Probabilistic Approximation of Stochastic Time Series Using Bayesian Recurrent Neural Network.

In this brief, we investigate the approximation theory (AT) of Bayesian recurrent neural network (BRNN) for stochastic time series forecasting (TSF) from a probabilistic standpoint. Due to the cumulative dependencies present in stochastic time series, which are incompatible with the recurrent structure of BRNN and further complicate the analysis of AT, we first perform marginalization and transform the time series into a probabilistically equivalent latent variable model (LVM). Subsequently, we analyze the AT by evaluating the approximation error between the output mean of BRNN and that of the LVM, which are derived through Taylor expansion-based uncertainty propagation and distribution parameterization, respectively.

Case report: A case study of neoadjuvant immunochemotherapy for locally advanced esophageal squamous carcinoma.

In locally advanced esophageal cancer, the controversy over the two traditional treatment modalities, neoadjuvant radiotherapy and neoadjuvant chemotherapy, has been unending and also challenged by the addition of neoadjuvant immunotherapy. Neoadjuvant immunotherapy has led to an increasing diversity of neoadjuvant combination treatment modalities, among which neoadjuvant immunochemotherapy has emerged, with current clinical studies initially demonstrating its efficacy and safety. We report a case of a patient with locally advanced esophageal cancer who underwent two cycles of neoadjuvant immunochemotherapy and successful surgery and achieved a pathological complete response (pCR).

Hybrid triggering design for global attitude synchronization of networked rigid bodies.

This paper is devoted to dealing with the problem of global attitude synchronization for quaternion-based multiple rigid bodies, regardless of the general directed topologies of networks and arbitrary initial orientations of rigid bodies. A novel canonical quaternion is constructed to represent all physical attitudes of rigid bodies such that the pseudo-synchronization of their quaternion representations (namely, the quaternions' vector parts of all rigid bodies reach agreement on some identical value, whereas their scalar parts do not) can be precluded. Moreover, to reduce unnecessary communication requirements of rigid bodies, a hybrid triggering mechanism involving both the time regulation and neighbors' non-real-time information is proposed, with which a distributed protocol is developed by leveraging the constructed canonical quaternion.

Accurate integrated position measurement system for mobile applications in GPS-denied coal mine.

The automatic positioning of underground mobile applications plays a crucial role in enabling intelligent coal mining. However, due to the diverse kinematics and dynamics of these applications, various positioning methods have been proposed to match different targets. Nonetheless, the accuracy and applicability of these methods still fall short of meeting the requirements for field applications.

Transcriptomic analysis reveals the contribution of to wheat root growth and development.

Roots are the major organs for water and nutrient acquisition and substantially affect plant growth, development and reproduction. Improvements to root system architecture are highly important for the increased yield potential of bread wheat. , a major stable quantitative trait locus (QTL) that controls maximum root length (MRL), essentially contributes to an improved root system in wheat.

Comparative genomic and transcriptomic analyses uncover the molecular basis of high nitrogen-use efficiency in the wheat cultivar Kenong 9204.

Studying the regulatory mechanisms that drive nitrogen-use efficiency (NUE) in crops is important for sustainable agriculture and environmental protection. In this study, we generated a high-quality genome assembly for the high-NUE wheat cultivar Kenong 9204 and systematically analyzed genes related to nitrogen uptake and metabolism. By comparative analyses, we found that the high-affinity nitrate transporter gene family had expanded in Triticeae.

Improving Tracking Accuracy for Repetitive Learning Systems by High-Order Extended State Observers.

For systems executing repetitive tasks, how to realize the perfect tracking objective is generally desirable, for which an effective method called "iterative learning control (ILC)" emerges thanks to the incorporation of the repetitive execution of systems into an ILC design framework. However, nonrepetitive (iteration-varying) uncertainties are often inevitable in practice and greatly degrade the tracking accuracy of ILC, which has not been treated well, regardless of considerable robust ILC results. This motivates this article to develop a new design method to improve the tracking accuracy of ILC by adopting a high-order extended state observer (ESO) to address ill effects of nonrepetitive uncertainties and uncertain system models.

Alternative Splicing of Differentially Regulates Grain Weight and Size in Bread Wheat.

The heterotrimeric G-protein mediates growth and development by perceiving and transmitting signals in multiple organisms. Alternative splicing (AS), a vital process for regulating gene expression at the post-transcriptional level, plays a significant role in plant adaptation and evolution. Here, we identified five splicing variants of G subunit gene ( to ), which showed expression divergence during wheat polyploidization, and differential function in grain weight and size determination.

