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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]

• 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]

• 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]

• 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]

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]

Seed germination directly affect maize yield and grain quality. Low-temperature reduces maize yield by affecting seed germination and seedling growth. However, the molecular mechanism of maize seed germination under low-temperature remains unclear. In this study, the transcriptome data of two maize inbred lines SCL127 (chilling-sensitive) and SCL326 (chilling-tolerant) were analyzed at five time points (0 H, 4 H, 12 H, 24 H, and 48 H) under low-temperature conditions. Through the comparison of SCL127-0 H-vs-SCL326-0 H (Group I), SCL127-4 H-vs-SCL326-4 H (Group Ⅱ), SCL127-12 H-vs-SCL326-12 H (Group Ⅲ), SCL127-24 H-vs-SCL326-24 H (Group Ⅳ), and SCL127-48 H-vs SCL326-48 H (Group Ⅴ), a total of 8,526 differentially expressed genes (DEGs) were obtained. Weighted correlation network analysis revealed that Zm00001d010445 was the hub gene involved in seed germination under low-temperature conditions. Zm00001d010445 -based association analysis showed that Hap Ⅱ (G) was the excellent haplotype for seed germination under low-temperature conditions. These findings provide a new perspective for the study of the genetic architecture of maize tolerance to low-temperature and contribute to the cultivation of maize varieties with low-temperature tolerance. • RNA-seq was performed on two maize lines under low-temperature. • Zm00001d010445 was identified to affect maize germination. [ABSTRACT FROM AUTHOR]

Aluminum (Al) toxicity is one of the primary factors limiting crop production in acid soils worldwide. The cell wall is the major target of Al toxicity owing to the presence of many Al binding sites. Previous studies have found that XTH , encoding xyloglucan endohydrolase (XEH) and xyloglucan endotransglucosylase (XET), could participate in cell wall extension and affect the binding ability of the cell wall to Al by impeding the activities of these two enzymes. In this study, we found that ZmXTH , an XTH gene in maize, was involved in Al detoxification. The Al-induced up-regulation of ZmXTH occurred in the roots, prominently in the root tips. Additionally, the expression of ZmXTH was specifically induced by Al3+ but no other divalent or trivalent cations. Compared with the wild-type Arabidopsis , ZmXTH overexpressing plants grew more healthy and had decreased Al content in their root and root cell wall after Al stress. Overall, the results suggest that ZmXTH could confer the Al tolerance of transgenic Arabidopsis plants by reducing the Al accumulation in their roots and cell walls. [ABSTRACT FROM AUTHOR]

• ZmMATE6 gene from maize was cloned and transformed into Arabidopsis thaliana. • Overexpression of ZmMATE6 in Arabidopsis resulted in enhancing the resistance to Al. • Transgenic Arabidopsis exhibited increased citrate exudation and decreased Al accumulation in roots. • The loss of function caused by the substitution of the fifth residue of CEM domain may also be related to other factors. The yields of cereal crops grown on acidic soils are often reduced by aluminum (Al) toxicity because the prevalence of toxic Al3+ cations increases as pH falls below 5.0. The Al-dependent release of citrate from resistant lines of maize is controlled by ZmMATE1 which encodes a multidrug and toxic compound extrusion (MATE) transporter protein. ZmMATE6 is another member of this family in maize whose expression is also increased by Al treatment. We investigated the function of this gene in more detail to determine whether it also contributes to Al resistance. Quantitative RT-PCR measurements found that ZmMATE6 was expressed in the roots and leaves of Al-resistant and sensitive inbred lines. Treatment with Al induced ZmMATE6 expression in all tissues but several other divalent or trivalent cations tested had no effect on expression. This expression pattern and the induction by Al treatment was confirmed in ZmMATE6 promoter–β-glucuronidase fusion lines. Heterogeneous expression of ZmMATE6 displayed a greater Al-activated release of citrate from the roots and was significantly resistant to Al toxicity than controls. This was associated with reduced accumulation of Al in the root tissues. Our results demonstrated that ZmMATE6 expression is induced by Al and functions as a citrate transporter. [ABSTRACT FROM AUTHOR]