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Keywords = formalization of domain knowledge

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23 pages, 57783 KiB  
Article
A Systematic Approach for Robotic System Development
by Simone Leone, Francesco Lago, Doina Pisla and Giuseppe Carbone
Technologies 2025, 13(8), 316; https://doi.org/10.3390/technologies13080316 (registering DOI) - 23 Jul 2025
Abstract
This paper introduces a unified and systematic design methodology for robotic systems that is generalizable across a wide range of applications. It integrates rigorous mathematical formalisms such as kinematics, dynamics, control theory, and optimization with advanced simulation tools, ensuring that each design decision [...] Read more.
This paper introduces a unified and systematic design methodology for robotic systems that is generalizable across a wide range of applications. It integrates rigorous mathematical formalisms such as kinematics, dynamics, control theory, and optimization with advanced simulation tools, ensuring that each design decision is grounded in provable theory. The approach defines clear phases, including mathematical modeling, virtual prototyping, parameter optimization, and theoretical validation. Each phase builds on the previous one to reduce unforeseen integration issues. Spanning from conceptualization to deployment, it offers a blueprint for developing mathematically valid and robust robotic solutions while streamlining the transition from design intent to functional prototype. By standardizing the design workflow, this framework reduces development time and cost, improves reproducibility across projects, and enhances collaboration among multidisciplinary teams. Such a generalized approach is essential in today’s fast-evolving robotics landscape where rapid innovation and cross-domain applicability demand flexible yet reliable methodologies. Moreover, it provides a common language and set of benchmarks that both novice and experienced engineers can use to evaluate performance, facilitate knowledge transfer, and future-proof systems against emerging application requirements. Full article
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33 pages, 2593 KiB  
Article
Methodological Exploration of Ontology Generation with a Dedicated Large Language Model
by Maria Assunta Cappelli and Giovanna Di Marzo Serugendo
Electronics 2025, 14(14), 2863; https://doi.org/10.3390/electronics14142863 - 17 Jul 2025
Viewed by 140
Abstract
Ontologies are essential tools for representing, organizing, and sharing knowledge across various domains. This study presents a methodology for ontology construction supported by large language models (LLMs), with an initial application in the automotive sector. Specifically, a user preference ontology for adaptive interfaces [...] Read more.
Ontologies are essential tools for representing, organizing, and sharing knowledge across various domains. This study presents a methodology for ontology construction supported by large language models (LLMs), with an initial application in the automotive sector. Specifically, a user preference ontology for adaptive interfaces in autonomous machines was developed using ChatGPT-4o. Based on this case study, the results were generalized into a reusable methodology. The proposed workflow integrates classical ontology engineering methodologies with the generative and analytical capabilities of LLMs. Each phase follows well-established steps: domain definition, term elicitation, class hierarchy construction, property specification, formalization, population, and validation. A key innovation of this approach is the use of a guiding table that translates domain knowledge into structured prompts, ensuring consistency across iterative interactions with the LLM. Human experts play a continuous role throughout the process, refining definitions, resolving ambiguities, and validating outputs. The ontology was evaluated in terms of logical consistency, structural properties, semantic accuracy, and inferential completeness, confirming its correctness and coherence. Additional validation through SPARQL queries demonstrated its reasoning capabilities. This methodology is generalizable to other domains, if domain experts adapt the guiding table to the specific context. Despite the support provided by LLMs, domain expertise remains essential to guarantee conceptual rigor and practical relevance. Full article
(This article belongs to the Special Issue Role of Artificial Intelligence in Natural Language Processing)
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16 pages, 236 KiB  
Article
Quality of Life for Patients with Down Syndrome and Their Caregivers: A Cross-Sectional Study from a Parental Perspective in Saudi Arabia
by Amal Khaleel AbuAlhommos, Maitham Abdullah Al Hawaj, Ashwaq Ali Alanazi, Hanadi Hwthael Alrashidi, Maha Faleh Aldawsari and Rasan Ali Alajmi
Healthcare 2025, 13(13), 1614; https://doi.org/10.3390/healthcare13131614 - 6 Jul 2025
Viewed by 314
Abstract
Background: Patients with Down syndrome (DS) commonly experience psychological and mental problems. Studying the quality of life (QoL) of children with DS is important because it increases knowledge related to understanding the challenges that this group may face. This study aims to examine [...] Read more.
