Abstract Genomic selection (GS) leverages genome-wide markers and phenotypes to predict breeding values, with its effectiveness largely dependent on the accuracy of genomic prediction (GP) models. However, GP methods often struggle to capture inter-individual variability and are limited by the curse of dimensionality, where the number of SNPs far exceeds the sample size. To address these challenges, we present CLCNet (Contrastive Learning and Chromosome-aware Network), a novel deep learning framework that integrates contrastive learning and chromosome-aware feature modeling. CLCNet comprises two key components: (i) a contrastive learning module that enhances the model’s ability to capture fine-grained, genotype-dependent phenotypic differences among individuals, and (ii) a chromosome-aware module that captures structured feature selection at both chromosome and genome levels, thereby distilling the most informative SNPs. We evaluated CLCNet across four crop species, covering ten agronomically important traits, and compared it with a diverse set of classical linear, machine learning, and deep learning models. CLCNet achieved superior prediction performance, with statistically significant improvements in Pearson correlation coefficient (PCC), ranging from 0.34% to 12.19% over baseline, together with reduced mean squared error (MSE). Performance gains were more pronounced for traits with moderate linkage disequilibrium (LD; r 2 = 0.21-0.36) and high heritability ( h 2 > 0.66), such as those in maize, rapeseed, and soybean. For cotton traits characterized by high LD ( r 2 = 0.74) and lower heritability ( h 2 < 0.50), CLCNet maintained robust performance without degradation. Overall, these results demonstrate that CLCNet is an effective framework for improving genomic prediction accuracy and holds strong potential for practical applications in plant breeding. Short abstract CLCNet is a novel deep learning framework for genomic prediction that integrates contrastive learning with chromosome-aware feature selection. By jointly modeling inter-individual genotype–phenotype variation and chromosomal genomic structure, CLCNet improves prediction accuracy under high-dimensional, low-sample-size conditions. Across four crop species and ten agronomic traits, CLCNet consistently outperformed classical statistical, machine learning, and existing deep learning models. The framework also identified biologically relevant SNPs and candidate genes, demonstrating its potential for practical applications in genomic selection and computational plant breeding. Key points We propose CLCNet, a multi-task deep learning framework that integrates contrastive learning with chromosome-aware feature selection for genomic prediction, under high-dimensional, low-sample-size conditions. The chromosome-aware module explicitly exploits genomic structural information to select representative and informative SNPs across chromosomes. Contrastive learning improves model robustness by stabilizing representation learning and reducing the influence of random effects across samples. By complementing GWAS analyses, CLCNet provides additional insights into genotype–phenotype relationships with potential relevance for gene discovery. Biographical Note Jiangwei Huang is a PhD candidate at the Institute of Genetics and Developmental Biology, Chinese Academy of Sciences. His research focuses on genomic prediction, deep learning, and computational plant breeding. Zhihan Yang is a PhD candidate at the Institute of Genetics and Developmental Biology, Chinese Academy of Sciences. Her research interests include genomic prediction and bioinformatics. Rongcheng Han is an associate professor at the Institute of Genetics and Developmental Biology, Chinese Academy of Sciences. His research focuses on bioinformatics and plant phenomics. Yuqiang Jiang is a professor at the Institute of Genetics and Developmental Biology, Chinese Academy of Sciences. His research interests include plant genomics, plant phenomics and genetic improvement. Organization description The Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, is a leading research institute focusing on genetics, genomics, molecular breeding, bioinformatics, and systems biology in plants and animals.

This article addresses the problem of distributed adaptive bipartite containment control for nonlinear multiagent systems under deception attacks and actuator faults. By constructing a modified coordinate transformation and designing a Lyapunov function based on predefined accuracy functions, a control scheme is developed that ensures bipartite containment errors converge to a user-defined value, even under deception attacks. To handle unknown time-varying attack signals and actuator faults, a two-step approach is employed in controller design. This guarantees that 1) the outputs of the followers converge to the convex hull formed by the leaders with cooperative relationships, and 2) all closed-loop signals remain bounded. Finally, the effectiveness of the proposed method is demonstrated through a simulation of an RLC circuit system.

