The article is concerned with active disturbance rejection control of a heat equation. The considered heat equation satisfies the Dirichlet boundary condition on one part of the boundary. On the ...other part of the boundary is located a Neumann boundary control. The heat equation system suffers from both a model uncertainty in the heat flow modeling and an unknown external disturbance. Our control approach is based on the design of an exponentially converging observer to estimates both the state and the unknown uncertainty. The estimated state and the estimated uncertainty are used to build a stabilizing feedback control law such that the closed-loop system is exponentially stabilized, and the external disturbance is rejected.
A photocontrolled reversible addition−fragmentation chain transfer (RAFT) process is developed by initiating polymerization through a 1,3‐diaminopropane‐triethylborane (DAPTB)−diphenyl iodonium salt ...(Ph2I+) complex (DAPTB/Ph2I+) under ambient temperature and atmospheric conditions. Upon demand, this air‐stable DAPTB/Ph2I+ complex is photolyzed to liberate a reactive triethylborane that consumes atmospheric oxygen and generates ethyl radicals, which initiate and mediate RAFT polymerization. Controlled RAFT polymerization is thus achieved without any prior deoxygenation using a novel RAFT chain transfer agent, BP‐FSBC, which contains both benzophenone and sulfonyl fluoride moieties. Furthermore, the kinetics of polymerization reveal that the reaction process is rapid, and well‐defined polymers are produced by a 61% conversion of 2‐hydroxyethyl acrylate (HEA) within 7 min and 77% conversion of N,N‐dimethylacrylamide (DMA) within 10.5 min. The temporal and spatial control of this photopolymerization is also demonstrated by an “on/off” switch of UV irradiation and a painting‐on‐a‐surface approach, respectively. In addition, active chain ends are demonstrated by preparing block copolymers by chain extension and click sulfur(VI)‐fluoride exchange postreaction using RAFT‐derived macrochain transfer agents.
A rapid, photocontrolled reversible addition−fragmentation chain transfer (RAFT) process is achieved without any prior deoxygenation under ambient conditions. Furthermore, the temporal and spatial control of this photopolymerization is demonstrated. In addition, facile and efficient techniques for preparing block copolymers by chain extension and click sulfur(VI)‐fluoride exchange postreaction using RAFT‐derived macro‐chain transfer agents are developed.
We consider the scaling of the optimal constant in Korn’s first inequality for elliptic and parabolic shells which was first given by Grabovsky and Harutyunyan with hints coming from the test ...functions constructed by Tovstik and Smirnov on the level of formal asymptotic expansions. Here, we employ the Bochner technique in Remannian geometry to remove the assumption that the middle surface of the shell is given by one single principal coordinate, in particularly, including closed elliptic shells.
The topological states in quantum Hall insulators and quantum spin Hall insulators that emerge helical are considered nondissipative. However, in crystalline systems without spin-orbit couplings, the ...existing higher-order topological states are considered not helical, and the energy suffers from dissipation during propagation. In this work, by introducing the intrinsic pseudospin degree of freedom, we theoretically and experimentally present the existence of the helical higher-order topological states in the C_{6}-symmetric topological crystalline insulators based on the acoustic samples. Crucially, rather than considering the global interaction of the large bulk, we further intuitively reveal the impacts of the geometries of the crystal on the generation mechanisms and natural behaviors of these states based on the simple equivalent models. These results provide a versatile way for guiding the design of the desired topological materials.
With the continuous prosperity of maritime transportation on a global scale and the resulting escalation in port trade volume, tugboats assume a pivotal role as essential auxiliary tools influencing ...the ingress and egress of vessels into and out of ports. As a result, the optimization of port tug scheduling becomes of paramount importance, as it contributes to the heightened efficiency of ship movements, cost savings in port operations, and the promotion of sustainable development within the realm of maritime transportation. However, a majority of current tugboat scheduling models tend to focus solely on the maximum operational time. Alternatively, the formulated objective functions often deviate from real-world scenarios. Furthermore, prevailing scheduling methods exhibit shortcomings, including inadequate solution accuracy and incompatibility with integer programming. Consequently, this paper introduces a novel multi-objective tugboat scheduling model to align more effectively with practical considerations. We propose a novel optimization algorithm, the Improved Grey Wolf Optimization (IGWO), for solving the tugboat scheduling model. The algorithm enhances convergence performance by optimizing convergence parameters and individual updates, making it particularly suited for solving integer programming problems. The experimental session designs several scale instances according to the reality of the port, carries out simulation experiments comparing several groups of intelligent algorithms, verifies the effectiveness of IGWO, and verifies it in the comprehensive port area of Huanghua Port to get the optimal scheduling scheme of this port area, and finally gives management suggestions to reduce the cost of tugboat operation through sensitivity analysis.
