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AI for Industry
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AI for Industry

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 July 2026 | Viewed by 952

Special Issue Editors

Prof. Dr. Zhenyan Ji

E-Mail Website
Guest Editor
School of Cyberspace Science and Technology, Beijing Jiaotong University, Beijing 100044, China
Interests: computer vision; multimodal data fusion; blockchain; data privacy protection
Dr. Longjie Li

E-Mail Website
Guest Editor
School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, China
Interests: big data analytics and applications; graph machine learning; complex network analysis
Assoc. Prof. Lijun Cheng

E-Mail Website
Guest Editor
School of Software, Shanxi Agricultural University, Taigu 030801, China
Interests: smart agriculture; blockchain technology
Dr. Weidong Wang

E-Mail Website
Guest Editor
College of Computer Science, Beijing University of Technology, Beijing 100124, China
Interests: artificial intelligence of things (AIoT); intelligent analysis of software vulnerabilities (IASV); mining software repositories (MSR)

Special Issue Information

Dear Colleagues,

This Special Issue “AI for Industry” brings together cutting-edge research on artificial intelligence applied in industry. Artificial intelligence (AI) is revolutionizing various industries by enhancing efficiency, driving innovation, ensuring data security, and enabling smarter decision-making across sectors such as manufacturing, transportation, healthcare, and finance. Topics of this Special Issue include Agent and Multi-Agent Systems, Artificial Neural Networks, Large Models, Autonomous and Ubiquitous Computing, Biologically Inspired Neural Networks, Collective Computational Intelligence, Computational Intelligence, Computer Vision, Convolutional Neural Networks, Crisis and Risk Management, Cybersecurity and AI, Blockchain, Data Privacy Protection, Data Fusion, Data Mining and Information Retrieval, Decision Support Systems, Distributed AI Systems and Architectures, Engineering and Industry, Environmental Intelligent Modelling, Evaluation of AI Systems, Evolving Systems Optimization, Expert Systems, Fuzzy Logic and Systems, Human–Machine Interaction/Presence Information and Optimization, Intelligent Real Time Monitoring and Control, Knowledge Acquisition and Representation, Mathematical Foundations of AI and Intelligent Computational Methods, Planning and Scheduling, Robotics and Virtual Reality, Signal and Image Processing, Time Series and Forecasting, and so on.

Prof. Dr. Zhenyan Ji
Dr. Longjie Li
Assoc. Prof. Lijun Cheng
Dr. Weidong Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • AI
  • industry
  • data mining
  • data privacy protection
  • blockchain

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Published Papers (2 papers)

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Research

16 pages, 20925 KB  
Article
RewriteGen: Autonomous Query Optimization for Retrieval-Augmented Large Language Models via Reinforcement Learning
by Yixuan Zhao, Zihao Fan, Yingying Cao, Zhengjia Lyu and Jingyuan Li
Electronics 2026, 15(5), 1058; https://doi.org/10.3390/electronics15051058 - 3 Mar 2026
Viewed by 473
Abstract
Large Language Models (LLMs) have achieved substantial progress in knowledge-intensive tasks, particularly through Retrieval-Augmented Generation (RAG) frameworks. However, existing RAG systems often suffer from performance degradation when input queries are misaligned with retrieval requirements, and effectively coordinating retrieval with reasoning remains challenging—especially for [...] Read more.
Large Language Models (LLMs) have achieved substantial progress in knowledge-intensive tasks, particularly through Retrieval-Augmented Generation (RAG) frameworks. However, existing RAG systems often suffer from performance degradation when input queries are misaligned with retrieval requirements, and effectively coordinating retrieval with reasoning remains challenging—especially for multi-hop questions requiring iterative retrieval steps. To address these challenges, we propose ReWriteGen, a unified framework that integrates query rewriting, retrieval augmentation, and complementary generation within a coordinated architecture, optimized using reinforcement learning (Group Relative Policy Optimization, GRPO) and Direct Preference Optimization (DPO). ReWriteGen introduces a retrieval-aware query rewriting mechanism to better align input queries with external knowledge. The framework optimizes retrieval-augmented answers without requiring supervised reasoning annotations.Our experiments show that ReWriteGen consistently outperforms traditional RAG baselines across three multi-hop QA benchmarks: HotpotQA, MuSiQue, and 2Wiki. On HotpotQA, ReWriteGen achieves improvements of 5.32 and 5.10 percentage points in EM and LLM-based evaluation, respectively, compared to the strongest baseline. Corresponding gains of 11.90 and 7.18 are observed on MuSiQue, and 15.45 and 18.60 on 2Wiki.ReWriteGen enhances the coordination between retrieval and reasoning in LLMs, delivering consistent performance gains while reducing reliance on supervised reasoning annotations and extensive task-specific engineering. Full article
(This article belongs to the Special Issue AI for Industry)
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23 pages, 2557 KB  
Article
MECFN: A Multi-Modal Temporal Fusion Network for Valve Opening Prediction in Fluororubber Material Level Control
by Weicheng Yan, Kaiping Yuan, Han Hu, Minghui Liu, Haigang Gong, Xiaomin Wang and Guantao Zhang
Electronics 2026, 15(4), 783; https://doi.org/10.3390/electronics15040783 - 12 Feb 2026
Viewed by 242
Abstract
During fluororubber production, strong material agitation and agglomeration induce severe dynamic fluctuations, irregular surface morphology, and pronounced variations in apparent material level. Under such operating conditions, conventional single-modality monitoring approaches—such as point-based height sensors or manual visual inspection—often fail to reliably capture the [...] Read more.
During fluororubber production, strong material agitation and agglomeration induce severe dynamic fluctuations, irregular surface morphology, and pronounced variations in apparent material level. Under such operating conditions, conventional single-modality monitoring approaches—such as point-based height sensors or manual visual inspection—often fail to reliably capture the true process state. This information deficiency leads to inaccurate valve opening adjustment and degrades material level control performance. To address this issue, valve opening prediction is formulated as a data-driven, control-oriented regression task for material level regulation, and an end-to-end multimodal temporal regression framework, termed MECFN (Multi-Modal Enhanced Cross-Fusion Network), is proposed. The model performs deep fusion of visual image sequences and height sensor signals. A customized Multi-Feature Extraction (MFE) module is designed to enhance visual feature representation under complex surface conditions, while two independent Transformer encoders are employed to capture long-range temporal dependencies within each modality. Furthermore, a context-aware cross-attention mechanism is introduced to enable effective interaction and adaptive fusion between heterogeneous modalities. Experimental validation on a real-world industrial fluororubber production dataset demonstrates that MECFN consistently outperforms traditional machine learning approaches and single-modality deep learning models in valve opening prediction. Quantitative results show that MECFN achieves a mean absolute error of 2.36, a root mean squared error of 3.73, and an R2 of 0.92. These results indicate that the proposed framework provides a robust and practical data-driven solution for supporting valve control and achieving stable material level regulation in industrial production environments. Full article
(This article belongs to the Special Issue AI for Industry)
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