Search Results (92)

The As-built Building Information Model (BIM) serves as a carrier of lifecycle information for the physical building and is a critical component of project handover. Despite the issuance of numerous delivery standards globally, efficient methods for reviewing information completeness remain insufficient, hindering the timely and effective delivery of project information. To address this gap, this research employs ontology technology as its core to develop a method for reviewing information completeness in as-built architectural engineering BIMs. An ontological model is constructed using Protégé, Semantic Web Rule Language (SWRL) rules for Completeness Review are defined, and a Revit data extraction add-in is developed to store model data in a database and map it to an ontological knowledge base. An integrated review system for As-built BIM information completeness is subsequently built. This system automates the entire review workflow, encompassing data import, semantic conversion, rule-based reasoning, and result output. Validated through a teaching building case study, the system successfully and accurately identified missing attributes and documents within the model, demonstrating high accuracy and review efficiency. The proposed method and its accompanying review system provide a scalable, rule-driven solution for ensuring BIM integrity, effectively supporting digital delivery in the architecture, engineering, and construction (AEC) industry. Full article

Building Information Modelling (BIM) offers a pivotal opportunity to automate Life Cycle Assessment (LCA) within the Architecture, Engineering, and Construction (AEC) industry. However, seamless integration is persistently hindered by a semantic gap, a critical misalignment between the object-oriented, geometric definitions of BIM and the process-based material data required by Life Cycle Inventory (LCI) databases. This paper presents a comprehensive review of data interoperability challenges and evaluates advanced methodologies designed to bridge this divide, moving beyond simple tool comparison to analyse structural integration barriers. Through a systematic review of 124 primary studies published between 2010 and 2025, this research inductively derives the BIM-LCA Interoperability Triad. This framework analyses causal dependencies across three dimensions, including Semantic and Ontological Structures, Workflow and Temporal Integration, and System Architecture and Interoperability. Furthermore, by establishing a comparative challenge–solution matrix, the analysis reveals a maturity paradox in current methodologies. While semi-automated commercial plugins dominate practice due to accessibility, they frequently function as opaque black boxes with limited transparency. Conversely, advanced approaches utilising Semantic Web technologies and Machine Learning demonstrate superior capability in resolving terminological mismatches but currently face significant barriers regarding infrastructure and expertise. This study contributes a novel theoretical model for understanding integration failures. It concludes that future research must pivot from static schema mapping towards AI-driven semantic healing, dynamic Digital Twins, and explicit system boundary harmonisation to achieve truly automated, context-aware environmental assessments and support whole-life circularity. Full article

Background: Healthcare supply chains (HSCs) are critical socio-technical systems that ensure the timely delivery of pharmaceuticals, medical devices, and electromedical equipment, yet they face increasing complexity due to regulatory constraints, demand uncertainty, and the growing digitalization of healthcare systems. This study aims to systematically map the HSC literature and identify its main thematic structures and research gaps. Methods: A systematic literature review was conducted following PRISMA guidelines, analyzing 705 peer-reviewed articles retrieved from the Web of Science database (PROSPERO registration: CRD42024605761). Natural language processing techniques were applied to support the analysis, including topic modeling, term frequency–inverse document frequency for keyword relevance, and Keyword in Context analysis for semantic interpretation. Results: The analysis identified six main thematic clusters and revealed a fragmented research landscape, characterized by limited integration across supply chain tiers, uneven attention to technological innovations, and marginal consideration of sustainability and implementation issues. The findings also highlight a gap between conceptual developments and real-world applications. Conclusions: This study provides a data-driven overview of the HSC research domain, highlighting key gaps and opportunities for more integrated, resilient, and efficient supply chain management. Full article

