network. Furthermore, because the vulnerable branch rankings change with the changing fault chain length, the ranking results' change rules are investigated. As a result, the branch vulnerabilities' characteristics are found to be different at different stages under sequential attacks. Inspired by the characteristics, the CFGs are divided into three sub-CFGs, based on load shedding threshold, to identify the vulnerable branches at different stages. The proposed method is used to identify the vulnerable branches of the IEEE 39- and 118-bus systems, and its effectiveness is validated by investigating load shedding of the systems under deliberate attacks. [ABSTRACT FROM AUTHOR]

• Investigate challenges of imbalanced dataset with limited fault samples and certain noise. • Propose an uncertainty-aware trustworthy FHPF risk prediction approach in a robust DGP framework for OCLs. • Develop an uncertainty decomposition scheme that estimates prediction uncertainty from epistemic and aleatory perspectives. Due to completely open-air operation, fog-haze has become the main cause of contamination on the insulator surface of overhead contact lines (OCLs), further leading to pollution flashover and a series of serious risk consequences. To perceive the fog-haze-caused pollution flashover (FHPF) risk of OCL insulator, a robust deep Gaussian process (DGP)-based uncertainty-aware trustworthy prediction approach is proposed, incorporating epistemic and aleatoric uncertainties. In particular, aiming at the imbalanced dataset with limited fault samples, the prediction of FHPF risk is cast as a classification problem, and solved by DGP using stochastic gradient Hamiltonian Monte Carlo (SGHMC) inference. The key parameters are identified investigating the influences of fog-haze on insulator surface contamination. Furthermore, the SGHMC sampling-based inference is utilized to efficiently capture the intractable posterior distribution, dealing with uncertainty and enhancing the flexibility of the prediction approach. Finally, extensive experiments on high-speed railway line validate the effectiveness and superior of the proposed approach, compared to other advanced predictive classification methods. In addition, it cannot only capture the prediction uncertainty over a limited number of fault samples, but also achieve favorable prediction performance under unseen noisy environments, ultimately ensuring robust and trustworthy FHPF risk predictions for OCLs. [ABSTRACT FROM AUTHOR]

Frequency response analysis (FRA) is an effective approach for detecting mechanical damage in the windings of a transformer. However, the application of FRA to estimate the winding condition of autotransformers (ATs) employed in the China High-Speed Railway is problematic. Because the split windings of the ATs are connected inside the tank, it is impossible to measure the FRA curves for each split winding outside the tank directly. Therefore, it is difficult to identify fault winding and effectively diagnose the overall condition of the windings. This paper explores the features of FRA curves under different fault conditions to help distinguish the fault winding. An equivalent circuit model is built for the AT with split windings, and the electrical parameters of the windings are determined using the finite-element method. The model is then employed to simulate typical axial displacement faults. The results show that the first antiresonance and resonance of the frequency response for connections involving series windings or series and common windings may be visible features for diagnosing winding faults and the fault level. Specifically, the first and second resonances of the frequency response for the common winding connection may be regarded as the primary feature for identifying fault windings. [ABSTRACT FROM PUBLISHER]

We propose a visual tracking method using multiple Hough detectors to address the problem of long-term robust object tracking in unconstrained environments. The method constructs the detectors based on the feature selection by the mutual information. These detectors serve to learn the partial appearances of target and synchronously evaluate image locations via the voting based detection with the generalized Hough transform. According to the result of detections, the best detector is selected by the minimum entropy criterion and delivers the final hypotheses for target location. The feature selection allows our tracker to be able to obtain and use the most discriminative parts of target and thus more robust to its changes, e.g. occlusion and deformation. The detector selection can correct undesirable model updates and restore the tracker after tracking failure. Meanwhile, the Hough-based detection can reduce the amount of noise introduced during online self-training and thus effectively prevent the tracker from drifting. The method is evaluated on the CVPR2013 Visual Tracker Benchmark and the experimental results demonstrate our method outperforms other tracking algorithms in terms of both success rate and precision. [ABSTRACT FROM AUTHOR]