Background: Patients with Down syndrome (DS) commonly experience psychological and mental problems. Studying the quality of life (QoL) of children with DS is important because it increases knowledge related to understanding the challenges that this group may face. This study aims to examine the QoL of children with DS from a parental perspective in terms of physical, emotional, social, and school domains, depending on several factors, and identify demographic characteristics of their parents that may affect their QoL. Methods: This online survey study was conducted in Saudi Arabia between November 2024 and March 2025. The inclusion criteria targeted parents of children with confirmed DS diagnoses aged between 8 and 18 years. Results: The findings of this study showed that children with DS aged between 0 and 2 years had significantly lower QoL scores (10.18 ± 3.83) compared to other age groups (p = 0.02). In addition, gender differences were significant in the emotional (p = 0.03), social (p = 0.01), and school (p = 0.01) domains, with females scoring lower QoL scores in all areas compared to males. Moreover, educational level showed significant results across all domains, particularly for children with no education, who had the lowest QoL scores in the physical domain (22.34 ± 7.53, p = 0.004), emotional domain (10.41 ± 3.79, p = 0.003), social domain (11.22 ± 4.06, p = 0.001), and school domain (8.75 ± 5.09, p = 0.001). The findings of this study showed that children with DS who are in primary school (odds ratio (OR) = 5.90, 95% confidence interval (CI): 1.85–18.78, p = 0.003) and middle school (OR = 5.27, 95% CI: 1.44–19.31, p = 0.012) had significantly higher odds of better QoL compared to children with no formal education. Additionally, children cared for by their fathers had significantly lower odds compared to those cared for by their mothers (OR = 0.07, 95% CI: 0.01–0.90, p = 0.041). None of the demographic characteristics of caregivers reached a statistical significance level to have influence on caregivers QoL (p > 0.05). Conclusions: The findings of this study demonstrated a low level of QoL, affecting the emotional, social, and school domains, especially among female children with DS aged between 0 and 2 years with no formal education and cared for by their fathers. Governments should develop a comprehensive plan to care for these children and families in order to enhance their rights and quality of life, thereby placing emphasis on those who exhibit parameters related to a lower QoL. Full article
27 pages, 1995 KiB  
Article
Polynomials—Unifying or Fragmenting High School Mathematics?
by Jelena Pleština, Željka Milin Šipuš and Matija Bašić
Educ. Sci. 2025, 15(7), 854; https://doi.org/10.3390/educsci15070854 - 3 Jul 2025
Viewed by 189
Abstract
This paper presents research on the origin, scope, evolution, and rationale of knowledge about polynomials in high school mathematics. Within the framework of the Anthropological Theory of the Didactic, Croatian high school curricula and textbooks were analyzed, and four models of knowledge to [...] Read more.
This paper presents research on the origin, scope, evolution, and rationale of knowledge about polynomials in high school mathematics. Within the framework of the Anthropological Theory of the Didactic, Croatian high school curricula and textbooks were analyzed, and four models of knowledge to be taught were identified in the period following the formal abandonment of New Math principles. None of the identified models provides a unified discourse that integrates knowledge about polynomials transposed from scholarly domains of algebra and mathematical analysis. In relation to other curricular content the knowledge about polynomials has two-fold importance: (1) contributing to the development of various techniques related to high school algebra and calculus; (2) serving as a fundamental example in the formation of the notion of a function. Thus, the observed reduction in polynomial content over the analyzed period affects both practical and theoretical knowledge. The findings suggest that curricular changes have primarily focused on the selection of knowledge, with scarce adaptations of knowledge to be taught compared to the knowledge before each curricular change. This has led to a persistent gap between algebraic and analytical approaches to polynomials, potentially influencing the learned knowledge even among the highest-achieving students. Despite polynomials’ epistemological and didactical potential to bridge high school algebra and calculus, their restriction to specific forms of algebraic expressions and linear and quadratic functions contributes more to the fragmentation of high school mathematics. Full article
(This article belongs to the Special Issue Curriculum Development in Mathematics Education)
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15 pages, 205 KiB  
Article
From the Philosopher’s Stone to AI: Epistemologies of the Renaissance and the Digital Age
by Bram Hennekes
Philosophies 2025, 10(4), 79; https://doi.org/10.3390/philosophies10040079 - 30 Jun 2025
Viewed by 467
Abstract
This paper reexamines the enduring role of esoteric traditions, as articulated by Frances Yates, in shaping the intellectual landscape of the scientific revolution and their resonance in the digital age. Challenging the linear, progress-centered narratives of traditional historiographies, it explores how esoteric principles—symbolized [...] Read more.
