Atmospheric wet and dry phosphorus deposition in Lake Erhai, China.

Lake Erhai is a potentially phosphorus (P)-limited lake and its water quality may have been affected by atmospheric P deposition. However, there have been few studies on atmospheric P deposition in this lake. In this study, we established five wet deposition monitoring sites and two dry deposition monitoring sites around Lake Erhai to quantify the wet and dry deposition of total phosphorus (TP), including dissolved inorganic phosphorus (DIP), dissolved organic phosphorus (DOP) and particulate phosphorus (PP) from July 2022 to June 2023. Wet deposition fluxes of P species were collected by automatic rainfall collection instrument, and dry deposition fluxes were estimated using airborne concentration measurements and inferential models. The results reveal that among the different P components, DOP had the highest contribution (50%) to wet TP deposition (average all sites 12.7 ± 0.7 mg P m2/yr), followed by PP (40%) and DIP (10%). Similarly, DOP (51%) was the major contributor to dry TP deposition (average two sites 2.4 ± 0.9 mg P m2/yr), followed by DIP (35%) and PP (14%). Wet deposition dominated the annual total TP deposition (wet plus dry), accounting for approximately 83%. The key seasons for dry deposition were spring and autumn, which accounted for 64% of the annual total dry TP deposition. In comparison, wet deposition was significantly higher in the summer, accounting for 73% of the annual total wet TP deposition. The results of the potential source contribution function and concentration-weighted trajectories analysis indicate that local source emission and long-range transport from surrounding cities jointly exerted a substantial influence on aerosol P concentrations, particularly in the eastern and northwestern regions of the lake. These findings provide a comprehensive understanding of the different P components in atmospheric deposition, which is beneficial for developing effective strategies to manage the P cycle in Lake Erhai.

Characterization and in silico analysis of the domain unknown function DUF568-containing gene family in rice (Oryza sativa L.).

BACKGROUND: Domains of unknown function (DUF) proteins are a number of uncharacterized and highly conserved protein families in eukaryotes. In plants, some DUFs have been predicted to play important roles in development and response to abiotic stress. Among them, DUF568-containing protein family is plant-specific and has not been described previously. A basic analysis and expression profiling was performed, and the co-expression and interaction networks were constructed to explore the functions of DUF568 family in rice. RESULTS: The phylogenetic tree showed that the 8, 9 and 11 DUF568 family members from rice, Arabidopsis and maize were divided into three groups. The evolutionary relationship between DUF568 members in rice and maize was close, while the genes in Arabidopsis were more distantly related. The cis-elements prediction showed that over 82% of the elements upstream of OsDUF568 genes were responsive to light and phytohormones. Gene expression profile prediction and RT-qPCR experiments revealed that OsDUF568 genes were highly expressed in leaves, stems and roots of rice seedling. The expression of some OsDUF568 genes varied in response to plant hormones (abscisic acid, 6-benzylaminopurine) and abiotic stress (drought and chilling). Further analysis of the co-expression and protein-protein interaction networks using gene ontology showed that OsDUF568 - related genes were enriched in cellular transports, metabolism and processes. CONCLUSIONS: In summary, our findings suggest that the OsDUF568 family may be a vital gene family for the development of rice roots, leaves and stems. In addition, the OsDUF568 family may participate in abscisic acid and cytokinin signaling pathways, and may be related to abiotic stress resistance in these vegetative tissues of rice.

Adaptive Neural Network-Based Filter Design for Nonlinear Systems With Multiple Constraints.

Filter design for nonlinear systems, especially time delayed nonlinear systems, has always been an important and challenging problem. This brief investigates the filter design problem of nonlinear systems with multiple constraints: time delay, actuator, and sensor faults, and a new adaptive neural network-based filter design method is proposed. Comparing with the existing works where there is a shortcoming that the designed filters contain unknown time delay(s), the design method proposed in this brief overcomes the shortcoming and only the estimation of the unknown time delay exists in the filter. Furthermore, not only the system states can be estimated, but also the unknown time delay with actuator and sensor faults can be estimated in this brief. Finally, simulation results are given to show the effectiveness of the proposed new design method.

Adaptive Tracking Control for Uncertain Cancer-Tumor-Immune Systems.

In this paper, the problem of control is investigated for cancer-tumor-immune systems, based on a two-dimension uncertain nonlinear model describing the interaction between immune and cancer cells in a body. First, the control problem is transformed into a state tracking problem. Second, an adaptive control method is proposed to track and stop the growth of cancer and maintain cancer and immune cells at an acceptable level. Different from the existing results in literature, the singularity problem in controller and the inaccuracy in control design have been overcome. From theoretical analysis, it is shown that the resulting closed-loop system is asymptotically stable and the tracking errors converge to the origin. Finally, simulation results illustrate not only the competitive relationship between immune system and tumor, but also the immune system has strong immunity to low level tumor volumes.

Neural Networks-Based Distributed Adaptive Control of Nonlinear Multiagent Systems.