Key message: Combined GWAS, WGCNA, and gene-based association studies identified the co-expression network and hub genes for maize EC induction. ZmARF23 bound to ZmSAUR15 promoter and regulated its expression, affecting EC induction. Embryonic callus (EC) induction in immature maize embryos shows high genotype dependence, which limits the application of genetic transformation in transgenic breeding and gene function elucidation in maize. Herein, we conducted a genome-wide association mapping (GWAS) for four EC induction-related traits, namely rate of embryonic callus induction (REC), increased callus diameter (ICD), ratio of shoot formation (RSF), and length of shoot (LS) across different environments. A total of 77 SNPs were significantly associated these traits under three environments and using the averages (across environments). Among these significant SNPs, five were simultaneously detected under multiple environments and 11 had respective phenotypic variation explained > 10%. A total of 257 genes were located in the linkage disequilibrium decay of these REC- and ICD-associated SNPs, of which 178 were responsive to EC induction. According to the expression values of the 178 genes, we performed a weighted gene co-expression network analysis (WGCNA) and revealed an EC induction-associated module and five hub genes. Hub gene-based association studies uncovered that the intragenic variations in GRMZM2G105473 and ZmARF23 influenced EC induction efficiency among different maize lines. Dual-luciferase reporter assay indicated that ZmARF23 bound to the promoter of a known causal gene (ZmSAUR15) for EC induction and positively regulated its expression on the transcription level. Our study will deepen the understanding of genetic and molecular mechanisms underlying EC induction and contribute to the use of genetic transformation in maize. [ABSTRACT FROM AUTHOR]

Lead (Pb) in the soil affects the growth and development of plants and causes damages to the human body through the food chain. Here, we identified and cloned a Pb-tolerance gene ZmPIP2;5 based on a weighted gene co-expression network analysis and gene-based association studies. We showed that ZmPIP2;5 encodes a plasma membrane aquaporin and positively regulated Pb tolerance and accumulation in Arabidopsis and yeast. Overexpression of Z mPIP2;5 increased root length and fresh weight of Arabidopsis seedlings under Pb stress. Heterologous expression of ZmPIP2;5 in yeast caused the enhanced growth speed under Pb treatment and Pb accumulation in yeast cells. A (T/A) SNP in the ZmPIP2;5 promoter affected the expression abundance of ZmPIP2;5 and thereby led to the difference in Pb tolerance among different maize lines. Our study helps to understand the mechanism underlying plant tolerance to Pb stress and provides new ideas for breeding Pb-tolerance maize varieties via molecular marker-assisted selection. • ZmPIP2;5 was identified using a gene-based association analysis. • ZmPIP2;5 was verified to positively regulate the Pb tolerance. • T/A variation in the promoter of ZmPIP2;5 led to the difference of Pb tolerance. • Increased ZmPIP2;5 promoted maize adaption to Pb stress. • ZmPIP2;5 improved plant tolerance to Pb probably by maintaining water balance. [ABSTRACT FROM AUTHOR]

Key message: Combined linkage and association analysis revealed five co-localized genetic loci across multiple environments. The key gene Zm00001d026491 was further verified to influence leaf length by candidate gene association analysis. Leaf morphology and number determine the canopy structure and thus affect crop yield. Herein, the genetic basis and key genes for 25 leaf-related traits, including leaf lengths (LL), leaf widths (LW), and leaf areas (LA) of eight continuous leaves under the tassel, and the number of leaves above the primary ear (LAE), were dissected by using an association panel and a biparental population. Using an intermated B73 × Mo17 (IBM) Syn10 doubled haploid (DH) population, 290 quantitative trait loci (QTL) controlling these traits were detected across different locations, among which 115 QTL were individually repeatedly identified in at least two environments. Using the association panel, 165 unique significant single-nucleotide polymorphisms (SNPs) were associated with target traits (P < 2.15E-06), of which 35 were separately detected across multiple environments. In total, 42 pleiotropic QTL/SNPs (pQTL/SNPs) were responsible for at least two of the LL, LW, LA, and LAE traits across multiple environments. Combining the QTL mapping and association study, five unique SNPs were located within the confidence intervals of seven QTL, and 77 genes were identified based on the linkage disequilibrium regions of co-localized SNP loci. Gene-based association studies verified that the intragenic variants in the candidate gene Zm00001d026491 influenced LL of the third leaf counted from the top node. These findings will provide vital information to understanding the genetic basis of leaf-related traits and help to cultivate maize varieties with ideal plant architecture. [ABSTRACT FROM AUTHOR]