This paper reexamines the enduring role of esoteric traditions, as articulated by Frances Yates, in shaping the intellectual landscape of the scientific revolution and their resonance in the digital age. Challenging the linear, progress-centered narratives of traditional historiographies, it explores how esoteric principles—symbolized by transformative motifs like the Philosopher’s Stone—provided a framework for early scientific inquiry by promoting hidden knowledge, experimentation, mathematics, and interdisciplinary synthesis. This paper argues that moments of accelerated scientific and technological development magnify the visibility of esoteric structures, demonstrating how the intellectual configurations of Renaissance learned circles persist in contemporary expert domains. In particular, artificial intelligence exemplifies the revival of esoteric modes of interpretation, as AI systems—much like their Renaissance predecessors—derive authority through the identification of unseen patterns and the extrapolation of hidden truths. By bridging Renaissance esotericism with the modern information revolution, this study highlights how such traditions are not mere relics of the past but dynamic paradigms shaping the present and future, potentially culminating in new forms of digital mysticism. This study affirms that the temporal gap during periods of rapid technological change between industrial practice and formal scientific treatises reinforces esoteric knowledge structures. Full article
17 pages, 1955 KiB  
Article
Development of Safety Domain Ontology Knowledge Base for Fall Accidents
by Hyunsoung Park and Sangyun Shin
Buildings 2025, 15(13), 2299; https://doi.org/10.3390/buildings15132299 - 30 Jun 2025
Viewed by 321
Abstract
Extensive research in the field of construction safety has predominantly focused on identifying the causes and impacts of construction accidents, evaluating safety plans, assessing the effectiveness of safety education materials, and analyzing relevant policies. However, comparatively limited attention has been given to the [...] Read more.
Extensive research in the field of construction safety has predominantly focused on identifying the causes and impacts of construction accidents, evaluating safety plans, assessing the effectiveness of safety education materials, and analyzing relevant policies. However, comparatively limited attention has been given to the systematic formation, management, and utilization of safety-related information and knowledge. Despite significant advancements in information and knowledge management technologies across the architecture, engineering, and construction (AEC) industries, their application in construction safety remains underdeveloped. This study addresses this gap by proposing a novel ontology-based framework specifically designed for construction safety management. Unlike previous models, the proposed ontology integrates diverse safety regulations and terminologies into a unified and semantically structured knowledge model. It comprises three primary superclasses covering key areas of construction safety, with an initial focus on fall hazards—one of the most frequent and severe risks, particularly in roofing activities. This domain-specific approach not only improves semantic clarity and standardization but also enhances reusability and extensibility for other risk domains. The ontology was developed using established methodologies and validated through reasoning tools and competency questions. By providing a formally structured, logic-driven knowledge base, the model supports automated safety reasoning, facilitates communication among stakeholders, and lays the foundation for future intelligent safety management systems in construction. This research contributes a validated, extensible, and regulation-aligned ontology model that addresses critical challenges in safety information integration, sharing, and application. Full article
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23 pages, 3590 KiB  
Article
Cost Efficiency in Buildings: An Ontological Perspective for Sustainable Life Cycle Management
by Martina Signorini, Chiara Gatto, Jacopo Cassandro, Alberto Pavan and Sonia Lupica Spagnolo
Sustainability 2025, 17(13), 5685; https://doi.org/10.3390/su17135685 - 20 Jun 2025
Viewed by 368
Abstract
The AECO (Architecture, Engineering, Construction, and Operation) sector is highly complex, involving multidisciplinary collaboration, extensive data management, and significant financial investments. Decisions in early phases significantly impact operational and maintenance costs, as well as the environmental and economic sustainability of a project over [...] Read more.