According to the tactical requirements of unmanned aerial vehicle (UAV) for tracking target and avoiding obstacle in complex dynamic environment, a three-dimensional (3D) real-time path planning ...method is proposed by combing the improved Lyapunov Guidance Vector Field (LGVF), the Interfered Fluid Dynamical System (IFDS) and the strategy of varying receding-horizon optimization from Model Predictive Control (MPC). First, in order to track the moving target in 3D environment, the LGVF method is improved by introducing flight height into the traditional Lyapunov function, and the generated velocity can guide UAV converge gradually to the limit cycle in horizontal plane and the optimal height in vertical plane. Then, the IFDS method imitating the phenomenon of fluid flow is utilized to plan the collision-free path. To achieve the mission of tracking moving target and avoid static or dynamic obstacle at the same time, the guidance vector field by LGVF is taken as the original fluid of IFDS. As the fluid system still remains stable under the influence of obstacles, the disturbed streamline from the interfered fluid can be regarded as the planned path. Third, as the quality of route is mainly influenced by the repulsive and tangential parameters of IFDS, the real-time suboptimal route can be planned by the varying receding-horizon optimization according to the predicted motion. The experimental results prove that the proposed hybrid method is applicable to various dynamic environments.
The utilisation of synthetic aperture radar (SAR) imagery for change detection can effectively circumvents the stringent limitations imposed by weather and lighting conditions, and is finding ...widespread applications in fields such as disaster monitoring and urban research. To address issues of edge blurring, severe noise interference and sample imbalance, an automated SAR change detection framework is proposed based on enhanced edge information and prototype constrained clustering. Firstly, a gradient-based neighbourhood ratio is designed to reinforce the edge information of the difference map, facilitating robust differential information representations. Subsequently, to obtain accurate samples in an unsupervised manner, we have developed prototype constrained hierarchical clustering for pre-classification. The quantity and quality of selected samples can be precisely guaranteed through the utilisation of histogram analysis and prototype constraints. In the sample learning and prediction phases, a class-balanced noise-tolerant change detection network is proposed that combines focal loss and mean absolute error loss, further tackling the sample imbalance issue, strengthening noise resistance and improving change detection accuracy. Comprehensive experimental results and analysis conducted on five benchmark datasets have validated the effectiveness and robustness of the proposed method.
Recently, accumulating evidence has demonstrated that RDW independently predicts clinically important outcomes in many populations. However, the role of RDW has not been elucidated in chronic kidney ...disease (CKD) patients. We conducted the present study with the aim to evaluate the predictive value of RDW in CKD patients.
A retrospective observational cohort study of 1075 stage 3-5 CKD patients was conducted in a medical center. The patients' baseline information included demographic data, laboratory values, medications, and comorbid conditions. The upper limit of normal RDW value (14.9%) was used to divide the whole population. Multivariate Cox regression analysis was used to determine the independent predictors of mortality.
Of the 1075 participants, 158 patients (14.7%) died over a mean follow-up of approximately 2.35 years. The crude mortality rate was significantly higher in the high RDW group (high RDW group, 22.4%; low RDW group 11%, p <0.001). From the adjusted model, the high RDW group was correlated with a hazard ratio of 2.19 for overall mortality as compared with the low RDW group (95% CI = 1.53-3.09, p<0.001). In addition, the high RDW group was also associated with an increased risk for cardiovascular disease (HR = 2.28, 95% CI = 1.14-4.25, p = 0.019) and infection (HR = 1.9, 95% CI = 1.15-3.14, p = 0.012)) related mortality in comparison with the low RDW group.
In stage 3-5 CKD patients, RDW was associated with patient mortality of all-cause, cardiovascular disease and infection. RDW should be considered as a clinical predictor for mortality when providing healthcare to CKD patients.
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