Manufacturing companies face significant challenges in leveraging artificial intelligence for equipment management due to high infrastructure costs and limited availability of labeled data for failures. While most manufacturing AI applications focus on structured sensor data, vast amounts of unstructured textual information containing valuable maintenance knowledge remain underutilized. This study presents a practical generative AI-based framework for structured information extraction that automatically converts unstructured equipment maintenance texts into predefined semantic fields to support predictive maintenance in manufacturing environments. We adopted and evaluated three representative generative models—Bidirectional and Auto-Regressive Transformers (BART) with KoBART, Text-to-Text Transfer Transformer (T5) with pko-t5-base, and the large language model Qwen—to generate structured outputs by extracting three predefined fields: failed components, failure types, and corrective actions. The framework enables the structuring of equipment management text data from Manufacturing Execution Systems (MES) to build predictive maintenance support systems. We validated the approach using a large-scale MES dataset consisting of 29,736 equipment maintenance records from a major automotive parts manufacturer, from which curated subsets were used for model training and evaluation. Our methodology employs Generative Pre-trained Transformer 4 (GPT-4) for initial dataset construction, followed by domain expert validation to ensure data quality. The trained models achieved promising performance when evaluated using extraction-aligned metrics, including exact match (EM) and token-level precision, recall, and F1-score, which directly assess field-level extraction correctness. ROUGE scores are additionally reported as a supplementary indicator of lexical overlap. Among the evaluated models, Qwen consistently outperformed BART and T5 across all extracted fields. The structured outputs are further processed through domain-specific dictionaries and regular expressions to create a comprehensive analytical database supporting predictive maintenance strategies. We implemented a web-based analytics platform enabling time-series analysis, correlation analysis, frequency analysis, and anomaly detection for equipment maintenance optimization. The proposed system converts tacit knowledge embedded in maintenance texts into explicit, actionable insights without requiring additional sensor installations or infrastructure investments. This research contributes to the manufacturing AI field by demonstrating a comprehensive application of generative language models to equipment maintenance text analysis, providing a cost-effective approach for digital transformation in manufacturing environments. The framework’s scalability and cloud-based deployment model present significant opportunities for widespread adoption in the manufacturing sector, supporting the transition from reactive to predictive maintenance strategies. Full article

With the acceleration of digital transformation, open-source software has become a fundamental component of modern smart grids and other critical infrastructures. However, the complex dependency structures of open-source ecosystems and the continuous emergence of vulnerabilities pose substantial challenges to software supply chain security. In power information networks and cyber–physical control systems, vulnerabilities in open-source components integrated into Supervisory Control and Data Acquisition (SCADA), Energy Management System (EMS), and Distribution Management System (DMS) platforms and distributed energy controllers may propagate along the supply chain, threatening system security and operational stability. In such application scenarios, large language models (LLMs) often suffer from limited semantic accuracy when handling domain-specific security terminology, as well as deployment inefficiencies that hinder their practical adoption in critical infrastructure environments. To address these issues, this paper proposes KD-SecBERT, a domain-specific semantic bidirectional encoder optimized through multi-level knowledge distillation for open-source software supply chain security in smart grid applications. The proposed framework constructs a hierarchical multi-teacher ensemble that integrates general language understanding, cybersecurity-domain knowledge, and code semantic analysis, together with a lightweight student architecture based on depthwise separable convolutions and multi-head self-attention. In addition, a dynamic, multi-dimensional distillation strategy is introduced to jointly perform layer-wise representation alignment, ensemble knowledge fusion, and task-oriented optimization under a progressive curriculum learning scheme. Extensive experiments conducted on a multi-source dataset comprising National Vulnerability Database (NVD) and Common Vulnerabilities and Exposures (CVE) entries, security-related GitHub code, and Open Web Application Security Project (OWASP) test cases show that KD-SecBERT achieves an accuracy of 91.3%, a recall of 90.6%, and an F1-score of 89.2% on vulnerability classification tasks, indicating strong robustness in recognizing both common and low-frequency security semantics. These results demonstrate that KD-SecBERT provides an effective and practical solution for semantic analysis and software supply chain risk assessment in smart grids and other critical-infrastructure environments. Full article

Today, relational databases are widely used in information systems. SQL (structured query language) is taught extensively in universities and professional schools across the globe as a programming language for its data management and accesses. Previously, we have studied a web-based programming learning assistant system (PLAS) to help novice students learn popular programming languages by themselves through solving various types of exercises. For SQL programming, we have implemented the grammar-concept understanding problem (GUP) and the comment insertion problem (CIP) for its initial studies. In this paper, we propose an SQL Query Description Problem (SDP) as a new exercise type for describing the SQL query to a specified request in a MySQL database system. To reduce teachers’ preparation workloads, we integrate a generative AI-assisted SQL query generator to automatically generate a new SDP instance with a given dataset. An SDP instance consists of a table, a set of questions and corresponding queries. Answer correctness is determined by enhanced string matching against an answer module that includes multiple semantically equivalent canonical queries. For evaluation, we generated 11 SDP instances on basic topics using the generator, where we found that Gemini 3.0 Pro exhibited higher pedagogical consistency compared to ChatGPT-5.0, achieving perfect scores in Sensibleness, Topicality, and Readiness metrics. Then, we assigned the generated instances to 32 undergraduate students at the Indonesian Institute of Business and Technology (INSTIKI). The results showed an average correct answer rate of 95.2% and a mean SUS score of 78, which demonstrates strong initial student performance and system acceptance. Full article

