Effects of short- and long-term plant functional group removal on alpine meadow community niche.

The rapid loss of global biodiversity affects the creation and maintenance of community biodiversity and ecosystem structure and function. Thus, it is insufficient to focus solely on the effects of biodiversity loss on community biodiversity without also considering other impacts such as community assembly, niches, interspecific relationships, community stability, and biodiversity-ecosystem function. In this study, a 3- and 10-year biodiversity manipulation experiment was conducted in an alpine meadow to examine the effects of the individual plant functional group (PFG) removal on the niches of community dominant species by removal of Gramineae, Cyperaceae, legumes, and other forbs.

Distributed adaptive safe fault-tolerant control for nonlinear systems based on Barrier function.

In this paper, a novel adaptive safe fault-tolerant (SFT) controller design framework is proposed to obtain stability with safety guarantee for a class of single systems and a class of interconnected nonlinear systems in the presence of unknown faults. Under the framework, an adaptive fault-tolerant controller for a class of single systems is designed to ensure safety and asymptotic stability simultaneously. For interconnected systems, the neural networks (NNs) is used to parameterize the unknown faults and interconnection terms.

Multitrophic Diversity of the Biotic Community Drives Ecosystem Multifunctionality in Alpine Grasslands.

Biodiversity and ecosystem multifunctionality are currently hot topics in ecological research. However, little is known about the role of multitrophic diversity in regulating various ecosystem functions, which limits our ability to predict the impact of biodiversity loss on human well-being and ecosystem multifunctionality. In this study, multitrophic diversity was divided into three categories: plant, animal, and microbial communities (i.

Reinforcement Learning-Based H Control of 2-D Markov Jump Roesser Systems With Optimal Disturbance Attenuation.

This article investigates model-free reinforcement learning (RL)-based H control problem for discrete-time 2-D Markov jump Roesser systems ( 2 -D MJRSs) with optimal disturbance attenuation level. This is compared to existing studies on H control of 2-D MJRSs with optimal disturbance attenuation levels that are off-line and use full system dynamics. We design a comprehensive model-free RL algorithm to solve optimal H control policy, optimize disturbance attenuation level, and search for the initial stabilizing control policy, via online horizontal and vertical data along 2-D MJRSs trajectories.

Deep Reinforcement Learning for Autonomous Driving with an Auxiliary Actor Discriminator.

In the research of robot systems, path planning and obstacle avoidance are important research directions, especially in unknown dynamic environments where flexibility and rapid decision makings are required. In this paper, a state attention network (SAN) was developed to extract features to represent the interaction between an intelligent robot and its obstacles. An auxiliary actor discriminator (AAD) was developed to calculate the probability of a collision.

Event-Triggered Constrained Optimal Control for Organic Rankine Cycle Systems via Safe Reinforcement Learning.

The organic Rankine cycle (ORC) is an effective application for converting low-grade heat sources into power and is crucial for environmentally friendly production and energy recovery. However, the inherent complexity of the mechanism, its strong and unidentified nonlinearity, and the presence of control constraints severely impair the design of its optimal controller. To solve these issues, this study provides a novel event-triggered (ET) constrained optimal control approach for the ORC systems based on a safe reinforcement learning technique to find the optimal control law.

Accelerating reinforcement learning with case-based model-assisted experience augmentation for process control.

In the context of intelligent manufacturing in the process industry, traditional model-based optimization control methods cannot adapt to the situation of drastic changes in working conditions or operating modes. Reinforcement learning (RL) directly achieves the control objective by interacting with the environment, and has significant advantages in the presence of uncertainty since it does not require an explicit model of the operating plant. However, most RL algorithms fail to retain transfer learning capabilities in the presence of mode variation, which becomes a practical obstacle to industrial process control applications.

Quality-Relevant Process Monitoring with Concurrent Locality-Preserving Dynamic Latent Variable Method.

A concurrent locality-preserving dynamic latent variable (CLDLV) method is proposed to extract the correlation between process variables and quality variables for quality-related dynamic process monitoring. Given that dynamic process data can easily be contaminated by noise and outliers and conventional dynamic latent variable models lack robustness, a low-rank autoregressive model is developed to deal with autocorrelation and cross-correlation properties among the data. Then neighborhood structure information is integrated into the partial least squares model, which can better reveal the essential structure of the data.

Effects of Expansion on Plant Community Characteristics and Soil Nutrients in an Alpine Grassland.

Pedicularis kansuensis is an indicator species of grassland degradation. Its population expansion dramatically impacts the production and service function of the grassland ecosystem, but the effects and mechanisms of the expansion are still unclear. In order to understand the ecological effects of P.

Biodiversity and Ecosystem Function under Simulated Gradient Warming and Grazing.

Biodiversity and ecosystem functions and their relationship with environmental response constitute a major topic of ecological research. However, the changes in and impact mechanisms of multi-dimensional biodiversity and ecosystem functions in continuously changing environmental gradients and anthropogenic activities remain poorly understood. Here, we analyze the effects of multi-gradient warming and grazing on relationships between the biodiversity of plant and soil microbial with productivity/community stability through a field experiment simulating multi-gradient warming and grazing in alpine grasslands on the Tibetan Plateau.

SVD-Based Robust Distributed MPC for Tracking Systems Coupled in Dynamics With Global Constraints.

This article presents a novel singular value decomposition (SVD)-based robust distributed model predictive control (SVD-RDMPC) strategy for linear systems with additive uncertainties. The system is globally constrained and consists of multiple interrelated subsystems with bounded disturbances, each of whom has local constraints on states and inputs. First, we integrate the steady-state target optimizer into the MPC problem through the offset cost function to formulate a modified single optimization problem for tracking changing targets from real-time optimization.