The AECO (Architecture, Engineering, Construction, and Operation) sector is highly complex, involving multidisciplinary collaboration, extensive data management, and significant financial investments. Decisions in early phases significantly impact operational and maintenance costs, as well as the environmental and economic sustainability of a project over its lifecycle. Cost efficiency and sustainability are critical and interconnected goals across the sector, spanning all phases of a building’s lifecycle. Ontologies, as formal and structured representations of knowledge within a particular domain, have the potential to enhance cost efficiency by improving decision-making, reducing redundancies, and optimizing resource allocation. Despite their relevance, cost ontologies are still lacking in the AECO sector. This paper addresses this gap by presenting both a methodological and conceptual contribution: it outlines a structured and iterative methodology for developing a cost ontology, and it defines the core concepts required to semantically represent construction cost information. The methodology emphasizes stakeholder engagement and refinement cycles, while the ontological structure ensures machine-readability and interoperability. The approach involves a preliminary analysis of the necessary cost parameters for defining the ontology and a subsequent validation of a practical case study. The results show the development of a heterogeneous and standardized data structure designed to define a cost ontology, aimed at improving the updatability, transparency, and sustainability-oriented interpretation of construction cost data by both humans and machines. Full article
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11 pages, 227 KiB  
Article
The Behaviours in Dementia Toolkit: A Descriptive Study on the Reach and Early Impact of a Digital Health Resource Library About Dementia-Related Mood and Behaviour Changes
by Lauren Albrecht, Nick Ubels, Brenda Martinussen, Gary Naglie, Mark Rapoport, Stacey Hatch, Dallas Seitz, Claire Checkland and David Conn
Geriatrics 2025, 10(3), 79; https://doi.org/10.3390/geriatrics10030079 - 11 Jun 2025
Viewed by 919
Abstract
Background: Dementia is a syndrome with a high global prevalence that includes a number of progressive diseases of the brain affecting various cognitive domains such as memory and thinking and the performance of daily activities. It manifests as symptoms which often include significant [...] Read more.
Background: Dementia is a syndrome with a high global prevalence that includes a number of progressive diseases of the brain affecting various cognitive domains such as memory and thinking and the performance of daily activities. It manifests as symptoms which often include significant mood and behaviour changes that are highly varied. Changed moods and behaviours due to dementia may reflect distress and may be stressful for both the person living with dementia and their informal and formal carers. To provide dementia care support specific to mood and behaviour changes, the Behaviours in Dementia Toolkit website (BiDT) was developed using human-centred design principles. The BiDT houses a user-friendly, digital library of over 300 free, practical, and evidence-informed resources to help all care partners better understand and compassionately respond to behaviours in dementia so they can support people with dementia to live well. Objective: (1) To characterize the users that visited the BiDT; and (2) to understand the platform’s early impact on these users. Methods: A multi-method, descriptive study was conducted in the early post-website launch period. Outcomes and measures examined included the following: (1) reach: unique visitors, region, unique visits, return visits, bounce rate; (2) engagement: engaged users, engaged sessions, session duration, pages viewed, engagement rate per webpage, search terms, resources accessed; (3) knowledge change; (4) behaviour change; and (5) website impact: relevance, feasibility, intention to use, improving access and use of dementia guidance, recommend to others. Data was collected using Google Analytics and an electronic survey of website users. Results: From 4 February to 31 March 2024, there were 76,890 unique visitors to the BiDT from 109 countries. Of 76,890 unique visitors to the BiDT during this period, 16,626 were engaged users as defined by Google Analytics (22%) from 80 countries. The highest number of unique engaged users were from Canada (n = 8124) with an engagement rate of 38%. From 5 March 2024 to 31 March 2024, 100 electronic surveys were completed by website users and included in the analysis. Website users indicated that the BiDT validated or increased their dementia care knowledge, beliefs, and activities (82%) and they reported that the website validated their current care approaches or increased their ability to provide care (78%). Further, 77% of respondents indicated that they intend to continue using the BiDT and 81.6% said that they would recommend it to others to review and adopt. Conclusions: The BiDT is a promising tool for sharing practical and evidence-informed information resources to support people experiencing dementia-related mood and behaviour changes. Early evaluation of the website has demonstrated significant reach and engagement with users in Canada and internationally. Survey data also demonstrated high ratings of website relevance, feasibility, intention to use, knowledge change, practice support, and its contribution to dementia guidance. Full article
26 pages, 3691 KiB  
Article
LLM-ACNC: Aerospace Requirement Texts Knowledge Graph Construction Utilizing Large Language Model
by Yuhao Liu, Junjie Hou, Yuxuan Chen, Jie Jin and Wenyue Wang
Aerospace 2025, 12(6), 463; https://doi.org/10.3390/aerospace12060463 - 23 May 2025
Viewed by 608
Abstract
Traditional methods for requirement identification depend on the manual transformation of unstructured requirement texts into formal documents, a process that is both inefficient and prone to errors. Although requirement knowledge graphs offer structured representations, current named entity recognition and relation extraction techniques continue [...] Read more.