This paper focuses on the adaptive neural network output feedback control problem for heterogeneous vehicle platoon system (HVPS) under the condition of sensor deception attack. Firstly, an event-triggered mechanism by double-channel transmissions is constructed, which considers both controller triggering and output triggering, effectively reducing the waste of communication resources and unnecessary data transmission. Secondly, in order to estimate the unmeasured states in the system, a neural network state observer is designed. It takes the output signals caused by output triggering and sensor failures as input to generate usable state estimates and ensure the smooth operation of the backstepping method. In addition, barrier Lyapunov functions (BLFs) are used to impose constraints on vehicle spacing errors to ensure that the errors will not expand infinitely. Combined with dynamic surface control (DSC) technology, an adaptive event-triggered output feedback control scheme based on backstepping is proposed in this paper. It guarantees both individual vehicle stability and weak string stability of HVPS. Through Lyapunov stability analysis, it is proved that all signals remain bounded. Finally, a simulation example verifies the effectiveness of the proposed method.

This work considers the adaptive output feedback consensus asymptotic tracking control problem for full-state-constrained nonlinear multiagent systems (MASs) under the denial-of-service (DoS) attacks and the actuator faults. Since MASs are regardered as a network, where each agent is considered as a network node, the DoS attacks that cut network communication links between agents are studied in this paper. Moreover, to compensate the unknown actuator fault signal, the Nussbaum function method is introduced into the controller design process for each agent. Due to the researched nonlinear MASs contain unknown nonlinearities and the system states of each agent are unmeasurable, an estimator based on fuzzy logic systems (FLSs) is constructed for each agent. Further, the secure control strategy based on the barrier Lyapunov functions (BLFs) is proposed for the researched nonlinear MASs, which guarantees the consensus tracking errors asymptotically converge to zero, all closed-loop signals remain bounded and the full-state constraint requirements are not broken. In the illustrative example, the proposed secure control strategy is applied to the MASs composed of multiple forced damped pendulums (FDPs).Note to Practitioners— In the framework of network communication, how to eliminate the impact of cyber attacks on nonlinear MASs that rely on communication links to transmit information is critical. Furthermore, due to practical systems are inevitably subject to multiple physical and environmental constraints, the problem of dealing with system constraints can not be ignored. Therefore, a secure control algorithm based on the BLFs is proposed to eliminate the effects of the DoS attacks on the researched nonlinear MASs and ensure that the full-state constraints are not broken. To realize the output feedback control and approximate the unknown nonlinearities of the studied system, the estimators combining with the intelligent approximation techniques based on FLSs are developed. The Nussbaum function is introduced into controller to solve the actuator faults problems for each agent.

This article primarily studies the adaptive output-feedback consensus tracking control issue for nonlinear multiagent systems (MASs) with unknown control directions against deception attacks. First, a composite observer combining the state observer and the disturbance observer is developed to concurrently estimate the states of confronting deception attacks and unmeasurable disturbances. Moreover, to resolve the unknown gains resulting from deception attacks, the adaptive attack compensator is proposed. Furthermore, in view of the logarithm Lyapunov function in the final step of the design process and the intelligent approximation technique, a new composite-observer-based adaptive consensus tracking control strategy is constructed. The suggested control strategy ensures the boundedness of all the closed-loop signals while also achieving synchronous tracking of the leader's output by the followers. Last but not least, the effectiveness of the suggested control strategy is validated through two simulation examples.

This article mainly considers the adaptive secure bipartite consensus tracking control (BCTC) problem for nonlinear multiagent systems (MASs) under false data injection (FDI) attacks with predefined accuracy. Since FDI attacks produce unknown attack gains, which increases the difficulty of the controller design, an adaptive secure control strategy is given based on the essential property of Nussbaum functions. By improving the traditional coordinate transformation in the current literatures that can only achieve unilateral consensus control, a backstepping-based control algorithm is put forward attaining bilateral consensus control. In addition, the appropriate Lyapunov functions are generated by a class of non-negative functions to construct the adaptive secure bipartite consensus controllers, which not only makes certain that the bilateral errors ultimately converge to a predefined interval, but also guarantees that all the closed-loop signals within the investigated system are bounded. Conclusively, a practical example is provided to validate the effectiveness of the proposed control strategy.