Ear shank length (ESL) has significant effects on grain yield and kernel dehydration rate in maize. Herein, linkage mapping and genome-wide association study were combined to reveal the genetic architecture of maize ESL. Sixteen quantitative trait loci (QTL) were identified in the segregation population, among which five were repeatedly detected across multiple environments. Meanwhile, 23 single nucleotide polymorphisms were associated with the ESL in the association panel, of which four were located in the QTL identified by linkage mapping and were designated as the population-common loci. A total of 42 genes residing in the linkage disequilibrium regions of these common variants and 12 of them were responsive to ear shank elongation. Of the 12 genes, five encode leucine-rich repeat receptor-like protein kinases, proline-rich proteins, and cyclin11, respectively, which were previously shown to regulate cell division, expansion, and elongation. Gene-based association analyses revealed that the variant located in Cyclin11 promoter affected the ESL among different lines. Cyclin11 showed the highest expression in the ear shank 15 days after silking among diverse tissues of maize, suggesting its role in modulating ESL. Our study contributes to the understanding of the genetic mechanism underlying maize ESL and genetic modification of maize dehydration rate and kernel yield. [ABSTRACT FROM AUTHOR]

Salinization seriously threatens the normal growth of maize, especially at the seedling stage. Recent studies have demonstrated that circular RNAs (circRNAs) play vital roles in the regulation of plant stress resistance. Here, we performed a genome-wide association study (GWAS) on the survival rate of 300 maize accessions under a salt stress treatment. A total of 5 trait-associated SNPs and 86 candidate genes were obtained by the GWAS. We performed RNA sequencing for 28 transcriptome libraries derived from 2 maize lines with contrasting salt tolerance under normal and salt treatment conditions. A total of 1217 highly expressed circRNAs were identified, of which 371 were responsive to a salt treatment. Using PCR and Sanger sequencing, we verified the reliability of these differentially expressed circRNAs. An integration of the GWAS and RNA-Seq analyses uncovered two differentially expressed hub genes (Zm00001eb013650 and Zm00001eb198930), which were regulated by four circRNAs. Based on these results, we constructed a regulation model of circRNA/miRNA/mRNA that mediated salt stress tolerance in maize. By conducting hub gene-based association analyses, we detected a favorable haplotype in Zm00001eb198930, which was responsible for high salt tolerance. These results help to clarify the regulatory relationship between circRNAs and their target genes as well as to develop salt-tolerant lines for maize breeding. [ABSTRACT FROM AUTHOR]

Voltage distortions are characterized by the harmonic spectrum of the nonlinear load (NLL) and harmonic impedance of the whole system. On one hand, the harmonic impedance is at the peak value when the frequency is at the resonance point, which leads to serious voltage distortions. On the other hand, harmonic distortions will be excited from excessive harmonic emission without resonance. In order to address the above causes, shifting the resonance frequency sensitivity (RFS) and the critical individual harmonic voltage distortion (IHDV) sensitivity (IHDS) indices are presented here to control the critical IHDV and total harmonic voltage distortion (THDV). The main idea is to find and adjust the most sensitive network parameter against these sensitivity indices until the harmonic distortion reaches the required value. A three-bus test system and the IEEE 14-bus system are used to verify the proposed method. Comparison of the results shows that these indices complement each other for solving the harmonics problems with or without harmonic resonance. The sensitivity-based methods can be effective for system optimization and filter design by considering harmonic resonance. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

In order to make high-speed trains (HSTs) lighter and more reliable, LC or LCL high-pass filters, which are widely adopted to mitigate high-order harmonics, are not installed in most of China HSTs. Therefore, the harmonic problem is a concern, because of the significant adverse impacts it has on the tractive drive system of the train and power quality of the utility system. The harmful harmonic distortions in high-speed railways (HSRs) are mainly caused by harmonic resonance and massive characteristic harmonics emission. This paper presents the results of harmonic assessment and harmonic filter design for a typical HSR line in China. Harmonic penetration analysis (HPA) is implemented and carried out to determine the harmonic distorted types for a wide range of possible train-operating conditions in a timetable. Both statistical field test and numerical calculation are used in passive filter design for HSRs. A C-type filter is designed here to address these typical harmonic distortions. The studies will be validated by detailed simulations based on the train timetable by counting the 95% index of the 24-h profile of harmonic results. [ABSTRACT FROM PUBLISHER]

Abstract—This article presents a fault-classification method for transmission lines based on voltage phasors using classification and regression trees. The proposed method is intended to aid system operators in understanding the outputs of a phasor measurement unit only state estimator. Faults are classified into four categories when the estimator is positive sequence and into ten categories when the estimator is three phase. The fault data are generated in PowerWorld® (PowerWorld Corporation, Champaign, IL, USA) and DSA Tools® (Powertech Labs Inc., Surrey, British Columbia, Canada). The pre-fault state consists of a variety of operating conditions and loading angles of faulted lines. The fault condition comprises different fault types, fault locations, fault impedances, and fault incidence angles. Fault classification is done using MATLAB® (The MathWorks, Natick, Massachusetts, USA).The approach is successfully tested on the IEEE-118 bus system. The results demonstrate that the technique developed here is effective and robust, irrespective of the pre-fault and fault conditions. [ABSTRACT FROM PUBLISHER]

