In this paper, we present a novel Byzantine fault-tolerant consensus protocol for sharded blockchain networks that does not rely on expensive leader-driven communication. The proposed protocol selects a single block proposer at a time and uses threshold signatures as a voting mechanism to confirm the validity of the proposed block. By using a gossip-like communication scheme, each node can collect and recover the group signature within $\mathrm {O(log}N)$ steps. With only one block proposer per consensus round, there is no possibility of conflicting blocks and resultant forks. Therefore, our consensus protocol requires only one round of one-way communication to achieve finality for each block. Our protocol guarantees safety and liveness while tolerating up to $f$ faulty participants among $2f+1$ nodes. Our performance study shows that the proposed protocol enables hundreds of nodes to participate in the agreement process, and can finalize large blocks in approximately 10 seconds.

With the rapidly growing popularity of cloud services, the cloud computing faces critical challenges to orchestrate the deployment and operation of cloud applications on heterogenous cloud platforms. Cloud applications are built on a platform model that abstracts away underlying platform-specific details, so that their orchestration can benefit from the abstract view and flexibility of the underlying platform configuration. However, considerable efforts are still required to properly manage complicated cloud applications. This paper proposes a model-driven approach to cloud application orchestration which promotes the concerns of distinct roles for cloud system provisioning and operation. By establishing a set of capabilities as modeling constructs, our approach allows TOSCA-based application topology itself and its orchestration needs to be specified in a way to provide a more targeted support for different needs and concerns of application developers and operators. With novel orchestration features like application topology description, platform capability modeling, and role-awareness for cloud application orchestration, it can significantly reduce the complexity of application orchestration in diverse cloud environments. To show the feasibility and effectiveness of our proposal for cloud application orchestration, we present a proof-of-concept orchestration system implementation and evaluate its deployment and orchestration results in a Kubernetes cluster.

Grain warehouses are critical nodes in food storage, circulation, and reserve systems, and their safe operation is relevant to occupational protection, reserve stability, and food-system resilience. However, unsafe warehouse behaviors are still mainly identified through manual patrols and fragmented inspections, making continuous observation, cross-site coordination, and early intervention difficult. To address this problem, this study proposes a grain warehouse safety governance framework with federated visual intelligence. The framework combines a real-time visual perception module, data-local federated learning, and a city-level digital supervision platform. Drawing on the Wenzhou grain reserve application scenario, the study defines twelve visually observable risk categories, including personal protective equipment (PPE) violations, and links artificial intelligence (AI) recognition with multi-source inspection, spatial mapping, warning generation, collaborative verification, work approval, closed-loop rectification, and large-model-assisted analysis. A four-client technical pilot compares isolated local, centralized, Federated Averaging (FedAvg), and Federated Proximal Optimization (FedProx) training across three selected recognition tasks. The task-specific results provide quantitative evidence from held-out warehouse-level validation and an intermittent-client stress test. The best held-out weighted F1-score (F1) values were 0.7712 for crossing over a conveyor, 0.5891 for passing under a conveyor, and 0.8481 for missing five-point safety harness. In the one-small-client-absent stress test, FedProx achieved weighted F1 scores of 0.5701, 0.4324, and 0.8064 for the same tasks, respectively. The framework translates visual detections into warning outputs, management indicators, and accountable safety workflows under data-governance constraints. It positions computer vision (CV) as an automated observation mechanism for responsible AI governance and embeds visual intelligence into a human-confirmed, data-local, and auditable safety-management workflow for sustainable food-storage infrastructure.