A distributed adaptive secure control algorithm is proposed for asynchronous switching nonlinear multi-agent systems (MASs) in pure-feedback form while considering the sensor attacks and actuator faults, which eliminates the effect of attack signals, actuator faults and uncertainties without knowing the sign of the control gain functions. Since the system states measured by the sensors are corrupted by the malicious attacker, only the post-attack system states can be obtained. Thus, an improved coordinate transformation is constructed by using the post-attack system states and a backstepping method based on the improved coordinate transformation is developed to solve the consensus tracking control problem of the researched systems under sensor attacks. The common Lyapunov function is designed to ensure the stability of all asynchronous switching subsystems of each agent. In the end, a simulation example is presented to illustrate the theoretical results’ validity.Note to Practitioners—This paper deal with the distributed adaptive secure consensus tracking control problem for asynchronous switching nonlinear MASs, whose models can describe many phenomena in nature such as biological processes, robot team formation and sensor networks. It is extremely challenging to achieve the tracking control problem of the researched asynchronous switching nonlinear system in an unsafe network environment, where the unknown sensor attacks signals, actuator faults coefficients, and randomly switched non-affine nonlinearities exist together for the followers. Furthermore, the control design and stability analysis of the researched system is based on the common Lyapunov approach, which makes the developed approach more engineering oriented.

In this paper, an adaptive finite-time bipartite consensus tracking control strategy is presented for a class of heterogeneous nonlinear nonstrict-feedback multi-agent systems (MASs) with output constraints. Firstly, to deal with the time-varying output constraints problem, an improved tan-type nonlinear mapping (NM) function is presented for the first time. And based on the improved NM function, a novel tracking error is constructed to design controller for each agent, which guarantees the bipartite consensus tracking is achieved while constraints requirement is not violated. Then, a state observer is designed to estimate the unmeasurable states of each agent. Moreover, in the case of unbalanced directed topological graph, a partition algorithm (PA) is employed to implement bipartite consensus tracking control. The developed distributed adaptive finite-time control strategy ensures that all the signals in the closed-loop system are bounded and the bipartite consensus tracking control is achieved in finite time. Finally, the validity of the designed control strategy is demonstrated by a simulation experiment.Note to Practitioners—At present, nonlinear MASs are widely used in practice, such as robots formation control, vehicular platoon systems control, etc. This paper investigated the adaptive finite-time bipartite consensus tracking control problem for a class of heterogeneous nonlinear nonstrict-feedback MASs with output constraints. In the scenarios of practical application, these two situations are common: 1) The communication topology graph of nonlinear MASs is unbalanced. 2) The output of each agent is constrained. Therefore, this paper presents an improved tan-type NM method to deal with the time-varying output constraints problem, and a partition algorithm is employed to implement bipartite consensus tracking control based on the unbalanced communication topology graph. Meanwhile, the nonsingular finite-time control strategy effectively improves the convergence of the studied nonlinear MASs. In addition, the system model and backstepping technology used in this paper are general and practical.

This paper investigates the adaptive finite-time consensus tracking control problem for a class of nonlinear nonstrict-feedback multi-agent systems (MASs) with output constraints. Firstly, to deal with the output constraints problem, an improved tan-type nonlinear mapping (NM) function is presented for the first time. Then, the singularity problem that exists in finite-time control is successfully avoided by employing a novel switching function. A modified tracking error involving the dependent variable of the designed NM function is constructed to guarantee that the outputs of all agnets achieve consensus tracking simultaneously and satisfy the constraints requirement. In addition, a switching threshold event-triggered control (ETC) strategy is applied to design controller for each agent, which combines the advantages of fixed threshold strategy and relative threshold strategy, and saves system communication resources well. The developed distributed adaptive finite-time control protocol ensures that all the signals in the closed-loop system are bounded and the consensus tracking control is achieved in finite time. Finally, the effectiveness of the designed control strategy is verified by a simulation experimentNote to Practitioners—Due to the wide application of nonlinear MASs in practice, such as unmanned aerial vehicles (UAVs) formation control, robots formation control and so on, the adaptive finite-time consensus tracking control problem for a class of nonlinear nonstrict-feedback MASs is studied in this paper. In practical applications, the practical fast finite-time strategy can effectively increase the system convergence, the NM method not only solves the output constraints problem well, but also overcomes the conservativeness of traditional barrier Lyapunov functions. Then, in order to reduce the communication burden of the nonlinear MASs, a switching threshold ETC is considered. In addition, the system model and backstepping technology used in this paper are general and practical.