SUMMARY: The ability of immature maize (Zea mays) embryos to form embryonic calluses (ECs) is highly genotype dependent, which limits transgenic breeding development in maize. Here, we report the association map‐based cloning of ZmSAUR15 using an association panel (AP) consisting of 309 inbred lines with diverse formation abilities for ECs. We demonstrated that ZmSAUR15, which encodes a small auxin‐upregulated RNA, acts as a negative effector in maize EC induction. Polymorphisms in the ZmSAUR15 promoter that influence the expression of ZmSAUR15 transcripts modulate the EC induction capacity in maize. ZmSAUR15 is involved in indole‐3‐acetic acid biosynthesis and cell division in immature embryo‐derived callus. The ability of immature embryos to induce EC formation can be improved by the knockout of ZmSAUR15, which consequently increases the callus regeneration efficiency. Our study provides new insights into overcoming the genotypic limitations associated with EC formation and improving genetic transformation in maize. Significance Statement: Embryonic callus formation is highly genotype dependent in maize, which largely limits maize transgenic breeding development. This study found that ZmSAUR15 negatively effects embryonic callus formation in maize. The polymorphisms in the ZmSAUR15 promoter influence its expression. Furthermore, ZmSAUR15 modulates indole‐3‐acetic acid biosynthesis and cell division in callus. [ABSTRACT FROM AUTHOR]

The discovery and characterization of the opaque endosperm gene provide ideas and resources for the production and application of maize. We found an o213 mutant whose phenotype was opaque and shrunken endosperm with semi-dwarf plant height. The protein, lipid, and starch contents in the o213 endosperm were significantly decreased, while the free amino acid content in the o213 endosperm significantly increased. The aspartic acid, asparagine, and lysine contents were raised in the o213 endosperm by 6.5-, 8.5-, and 1.7-fold, respectively. Genetic analysis showed that this o213 mutant is a recessive single-gene mutation. The position mapping indicated that o213 is located in a 468-kb region that contains 11 protein-encoding genes on the long arm of chromosome 5. The coding sequence analysis of candidate genes between the WT and o213 showed that ZmYSL2 had only a single-base substitution (A–G) in the fifth exon, which caused methionine substitution to valine. Sequence analysis and the allelic test showed that o213 is a new mutant allele of ZmYSL2. The qRT-PCR results indicated that o213 is highly expressed in the stalks and anthers. Subcellular localization studies showed that o213 is a membrane transporter. In the variation analysis of o213, the amplification of 65 inbred lines in GWAS showed that this 3-bp deletion of the first exon of o213 was found only in temperate inbred lines, implying that the gene was artificially affected in the selection process. Our results suggest that o213 is an important endosperm development gene and may serve as a genetic resource. [ABSTRACT FROM AUTHOR]

Key message: Two read depth methods were jointly used in next-generation sequencing data to identify deletions in maize population. GWAS by deletions were analyzed for gene expression pattern and classical traits, respectively. Many studies have confirmed that structural variation (SV) is pervasive throughout the maize genome. Deletion is one type of SV that may impact gene expression and cause phenotypic changes in quantitative traits. In this study, two read count approaches were used to analyze the deletions in the whole-genome sequencing data of 270 maize inbred lines. A total of 19,754 deletion windows overlapped 12,751 genes, which were unevenly distributed across the genome. The deletions explained population structure well and correlated with genomic features. The deletion proportion of genes was determined to be negatively correlated with its expression. The detection of gene expression quantitative trait loci (eQTL) indicated that local eQTL were fewer but had larger effects than distant ones. The common associated genes were related to basic metabolic processes, whereas unique associated genes with eQTL played a role in the stress or stimulus responses in multiple tissues. Compared with the eQTL detected by SNPs derived from the same sequencing data, 89.4% of the associated genes could be detected by both markers. The effect of top eQTL detected by SNPs was usually larger than that detected by deletions for the same gene. A genome-wide association study (GWAS) on flowering time and plant height illustrated that only a few loci could be consistently captured by SNPs, suggesting that combining deletion and SNP for GWAS was an excellent strategy to dissect trait architecture. Our findings will provide insights into characteristic and biological function of genome-wide deletions in maize. [ABSTRACT FROM AUTHOR]