Distributed Control Problems on Signed Networks Under Mixed Static and Dynamic Protocols.

This article aims at exploring the dynamic behaviors of signed networks under the mixed static and dynamic control protocols, which reflect the existence of two classes of communication channels. An extended leader-follower framework admitting multiple dynamic leaders is established to identify the roles of all nodes in signed networks, depending on the union of two related signed digraphs. It is shown that bipartite containment tracking is achieved for signed networks despite any topology conditions.

Iterative Rectifying Methods for Nonrepetitive Continuous-Time Learning Control Systems.

To implement iterative learning control (ILC), one of the most fundamental hypotheses is the strict repetitiveness (i.e., iteration-independence) of the controlled systems, especially of their plant models.

Transient Bipartite Synchronization for Cooperative-Antagonistic Multiagent Systems With Switching Topologies.

This article aims at addressing the transient bipartite synchronization problem for cooperative-antagonistic multiagent systems with switching topologies. A distributed iterative learning control protocol is presented for agents by resorting to the local information from their neighbor agents. Through learning from other agents, the control input of each agent is updated iteratively such that the transient bipartite synchronization can be achieved over the targeted finite horizon under the simultaneously structurally balanced signed digraph.

Enhances Root System, Biomass, Nitrogen Accumulation and Yield in Bread Wheat.

Genetic improvement of root systems is an efficient approach to improve yield potential and nitrogen use efficiency (NUE) of crops. was a major stable quantitative trait locus (QTL) controlling the maximum root length in wheat ( L). Two types of near isogenic lines (A-NILs with superior and B-NILs with inferior alleles) were used to specify the effects of on root, grain output and nitrogen-related traits under both low nitrogen (LN) and high nitrogen (HN) environments.

Nonsynchronous Model Reduction for Uncertain 2-D Markov Jump Systems.

Mode information is of great significance when investigating the Markov jump systems (MJSs). However, it is common in practical scenarios that the mode information is not completely accessible, which probably induces nonsynchronization problems. Taking this into consideration, in this article, we study nonsynchronous H model order reduction for 2-D MJSs with model uncertainty.

Design and Analysis of Data-Driven Learning Control: An Optimization-Based Approach.

Learning to perform perfect tracking tasks based on measurement data is desirable in the controller design of systems operating repetitively. This motivates this article to seek an optimization-based design and analysis approach for data-driven learning control systems by focusing on iterative learning control (ILC) of repetitive systems with unknown nonlinear time-varying dynamics. It is shown that perfect output tracking can be realized with updating inputs, where no explicit model knowledge but only measured input-output data are leveraged.

Fully Distributed Synchronization of Complex Networks With Adaptive Coupling Strengths.

This article considers the fully distributed leaderless synchronization in a complex network by only utilizing local neighboring information to design and tune the coupling strength of each node such that the synchronization problem can be solved without involving any global information of the network. For an undirected network, a fully distributed synchronization algorithm is presented to adjust the coupling strength of each node based on a simple adaptive law. When the topology of a network is directed, two different types of adaptive algorithms are developed to achieve synchronization in a fully distributed manner, where the coupling strength of each node is designed to be either the sum or product of two non-negative scalar functions.

Adaptive robust precision motion control of single PAM actuated servo systems with non-local memory hysteresis force compensation.

An adaptive robust controller with non-local memory hysteresis force compensation is investigated for the precision tracking control of pneumatic artificial muscle (PAM). The proposed controller presents a two-layer cascade structure, and each layer has an adaptive law part and a robust control law part. A modified operator based Prandtl-Ishlinskii (PI) model is employed in the development of the robust control algorithm with the hysteresis feedback linearization compensation.

Distributed Controller Design and Analysis of Second-Order Signed Networks With Communication Delays.

This article concentrates on dealing with distributed control problems for second-order signed networks subject to not only cooperative but also antagonistic interactions. A distributed control protocol is proposed based on the nearest neighbor rules, with which necessary and sufficient conditions are developed for consensus of second-order signed networks whose communication topologies are described by strongly connected signed digraphs. Besides, another distributed control protocol in the presence of a communication delay is designed, for which a time margin of the delay can be determined simultaneously.

Convergence Analysis of Robust Iterative Learning Control Against Nonrepetitive Uncertainties: System Equivalence Transformation.