The majority of the results on constrained nonlinear multi-agent systems (MASs) control focused on output or state constraints without considering the saving of communication resources. In this paper, for a class of uncertain nonlinear MASs, we first present a new adaptive bounded consensus tracking control scheme in which the asymmetric and full-state constraints are jointly synthesized with a switching threshold event-triggered strategy, such that the communication resources are effectively utilized. The key to accomplishing the asymmetric and full-state constraints is that a kind of improved$tan$-type barrier Lyapunov functions are constructed. The controller constructed for each agent by the switching threshold event-triggered strategy guarantees that the asymmetric and full-state constraints are not violated and the output of each agent can track the leader’s trajectory with an adjustable bounded tracking error. Furthermore, to achieve the asymptotic consensus tracking control, we give another kind of novel$tan$-type barrier Lyapunov functions to design the desired controller for each agent. A simulation example of five single-link robots is proposed to illustrate the effectiveness of our control scheme.Note to Practitioners—In this paper, the adaptive bounded consensus tracking control problem is considered for nonlinear MASs subject to full-state constraints, whose models are capable of describing a multitude of critical applications, including the formation of unmanned vehicles and robots. The research on the tracking control problem will be rather complicated yet challenging if the asymmetric and full-state constraints are taken into account in a complex environment. Additionally, the incorporation of an event-triggered mechanism aids in the reduction of communication resource usage, enhancing the ease of implementation and improving the user-friendliness of the proposed control scheme.

Under a directed graph, a distributed nonsingular finite time adaptive control algorithm is proposed in this paper for a class of nonlinear multiagent systems (MASs) with parametric uncertainties and completely unknown non-identical control directions. Firstly, a modified coordinate transformation is proposed by introducing the dynamic surface control (DSC) technique, which avoids the massive use of neighbor informations and overcomes the “explosion of complexity” challenge arising from iterative differentiation concerning the virtual controls. Secondly, we adopt a set of Nussbaum functions with different frequencies for each agent to deal with the unknown control directions and make the effects of the multiple Nussbaum functions enhance each other instead of counteracting. In addition, by introducing a piecewise continuous function in each step of the backstepping procedure, a novel nonsingular finite time adaptive tracking controller is designed, which eliminates the possible singularity problems resulting from the presence of negative fractional powers. Based on the finite time stability criterion, it is proved that all closed-loop signals are bounded and the tracking errors can converge to a small residual set in a finite time. Finally, a simulation example of four DC motors is provided to demonstrate the effectiveness of the proposed algorithm.Note to Practitioners—This paper investigates the distributed nonsingular finite time adaptive consensus tracking control problem for the nonlinear MASs with parametric uncertainties and completely unknown non-identical control directions, whose models can describe many critical applications, such as unmanned vehicle formation and robot formation. The proposed algorithm provides an intuitive design paradigm for the consensus tracking control of uncertain nonlinear MASs with completely unknown non-identical control directions. In addition, the developed finite time control method can effectively increase the system convergence, which makes the designed algorithm more engineering oriented.

Recently big data have become a research hotspot and been successfully exploited in a few applications such as data mining and business modeling. Although big data contain a plenty of treasures for all the fields of computer science, it is very difficult for the current computing paradigms and computer hardware to efficiently process and utilize big data to attain what are looked forward to. In this work, we explore the possibility of employing big data in recommendation systems. We have proposed a simple recommendation system framework BDRSF (Big Data Recommendation System Framework), which is based on big data with social context theories and has abilities in obtaining the Recommender based on the idea of supervised learning through big data training. Its main idea can be divided into three parts: (1) reduce the scale of the current recommendation problems according to the essence of recommending; (2) design a rational Recommender and propose a novel supervised learning algorithm to get it; (3) utilize the Recommender to deal with the later recommendation problems. Experimental results show that BDRSF outperforms conventional recommendation systems, which clearly indicates the effectiveness and efficiency of big data with social context in personalized recommendation.