This paper investigates the effective management of distributed Renewable Energy (RE) sources in multi-user Park-level Integrated Energy Systems (PIESs), aiming to coordinate power supply, cooling, and other forms of energy through a Virtual Power Plant (VPP) while enhancing environmental and economic benefits in regulation markets. Beyond the uncertain influence of solar PhotoVoltaic (PV) power on VPP operation, the challenge exists in establishing carbon trading and Integrated Demand Response (IDR) mechanisms to enable internal regulation of VPP. To address the above problem, a data-driven two-stage Distributed Robust Optimization (2-DRO) model is proposed. The overall goal is to minimize the operating costs of VPP by maintaining a supply-demand balance in PIESs along with protecting the electricity for production tasks. A case study of an actual PIES in eastern China reveals that our model is more practical and cost-effective. It integrates the deployment of both electricity and cooling energy under the mechanism of IDR and carbon emissions, realising flexible scheduling for multi-scenario operation of PIES.

Integrating Knowledge Graphs (KGs) into recommendation systems as supplementary information has become a prevalent strategy. By leveraging the semantic relationships between entities in KGs, recommendation systems can better comprehend user preferences. Due to the unique structure of KGs, methods based on Graph Neural Networks (GNNs) have emerged as the current technical trend. However, existing GNN-based methods struggle to (1) filter out noisy information in real-world KGs, and (2) differentiate the item representations obtained from the knowledge graph and bipartite graph. In this paper, we introduce a novel model called Attention-enhanced and Knowledge-fused Dual item representations Network for recommendation (namely AKDN) that employs attention and gated mechanisms to guide aggregation on both knowledge graphs and bipartite graphs. In particular, we firstly design an attention mechanism to determine the weight of each edge in the information aggregation on KGs, which reduces the influence of noisy information on the items and enables us to obtain more accurate and robust representations of the items. Furthermore, we exploit a gated aggregation mechanism to differentiate collaborative signals and knowledge information, and leverage dual item representations to fuse them together for better capturing user behavior patterns. We conduct extensive experiments on two public datasets which demonstrate the superior performance of our AKDN over state-of-the-art methods, like Knowledge Graph Attention Network (KGAT) and Knowledge Graph-based Intent Network (KGIN).

Heterogeneous unmanned aerial vehicle (UAV) swarms have garnered significant attention from researchers worldwide due to their remarkable flexibility, diverse mission capabilities, and wide-ranging potential applications. Mission planning stands at the core of UAV swarm operations, requiring consideration of various factors including mission environment, requirements, and inherent characteristics. In this paper, we investigate the model of the cooperative tasking problem in heterogeneous UAV swarms. We provide a comprehensive review of artificial intelligence algorithms applied in UAV swarm mission planning, analyzing their strengths and weaknesses in multi-UAV cooperative environments. By discussing these key techniques and their practical applications, the article highlights future research trends and challenges. This review serves as a valuable reference for understanding the current state of AI algorithm applications in heterogeneous UAV swarm task assignments.

The Correlation Clustering Problem (CorCP) is a significant clustering problem based on the similarity of data. It has significant applications in different fields, such as machine learning, biology, and data mining, and many different problems in other areas. In this paper, the Balanced $\mathrm{2}$-CorCP (B $\mathrm{2}$-CorCP) is introduced and examined, and a new interesting variant of the CorCP is described. The goal of this clustering problem is to partition the vertex set into two clusters with equal size, such that the number of disagreements is minimized. We first present a polynomial time algorithm for the B $\mathrm{2}$-CorCP on $M$-positive edge dominant graphs $\mathrm{(}M\mathrm{⩾}\mathrm{3}\mathrm{)}$. Then, we provide a series of numerical experiments, and the results show the effectiveness of our algorithm.

In this paper, we study the prize-collecting $k$-Steiner tree (PC $k$ST) problem. We are given a graph $G\mathrm{=}\mathrm{(}V\mathrm{,}E\mathrm{)}$ and an integer $k$. The graph is connected and undirected. A vertex $r\mathrm{\in }V$ called root and a subset $R\mathrm{\subseteq }V$ called terminals are also given. A feasible solution for the PC $k$ST is a tree $F$ rooted at $r$ and connecting at least $k$ vertices in $R$. Excluding a vertex from the tree incurs a penalty cost, and including an edge in the tree incurs an edge cost. We wish to find a feasible solution with minimum total cost. The total cost of a tree $F$ is the sum of the edge costs of the edges in $F$ and the penalty costs of the vertices not in $F$. We present a simple approximation algorithm with the ratio of $\mathrm{5.9672}$ for the PC $k$ST. This algorithm uses the approximation algorithms for the prize-collecting Steiner tree (PCST) problem and the $k$-Steiner tree ( $k$ST) problem as subroutines. Then we propose a primal-dual based approximation algorithm and improve the approximation ratio to $\mathrm{5}$.