Vitamin A deficiency (VAD) compromises immune function and is the leading cause of preventable blindness in children in many developing countries. Biofortification, or breeding staple food crops that are rich in micronutrients, provides a sustainable way to fight VAD and other micronutrient malnutrition problems. Polymorphisms, with associated molecular markers, have recently been identified for two loci, LcyE (lycopene epsilon cyclase) and CrtRB1 (β-carotene hydroxylase 1) that govern critical steps in the carotenoid biosynthetic pathway in maize endosperm, thereby enabling the opportunity to integrate marker-assisted selection (MAS) into carotenoid breeding programs. We validated the effects of 3 polymorphisms (LcyE5′TE, LcyE3′Indel and CrtRB1-3′TE) in 26 diverse tropical genetic backgrounds. CrtRB1-3′TE had a two-ten fold effect on enhancing beta-carotene (BC) and total provitamin A (proA) content. Reduced-function, favorable polymorphisms within LcyE resulted in 0-30 % reduction in the ratio of alpha- to beta-branch carotenoids, and increase in proA content (sometimes statistically significant). CrtRB1-3′TE had large, significant effect on enhancing BC and total ProA content, irrespective of genetic constitution for LcyE5′TE. Genotypes with homozygous favorable CrtRB1-3′TE alleles had much less zeaxanthin and an average of 25 % less total carotenoid than other genotypes, suggesting that feedback inhibition may be reducing the total flux into the carotenoid pathway. Because this feedback inhibition was most pronounced in the homozygous favorable LcyE (reduced-function) genotypes, and because maximum total proA concentrations were achieved in genotypes with homozygous unfavorable or heterozygous LcyE, we recommend not selecting for both reduced-function genes in breeding programs. LcyE exhibited significant segregation distortion (SD) in all the eight, while CrtRB1 in five of eight digenic populations studied, with favorable alleles of both the genes frequently under-represented. MAS using markers reported herein can efficiently increase proA carotenoid concentration in maize. [ABSTRACT FROM AUTHOR]

Background: Long noncoding RNAs (lncRNAs) play important roles in essential biological processes. However, our understanding of lncRNAs as competing endogenous RNAs (ceRNAs) and their responses to nitrogen stress is still limited. Results: Here, we surveyed the lncRNAs and miRNAs in maize inbred line P178 leaves and roots at the seedling stage under high-nitrogen (HN) and low-nitrogen (LN) conditions using lncRNA-Seq and small RNA-Seq. A total of 894 differentially expressed lncRNAs and 38 different miRNAs were identified. Co-expression analysis found that two lncRNAs and four lncRNA-targets could competitively combine with ZmmiR159 and ZmmiR164, respectively. To dissect the genetic regulatory by which lncRNAs might enable adaptation to limited nitrogen availability, an association mapping panel containing a high-density single–nucleotide polymorphism (SNP) array (56,110 SNPs) combined with variable LN tolerant-related phenotypes obtained from hydroponics was used for a genome-wide association study (GWAS). By combining GWAS and RNA-Seq, 170 differently expressed lncRNAs within the range of significant markers were screened. Moreover, 40 consistently LN-responsive genes including those involved in glutamine biosynthesis and nitrogen acquisition in root were identified. Transient expression assays in Nicotiana benthamiana demonstrated that LNC_002923 could inhabit ZmmiR159-guided cleavage of Zm00001d015521. Conclusions: These lncRNAs containing trait-associated significant SNPs could consider to be related to root development and nutrient utilization. Taken together, the results of our study can provide new insights into the potential regulatory roles of lncRNAs in response to LN stress, and give valuable information for further screening of candidates as well as the improvement of maize resistance to LN stress. [ABSTRACT FROM AUTHOR]

The free moisture in crop kernels after being naturally dried is referred to as kernel moisture content (KMC). Maize KMC reflects grain quality and influences transportation and storage of seeds. We used an IBM Syn10 DH maize population consisting of 249 lines and an association panel comprising 310 maize inbred lines to identify the genetic loci affecting maize KMC in three environments. Using the IBM population detected 13 QTL on seven chromosomes, which were clustered into nine common QTL. Genome‐wide association analysis (GWAS) identified 16 significant SNPs across the 3 environments, which were linked to 158 genes across the three environments. Combined QTL mapping and GWAS found two SNPs that were located in two of the mapped QTL, respectively. Twenty‐three genes were linked with the loci co‐localized in both populations. Of these 181 genes, five have previously been reported to be associated with KMC or to regulate seed development. These associations were verified by candidate gene association analysis. Two superior alleles and one favorable haplotype for Zm00001d007774 and Zm00001d047868 were found to influence KMC. These findings provide insights into molecular mechanisms underlying maize KMC and contribute to the use of marker‐assisted selection for breeding low‐KMC maize. [ABSTRACT FROM AUTHOR]