Background/Objectives: Primary progressive aphasia (PPA) is characterised by progressive decline in language and communication. However, existing diagnostic frameworks and assessment tools are largely based on Indo-European languages, which limits their applicability to Chinese bilingual speakers whose linguistic profiles differ markedly in tonal phonology, logographic writing, and bilingual organisation. This review aimed to (a) describe how PPA presents in Chinese bilingual speakers, (b) evaluate how well current speech–language and neuropsychological assessments capture these impairments, and (c) identify linguistically and culturally informed strategies to improve clinical practice. Methods: A systematic review was conducted in accordance with the PRISMA-ScR guidelines. Four databases (PubMed, Scopus, Web of Science, PsycINFO) were searched, complemented by backward and forward citation chaining. Eight empirical studies met the inclusion criteria. Data were extracted on participant characteristics, PPA variant, language background, speech–language and writing profiles, and assessment tools used. Thematic analysis was applied to address the research questions. Results: Across variants, Chinese bilingual speakers demonstrated universal PPA features expressed through language-specific pathways. Mandarin speakers exhibited tone-segment integration errors, tonal substitution, and disruptions in logographic writing. Lexical-semantic degradation reflected homophony and compounding characteristics. Bilingual individuals showed parallel or asymmetric decline influenced by dominance and usage. Standard English-based naming, repetition, and writing assessments did not reliably capture tone accuracy, radical-level writing errors, or bilingual patterns. Sociocultural factors, including stigma, delayed help-seeking, and family-centred care expectations, further shaped diagnostic pathways. Conclusions: Chinese PPA cannot be meaningfully assessed using tools designed for Indo-European languages. Findings highlight the need for tone-sensitive repetition tasks, logographic writing assessments, bilingual diagnostic protocols, and culturally responsive communication-partner support. This review provides a comprehensive synthesis to date on Chinese bilingual PPA and establishes a foundation for linguistically inclusive diagnostic and clinical models. Full article

Urban Digital Twins (UDTs) demand both simplified geometry and rich semantic information from Building Information Models (BIM) to be effectively integrated into Geospatial Information Systems (GIS). However, current BIM-to-GIS conversion methods struggle with geometric complexity and semantic loss, particularly at scale. This paper proposes a novel, scalable methodology for comprehensive BIM/GIS integration, addressing both geometric and semantic challenges. The approach introduces a geometry conversion workflow that transforms solid BIMs into valid, simplified CityGML representations through a level-by-level detection of building elements and outer surface extraction. To preserve semantic richness, all entities, attributes, and relationships—including implicit connections—are automatically extracted and stored in a Labeled Property Graph (LPG) database. The method is further extended with a new CityGML Application Domain Extension (ADE) that supports Multi-LoD4 representations, enabling selective interior visualization and efficient rendering. A web-based urban digital twin platform demonstrates the integration, allowing dynamic semantic querying and scalable 3D visualization. Results show a significant reduction in geometric complexity, full semantic retention, and robust performance in visualization and querying, offering a practical pathway for advanced UDT development. Full article

Effective web service selection and recommendation are critical for ensuring high-quality performance in distributed and service-oriented systems. Recent research has increasingly explored the use of BERT (Bidirectional Encoder Representations from Transformers) to enhance semantic understanding of service descriptions, user requirements, and Quality of Service (QoS) prediction. This systematic review examines the application of BERT-based models in QoS-aware web service selection and recommendation. A structured database search was conducted across IEEE, ACM, ScienceDirect, and Google Scholar covering studies published between 2020 and 2024, resulting in twenty-five eligible articles based on predefined inclusion criteria and PRISMA screening. The review shows that BERT improves semantic representation and mitigates cold-start and sparsity issues, contributing to better service ranking and QoS prediction accuracy. However, challenges persist, including limited availability of benchmark datasets, high computational overhead, and limited interpretability of model decisions. The review identifies five key research gaps and outlines future directions, including domain-specific pre-training, hybrid semantic–numerical models, multi-modal QoS reasoning, and lightweight transformer architectures for deployment in dynamic and resource-constrained environments. These findings highlight the potential of BERT to support more intelligent, adaptive, and scalable web service management. Full article

