The tempered pullback dynamics of the 3D Brinkman–Forchheimer equation with variable delay has been studied in this paper. With the different universes which has some topology property, the existence of minimal and unique family of pullback attractors were obtained. Moreover, the convergence of pullback attractors for the 3D Brinkman–Forchheimer equation as delay term vanishes is also been proved, i.e., the upper semi-continuity of attractors.

In the science museum industry, exhibits related to virtual reality(VR) technology have grown rapidly in number. Demonstrating and using virtual reality technology in exhibits is becoming a new hotspot in science museums. However, most of these exhibits are non-interactive or only allow one person to interact with virtual environment. Few exhibits could realize multiplayer real-time interaction. Taking the exhibit of \"Virtual Experience on an Aircraft Carrier\" in China Science and Technology Museum as an example, this paper elaborates the design of a multiplayer interactive exhibit based on VR technology, including the game content and interactive ways of the exhibit, the software system architecture, the function of software system modules, software development tools, the hardware system and the equipment required. Through this, the paper is aimed to provide useful ideas for such multiplayer interactive exhibits based on virtual reality technology, which may make exhibitions in science museums more interesting and improve their science popularization effects.

Analysis of dynamic metabolomics data holds the promise to improve our understanding of underlying mechanisms in metabolism. For example, it may detect changes in metabolism due to the onset of a disease. Dynamic or time-resolved metabolomics data can be arranged as a three-way array with entries organized according to a subjects mode, a metabolites mode and a time mode. While such time-evolving multiway data sets are increasingly collected, revealing the underlying mechanisms and their dynamics from such data remains challenging. For such data, one of the complexities is the presence of a superposition of several sources of variation: induced variation (due to experimental conditions or inborn errors), individual variation, and measurement error. Multiway data analysis (also known as tensor factorizations) has been successfully used in data mining to find the underlying patterns in multiway data. To explore the performance of multiway data analysis methods in terms of revealing the underlying mechanisms in dynamic metabolomics data, simulated data with known ground truth can be studied. We focus on simulated data arising from different dynamic models of increasing complexity, i.e., a simple linear system, a yeast glycolysis model, and a human cholesterol model. We generate data with induced variation as well as individual variation. Systematic experiments are performed to demonstrate the advantages and limitations of multiway data analysis in analyzing such dynamic metabolomics data and their capacity to disentangle the different sources of variations. We choose to use simulations since we want to understand the capability of multiway data analysis methods which is facilitated by knowing the ground truth. Our numerical experiments demonstrate that despite the increasing complexity of the studied dynamic metabolic models, tensor factorization methods CANDECOMP/PARAFAC(CP) and Parallel Profiles with Linear Dependences (Paralind) can disentangle the sources of variations and thereby reveal the underlying mechanisms and their dynamics.

This paper proposes a novel heading control law for an unmanned surface vehicle subject to external disturbance. A robust heading autopilot is developed with trajectory linearization control algorithm and disturbance observer. In the past research, trajectory linearization control is often used in the field of motion control of aircraft. However, in this paper, it will be applied to the heading control strategy of the unmanned surface vehicle. The advantage of trajectory linearization control technology is its simple structure and the robustness of the controlled system can be guaranteed by dealing with external disturbance through a feedback term. Nevertheless, the ability of trajectory linearization control to deal with external disturbance is ultimately limited, so in order to further improve the robustness of heading control strategy, a disturbance observer is designed to compensate external disturbance. Finally, the numerical simulation results of keeping keeping and tracking are given to verify the feasibility and correctness of the proposed scheme.

Redundant parameters in robot kinematic error model may affect the accuracy and robustness of identification results, and should be removed before calibration. In this paper, the pose error model of end-effector was derived, and redundant parameters of robot with arbitrary structure were obtained by analyzing the linear correlation columns of Jacobian matrix. To verify the correctness of the proposed method, a self-calibration method based on plane constraint was proposed. The simulation results show that non-redundant parameter identification model can improve the position accuracy regardless of the presence of noise, while the full-parameter identification model is susceptible to noise and the identification results do not converge. The correctness of the parameter redundancy analysis is proved by the experiments on Franka Emika serial robot. After removing redundant parameters, the position errors reduced from 2.41mm to 1.20mm.