Corrigendum to“GmTOC1b negatively regulates resistance to Soybean mosaic virus”.[Crop J.11(2023)1762-1773]

The authors regret to report a mistake in the text and an associated change necessary to section 3.6 of the paper.On page 1766 in the right-hand column,line 4,the heading of subsection 3.6“GmWRKY40 represses the expression of PR genes”should be changed to“GmWRKY40 promotes the expression of PR genes”.The authors would like to apologize for any inconvenience caused.

Blockchain-Enabled Federated Learning with Differential Privacy for Internet of Vehicles

The rapid evolution of artificial intelligence(AI)technologies has significantly propelled the advancement of the Internet of Vehicles(IoV).With AI support,represented by machine learning technology,vehicles gain the capability to make intelligent decisions.As a distributed learning paradigm,federated learning(FL)has emerged as a preferred solution in IoV.Compared to traditional centralized machine learning,FL reduces communication overhead and improves privacy protection.Despite these benefits,FL still faces some security and privacy concerns,such as poisoning attacks and inference attacks,prompting exploration into blockchain integration to enhance its security posture.This paper introduces a novel blockchain-enabled federated learning(BCFL)scheme with differential privacy(DP)tailored for IoV.In order to meet the performance demanding IoV environment,the proposed methodology integrates a consortium blockchain with Practical Byzantine Fault Tolerance(PBFT)consensus,which offers superior efficiency over the conventional public blockchains.In addition,the proposed approach utilizes the Differentially Private Stochastic Gradient Descent(DP-SGD)algorithm in the local training process of FL for enhanced privacy protection.Experiment results indicate that the integration of blockchain elevates the security level of FL in that the proposed approach effectively safeguards FL against poisoning attacks.On the other hand,the additional overhead associated with blockchain integration is also limited to a moderate level to meet the efficiency criteria of IoV.Furthermore,by incorporating DP,the proposed approach is shown to have the(ε-δ)privacy guarantee while maintaining an acceptable level of model accuracy.This enhancement effectively mitigates the threat of inference attacks on private information.

Shared Control of Highly Automated Vehicles Using Steer-By-Wire Systems

A shared control of highly automated Steer-by-Wire system is proposed for cooperative driving between the driver and vehicle in the face of driver's abnormal driving. A fault detection scheme is designed to detect the abnormal driving behaviour and transfer the control of the car to the automatic system designed based on a fault tolerant model predictive control(MPC) controller driving the vehicle along an optimal safe path.The proposed concept and control algorithm are tested in a number of scenarios representing intersection, lane change and different types of driver's abnormal behaviour. The simulation results show the feasibility and effectiveness of the proposed method.

GmTOC1b negatively regulates resistance to Soybean mosaic virus

Soybean(Glycine max)is a major oil and feed crop worldwide.Soybean mosaic virus(SMV)is a globally occurring disease that severely reduces the yield and quality of soybean.Here,we characterized the role of the clock gene TIMING OF CAB EXPRESSION 1b(GmTOC1b)in the resistance of soybean to SMV.Homozygous Gmtoc1b mutants exhibited increased tolerance to SMV strain SC3 due to the activation of programmed cell death triggered by a hypersensitive response.Transcriptome deep sequencing and RT-qPCR analysis suggested that GmTOC1b likely regulates the expression of target genes involved in the salicylic acid(SA)signaling pathway.GmTOC1b binds to the promoter of GmWRKY40,which encodes a protein that activates the expression of SA-mediated defense-related genes.Moreover,we revealed that the GmTOC1bH1 haplotype,which confers increased tolerance to SMV,was artificially selected in improved cultivars from the Northern and Huang-Huai regions of China.Our results therefore identify a previously unknown SMV resistance component that could be deployed in the molecular breeding of soybean to enhance SMV resistance.

Mechanisms underlying key agronomic traits and implications for molecular breeding in soybean

Soybean(Glycine max[L.]Merr.)is an important crop that provides protein and vegetable oil for human consumption.As soybean is a photoperiod-sensitive crop,its cultivation and yield are limited by the photoperiodic conditions in the field.In contrast to other major crops,soybean has a special plant architecture and a special symbiotic nitrogen fixation system,representing two unique breeding directions.Thus,flowering time,plant architecture,and symbiotic nitrogen fixation are three critical or unique yielddetermining factors.This review summarizes the progress made in our understanding of these three critical yield-determining factors in soybean.Meanwhile,we propose potential research directions to increase soybean production,discuss the application of genomics and genomic-assisted breeding,and explore research directions to address future challenges,particularly those posed by global climate changes.

Understandings and future challenges in soybean functional genomics and molecular breeding

Soybean(Glycine max)is a major source of plant protein and oil.Soybean breeding has benefited from advances in functional genomics.In particular,the release of soybean reference genomes has advanced our understanding of soybean adaptation to soil nutrient deficiencies,the molecular mechanism of symbiotic nitrogen(N)fixation,biotic and abiotic stress tolerance,and the roles of flowering time in regional adaptation,plant architecture,and seed yield and quality.Nevertheless,many challenges remain for soybean functional genomics and molecular breeding,mainly related to improving grain yield through high-density planting,maize-soybean intercropping,taking advantage of wild resources,utilization of heterosis,genomic prediction and selection breeding,and precise breeding through genome editing.This review summarizes the current progress in soybean functional genomics and directs future challenges for molecular breeding of soybean.

Constrained nonlinear MPC for accelerated tracking piece-wise references and its applications to thermal systems

In this paper,we propose a model predictive control(MPC)strategy for accelerated offset-free tracking piece-wise constant reference signals of nonlinear systems subject to state and control constraints.Some special contractive constraints on tracking errors and terminal constraints are embedded into the tracking nonlinear MPC formulation.Then,recursive feasibility and closed-loop convergence of the tracking MPC are guaranteed in the presence of piece-wise references and constraints by deriving some sufficient conditions.Moreover,the local optimality of the tracking MPC is achieved for unreachable output reference signals.By comparing to traditional tracking MPC,the simulation experiment of a thermal system is used to demonstrate the acceleration ability and the effectiveness of the tracking MPC scheme proposed here.

Quality-related locally weighted soft sensing for non-

Soft sensors are widely used to predict quality variables which are usually hard to measure.It is necessary to construct an adaptive model to cope with process non-stationaries.In this study,a novel quality-related locally weighted soft sensing method is designed for non-stationary processes based on a Bayesian network with latent variables.Specifically,a supervised Bayesian network is proposed where quality-oriented latent variables are extracted and further applied to a double-layer similarity meas-urement algorithm.The proposed soft sensing method tries to find a general approach for non-stationary processes via quality-related information where the concepts of local similarities and window confidence are explained in detail.The performance of the developed method is demonstrated by application to a numerical example and a debutanizer column.It is shown that the proposed method outperforms competitive methods in terms of the accuracy of predicting key quality variables.