AI-based educational chatbots can expand access to learning, but many remain limited to text-only interfaces and fixed infrastructures, while purely generative responses raise concerns of reliability and consistency. In this context, we present ROboMC, a portable and multimodal system that combines a validated knowledge base with generative responses (OpenAI) and voice–text interaction, designed to enable both text and voice interaction, ensuring reliability and flexibility in diverse educational scenarios. The system, developed in Django, integrates two response pipelines: local search using normalized keywords and fuzzy matching in the LocalQuestion database, and fallback to the generative model GPT-3.5-Turbo (OpenAI, San Francisco, CA, USA) with a prompt adapted exclusively for Romanian and an explicit disclaimer. All interactions are logged in AutomaticQuestion for later analysis, supported by a semantic encoder (SentenceTransformer—paraphrase-multilingual-MiniLM-L12-v2’, Hugging Face Inc., New York, NY, USA) that ensures search tolerance to variations in phrasing. Voice interaction is managed through gTTS (Google LLC, Mountain View, CA, USA) with integrated audio playback, while portability is achieved through deployment on a Raspberry Pi 4B (Raspberry Pi Foundation, Cambridge, UK) with microphone, speaker, and battery power. Voice input is enabled through a cloud-based speech-to-text component (Google Web Speech API accessed via the Python SpeechRecognition library, (Anthony Zhang, open-source project, USA) using the Google Web Speech API (Google LLC, Mountain View, CA, USA; language = “ro-RO”)), allowing users to interact by speaking. Preliminary tests showed average latencies of 120–180 ms for validated responses on laptop and 250–350 ms on Raspberry Pi, respectively, 2.5–3.5 s on laptop and 4–6 s on Raspberry Pi for generative responses, timings considered acceptable for real educational scenarios. A small-scale usability study (N ≈ 35) indicated good acceptability (SUS ~80/100), with participants valuing the balance between validated and generative responses, the voice integration, and the hardware portability. Although system validation was carried out in the eHealth context, its architecture allows extension to any educational field: depending on the content introduced into the validated database, ROboMC can be adapted to medicine, engineering, social sciences, or other disciplines, relying on ChatGPT only when no clear match is found in the local base, making it a scalable and interdisciplinary solution. Full article

Ports serve as key nodes in eco-friendly and digital logistics networks, and the volume of cargo handled continues to increase in response to growing international trade. However, the increased workload within limited spaces heightens the risk of safety accidents, and the number of casualties in port stevedoring operations has continued to rise. As the era of transition toward eco-friendly and digital paradigms unfolds, the adoption of new technologies in ports presents a strategic opportunity to enhance safety management. As of 13 May 2025, the study conducted a text-mining analysis based on research abstracts related to the keyword “New technology and port safety,” in the context of internal and external environmental changes. Specifically, a total of 639 research abstracts were collected, but 138 abstracts, which were unrelated to port safety, were excluded, and 501 abstracts from the Clarivate Web of Science database were analyzed, focusing on 2676 words that appeared at least twice. The study applied Term Frequency (TF) analysis, TF–Inverse Document Frequency analysis, Semantic Network Analysis, and Topic Modeling. The results indicate that Internet of Things emerged as a core solution for strengthening port safety management. However, challenges remain, including the prevention of security breaches, high infrastructure implementation costs, and limitations in battery life. Full article

