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This paper studies the resilient group consensus of continuous-time second-order multi-agent systems (MASs) with malicious agents. Adopting the idea that each normal agent ignores the most extreme values from neighbors, synchronous resilient impulsive algorithm based on sampled data is proposed for normal agents with bounded communication delays to achieve group consensus. Meanwhile, asynchronous resilient impulsive algorithm is also proposed for MASs where each agent has its own time clock. Sufficient topological conditions are obtained for solving resilient group consensus under synchronous and asynchronous settings, respectively. Numerical examples are provided to illustrate the effectiveness of the theoretical results. • Resilient group consensus is investigated for second-order multi-agent systems. • Resilient impulsive algorithm is proposed under the asynchronous setting. • Some sufficient conditions are established for solving resilient group consensus. [ABSTRACT FROM AUTHOR]

In this work, we present a scalable in-situ growth strategy for self-assembling a carbon felt supported Bi 2 O 3 fractal nanostructure (f-Bi 2 O 3 /CF), enabling superior performance towards electrocatalytic CO 2 -to-formate conversion with an optimal Faradaic efficiency of 95.1% at −0.9 V (vs. RHE) in flow cell. More impressively, the current density reaches an industrially relevant 235.7 mA cm−2 and remains a high FE formate of 87.7%. The enhanced catalytic activity of the f-Bi 2 O 3 /CF model is attributed to the increased adsorption energy of OCHO* intermediate by changing the surface charge distribution according to DFT calculations. Using the aforementioned method, a series of other CF supported metal oxide catalysts (i.e., CuO, In 2 O 3 , and SnO 2) are easily obtained. A brief techno-economic analysis (TEA) model demonstrates the economic potential of electrocatalytic hydrogenation of CO 2 into formate in flow cell. Our findings offer a promising pathway for the fabrication of stable and accessible fractal structure, applicable to a wide range of scenarios. [Display omitted] • A Bi 2 O 3 -based fractal nanostructure is obtained via a scalable in-situ fabrication strategy. • The f-Bi 2 O 3 /CF exhibits a 95.1% FE formate and maintains a high value of 87.7% under industrially relevant current density of 235.7 mA cm-2 in flow cell. • DFT calculations reveal the origin of enhanced CO 2 RR activity for f-Bi 2 O 3 /CF. • TEA model demonstrates the profitable electrocatalytic hydrogenation of CO 2 over f-Bi 2 O 3 /CF. • A series of novel CF supported metal oxide catalysts (i.e., CuO, In 2 O 3 and SnO 2) were gained using the same synthetic route. [ABSTRACT FROM AUTHOR]

This paper establishes a novel finite-time containment control framework for multi-agent systems such that the containment control problem can be solved at any preset time with static or dynamic leaders. In order to reach this goal, nonlinear feedback control protocols are introduced. We prove that the proposed protocols can solve containment problems at the preset time if the communication graph has a spanning forest. Numerical simulations are presented to illustrate the effectiveness of the obtained theoretical results. [ABSTRACT FROM AUTHOR]

Consensus can be achieved under various topologies for multi-agent systems. Then, which one is the optimal? In this paper, we consider the problem of optimal topology for consensus of heterogeneous multi-agent systems. We assume that the system consists of a leader and several followers with heterogeneous dynamics. Firstly, we define a quadratic cost function composed of consensus error and control effort for the heterogeneous multi-agent system. Secondly, by using linear-quadratic regulator theory and matrix theory, we prove that the optimal position topology and the optimal velocity topology are star graphs. Finally, simulations are carried out to illustrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

• A multifactor approach is presented to mine the inner patterns of flight delays. • High-dimensional data from Beijing International Airport (PEK) is analyzed. • Traditional macro-level factors are replaced with specific micro influential factors. • A novel prediction model based on deep belief network (DBN) is employed. • Support vector regression (SVR) is used to perform a supervised fine-tuning. This study analyzes high-dimensional data from Beijing International Airport and presents a practical flight delay prediction model. Following a multifactor approach, a novel deep belief network method is employed to mine the inner patterns of flight delays. Support vector regression is embedded in the developed model to perform a supervised fine-tuning within the presented predictive architecture. The proposed method has proven to be highly capable of handling the challenges of large datasets and capturing the key factors influencing delays. This ultimately enables connected airports to collectively alleviate delay propagation within their network through collaborative efforts (e.g., delay prediction synchronization). [ABSTRACT FROM AUTHOR]