Traditional methods for requirement identification depend on the manual transformation of unstructured requirement texts into formal documents, a process that is both inefficient and prone to errors. Although requirement knowledge graphs offer structured representations, current named entity recognition and relation extraction techniques continue to face significant challenges in processing the specialized terminology and intricate sentence structures characteristic of the aerospace domain. To overcome these limitations, this study introduces a novel approach for constructing aerospace-specific requirement knowledge graphs using a large language model. The method first employs the GPT model for data augmentation, followed by BERTScore filtering to ensure data quality and consistency. An efficient continual learning based on token index encoding is then implemented, guiding the model to focus on key information and enhancing domain adaptability through fine-tuning of the Qwen2.5 (7B) model. Furthermore, a chain-of-thought reasoning framework is established for improved entity and relation recognition, coupled with a dynamic few-shot learning strategy that selects examples adaptively based on input characteristics. Experimental results validate the effectiveness of the proposed method, achieving F1 scores of 88.75% in NER and 89.48% in relation extraction tasks. Full article
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38 pages, 3033 KiB  
Article
Nomological Deductive Reasoning for Trustworthy, Human-Readable, and Actionable AI Outputs
by Gedeon Hakizimana and Agapito Ledezma Espino
Algorithms 2025, 18(6), 306; https://doi.org/10.3390/a18060306 - 23 May 2025
Viewed by 407
Abstract
The lack of transparency in many AI systems continues to hinder their adoption in critical domains such as healthcare, finance, and autonomous systems. While recent explainable AI (XAI) methods—particularly those leveraging large language models—have enhanced output readability, they often lack traceable and verifiable [...] Read more.
The lack of transparency in many AI systems continues to hinder their adoption in critical domains such as healthcare, finance, and autonomous systems. While recent explainable AI (XAI) methods—particularly those leveraging large language models—have enhanced output readability, they often lack traceable and verifiable reasoning that is aligned with domain-specific logic. This paper presents Nomological Deductive Reasoning (NDR), supported by Nomological Deductive Knowledge Representation (NDKR), as a framework aimed at improving the transparency and auditability of AI decisions through the integration of formal logic and structured domain knowledge. NDR enables the generation of causal, rule-based explanations by validating statistical predictions against symbolic domain constraints. The framework is evaluated on a credit-risk classification task using the Statlog (German Credit Data) dataset, demonstrating that NDR can produce coherent and interpretable explanations consistent with expert-defined logic. While primarily focused on technical integration and deductive validation, the approach lays a foundation for more transparent and norm-compliant AI systems. This work contributes to the growing formalization of XAI by aligning statistical inference with symbolic reasoning, offering a pathway toward more interpretable and verifiable AI decision-making processes. Full article
(This article belongs to the Section Algorithms for Multidisciplinary Applications)
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20 pages, 7113 KiB  
Article
Juggling Balls and Mathematics: An Ethnomathematical Exploration
by Giovanna Zito and Veronica Albanese
Educ. Sci. 2025, 15(3), 387; https://doi.org/10.3390/educsci15030387 - 20 Mar 2025
Cited by 1 | Viewed by 505
Abstract
Ethnomathematics, as a field of study, promotes recognizing the diversity in ways of thinking and doing mathematics, challenging the hierarchies and exclusions typical of traditional mathematics education. This research explores the practice of juggling, specifically analyzing three-ball juggling sequences to uncover the mathematical [...] Read more.