This article is concerned with the robust convergence analysis of iterative learning control (ILC) against nonrepetitive uncertainties, where the contradiction between convergence conditions for the output tracking error and the input signal (or error) is addressed. A system equivalence transformation (SET) is proposed for robust ILC such that given any desired reference trajectories, the output tracking problems for general nonsquare multi-input, multi-output (MIMO) systems can be equivalently transformed into those for the specific class of square MIMO systems with the same input and output numbers. As a benefit of SET, a unified condition is only needed to guarantee both the uniform boundedness of all system signals and the robust convergence of the output tracking error, which avoids causing the condition contradiction problem in implementing the double-dynamics analysis approach to ILC.

Distributed Control of Time-Varying Signed Networks: Theories and Applications.

Signed networks admitting antagonistic interactions among agents may polarize, cluster, or fluctuate in the presence of time-varying communication topologies. Whether and how signed networks can be stabilized regardless of their sign patterns is one of the fundamental problems in the network system control areas. To address this problem, this paper targets at presenting a self-appraisal mechanism in the protocol of each agent, for which a notion of diagonal dominance degree is proposed to represent the dominant role of agent's self-appraisal over external impacts from all other agents.

Convergence Analysis of Saturated Iterative Learning Control Systems With Locally Lipschitz Nonlinearities.

In this article, the robust trajectory tracking problem of iterative learning control (ILC) for uncertain nonlinear systems is considered, and the effects from locally Lipschitz nonlinearities, input saturations, and nonzero system relative degrees are treated. A saturated ILC algorithm is given, with the convergence analysis exploited using a composite energy function-based approach. It is shown that the tracking error can be guaranteed to converge both pointwisely and uniformly.

Further Results for Edge Convergence of Directed Signed Networks.

The edge convergence problems have been explored for directed signed networks recently in 2019 by Du, Ma, and Meng, of which the analysis results, however, depend heavily on the strong connectivity of the network topologies. The question asked in this article is: whether and how can the edge convergence be achieved when the strong connectivity is not satisfied? The answer for the case of spanning tree is given. It is shown that if a signed network is either structurally balanced or r-structurally unbalanced, then the edge state can be ensured to converge to a constant vector.

Dissection of Pleiotropic QTL Regions Controlling Wheat Spike Characteristics Under Different Nitrogen Treatments Using Traditional and Conditional QTL Mapping.

Optimal spike characteristics are critical in improving the sink capacity and yield potential of wheat even in harsh environments. However, the genetic basis of their response to nitrogen deficiency is still unclear. In this study, quantitative trait loci (QTL) for six spike-related traits, including heading date (HD), spike length (SL), spikelet number (SN), spike compactness (SC), fertile spikelet number (FSN), and sterile spikelet number (SSN), were detected under two different nitrogen (N) supplies, based on a high-density genetic linkage map constructed by PCR markers, DArTs, and Affymetrix Wheat 660 K SNP chips.

Convergence Conditions for Solving Robust Iterative Learning Control Problems Under Nonrepetitive Model Uncertainties.

Learning from saved measurement and control data to refine the performance of output tracking is the core feature of iterative learning control (ILC). Even though this implementation process of ILC does not need any model knowledge, ILC typically requires the strict repetitiveness of the control systems, especially on the plant models of them. The questions of interest in this paper are: 1) whether and how can robust ILC problems be solved with respect to the nonrepetitive (or iteration-dependent) model uncertainties and 2) can convergence conditions be developed with the effective contraction mapping (CM)-based approach to ILC? The answers to these questions are affirmative, and the CM-based approach is applicable to robust ILC that accommodates certain nonrepetitive uncertainties, especially in the plant models.

QTL Detection for Kernel Size and Weight in Bread Wheat ( L.) Using a High-Density SNP and SSR-Based Linkage Map.

High-density genetic linkage maps are essential for precise mapping quantitative trait loci (QTL) in wheat ( L.). In this study, a high-density genetic linkage map consisted of 6312 SNP and SSR markers was developed to identify QTL controlling kernel size and weight, based on a recombinant inbred line (RIL) population derived from the cross of Shixin828 and Kenong2007.

Edge Convergence Problems on Signed Networks.

This paper focuses on characterizing edge dynamics of signed networks subject to both cooperative and antagonistic interactions and copes with the state convergence problems of the resulting edge systems. To represent the two competitive classes of interactions that emerge in signed networks, signed digraphs are adopted and the relevant edge Laplacian matrices are introduced, with which an edge-based distributed protocol is presented. The relation between the edge Laplacian matrix and the structural balance (or unbalance) of a signed digraph is disclosed by taking advantage of properties of undirected cycles.