The cooperative control problem of nonlinear multiagent systems is studied in this paper. The followers in the communication network are subject to unmodeled dynamics. A fully distributed neural-networks-based adaptive control strategy is designed to guarantee that all the followers are asymptotically synchronized to the leader, and the synchronization errors are within a prescribed level, where some global information, such as minimum and maximum singular value of graph adjacency matrix, is not necessarily to be known. Based on the Lyapunov stability theory and algebraic graph theory, the stability analysis of the resulting closed-loop system is provided. Finally, an numerical example illustrates the effectiveness and potential of the proposed new design techniques.

Robust Gene Circuit Control Design for Time-Delayed Genetic Regulatory Networks Without SUM Regulatory Logic.

This paper investigates the gene circuit control design problem of time-delayed genetic regulatory networks. In the genetic regulatory networks, the time delays are unknown constants, and the genetic regulatory is not conventional SUM regulatory logic and can be modeled to be an unknown nonlinear function of the time-delayed states of the other genes in a cell. By Lyapunov stability, a novel adaptive gene circuit control design approach is proposed for the genetic regulatory networks, where the unknown time delays are estimated online by adaptive algorithms and the unknown regulatory functions are approximated by neural networks. The design approach in this paper is delay-dependent and has less conservatism than the delay-independent approach. From theoretical analysis, the closed-loop system is asymptotically stable and all the signals in the system converge to an adjustable neighborhood of the origin. Finally, a numerical example is given to show the effectiveness of the new design approach.

Filter Design with Adaptation to Time-Delay Parameters for Genetic Regulatory Networks.

In existing works, the filters designed for delayed genetic regulatory networks contain time delay. If the time delay is unknown, the filters do not work in practical applications. In order to overcome the shortcoming in such existing works, this paper investigates the filter design problem of genetic regulatory networks with unknown constant time delay, and a novel adaptive filter is introduced, which can estimate online not only unknown network parameters but also the unknown time delay. By the Lyapunove approach, it is shown that the estimating errors asymptotically converge to the origin. Finally, simulation results are presented to illustrate the effectiveness of the proposed new design method.

Distributed Adaptive Fuzzy Control for Nonlinear Multiagent Systems Via Sliding Mode Observers.

In this paper, the problem of distributed adaptive fuzzy control is investigated for high-order uncertain nonlinear multiagent systems on directed graph with a fixed topology. It is assumed that only the outputs of each follower and its neighbors are available in the design of its distributed controllers. Equivalent output injection sliding mode observers are proposed for each follower to estimate the states of itself and its neighbors, and an observer-based distributed adaptive controller is designed for each follower to guarantee that it asymptotically synchronizes to a leader with tracking errors being semi-globally uniform ultimate bounded, in which fuzzy logic systems are utilized to approximate unknown functions. Based on algebraic graph theory and Lyapunov function approach, using Filippov-framework, the closed-loop system stability analysis is conducted. Finally, numerical simulations are provided to illustrate the effectiveness and potential of the developed design techniques.

Novel neural networks-based fault tolerant control scheme with fault alarm.

In this paper, the problem of adaptive active fault-tolerant control for a class of nonlinear systems with unknown actuator fault is investigated. The actuator fault is assumed to have no traditional affine appearance of the system state variables and control input. The useful property of the basis function of the radial basis function neural network (NN), which will be used in the design of the fault tolerant controller, is explored. Based on the analysis of the design of normal and passive fault tolerant controllers, by using the implicit function theorem, a novel NN-based active fault-tolerant control scheme with fault alarm is proposed. Comparing with results in the literature, the fault-tolerant control scheme can minimize the time delay between fault occurrence and accommodation that is called the time delay due to fault diagnosis, and reduce the adverse effect on system performance. In addition, the FTC scheme has the advantages of a passive fault-tolerant control scheme as well as the traditional active fault-tolerant control scheme's properties. Furthermore, the fault-tolerant control scheme requires no additional fault detection and isolation model which is necessary in the traditional active fault-tolerant control scheme. Finally, simulation results are presented to demonstrate the efficiency of the developed techniques.

Novel neural control for a class of uncertain pure-feedback systems.

This paper is concerned with the problem of adaptive neural tracking control for a class of uncertain pure-feedback nonlinear systems. Using the implicit function theorem and backstepping technique, a practical robust adaptive neural control scheme is proposed to guarantee that the tracking error converges to an adjusted neighborhood of the origin by choosing appropriate design parameters. In contrast to conventional Lyapunov-based design techniques, an alternative Lyapunov function is constructed for the development of control law and learning algorithms. Differing from the existing results in the literature, the control scheme does not need to compute the derivatives of virtual control signals at each step in backstepping design procedures. Furthermore, the scheme requires the desired trajectory and its first derivative rather than its first n derivatives. In addition, the useful property of the basis function of the radial basis function, which will be used in control design, is explored. Simulation results illustrate the effectiveness of the proposed techniques.