In this article, a new testing method for the diagnosis of winding faults based on the resonant frequency of the transformer is proposed. The system that consists of the high-voltage dc power and high-voltage insulated gate bipolar transistor (IGBT) could charge the transformer periodically. The response measured from the bushing of the winding is the oscillating wave that can be used for evaluating the status. Carrying out the new test on a 220-kV three-winding, three-phase transformer in the field, the tests of insulation and the winding have been completed in one test. The calculated frequency verifies the feasibility and applicability of the method in theory and the voltage does not affect the resonant frequency in the test. The three-winding, three-phase model transformer was built to simulate the various parameters of the power supply, which proves that the features of the oscillating waves are brought about by the properties of the transformer. Also, the new testing method provides a new opportunity to do parameter identification of axial displacement and short-circuit (SC) fault. The abovementioned research shows that the proposed method can conduct the test of winding and insulation in one test for a short time, which proves a reference in the field. [ABSTRACT FROM AUTHOR]

Key message: Using GWAS and QTL mapping identified 100 QTL and 138 SNPs, which control yield-related traits in maize. The candidate gene GRMZM2G098557 was further validated to regulate ear row number by using a segregation population. Understanding the genetic basis of yield-related traits contributes to the improvement of grain yield in maize. This study used an inter-mated B73 × Mo17 (IBM) Syn10 doubled-haploid (DH) population and an association panel to identify the genetic loci responsible for nine yield-related traits in maize. Using quantitative trait loci (QTL) mapping, 100 QTL influencing these traits were detected across different environments in the IBM Syn10 DH population, with 25 co-detected in multiple environments. Using a genome-wide association study (GWAS), 138 single-nucleotide polymorphisms (SNPs) were identified as correlated with these traits (P < 2.04E−06) in the association panel. Twenty-one pleiotropic QTL/SNPs were identified to control different traits in both populations. A combination of QTL mapping and GWAS uncovered eight significant SNPs (PZE-101097575, PZE-103169263, ZM011204-0763, PZE-104044017, PZE-104123110, SYN8062, PZE-108060911, and PZE-102043341) that were co-located within seven QTL confidence intervals. According to the eight co-localized SNPs by the two populations, 52 candidate genes were identified, among which the ear row number (ERN)-associated SNP SYN8062 was closely linked to SBP-transcription factor 7 (GRMZM2G098557). Several SBP-transcription factors were previously demonstrated to modulate maize ERN. We then validated the phenotypic effects of SYN8062 in the IBM Syn10 DH population, indicating that the ERN of the lines with the A-allele in SYN8062 was significantly (P < 0.05) larger than that of the lines with the G-allele in SYN8062 in each environment. These findings provide valuable information for understanding the genetic mechanisms of maize grain yield formation and for improving molecular marker-assisted selection for the high-yield breeding of maize. [ABSTRACT FROM AUTHOR]

Ear tip‐barrenness (ETB) phenotype threatens crop yield, because it reduces the kernel number per ear. The genetic basis of ETB in maize remains largely unknown. Herein, a genome‐wide association study (GWAS) and quantitative trait loci (QTL) mapping were jointly applied to identify the significant genetic loci interrelated with ETB. Six significant SNPs were detected at a stringent P‐value threshold (1.95 × 10−6). Additionally, four environment‐stable SNPs were co‐detected across a single environment and best linear unbiased prediction (BLUP) model at a less stringent P‐value threshold (1 × 10−4). The above 10 SNPs were closely linked to 6 candidate genes, which mainly involved seed development, photosynthesis and ethylene response. Moreover, the ratio of superior allele at each significant SNP ranged from 0 to 83.33% in 30 investigated maize elite lines. QTL mapping identified 14 QTL with phenotypic variation explained (PVE) ranging from 3.64 to 7.09%, of which one QTL (qETB2‐1) was repeatedly identified in two environments. Combined analysis of GWAS and QTL mapping showed that one SNP (PZE‐102175229, chromosome 2: 217 66 Mb) was located in the QTL (qETB2‐2, chromosome 2: 215 90–217 82 Mb). Eighteen gene models situated in the linkage disequilibrium (LD) region of the co‐localized SNP were further used to evaluate their correlation with ETB by candidate gene association analysis. Two superior haplotypes and two superior alleles were detected among 74 lines for Zm00001d007195, Zm00001d007197 and Zm00001d007201. These results provide more information for clarifying the molecular mechanism of ETB and for speeding up the genetic improvement of maize varieties. [ABSTRACT FROM AUTHOR]

