Journal of Industrial Information Integration最新文献

{"title":"Enhancing semantic search using ontologies: A hybrid information retrieval approach for industrial text","authors":"Syed Meesam Raza Naqvi ,&nbsp;Mohammad Ghufran ,&nbsp;Christophe Varnier ,&nbsp;Jean-Marc Nicod ,&nbsp;Noureddine Zerhouni","doi":"10.1016/j.jii.2025.100835","DOIUrl":"10.1016/j.jii.2025.100835","url":null,"abstract":"<div><div>Despite the increased focus on data in Industry 4.0, textual data has received little attention in the production and engineering management literature. Data sources such as maintenance records and machine documentation usually are not used to help maintenance decision-making. Available studies mainly focus on categorizing maintenance records or extracting meta-data, such as time of failure, maintenance cost, etc. One of the main reasons behind this underutilization is the complexity and unstructured nature of the industrial text. In this study, we propose a novel hybrid information retrieval approach for industrial text using multi-modal learning. Maintenance operators can use the proposed system to query maintenance records and find similar solutions to a given problem. The proposed system utilizes heterogeneous (multi-modal) data, a combination of maintenance records, and machine ontology to enhance semantic search results. We used the state-of-the-art Large Language Models (LLMs); BERT (Bidirectional Encoder Representations from Transformers) for textual similarity. For similarity among ontology labels, we used a modified version of Wu-Palmer’s similarity. A hybrid weighted similarity is proposed, incorporating text and ontology similarities to enhance semantic search results. The proposed approach was validated using an open-source dataset of real maintenance records from excavators collected over ten years from different mining sites. A retrieval comparison using only text and multi-modal data is performed to estimate the proposed system’s effectiveness. Quantitative and qualitative analysis of results indicates a performance improvement of 8% using the proposed hybrid similarity approach compared to only text-based retrieval. To the best of our knowledge, this is the first study to combine LLMs and machine ontology for semantic search in maintenance records.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"45 ","pages":"Article 100835"},"PeriodicalIF":10.4,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143677755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"STEP: A structured prompt optimization method for SCADA system tag generation using LLMs","authors":"Fuyu Ma ,&nbsp;Dong Li ,&nbsp;Yu Liu ,&nbsp;Dapeng Lan ,&nbsp;Zhibo Pang","doi":"10.1016/j.jii.2025.100832","DOIUrl":"10.1016/j.jii.2025.100832","url":null,"abstract":"<div><div>In the domain of industrial control, supervisory control and data acquisition (SCADA) systems are essential for real-time monitoring and efficient data acquisition. However, as industrial systems grow in scale and complexity, conventional tag configuration methods face challenges in balancing precision and operational efficiency. Addressing these challenges requires innovative solutions. The rapid evolution of generative artificial intelligence, particularly large language models (LLMs), offers a transformative approach. This study introduces a structured prompt optimization strategy, termed structured tag engineering prompt (STEP), to increase the ability of LLMs to generate high-quality tag files. To validate the STEP method, we assessed five mainstream LLMs on basic tag generation tasks via the CodeBERTScore and pass@k metrics. The results revealed that performance of all models has been improved, thus validating the effectiveness of the proposed optimization method. On the basis of these findings, a tag generation framework grounded in the STEP method was developed and validated through case studies and practical industrial scenarios. These validations confirmed the STEP method’s applicability, demonstrating its value and potential to advance prompt engineering for SCADA systems. In summary, this study contributes to the automation and intelligence of industrial control systems while providing unique insights through the application of LLMs combined with prompt engineering in addressing complex industrial tasks.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"45 ","pages":"Article 100832"},"PeriodicalIF":10.4,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bridging the gap between Industry 4.0 and manufacturing SMEs: A framework for an end-to-end Total Manufacturing Quality 4.0’s implementation and adoption","authors":"Badreddine Tanane ,&nbsp;Mohand-Lounes Bentaha ,&nbsp;Baudouin Dafflon ,&nbsp;Néjib Moalla","doi":"10.1016/j.jii.2025.100833","DOIUrl":"10.1016/j.jii.2025.100833","url":null,"abstract":"<div><div>Manufacturing is one of the industrial sectors taking benefit from the 4th industrial revolution and bringing existing production capacities closer to the ”factory of the future”. Quality, as a main concern in manufacturing, is also to benefit from this change of paradigm by introducing new key enabling technologies such as Internet of Things (IoT) and Artificial Intelligence (AI) into quality management, earning it the label of Quality 4.0 (Q4.0). The implementation of these paradigms is still gathering research efforts as it is arduous to design and realize effective end-to-end Decision