Ethnomathematics, as a field of study, promotes recognizing the diversity in ways of thinking and doing mathematics, challenging the hierarchies and exclusions typical of traditional mathematics education. This research explores the practice of juggling, specifically analyzing three-ball juggling sequences to uncover the mathematical structures and patterns embedded in this ancient art form. In a social association during a workshop, two jugglers and seven juggling learners interact with one of the researchers, a mathematics educator, to co-construct a shared model establishing a symmetrical dialogue based on the Alangui’s principles of “mutual interrogation” between the practice of juggling and the domain of mathematics. The knowledge exchange process is envisioned as a “barter” where both the mathematics educator and the jugglers contribute their unique perspectives to generate new and hybrid understandings. With a qualitative approach, from the analysis of the data collected during the ethnographic field work (notes, audiovisual recordings) emerges how the initial model, created by mathematicians and jugglers, was reinterpreted to better align with the cultural community’s practice. The research revealed that juggling serves as a concrete context for exploring abstract mathematical concepts and that mathematical analysis of juggling sequences helps jugglers gain a deeper understanding of underlying structures, enhancing their creativity. The hybrid model developed in this study offers a promising resource to integrating ethnomathematical perspectives into formal mathematics education, fostering a more situated and engaging learning experience for students. Full article
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39 pages, 24264 KiB  
Article
Digital Health Transformation: Leveraging a Knowledge Graph Reasoning Framework and Conversational Agents for Enhanced Knowledge Management
by Abid Ali Fareedi, Muhammad Ismail, Stephane Gagnon, Ahmad Ghazanweh and Zartashia Arooj
Systems 2025, 13(2), 72; https://doi.org/10.3390/systems13020072 - 22 Jan 2025
Viewed by 1478
Abstract
The research focuses on the limitations of traditional systems in optimizing information flow in the healthcare domain. It focuses on integrating knowledge graphs (KGs) and utilizing AI-powered applications, specifically conversational agents (CAs), particularly during peak operational hours in emergency departments (EDs). Leveraging the [...] Read more.
The research focuses on the limitations of traditional systems in optimizing information flow in the healthcare domain. It focuses on integrating knowledge graphs (KGs) and utilizing AI-powered applications, specifically conversational agents (CAs), particularly during peak operational hours in emergency departments (EDs). Leveraging the Cross Industry Standard Process for Data Mining (CRISP-DM) framework, the authors tailored a customized methodology, CRISP-knowledge graph (CRISP-KG), designed to harness KGs for constructing an intelligent knowledge base (KB) for CAs. This KG augmentation empowers CAs with advanced reasoning, knowledge management, and context awareness abilities. We utilized a hybrid method integrating a participatory design collaborative methodology (CM) and Methontology to construct a domain-centric robust formal ontological model depicting and mapping information flow during peak hours in EDs. The ultimate objective is to empower CAs with intelligent KBs, enabling seamless interaction with end users and enhancing the quality of care within EDs. The authors leveraged semantic web rule language (SWRL) to enhance inferencing capabilities within the KG framework further, facilitating efficient information management for assisting healthcare practitioners and patients. This innovative assistive solution helps efficiently manage information flow and information provision during peak hours. It also leads to better care outcomes and streamlined workflows within EDs. Full article
(This article belongs to the Special Issue Integration of Cybersecurity, AI, and IoT Technologies)
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19 pages, 333 KiB  
Article
Bi-Fuzzy S-Approximation Spaces
by Ronghai Wang, Xiaojie Xie and Huilai Zhi
Mathematics 2025, 13(2), 324; https://doi.org/10.3390/math13020324 - 20 Jan 2025
Viewed by 674
Abstract
The S-approximation spaces are significant extension of the rough set model and have been widely applied in intelligent decision-making. However, traditional S-approximation spaces are limited to two crisp universes, whereas bi-fuzzy universes (i.e., two distinct fuzzy domains) are more prevalent in practical applications. [...] Read more.