Following the stablecoin legislation (GENIUS Act) enacted under the second Trump administration in 2025, blockchain has become core digital economy infrastructure. However, privacy risks from decentralization and transparency constrain adoption in regulated industries, requiring solutions that harmonize blockchain architecture with regulatory compliance. Existing research relies on reactive auditing or post-execution rule checking, which wastes computational resources or provides only basic encryption or access controls without comprehensive privacy compliance. The proposed Artificial Intelligence-enhanced Regulatory Proof-of-Compliance (AIRPoC) framework addresses this gap through a two-phase consensus mechanism that integrates AI legal agents with semantic web technologies for autonomous regulatory compliance enforcement. Unlike existing research, AIRPoC implements a dual-layer architecture where AI-powered regulatory validation precedes consensus execution, ensuring that only compliant transactions proceed to blockchain finalization. The system employs AI legal agents that automatically construct and update regulatory databases via multi-oracle networks, using SPARQL-based inference engines for real-time General Data Protection Regulation (GDPR) compliance validation. A simulation-based experimental evaluation conducted across 24 tests with 116,200 transactions in a controlled environment demonstrates 88.9% compliance accuracy, with 9502 transactions per second (TPS) versus 11,192 TPS for basic Proof-of-Stake (PoS) (4.5% overhead). This research represents a paradigm shift to dynamic, transaction-based regulatory models that preserve blockchain efficiency. Full article

Background: Chronic low back pain is often associated with impaired balance and reduced functional mobility. Recent studies suggest that virtual reality-based interventions may be effective in improving balance outcomes in individuals with chronic low back pain. Objective: In this systematic review and meta-analysis, we aimed to investigate the impact of virtual reality training on static and dynamic balance outcomes in patients with chronic low back pain. Methods: Two independent reviewers searched English-language studies from inception to 1 July 2024, using the following databases: PubMed, Web of Science, Scopus, Dimensions, Semantic Scholar, and ProQuest. Randomized clinical trials with a PEDro score of ≥6 were included. Fixed- and random-effects meta-analyses were conducted on eligible trials. Results: Of 3172 records screened, 13 trials were eligible. Meta-analyses of six trials (n = 183) across diverse adults using 2–8 week interventions showed that virtual reality training improved dynamic balance: timed up and go (mean difference: −2.29 s; 95% confidence interval: −2.91 to −1.66; I² = 0%; p < 0.00001) and forward reach (mean difference: 7.80 cm; 95% confidence interval: 2.08 to 13.52; I² = 0%; p = 0.008). However, no significant effects were found for static balance, single-leg stance, center of pressure medio-lateral displacement, or center of pressure velocity, compared with controls. Conclusions: Virtual reality-based training seems to be more effective than control interventions in improving dynamic and functional balance, but not static balance, in patients with chronic low back pain. Full article

In an era of escalating digital threats and growing dependence on online systems, cybersecurity and user data protection have become critical global priorities. This study aims to identify cutting-edge discoveries and emerging trends in the field through a bibliometric analysis of 708 articles indexed in the Web of Science database. The first part of the analysis presents publication trends, while a semantic analysis of the scientific output highlights the main research directions within the field. For each research direction, emerging terms were identified and used to map the key trends shaping current developments. To fully capture the nuances of the relationships among emerging terms, the identified trends were discussed and contextualized to enhance their understanding. The results provide a comprehensive overview of the evolving dynamics of cybersecurity and user data protection research, and they underscore the dynamic areas of interest driving innovation in this domain. Full article

The urban heat island (UHI) effect, intensified by urbanisation and climate change, leads to increased urban temperatures and poses a serious environmental challenge. Understanding its causes, impacts, and mitigation strategies is essential for sustainable urban planning. The aim of this study is to systematically analyse how the Urban Heat Island (UHI) effect has been addressed in the scientific literature, to identify key research themes and their temporal evolution, and to critically highlight knowledge gaps in order to provide guidance for future research and urban planning policies. Using BERTopic, an advanced natural language processing (NLP) tool, the study extracts dominant themes from a large corpus of academic literature and tracks their evolution over time. A total of 9061 research articles from the Web of Science database were collected, pre-processed, and analysed. BERTopic clustered semantically related topics and revealed their temporal dynamics, offering insights into emerging and declining research areas. The results show that pavement materials and urban vegetation are among the most studied themes, highlighting the importance of surface materials and green infrastructure in mitigating UHI. In line with this aim, the study identifies a rising interest in urban cooling strategies, particularly reflective surfaces and ventilation corridors. Consistent with its aim, the study provides a comprehensive overview of UHI literature, critically identifies existing gaps, and proposes clear directions for future research. It provides supports for urban planners, policymakers, and researchers in developing data-driven strategies to mitigate UHI impacts and strengthen enhance urban climate resilience. Full article