The growing complexity of industrial systems demands a transition from passive monitoring to proactive decision support, a shift that hinges on advanced intelligent perception. However, most systems remain confined to brittle, rule-based logic, which operates on fixed symptom-to-action mappings and thus cannot perceive the underlying, context-dependent operational intent. This perceptual gap is particularly detrimental especially in fault diagnosis, where this inability to adapt leads to frequent misdiagnoses and costly downtime. To overcome this limitation, this paper introduces the Intent-Aware Enhancement Framework (IAEF)1, which replaces static rules by actively creating and reasoning over a dynamic causal model. This is achieved through two core method. The Information Theory-guided Causal Graph Revision (ITCGR) algorithm provides the foundation by constructing a reliable causal model, uniquely leveraging information-theoretic metrics to guide an LLM for verifiable revisions on sparse data. Building on this model, the Multi-scale Adaptive Path Reasoning (MAPR) method then infers the true operational intent, employing a novel adaptive fusion model to robustly navigate complex inference chains. Experimental validation in a real-world case demonstrates the framework’s ability to accurately diagnose fault root causes under varying conditions, a task where traditional systems fail. The proposed approach significantly outperforms baselines, providing a foundational methodology for advancing industrial intelligence.

The demand, technologies, and market all put forward the request for platformization of the vehicle logistics industry. In China, a few logistics companies have plans but only one has initially built a platform for vehicle logistics. This platform has also attracted a considerable number of service providers to join, which has led to competition between service providers and the platform. To promote the platformization of the vehicle logistics industry and attract more customers to use platform-based services, how to leverage the incentive effect of subsidies is a topic of concern for the government. Motivated by the above, this article discusses whether the exogenous subsidies (provided to customers for platform service or providers service) can accelerate the process of the vehicle logistics industry. We explore the role of subsidies by developing a model (a hotelling line) that describes the differentiated competition between the platform and service providers. Our study found that subsidies do not always work as expected. The effectiveness of the subsidy depends on service advantages and customer preferences. Subsidies may even be counterproductive if customers prefer service providers or if platform services lack advantages. This is a reminder for both the government and industry that to accelerate the platformization of the vehicle logistics industry, it is necessary to combine reality and not blindly subsidize.

Electroencephalogram (EEG) has been widely utilized in emotion recognition due to its high temporal resolution and reliability. However, the individual differences and non-stationary characteristics of EEG, along with the complexity and variability of emotions, pose challenges in generalizing emotion recognition models across subjects. In this paper, an end-to-end framework is proposed to improve the performance of cross-subject emotion recognition. A novel evolutionary programming (EP)-based optimization strategy with neural network (NN) as the base classifier termed NN ensemble with EP (EPNNE) is designed for cross-subject emotion recognition. The effectiveness of the proposed method is evaluated on the publicly available DEAP, FACED, SEED, and SEED-IV datasets. Numerical results demonstrate that the proposed method is superior to state-of-the-art cross-subject emotion recognition methods. The proposed end-to-end framework for cross-subject emotion recognition aids biomedical researchers in effectively assessing individual emotional states, thereby enabling efficient treatment and interventions.

As an emerging delivery pattern, long-distance non-standardized delivery (LND) is receiving the attention of large logistics companies. Despite the growing demand and customer market for LND, so far there is no mature logistics platform for LND. For logistics companies planning to carry out LND services, there is currently no feasible and effective operations methodology. This paper summarizes the characteristics of LND from the perspective of operations and preliminarily explores the operations methodology of LND. Specifically, the two issues, driver assignment and order settlement for LND, are discussed in detail. To the best of our knowledge, our paper is the first one to study the complete operations methodology for LND. Our proposed methodology could help these large logistics companies that have already launched long-distance standardized delivery services expand the scope of their service. Experimental results demonstrate the feasibility and effectiveness of the proposed driver assignment and order settlement methods. Finally, the paper discusses the impact of drivers’ human factors on LND operations and concludes with several interesting findings. These findings can help the future exploration of how to design a more efficient and beneficial logistics platform for LND.

The integration of the industrial Internet of Things (IoT) and blockchain technology is changing the business and management model of the supply chain. Much work is focused on how to promote the application of blockchain-based industrial IoT in the supply chain from both academic research and industrial practice. However, due to the different industrial requirements in industrial scenes, the gap between technology researches and industrial applications is still large. Therefore, the article adopts the mixed method of enterprise survey and literature review to identify the actual industrial requirements in different supply chain scenes. Also, the characteristics and applicable scenarios of industrial IoT and blockchain have been analyzed. Then, the potential application opportunities of blockchain-based industrial IoT in nine scenes are discussed in detail. This study reveals the technical challenges and practical challenges of these applications, which potentially guides research on applying industrial IoT and blockchain technology in the supply chain.