In response to the unbalanced DC-port fault of three-level neutral point clamped cascaded converter (3LNPC-CC), a sequence pulse modulation (SPM) voltage-balancing strategy is proposed in this paper to balance DC-link voltage, not only within the module but also among modules. With the steps of carrier cascaded calculation and sequence pulse generator, the voltage level of cascaded modules would take a smooth transition. Then the limitation of the SPM strategy is calculated according to the law of volt-second balance and the law of energy conservation. The proposed strategy has the advantage of simple calculation and control stability. Simulation and experimental results show the superiority of the proposed strategy. [ABSTRACT FROM AUTHOR]

Phosphorus deficiency is one of the major factors limiting plant growth and grain yield. Improving plant tolerance to Pi deficiency through genetic mechanism is an important source to increase grain yield. In this study, bioinformatics analysis, expression, and natural variation in ZmNAC134 gene with low Pi tolerant traits were analyzed under low Pi conditions. A total of 177 diverse heterotic groups of maize inbreds were used to identify the nucleotide diversity and alleles of ZmNAC134, which plays a vital role in the regulation of root architecture in response to Pi deficiency evolutionary results showed that ZmNAC134 has 3 homologous genes and these genes have one exon and conserved domain. The phenotypic traits showed significant difference for each of the 22 traits under deficient and sufficient Pi conditions. A total of 27 SNPs were identified in the coding region of ZmNAC134 and no InDels among 177 inbred lines. Among 27 SNPs, a total of 8 sites were highly significantly associated with multiple traits of low Pi tolerant trait index at − log10 P = 3.43. Although, 7 sites under Pi normal and 9 sites under Pi deficient conditions, of which four synonymous sites (position S24, S82, S164, and S1037) were associated with diverse number of traits in low Pi and normal Pi conditions and LD was not tight among these sites. Furthermore, the expression pattern was compared between Pi tolerant 178 and Pi sensitive 9782 inbred lines. ZmNAC134 was highly up-regulated in the roots and leaves of Pi tolerant 178 inbred line at 3 days, 7 days, and 9 days in roots and 7 days and 12 days in leaves while down-regulated in roots and leaves of Pi sensitive 9782 inbred line. In addition, protein encoded by ZmNAC134 was located in both nucleus and cytoplasm. Our findings provide the new insight of ZmNAC134 gene involved in low Pi stress and responsible against low Pi condition and the significant association of polymorphic loci with traits could be helpful to find out the molecular marker for genetic resources for further molecular maize breeding program. [ABSTRACT FROM AUTHOR]

Kernel weight in a unit volume is referred to as kernel test weight (KTW) that directly reflects maize (Zea mays L.) grain quality. In this study, an inter-mated B73 × Mo17 (IBM) Syn10 doubled haploid (DH) population and an association panel were used to identify loci responsible for KTW of maize across multiple environments. A total of 18 significant KTW-related single-nucleotide polymorphisms (SNPs) were identified using genome-wide association study (GWAS); they were closely linked to 12 candidate genes. In the IBM Syn10 DH population, linkage analysis detected 19 common quantitative trait loci (QTL), five of which were repeatedly detected among multiple environments. Several verified genes that regulate maize seed development were found in the confidence intervals of the mapped QTL and the LD regions of GWAS, such as ZmYUC1, BAP2, ZmTCRR-1, dek36 and ZmSWEET4c. Combined QTL mapping and GWAS identified one significant SNP that was co-identified in the both populations. Based on the co-localized SNP across the both populations, 17 candidate genes were identified. Of them, Zm00001d044075, Zm00001d044086, and Zm00001d044081 were further identified by candidate gene association study for KTW. Zm00001d044081 encodes homeobox-leucine zipper protein ATHB-4, which has been demonstrated to control apical embryo development in Arabidopsis. Our findings provided insights into the mechanism underlying maize KTW and contributed to the application of molecular-assisted selection of high KTW breeding in maize. [ABSTRACT FROM AUTHOR]