The S-approximation spaces are significant extension of the rough set model and have been widely applied in intelligent decision-making. However, traditional S-approximation spaces are limited to two crisp universes, whereas bi-fuzzy universes (i.e., two distinct fuzzy domains) are more prevalent in practical applications. To bridge this gap, this study introduces the bi-fuzzy S-approximation spaces (BFS approximation spaces) as an advancement of knowledge space theory’s fuzzy extension. Upper and lower approximation operators are formally defined, and the properties of BFS approximation spaces under various operations, such as complement, intersection and union are systematically explored. Special attention is given to a significant form of these operators, under which the monotonicity and complementary compatibility of BFS approximation spaces are rigorously analyzed. These results not only extend the theoretical framework of S-approximation spaces but also pave the way for further exploration of fuzzy extensions within knowledge space theory. Full article
(This article belongs to the Special Issue Fuzzy Convex Structures and Some Related Topics, 2nd Edition)
32 pages, 4167 KiB  
Article
Ontology-Driven Mixture-of-Domain Documentation: A Backbone Approach Enabling Question Answering for Additive Construction
by Chao Li and Frank Petzold
Buildings 2025, 15(1), 133; https://doi.org/10.3390/buildings15010133 - 4 Jan 2025
Cited by 2 | Viewed by 1936
Abstract
Advanced construction techniques, such as additive manufacturing (AM) and modular construction, offer promising solutions to address labor shortages, reduce CO2 emissions, and enhance material efficiency. Despite their potential, the adoption of these innovative methods is hindered by the construction industry’s fragmented expertise. [...] Read more.
Advanced construction techniques, such as additive manufacturing (AM) and modular construction, offer promising solutions to address labor shortages, reduce CO2 emissions, and enhance material efficiency. Despite their potential, the adoption of these innovative methods is hindered by the construction industry’s fragmented expertise. Building Information Modeling (BIM) is frequently suggested to integrate this diverse knowledge, but existing BIM-based approaches lack a robust framework for systematically documenting and retrieving the cross-domain knowledge essential for construction projects. To bridge this gap, this paper presents an ontology-driven methodology for documenting and utilizing expert knowledge, with a focus on AM in construction. Based on a well-founded ontological framework, a set of modular ontologies is formalized for individual domains. Additionally, a prototypical documentation tool is developed to elevate recorded information and BIM models as a knowledge graph. This knowledge graph will interface with advanced large language models (LLMs), enabling effective question answering and knowledge retrieval. Full article
(This article belongs to the Special Issue Architectural Design Supported by Information Technology: 2nd Edition)
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73 pages, 3621 KiB  
Review
Hardware Design and Verification with Large Language Models: A Scoping Review, Challenges, and Open Issues
by Meisam Abdollahi, Seyedeh Faegheh Yeganli, Mohammad (Amir) Baharloo and Amirali Baniasadi
Electronics 2025, 14(1), 120; https://doi.org/10.3390/electronics14010120 - 30 Dec 2024
Cited by 1 | Viewed by 7883
Abstract
Background: Large Language Models (LLMs) are emerging as promising tools in hardware design and verification, with recent advancements suggesting they could fundamentally reshape conventional practices. Objective: This study examines the significance of LLMs in shaping the future of hardware design and verification. It [...] Read more.
Background: Large Language Models (LLMs) are emerging as promising tools in hardware design and verification, with recent advancements suggesting they could fundamentally reshape conventional practices. Objective: This study examines the significance of LLMs in shaping the future of hardware design and verification. It offers an extensive literature review, addresses key challenges, and highlights open research questions in this field. Design: in this scoping review, we survey over 360 papers most of the published between 2022 and 2024, including 71 directly relevant ones to the topic, to evaluate the current role of LLMs in advancing automation, optimization, and innovation in hardware design and verification workflows. Results: Our review highlights LLM applications across synthesis, simulation, and formal verification, emphasizing their potential to streamline development processes while upholding high standards of accuracy and performance. We identify critical challenges, such as scalability, model interpretability, and the alignment of LLMs with domain-specific languages and methodologies. Furthermore, we discuss open issues, including the necessity for tailored model fine-tuning, integration with existing Electronic Design Automation (EDA) tools, and effective handling of complex data structures typical of hardware projects. Conclusions: this survey not only consolidates existing knowledge but also outlines prospective research directions, underscoring the transformative role LLMs could play in the future of hardware design and verification. Full article
(This article belongs to the Special Issue Machine Learning in Network-on-Chip Architectures)
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