Partial Differential Equations (PDEs) are fundamental for modeling physical systems, yet solving them in a generic and efficient manner using machine learning-based approaches remains challenging due to limited multi-input and multi-scale generalization capabilities, as well as high computational costs. This paper proposes the Multi-input and Multi-scale Efficient Transformer (MMET), a novel framework designed to address the above challenges. MMET decouples mesh and query points as two sequences and feeds them into the encoder and decoder, respectively, and uses a Gated Condition Embedding (GCE) layer to embed input variables or functions with varying dimensions, enabling effective solutions for multi-scale and multi-input problems. Additionally, a Hilbert curve-based reserialization and patch embedding mechanism decrease the input length. This significantly reduces the computational cost when dealing with large-scale geometric models. These innovations enable efficient representations and support multi-scale resolution queries for large-scale and multi-input PDE problems. Experimental evaluations on diverse benchmarks spanning different physical fields demonstrate that MMET outperforms SOTA methods in both accuracy and computational efficiency. This work highlights the potential of MMET as a robust and scalable solution for real-time PDE solving in engineering and physics-based applications, paving the way for future explorations into pre-trained large-scale models in specific domains. This work is open-sourced at https://github.com/YichenLuo-0/MMET.

As traditional analog industrial automation transitions toward digitalization, the efficient deployment of control systems becomes increasingly critical. Open programmable logic controllers (OpenPLC) and the Modbus communication protocol, leveraging the widespread adoption of containerization technologies, are gradually migrating to native cloud architectures. This paper investigates the deployment of OpenPLC in local environments, Docker containers, and Kubernetes clusters, evaluating several key performance indicators, including resource consumption, Modbus communication response time, throughput, multi-user handling, stability, and fault recovery. By designing experimental platforms tailored to different environments and conducting validation across multiple platforms, the experimental results indicate that, although local deployment performs better in terms of low latency and resource consumption, deployments on the Kubernetes platform exhibit significant advantages in fault recovery, throughput, and system stability. In contrast, Docker-based deployments demonstrate more balanced performance, but lag behind Kubernetes in scalability and fault tolerance. Based on the experimental findings, this study provides valuable insights for selecting the optimal deployment strategy for industrial automation systems, taking into account OpenPLC, containerization technologies, performance requirements, and the integration of cloud-fog automation for enhanced flexibility and scalability.

This article presents a Kubernetes-based database benchmarking framework for Cloud-Fog Automation (CFA) in industrial systems, integrating Locust (dynamic load simulation) and Prometheus (resource monitoring) to evaluate four databases under industrial workloads: write-heavy , query-intensive (real-time analytics), and 6:4 read-write hybrid (control-logic scenarios). Kubernetes, as a representative of cloud-native technologies, is a crucial support for industrial CPS, but the database performance for industrial data management is yet to be fully determined. In 1k-concurrent-user tests (emulating distributed CFA edge nodes), ReductStore delivered 649.63 req/s throughput with <6 ms latency, ideal for fog-level real-time control, while OpenGauss achieved P99 latency <1 ms (critical for PLC synchronization) at 4.69 Gi memory cost—quantifying trade-offs for resource-constrained fog deployments. The framework demonstrates Kubernetes’ role in elastic cloud-fog orchestration, aligning with CFA’s industrial demands: InfluxDB suits massive IIoT data aggregation, while OpenGauss optimizes mission-critical latency. Our results bridge cloud-native scalability with deterministic industrial performance, enabling cost-efficient DBMS selection for smart factories.

Virtual Programmable Logic Controllers (vPLCs), as a newborn technology, are becoming increasingly important in modern industrial automation due to their flexibility and scalability. There is lack of researches on data synchronization and redundancy mechanisms for vPLCs, limiting applications of vPLCs in critical industrial scenarios. This paper designs and implements a data synchronization and redundancy mechanism between vPLCs based on heartbeat detection to enhance the reliability of vPLC systems. The mechanism continuously monitors for failures and synchronizes data between vPLCs to ensure seamless control task takeover in the event of a failure. Experimental results demonstrate the mechanism’s high effectiveness in fault detection and recovery, achieving a redundancy switchover time that meets industrial application requirements.

With the rapid development of the industrial internet of things and automation control systems, supervisory control and data acquisition (SCADA) systems have been widely adopted in industrial manufacturing due to their flexibility and scalability. The cloud-fog automation (CFA) paradigm is emerging to address higher real-time and computing demands in complex industrial environments. To fully leverage the efficiency, flexibility, and scalability of Kubernetes, an open-source container orchestration platform Kubernetes in managing containerized applications, this article investigates methods for deploying SCADA systems on the Kubernetes platform. This approach aims to capitalize on Kubernetes’ benefits, such as automated deployment, elastic scaling, and high availability, to optimize resource management and enhance system performance. To validate the proposed solution, we employs testing tools such as wrk and tc, along with monitoring tools like Prometheus and Grafana, to conduct a comprehensive evaluation of Kubernetes’ advantages in various scenarios. We focus on three key aspects: reliability, scalability, and security. The results demonstrate that Kubernetes can significantly improve the scalability, fault recovery capabilities, and stability of SCADA systems.

The advancement of large models has initiated a transformation in the field of time-series forecasting. Both the repurposing of existing large models and the development of large models tailored for time-series analysis have exhibited impressive performance. In industrial applications, challenges, such as limited data availability and constrained computational resources, render the first approach viable. However, it is important to note that this approach is still in its infancy and lacks both a thorough technical analysis and a unified effective framework. Meanwhile, as large models become a mainstream artificial intelligence paradigm, it is urgent to discuss typical industrial scenarios, such as how automated systems can transition from intelligent to collaborative operation and maintenance. In light of this premise, this article endeavors to advance a generalized technical framework for large model-driven time-series forecasting, under which existing methods can be subsumed. Then, within this overarching technical paradigm, the technical advancements facilitated by diverse methods will be systematically elucidated and analyzed, along with a comparative evaluation conducted across seven benchmark datasets. Concluding this analysis, the implementation pathway for the industrial automation system is delineated that integrates operator action commands to forecast post-action trends to assess action correctness in advance. Finally, the challenges and future directions of large model-based time-series forecasting are outlined.

The need for energy efficiency in wireless communication is prevalent in all areas, but to an even greater extent in low-power and lossy networks that rely on resource-constrained devices. This paper seeks to address the problem of modeling the battery lifetime of a duty-cycled node, participating in a wireless sensor network that is typically used in smart home and building applications. Modeling in MATLAB and experimentation with prototype testing are employed to predict and validate the battery lifetime. Various scenarios including sleepy end devices in a wireless sensor network are modeled and validated. They range from variable wake-up frequency and packet payload transmission to increasing network contention with the addition of network load. A comprehensive analysis of the main factors contributing to wasteful energy usage is provided. It can be concluded that the model can estimate the battery lifetime under different testing scenarios with an error rate less than 5%.

Motivated by the proliferation of wireless building automation systems (BAS) and increasing security-awareness among BAS operators, in this paper, we propose a taxonomy for the security assessment of BASs. We apply the proposed taxonomy to Thread, an emerging native IP-based protocol for BAS. Our analysis reveals a number of potential weaknesses in the design of Thread. We propose potential solutions for mitigating several identified weaknesses and discuss their efficacy. We also provide suggestions for improvements in future versions of the standard. Overall, our analysis shows that Thread has a well-designed security control for the targeted use case, making it a promising candidate for communication in next generation BASs.