Support Systems (DSSs) for Q4.0, with several dimensions to consider when integrating digitalization with quality. This is an even more challenging task when it comes to SMEs’ efforts to implement these concepts, given the particularities of these entities. This paper presents an approach to design a Total Manufacturing Quality 4.0 (TMQ 4.0) DSS by combining Sensor Network (SN) data and historical data in an end-to-end framework. Furthermore, the paper presents the validation of the approach through a case study application in a metal-cutting high-precision manufacturing SME. It shows promising Q4.0 estimations with regular Machine Learning (ML) algorithms (kNN, Random Forest, Logistic Regression, XGboost, feed-forward Deep Neural Network) when the steps of tending to data quality, data augmentation, and end-to-end design and implementation are applied. By providing building blocks for an end-to-end Q4.0 DSS design and implementation in an integrated quality control application, this approach aims at supporting end-users in the in-process quality control of their manufacturing operations.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"45 ","pages":"Article 100833"},"PeriodicalIF":10.4,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143678047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing software security: DCodeBERT for automatic vulnerability detection and repair","authors":"Ahmed Bensaoud,&nbsp;Jugal Kalita","doi":"10.1016/j.jii.2025.100834","DOIUrl":"10.1016/j.jii.2025.100834","url":null,"abstract":"<div><div>The exponential growth of software complexity has led to a corresponding increase in software vulnerabilities, necessitating robust methods for automatic vulnerability detection and repair. This paper proposes DCodeBERT, a large language model (LLM) fine-tuned for vulnerability detection and repair in software code. Leveraging the pre-trained CodeBERT model, DCodeBERT is designed to understand both natural language and programming language context, enabling it to effectively identify vulnerabilities and suggest repairs. We conduct experiments to evaluate DCodeBERT’s performance, comparing it against several baseline models. The results demonstrate that DCodeBERT outperforms the baselines in both vulnerability detection and repair tasks across multiple programming languages, showcasing its effectiveness in enhancing software security.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"45 ","pages":"Article 100834"},"PeriodicalIF":10.4,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143703999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-objective and multi-level models to optimize integration of hybrid renewable energy systems","authors":"Eghbal Hosseini ,&nbsp;Dler Hussein Kadir ,&nbsp;Abbas M. Al-Ghaili ,&nbsp;Muhammet Deveci","doi":"10.1016/j.jii.2025.100826","DOIUrl":"10.1016/j.jii.2025.100826","url":null,"abstract":"<div><div>In contemporary practical scenarios, the integration of diverse renewable energy sources, such as solar, wind, hydro, biomass, geothermal, and energy storage solutions like batteries, presents complex challenges. These challenges demand simultaneous optimization of energy production, system reliability enhancement, and cost minimization including those related to fossil fuels and greenhouse gas emissions. Hence, it is imperative to develop comprehensive models that address all these objectives. This paper proposes novel multi-objective and multi-level mathematical models tailored for Hybrid Renewable Energy Systems (HRESs), facilitating the simultaneous consideration of diverse objectives and decision-making levels within renewable energy integration frameworks. To effectively tackle the complexity of these models, two efficient hybrid algorithms are introduced. The first algorithm employs a combined smoothing approach to address multi-level problems, leveraging Karush–Kuhn–Tucker (KKT) conditions, mathematical principles, and heuristic functions to smooth the multi-level model. Additionally, Taylor approximation is employed to further refine the smoothed problem. The second algorithm, tailored for multi-objective models, operates in two phases: initially, a heuristic algorithm simplifies objective functions through interpolation; subsequently, the population is optimized using the Laying Chicken Algorithm (LCA), with a neural network refining the best LCA generation to identify the Pareto front in multi-objective problems. The proposed algorithms significantly improve system efficiency by optimizing the integration of diverse renewable energy sources and energy storage, leading to reduced operational costs and enhanced sustainability outcomes. These advancements offer promising real-world applications in optimizing energy systems, supporting the transition to cleaner, more sustainable energy infrastructure globally. Experimental results show that the proposed algorithm outperforms state-of-the-art methods, achieving Avg HV improvements of 1.50% for DTLZ1, 1.30% for DTLZ2, 3.28% for DTLZ3, 0.57% for DTLZ4, and 1.05% for DTLZ5. It also achieves significant reductions in Std Dev, with improvements of 98.37% for DTLZ1, 48.46% for DTLZ2, 20.41% for DTLZ3, 26.61% for DTLZ4, and 6.87% for DTLZ5, demonstrating its robustness and efficiency for complex multi-objective optimization problems.