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The effect of heat treatment on the microstructure evolution of a high Nb containing TiAl alloy has been studied. The results indicate that β-segregation, α-segregation and S-segregation in the as-cast and as-forged alloys can be effectively eliminated at the temperature above T α (1350-1400°C) for long holding time (12-24 h) and the full lamellar (FL) microstructure is gained. For the two alloys, the lamellar colony sizes are 120 μm and 2000 μm, respectively after heat treatment at 1400°C for 12 h. Meanwhile, the sizes are 210 μm and 3000 μm, respectively at 1350°C for 24 h. To get a fine homogenous microstructure, the primary as-cast alloy is first subjected to preheat treatment for eliminating the segregations. After the preheat treatment, the alloy is processed by the multi-step canned forging to attain the microstructure with fine grain size.

Considering four popular Active Queue Management (AQM) schemes, we demonstrate by simulations that loss and delay of acknowledgement (ACK) packets, due to congestion, may significantly degrade the stability of a TCP/AQM system. We demonstrate that giving priorities to the ACK packets can stabilize the system.

This paper investigates the application of semi-active inerter in semi-active suspension. A semi-active inerter is defined as an inerter whose inertance can be adjusted within a finite bandwidth by online control actions. A force-tracking approach to designing semi-active suspension with a semi-active inerter and a semi-active damper is proposed in this paper. Two parts are required in the force-tracking strategy: a target active control law and a proper algorithm to adjust the inertance and the damping coefficient online to track the target active control law. The target active control law is derived based on the state-derivative feedback control methodology in the “reciprocal state-space” (RSS) framework, which has the advantage that it is straightforward to use the acceleration information in the controller design. The algorithm to adjust the inertance and the damping coefficient is to saturate the active control force between the maximal and the minimal achievable suspension forces of the semi-active suspension. Both a quarter-car model and a full-car model are considered in this paper. Simulation results demonstrate that the semi-active suspension with a semi-active inerter and a semi-active damper can track the target active control force much better than the conventional semi-active suspension (which only contains a semi-active damper) does. As a consequence, the overall performance in ride comfort, suspension deflection, and road holding is improved, which effectively demonstrates the necessity and the benefit of introducing semi-active inerter in vehicle suspension.

The wavelet function feedback method is proposed for controlling beam halo-chaos for high intensity proton beam in a periodic-focusing channel.After control for four different primary proton distributions,the halo intensity factor become zero,other statistical physical quantities (such as rrms and P2av) and relative average emittances are more than doubly reduced.Simulation results show that the method is very effective and has its prospect for application.

This paper addresses the problem of distributed online generalized Nash equilibrium (GNE) learning for multi-cluster games with delayed feedback information. Specifically, each agent in the game is assumed to be informed a sequence of local cost functions and constraint functions, which are known to the agent with time-varying delays subsequent to decision-making at each round. The objective of each agent within a cluster is to collaboratively optimize the cluster's cost function, subject to time-varying coupled inequality constraints and local feasible set constraints over time. Additionally, it is assumed that each agent is required to estimate the decisions of all other agents through interactions with its neighbors, rather than directly accessing the decisions of all agents, i.e., each agent needs to make decision under partial-decision information. To solve such a challenging problem, a novel distributed online delay-tolerant GNE learning algorithm is developed based upon the primal-dual algorithm with an aggregation gradient mechanism. The system-wise regret and the constraint violation are formulated to measure the performance of the algorithm, demonstrating sublinear growth with respect to time under certain conditions. Finally, numerical results are presented to verify the effectiveness of the proposed algorithm.

Uncovering higher-order spatiotemporal dependencies within human mobility networks offers valuable insights into the analysis of urban structures. In most existing studies, human mobility networks are typically constructed by aggregating all trips without distinguishing who takes which specific trip. Instead, we claim individual mobility motifs, higher-order structures generated by daily trips of people, as fundamental units of human mobility networks. In this paper, we propose two network construction frameworks at the level of mobility motifs in characterizing regional importance in cities. Firstly, we enhance the structural dependencies within mobility motifs and proceed to construct mobility networks based on the enhanced mobility motifs. Secondly, taking inspiration from PageRank, we speculate that people would allocate values of importance to destinations according to their trip intentions. A motif-wise network construction framework is proposed based on the established mechanism. Leveraging large-scale metro data across cities, we construct three types of human mobility networks and characterize the regional importance by node importance indicators. Our comparison results suggest that the motif-based mobility network outperforms the classic mobility network, thus highlighting the efficacy of the introduced human mobility motifs. Finally, we demonstrate that the performance in characterizing the regional importance is significantly improved by our motif-wise framework.