This article investigates the optimization consensus and constraint issues of multi-agent systems within a given time. By adopting a barrier function and the improved non-negative function, a new finite-horizon cost function is constructed. Based on this cost function, a simple single-critic network framework is proposed, which avoids the complex interaction and iterative update process between the actor and critic networks, thereby reducing the computational complexity. Under this framework, the temporal differential (TD) signal generated by the integral Bellman equation and the terminal constraint (TC) error are used to jointly guide the update of the critic learning law, and a concurrent learning technique is employed to adaptively adjust the weights without requiring persistent excitation conditions. The proposed adaptive dynamic programming (ADP) control strategy does not rely on the system model information of the controlled plant, enhancing the applicability of the algorithm in unknown dynamic environments. Furthermore, by incorporating the prescribed-time (PT) criterion into the optimization design process, a collaborative control protocol with explicit time constraints is designed, which strictly ensures that the controlled system achieves consensus within the specified time. At the same time, by adjusting the parameters of the PT criterion, the convergence rate and transient performance can be flexibly controlled. Finally, simulation results verify the effectiveness of the designed control algorithm and illustrate the impact of key parameters on system performance.

This article considers adaptive hierarchical sliding mode control (HSMC) for hyperbolic partial differential equation (PDE)-ordinary differential equation (ODE) cascade systems. Unlike existing PDE-ODE cascade systems, the ODE subsystem studied in this paper involves unknown nonlinear functions and unknown gain functions, and thus fuzzy logic systems (FLSs) are introduced to approximate them. Meanwhile, a projection algorithm is proposed to ensure that the designed controller does not have singularity issues. Subsequently, with the aid of integration by parts and Volterra integral (VI) transformation, the original system is equivalently transformed into a new target one. For this target system, an adaptive fuzzy control approach based on hierarchical sliding mode (HSM) is developed. Compared with the commonly used backstepping method, this method simplifies the controller design steps while also eliminating the algebraic loop and “complexity explosion” issues. In addition, a triggering mechanism with a switching threshold is introduced to alleviate communication burden. Ultimately, the proposed control algorithm ensures that all signals including system states and actuator states are bounded, and this fact is verified through a Josephson junction circuit simulation.

This paper investigates the deferred output constraint (DOC) issues of high-order random nonlinear systems. The DOC indicates that the system output is constrained within a given range after the system running for a period of time, rather than being constrained at the beginning. This leads to an obstacle that the well used barrier Lyapunov function method is invalid because it is not defined at the initial stage. For this reason, a prescribed-time scaling function and a barrier function are introduced. Then, to more accurately describe the disturbances in nature, this paper considers a more common stochastic process than white noise, which is called colored noise. In addition, a dynamic-memory event-triggered mechanism (DMETM) that takes into account the influence of historical information of internal dynamic variables on trigger conditions is proposed to reduce the waste of communication resources. With the assistance of Lyapunov stability theory and backstepping approach, it is shown that our developed DMETM-based control scheme can guarantee that all signals are bounded in the presence of colored noises. Ultimately, two simulation examples are included to demonstrate the effectiveness of the proposed theory.Note to Practitioners—Since higher-order nonlinear systems can describe nonlinear features in system dynamics, some complex actual systems such as induction motorcycles and space vehicles can be modeled as such systems. In engineering applications, controlled systems are usually affected by random noises from external environment, thus colored noises are introduced in this paper. Compared with white noises, colored noises can describe disturbances in nature more accurately and reasonably. In addition, to ensure that practical systems such as car operation systems and robotic arm systems can work safely, their DOC issues need to be considered. However, the traditional barrier Lyapunov functions (BLFs) cannot solve the DOC problems because they are not defined in the initial stage, hence the prescribed-time scaling and barrier functions are proposed. Meanwhile, to circumvent the problem of limited communication bandwidth, a DMETM with delay terms of internal variables is devised.

MOTIVATION: Although long-read sequencing technologies can produce genomes with long contiguity, they suffer from high error rates. Thus, we developed NextPolish, a tool that efficiently corrects sequence errors in genomes assembled with long reads. This new tool consists of two interlinked modules that are designed to score and count K-mers from high quality short reads, and to polish genome assemblies containing large numbers of base errors. RESULTS: When evaluated for the speed and efficiency using human and a plant (Arabidopsis thaliana) genomes, NextPolish outperformed Pilon by correcting sequence errors faster, and with a higher correction accuracy. AVAILABILITY AND IMPLEMENTATION: NextPolish is implemented in C and Python. The source code is available from https://github.com/Nextomics/NextPolish. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Orthology characterizes genes of different organisms that arose from a single ancestral gene via speciation, in contrast to paralogy, which is assigned to genes that arose via gene duplication. An accurate orthology assignment is a crucial step for comparative genomic studies. Orthologous genes in two organisms can be identified by applying a so-called reciprocal search strategy, given that complete information of the organisms' gene repertoire is available. In many investigations, however, only a fraction of the gene content of the organisms under study is examined (e.g., RNA sequencing). Here, identification of orthologous nucleotide or amino acid sequences can be achieved using a graph-based approach that maps nucleotide sequences to genes of known orthology. Existing implementations of this approach, however, suffer from algorithmic issues that may cause problems in downstream analyses. RESULTS: We present a new software pipeline, Orthograph, that addresses and solves the above problems and implements useful features for a wide range of comparative genomic and transcriptomic analyses. Orthograph applies a best reciprocal hit search strategy using profile hidden Markov models and maps nucleotide sequences to the globally best matching cluster of orthologous genes, thus enabling researchers to conveniently and reliably delineate orthologs and paralogs from transcriptomic and genomic sequence data. We demonstrate the performance of our approach on de novo-sequenced and assembled transcript libraries of 24 species of apoid wasps (Hymenoptera: Aculeata) as well as on published genomic datasets. CONCLUSION: With Orthograph, we implemented a best reciprocal hit approach to reference-based orthology prediction for coding nucleotide sequences such as RNAseq data. Orthograph is flexible, easy to use, open source and freely available at https://mptrsen.github.io/Orthograph . Additionally, we release 24 de novo-sequenced and assembled transcript libraries of apoid wasp species.

MOTIVATION: Transcriptome sequencing has long been the favored method for quickly and inexpensively obtaining a large number of gene sequences from an organism with no reference genome. Owing to the rapid increase in throughputs and decrease in costs of next- generation sequencing, RNA-Seq in particular has become the method of choice. However, the very short reads (e.g. 2 × 90 bp paired ends) from next generation sequencing makes de novo assembly to recover complete or full-length transcript sequences an algorithmic challenge. RESULTS: Here, we present SOAPdenovo-Trans, a de novo transcriptome assembler designed specifically for RNA-Seq. We evaluated its performance on transcriptome datasets from rice and mouse. Using as our benchmarks the known transcripts from these well-annotated genomes (sequenced a decade ago), we assessed how SOAPdenovo-Trans and two other popular transcriptome assemblers handled such practical issues as alternative splicing and variable expression levels. Our conclusion is that SOAPdenovo-Trans provides higher contiguity, lower redundancy and faster execution. AVAILABILITY AND IMPLEMENTATION: Source code and user manual are available at http://sourceforge.net/projects/soapdenovotrans/.