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"46 ","pages":"Article 100826"},"PeriodicalIF":10.4,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143916614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the disagreement problem in Human-in-the-Loop federated machine learning","authors":"Matthias Huelser ,&nbsp;Heimo Mueller ,&nbsp;Natalia Díaz-Rodríguez ,&nbsp;Andreas Holzinger","doi":"10.1016/j.jii.2025.100827","DOIUrl":"10.1016/j.jii.2025.100827","url":null,"abstract":"<div><div>The popularity of Artificial Intelligence (AI) has risen sharply in recent years, revolutionizing applications in most sectors with unprecedented functionalities. Milestones and achievements like ChatGPT demonstrate not only the impressive capabilities of AI, but also how accessible such technologies have become in recent times. However, the success of AI applications depends heavily on the underlying information integration processes. Among the most important processes are the training of the AI model at the core of the application and the collection and pre-processing of training data. In particular, the task of collecting high-quality training data can be very costly and resource-intensive, as in many cases large amounts of data have to be annotated manually. Human annotators must have extensive expertise for certain tasks in order to provide high-quality training data. In this paper, we present a framework to maximize the efficiency of human experts in a Machine Learning (ML) scenario, with the aim of optimizing the use of human expertise in active learning. This is done by constantly measuring the quality of human experts’ input, as well as by involving human annotators only when needed. We showcase the benefits of our proposed framework by applying it to a problem in image classification, proving its usefulness to reduce the cost of annotating training data. The source code of the framework is publicly available at <span><span>https://github.com/human-centered-ai-lab/app-HITL-annotator</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"45 ","pages":"Article 100827"},"PeriodicalIF":10.4,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143677757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating digital transformation readiness in prefabricated construction supply chains: A multi-level model and fairness-aware optimization approach","authors":"Zhen-Song Chen ,&nbsp;Kou-Dan Chen ,&nbsp;Kannan Govindan ,&nbsp;Maxwell Fordjour Antwi-Afari","doi":"10.1016/j.jii.2025.100831","DOIUrl":"10.1016/j.jii.2025.100831","url":null,"abstract":"<div><div>Prefabricated construction is revolutionizing the industry by promoting efficiency and sustainability through off-site manufacturing and on-site assembly. Despite its potential, the digital transformation of prefabricated construction supply chains (PCSCs) has not kept pace with Industry 4.0 advancements, resulting in fragmented information and operational inefficiencies. Even more, limited attention has been paid to establishing both a well-structured readiness model for PCSC digital transformation and an actionable assessment framework. This study addresses this gap by developing a comprehensive, multi-level readiness model tailored specifically for PCSCs, encompassing dimensions of technology, strategy, partnership, and personnel. To accurately evaluate digital transformation readiness, we propose a fairness-aware bi-objective optimization model that aggregates probabilistic expert opinions while considering individual behavioral factors related to fairness concerns. The model was applied in a case study of a PCSC project, demonstrating its feasibility and effectiveness. Results validated the model’s capability to provide a robust readiness assessment, identifying critical areas for improvement and offering actionable insights for stakeholders. This research fills a critical gap in the literature by providing practical guidance for PCSCs embarking on their digital transformation journey. The proposed framework serves as a valuable tool for organizations seeking to transition to digital supply chains by addressing both technical and managerial aspects of digital transformation.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"45 ","pages":"Article 100831"},"PeriodicalIF":10.4,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interconnected Industry 4.0 technologies: Identifying current network value and integration opportunities","authors":"Vincenzo Varriale,&nbsp;Antonello Cammarano,&nbsp;Francesca Michelino,&nbsp;Mauro Caputo","doi":"10.1016/j.jii.2025.100838","DOIUrl":"10.1016/j.jii.2025.100838","url":null,"abstract":"<div><div>The basic principle of Industry 4.0 (I4.0) is the integration of digital systems and technologies to achieve complete automation and optimization. Despite the importance of integrating and interconnecting technologies according to I4.0 principles, the literature has mainly focused on the 'standalone' value of individual technologies while neglecting their potential network value. Given the high complexity of evaluating technologies in an integrated system, this study goes beyond the limitations of the 'standalone' analysis of I4.0 technologies. Specifically, the study proposes a methodology that identifies I4.0 technologies that play a central role in an integrated and interconnected network of technologies. By analyzing the literature, 5,271 business practices were collected in which I4.0 technologies are integrated to achieve specific impacts. Then, this paper employed social network analysis to explore the value of each I4.0 technology in a technology network. Using specific centrality indicators, current enabling, and central technologies were identified in an interconnected and integrated system following I4.0 principles. Finally, using the methodology of brokerage roles, the study reveals the I4.0 technologies, considered as 'drivers', that could have the greatest potential to unlock new future interconnection opportunities.