Rapid advancements in digitalization and artificial intelligence (AI) have catalyzed the adoption of digital twin technologies in the construction sector, enabling real-time synchronization between virtual models and physical systems. Simultaneously, on-site robotic automation has shown promise for reducing physical workloads, enhancing productivity, and contributing to sustainability goals that are key values of Industry 5.0. However, current digital twin implementations rarely incorporate multirobot construction systems, often relying on single-robot approaches or purely offline simulations. This gap hinders the realization of truly integrated construction environments that combine sensing, data analytics, wireless communications, and multirobot coordination. In response, this article proposes ConstrucTwin, a digital twin-driven multirobot construction framework designed to support complex construction tasks in real-world settings. By combining a 5G communication estimation-involved architecture and a cross-level planning strategy, ConstrucTwin streamlines interactions between physical robots and their digital counterparts. Essential tasks such as motion and task-level planning, as well as remote human-in-the-loop (HIL) oversight, are orchestrated within a single unified architecture. Through case studies involving rebar cage and brick wall construction, we demonstrate how an integrated approach to vision-based servoing and multirobot coordination enhances execution speed, precision, and scalability. The results underscore the system’s potential to advance human-centric, resilient, and sustainable construction, thereby aligning with the broader vision of Industry 5.0.

Existing studies on traffic flow prediction primarily rely on on-board devices to collect vehicle trajectory data , which can potentially infringe upon the privacy of users and limit the applicability of the method. Additionally, traffic flow prediction remains challenging due to the complex spatial and temporal dependencies within real-world traffic networks. To address these limitations, this paper introduces a framework for analyzing discrete vehicle trajectory data at urban intersections. By incorporating various external physical factors into traffic flow prediction, this framework derives embedding vectors from vehicle trajectory sequences and road network topology , modeling their spatio-temporal dependencies using Skip-Gram and GraphSAGE, respectively. Additionally, the intersection similarity is introduced to capture and integrate traffic flow patterns between the target intersection and similar intersections. A Spatio-Temporal Graph Convolutional Neural Network (ST-GCN) algorithm, which combines Graph Convolutional Networks (GCN) with Long Short-Term Memory (LSTM), is developed to achieve precise traffic flow prediction. Extensive experiments on a real-world traffic flow dataset from Qingdao, China, validate that the proposed method outperforms state-of-the-art baseline methods .

Industrial Internet of Things (IIoT) plays a crucial role in advancing smart manufacturing by connecting numerous devices, enabling data exchanges, and supporting industrial applications. Yet, the timely and proper scheduling of asynchronously generated Heterogeneous Computing Tasks (HCTs) in IIoT environments remains a significant challenge. In this article, we first introduce the representation and notation of such HCTs and define a computing network structure. We then propose an initial mathematical programming-based scheduling model aimed at minimizing HCT completion time. To make this model easy to solve, we reformulate it by using logical constraints and derive a constraint programming-based model, for which a feasibility-guaranteed solution algorithm is developed. This algorithm leverages two easily-verified propositions to either identify feasible solutions or demonstrate the infeasibility of the problem.Furthermore, we have proven a critical proposition that facilitates the development of a hybrid programming-based scheduling approach, effectively combining the strengths of both mathematical and constraint programming models. As demonstrated through extensive computational experiments, our proposed approach achieves an average reduction of 20% in HCT completion time in comparison with its existing peers. It consistently and timely provides the high-quality solutions that meet the required deadlines.