Summary: Kernel size‐related traits are the most direct traits correlating with grain yield. The genetic basis of three kernel traits of maize, kernel length (KL), kernel width (KW) and kernel thickness (KT), was investigated in an association panel and a biparental population. A total of 21 single nucleotide polymorphisms (SNPs) were detected to be most significantly (P < 2.25 × 10−6) associated with these three traits in the association panel under four environments. Furthermore, 50 quantitative trait loci (QTL) controlling these traits were detected in seven environments in the intermated B73 × Mo17 (IBM) Syn10 doubled haploid (DH) population, of which eight were repetitively identified in at least three environments. Combining the two mapping populations revealed that 56 SNPs (P < 1 × 10−3) fell within 18 of the QTL confidence intervals. According to the top significant SNPs, stable‐effect SNPs and the co‐localized SNPs by association analysis and linkage mapping, a total of 73 candidate genes were identified, regulating seed development. Additionally, seven miRNAs were found to situate within the linkage disequilibrium (LD) regions of the co‐localized SNPs, of which zma‐miR164e was demonstrated to cleave the mRNAs of Arabidopsis CUC1,CUC2 and NAC6 in vitro. Overexpression of zma‐miR164e resulted in the down‐regulation of these genes above and the failure of seed formation in Arabidopsis pods, with the increased branch number. These findings provide insights into the mechanism of seed development and the improvement of molecular marker‐assisted selection (MAS) for high‐yield breeding in maize. [ABSTRACT FROM AUTHOR]

The windings of a traction transformer are always be threatened by the fluctuation of the load. Frequency response analysis (FRA) is regarded as the most effective method to diagnose the winding deformation. Appropriate FRA modelling method may help to predict typical winding faults for large transformers. The existing model usually simplify dozens of winding disks into several disks, it is applicable when analysing small displacement. However, when the disks between windings are seriously misplaced, the conventional model may not be suitable. This study presents an improved lumped parameter circuit model considering intersection capacitances and mutual inductance between windings. The feasibility of the proposed model is verified based on a 10 kV test transformer. Finally, the model is applied for a 220 kV traction transformer, the variation of parameters and features are analysed for axial displacement of different windings. The results show that the axial displacement may lead to increase of resonance frequency and decrease of amplitude. The low‐frequency band (1–30 kHz) may be regarded as visible features for diagnosing axial displacement of H winding and the middle frequency band (50–200 kHz) may be a characteristic band for diagnosing axial displacement of T winding. [ABSTRACT FROM AUTHOR]

The low frequency oscillation (LFO) phenomenon has frequently occurred in railway electrification systems (RESs) and deteriorates the safety and reliability of RESs. This article proposes a simplified and analytical impedance model of the electric train and traction network interaction system (hereinafter train-network system) in the synchronous frame (-dq), which reveals that the dc-link voltage proportional-integral controller mainly brings the negative resistor to the d–d channel impedance of the train in the low frequency range. The in-depth stability analysis elaborates that the introduced negative resistor will lead the phase margin of eigenvalues of the impedance ratio matrix to be negative under some conditions and then causes the LFO issue. Moreover, case studies also illustrate that the narrower negative real-part area (NRA) caused by the negative resistor and the higher magnitude of the train impedance in the low frequency range can enhance the stability of the train-network system. Therefore, for suppressing the LFO, a proportion-derivation feedback of the current based on the analytical impedance model is then proposed to decrease the NRA and increase the magnitude of the train impedance at the same time in the low frequency range. Both simulation and experimental results validate the effectiveness and correctness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

Misalignment in an inductive power transfer (IPT) system is inevitable for most of the cases, which leads to system performance degradation due to the variation of system parameters. A novel IPT topology with two intermediate coils, one for primary side and the other for secondary one, is proposed to improve an anti-misalignment characteristic. With the merit of the proposed topology, a constant-current output characteristic is achieved, and the system can operate safely without any secondary side. Then, the parameter design and optimization method of the magnetic coupler with intermediate coils are elaborated to improve misalignment performance. Finally, a 3.4-kW experimental setup is built to verify the feasibility of the proposed method. Experimental results show that the four-coil IPT system can tolerate ±225 mm X-misalignment, −30 to +50 mm Y-misalignment, and −20 to +70 mm Z-misalignment with load varying from 40 to 60 Ω, while the fluctuation of the output current is within ±5%. The system efficiency of the IPT system is from 92.1% up to 96.3% with misalignment and variable load (40–60 Ω). [ABSTRACT FROM AUTHOR]