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"45 ","pages":"Article 100838"},"PeriodicalIF":10.4,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143677756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An integrated decision support framework for exploring the barriers and potential application scenarios in metaverse hospitality","authors":"Qun Wu ,&nbsp;Weiqi Tan ,&nbsp;Ligang Zhou ,&nbsp;Muhammet Deveci ,&nbsp;Dragan Pamucar ,&nbsp;Witold Pedrycz","doi":"10.1016/j.jii.2025.100825","DOIUrl":"10.1016/j.jii.2025.100825","url":null,"abstract":"<div><div>The global hospitality industry is undergoing a profound transformation, primarily driven by the internet era within the context of Industry 4.0 and the continuous evolution of the metaverse. The metaverse offers limitless possibilities for creating immersive and personalized accommodation experiences by closely integrating the virtual with the real world. It also provides a new perspective and mindset for guiding the hospitality industry to rethink and redesign service models and business processes. Although the metaverse application in the hospitality industry has already made significant progress, this process still faces numerous barriers, and the specific application scenarios of the metaverse hospitality industry (MHI) remain unclear. This study aims to develop an integrated decision support framework for exploring the barriers and potential application scenarios in metaverse hospitality. First, the interval type-2 fuzzy sets (IT2FSs) is introduced to quantify the complex and uncertain information in the decision-making procedure. Second, the IT2F-BWM with the IT2F weighted averaging (IT2FWA) operator is proposed to calculate the barriers’ weights considering the group opinions. Third, the IT2FWA operator and IT2F-ExpTODIM method are incorporated to prioritize the potential application scenarios in MHI. Finally, a case study on the prioritization of barriers and application scenarios in MHI is utilized to test the effectiveness and practicality of the presented framework. Results show that the technology barrier (0.5217) and the interaction technology (0.2483) have the highest levels among the main and sub-main barrier hierarchies, respectively. The findings of this article may enrich the theories of uncertain decision-making methods and provide instructive suggestions for the operation and management of metaverse hospitality practice.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"45 ","pages":"Article 100825"},"PeriodicalIF":10.4,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge-enhanced ontology-to-vector for automated ontology concept enrichment in BIM","authors":"Yinyi Wei,&nbsp;Xiao Li","doi":"10.1016/j.jii.2025.100836","DOIUrl":"10.1016/j.jii.2025.100836","url":null,"abstract":"<div><div>Building Information Modeling (BIM) relies on standardized ontologies like IfcOWL to address interoperability. However, the increasing complexity and diversity of construction information requirements demand automated enrichment of BIM ontologies, which is hindered by several factors, including complexity in ontology structure, scalability limitations, and domain-specific issues. Manual curation and maintenance of ontologies are labor-intensive and time-consuming, particularly as the scope of BIM projects expands. Despite these challenges, the construction industry lacks an effective automated approach for ontology concept enrichment. Thus, this study proposes a knowledge-enhanced ontology-to-vector (Keno2Vec) approach for automated BIM ontology concept enrichment, which can (1) encode ontology elements into meaningful and semantically rich embeddings by employing the BERT model to integrate both ontological information (names and labels) and external knowledge (definitions from authoritative knowledge bases), effectively addressing the domain expression specificity and complexity of BIM ontologies; and (2) provide a flexible framework that supports various downstream tasks of ontology concept enrichment by utilizing the resulting embeddings, thereby improving the task-specific adaptability and variability. Experimental results on datasets derived from the large-scale ifcOWL and two smaller BIM ontologies demonstrate that Keno2Vec significantly outperforms existing ontology embedding approaches in terms of accuracy and adaptability. For example, Keno2Vec achieves F1 scores on ifcOWL of nearly 87 % for subsumption prediction, 60 % for property identification, 95 % for membership recognition, and 100 % and 90 % for category-based and schema-based concept classification, respectively. Additional analysis highlights the potential of Keno2Vec for improving BIM ontology encoding and benefiting downstream applications.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"45 ","pages":"Article 100836"},"PeriodicalIF":10.4,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143678081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}