Expression of LncRNA KCNQ1Ot1 in diabetic nephropathy and its correlation with MEK/ERK signaling pathway.

Objective: To explore the expression of LncRNA KCNQ1OT1 in diabetic nephropathy (DN), and its correlation with MEK/ERK signaling pathway.

Methods: 148 patients with type 2 diabetes in our hospital were selected as research subjects, including 83 patients with simple type 2 diabetes (T2D group) and 65 patients with type 2 diabetes with DN (DN group). Another 50 non-diabetic patients were enrolled as the control group.

Automated rock mass condition assessment during TBM tunnel excavation using deep learning.

Rock mass condition assessment during tunnel excavation is a critical step for the intelligent control of tunnel boring machine (TBM). To address this and achieve automatic detection, a visual assessment system is installed to the TBM and a lager in-situ rock mass image dataset is collected from the water conveyance channel project. The rock mass condition assessment task is transformed into a fine-grain classification task.

Effects of liraglutide on lipolysis and the AC3/PKA/HSL pathway.

Background: Liraglutide reduces blood glucose, body weight and blood lipid levels. Hormone-sensitive lipase (HSL) is a key enzyme in lipolysis. Evidence from our and other studies have demonstrated that adenylate cyclase 3 (AC3) is associated with obesity and can be upregulated by liraglutide in obese mice.

An improved mixture robust probabilistic linear discriminant analyzer for fault classification.

This article introduces a novel fault classification method based on the mixture robust probabilistic linear discriminant analysis (MRPLDA). Unlike conventional probabilistic models like probabilistic principal component analysis (PPCA), probabilistic linear discriminant analysis (PLDA) introduces two sets of latent variables to represent the within-class and between-class information, resulting in an enhanced classification capability. In order to deal with outliers and non-Gaussian distributed variables commonly encountered in industrial processes, a mixture of robust PLDA model is considered by imposing the Student's t-priors on the noise and hidden variables of the PLDA model.

H∞ Output Consensus for Markov Jump Multiagent Systems With Uncertainties.

This paper investigates the H∞ output consensus problem for multiagent systems with Markov jump and external disturbance in both continuous-time and discrete-time domains. The communication network is directed and fixed with uncertainties. Based on the hidden Markov model, an output feedback controller is constructed.

Nonfragile State Estimation of Quantized Complex Networks With Switching Topologies.

This paper considers the nonfragile $H_\infty $ estimation problem for a class of complex networks with switching topologies and quantization effects. The network architecture is assumed to be dynamic and evolves with time according to a random process subject to a sojourn probability. The coupled signal is to be quantized before transmission due to power and bandwidth constraints, and the quantization errors are transformed into sector-bounded uncertainties.

Quantized Feedback Control of Fuzzy Markov Jump Systems.

This paper addresses the problem of quantized feedback control of nonlinear Markov jump systems (MJSs). The nonlinear plant is represented by a class of fuzzy MJSs with time-varying delay based on a Takagi-Sugeno fuzzy model. The quantized signal is utilized for control purpose and the sector bound approach is exploited to deal with quantization errors.

Hidden-Markov-Model-Based Asynchronous Filter Design of Nonlinear Markov Jump Systems in Continuous-Time Domain.

This paper addresses the dissipative asynchronous filtering problem for a class of Takagi-Sugeno fuzzy Markov jump systems in the continuous-time domain. The hidden Markov model is applied to describe the asynchronous situation between the designed filter and the original system. Based on the stochastic Lyapunov function, a sufficient condition is developed to guarantee the stochastic stability of the filtering error systems with a given dissipative performance.

Asynchronous and Resilient Filtering for Markovian Jump Neural Networks Subject to Extended Dissipativity.

The problem of asynchronous and resilient filtering for discrete-time Markov jump neural networks subject to extended dissipativity is investigated in this paper. The modes of the designed resilient filter are assumed to run asynchronously with the modes of original Markov jump neural networks, which accord well with practical applications and are described through a hidden Markov model. Due to the fluctuation of the filter parameters, a resilient filter taking into account parameter uncertainty is adopted.

Quantized Control of Markov Jump Nonlinear Systems Based on Fuzzy Hidden Markov Model.

This paper considers the problem of asynchronous guaranteed cost control (GCC) for nonlinear Markov jump systems with stochastic quantization. Hidden Markov model is used to describe the nonsynchronous controller and the random quantization phenomenon. Based on Takagi-Sugeno fuzzy technique and Lyapunov function approach, a sufficient condition is obtained, which can not only ensure the asymptotic stability of the closed-loop system and existence of the desired controller, but also can yield the minimal upper bound of GCC performance.

Asynchronous Dissipative Control for Fuzzy Markov Jump Systems.

The problem of asynchronous dissipative control is investigated for Takagi-Sugeno fuzzy systems with Markov jump in this paper. Hidden Markov model is introduced to represent the nonsynchronization between the designed controller and the original system. By the fuzzy-basis-dependent and mode-dependent Lyapunov function, a sufficient condition is achieved such that the resulting closed-loop system is stochastically stable with a strictly ( , , )- -dissipative performance.

Dissipativity-Based Resilient Filtering of Periodic Markovian Jump Neural Networks With Quantized Measurements.

The problem of dissipativity-based resilient filtering for discrete-time periodic Markov jump neural networks in the presence of quantized measurements is investigated in this paper. Due to the limited capacities of network medium, a logarithmic quantizer is applied to the underlying systems. Considering the fact that the filter is realized through a network, randomly occurring parameter uncertainties of the filter are modeled by two mode-dependent Bernoulli processes.