Search Results (63)

Academic researchers in technical and policy fields tend to pay little attention to the metaphysical and ontological ‘priors’ that nevertheless structure and determine scientific strategies and results. Green political agendas rooted in ecological modernization (EM) are distinguished from antecedent visions predicated on biophysical limits. Net zero is shown to be rooted in a project of global EM. Ecomodernism is analyzed in relation to its principal actors, geopolitical context and underlying metaphysics and anthropology. It is driven by non-negotiable societal priorities (‘ends’), which themselves derive from a particular set of technical ‘means’. The top-down version of the Fourth Industrial Revolution (IR4.0) and new paradigm of global net zero constitute an integrated agenda of eco-modernism. Global net zero cannot hope to achieve its own metabolic goals in respect of either energy flows or the circular economy. A competing, bottom-up and distributed model of the IR4.0 could potentially achieve these targets without falling prey to the Jevons paradox. This potential turns on the greater capacity of low-overhead, prosumer models to nurture less materialist cultural priorities that are more communitarian and family-oriented. A smart energy system that emerges in the context of distributed, domestic and informal production is much more likely to mirror the complex, infinitely gradated and granular pattern of oscillating energy transfers that are characteristic of biological systems. From an ecological economic perspective, such a bottom-up approach to the IR4.0 is much more likely to see the orders of magnitude reduction in the unit energetic cost of social complexity envisaged, in principle, by net zero. Through this comprehensive review of the metaphysical and ontological priors of mainstream IR4.0, researchers in the linked fields of energy and circular economy are presented with a wider range of potential options less constrained by preconceived assumptions about the ‘ends’ of societal development and progress. Full article

Traditional Case-Based Reasoning (CBR) methods face significant methodological challenges, including limited information resources in case databases, methodologically inadequate similarity calculation approaches, and a lack of standardized case revision mechanisms. These limitations lead to suboptimal case matching and insufficient solution adaptation, highlighting critical gaps in the development of CBR methodologies. This paper proposes a novel CBR framework enhanced by generative AI, aiming to improve and innovate existing methods in three key stages of traditional CBR, thereby enhancing the accuracy of retrieval and the scientific nature of corrections. First, we develop an ontology model for comprehensive case representation, systematically capturing scenario characteristics, risk typologies, and strategy frameworks through structured knowledge representation. Second, we introduce an advanced similarity calculation method grounded in triangle theory, incorporating three computational dimensions: attribute similarity measurement, requirement similarity assessment, and capability similarity evaluation. This multi-dimensional approach provides more accurate and robust similarity quantification compared to existing methods. Third, we design a generative AI-based case revision mechanism that systematically adjusts solution strategies based on case differences, considering interdependence relationships and mutual influence patterns among risk factors to generate optimized solutions. The methodological framework addresses fundamental limitations in existing CBR approaches through systematic improvements in case representation, similarity computation, and solution adaptation processes. Experimental validation using actual case data demonstrates the effectiveness and scientific validity of the proposed methodological framework, with applications in risk assessment and emergency response scenarios. The results show significant improvements in case-matching accuracy and solution quality compared to traditional CBR approaches. This method provides a robust methodological foundation for CBR-based decision-making systems and offers practical value for risk management applications. Full article

The rapid growth in scientific knowledge has created a critical need for advanced systems capable of managing mathematical knowledge at scale. This study presents a novel approach that integrates ontology-based knowledge representation with large language models (LLMs) to automate the extraction, organization, and reasoning of mathematical knowledge from LaTeX documents. The proposed system enhances Mathematical Knowledge Management (MKM) by enabling structured storage, semantic querying, and logical validation of mathematical statements. The key innovations include a lightweight ontology for modeling hypotheses, conclusions, and proofs, and algorithms for optimizing assumptions and generating pseudo-demonstrations. A user-friendly web interface supports visualization and interaction with the knowledge graph, facilitating tasks such as curriculum validation and intelligent tutoring. The results demonstrate high accuracy in mathematical statement extraction and ontology population, with potential scalability for handling large datasets. This work bridges the gap between symbolic knowledge and data-driven reasoning, offering a robust solution for scalable, interpretable, and precise MKM. Full article

The enhancement of remote sensing interpretation accuracy for rock strata in complex terrain areas has long been limited by challenges in field validation and the insufficient integration of geological knowledge in traditional spectral–spatial feature selection methods. This study proposes a geological remote sensing interpretation framework that integrates textual geological data, which enhances lithological identification accuracy by systematically combining multi-source geological knowledge with machine learning algorithms. Using a dataset of 2591 geological survey reports and scientific literature, a remote sensing interpretation ontology model was established, featuring four core entities (rock type, stratigraphic unit, spectral feature, and geomorphological indicator). A hybrid information extraction process combining rule-based parsing and a fine-tuned Universal Information Extraction (UIE) model was employed to extract knowledge from unstructured texts. A knowledge graph constructed using the TransE algorithm consists of 766 entity nodes and 1008 relationships, enabling a quantitative evaluation of feature correlations based on semantic similarity. When combined with Landsat multispectral data and digital elevation model (DEM)-derived terrain parameters, the knowledge-enhanced Random Forest (81.79%) and Support Vector Machine (75.76%) models demonstrated excellent performance in identifying rock-stratigraphic assemblages in the study area. While reducing subjective biases in manual interpretation, the method still has limitations. These include limited use of cross-modal data (e.g., geochemical tables, outcrop images) and a reliance on static knowledge representations. Future research will introduce dynamic graph updating mechanisms and multi-modal fusion architectures to improve adaptability across diverse geological lithological and structural environments. Full article

Software engineering occupies a prominent place in the theory and practice of simulation modeling, which necessitates scientific research in the field of methodological principles for forming software product quality. The problem of determining the optimal option for software development is one of the key ones in the field of information technology because it determines the quality of the final product and the efficiency of project management. The article considers the concept of developing an automated system, the basis of which is the software for assessing alternatives in the process of creating innovative IT projects. The main goal of the study is to model alternatives and select the optimal option for the process of creating an IT project using modern methodological approaches. For this purpose, the methods of ontological analysis, expert evaluation, multi-criteria optimization, pairwise comparisons and multi-factor selection of alternatives are applied. In the course of the research, a subset of Pareto factors is singled out and alternative development options are formed based on the method of linear convolution of criteria. The proposed methodology allows for assessing the importance of key factors and selecting the optimal option for the software development process. As a result, the developed approach contributes to strategic planning and increases the transparency of the decision-making process. The key result of the research is the created software product that allows one to automate the procedure for selecting the optimal solution for the IT project development process, providing reliable support for simulation modeling and increasing the efficiency of project management. The proposed methodology creates a new paradigm for making informed decisions regarding systems for creating complex software complexes. Full article

Typhoon disasters not only trigger secondary disasters, such as rainstorms and flooding, but also bring many negative impacts on the normal operation of urban infrastructure and the safety of people’s lives and property. In order to effectively prevent the risks of typhoon disaster chain, this paper proposes a joint entity and relation extraction model based on RoBERTa-Adv-GPLinker. Then, relying on the ontology theory and methodology, we establish a knowledge graph of typhoon disaster chain. The results show that the joint extraction model based on RoBERTa-Adv-GPLinker outperforms other baseline models in all assessment indexes. Moreover, the constructed knowledge graph of typhoon disaster chain includes secondary disasters and derived disaster impacts. This can largely depict the evolution process of typhoon disaster secondary derivations. On this basis, a risk assessment model of typhoon disaster chain based on Bayesian network is established. Taking Fujian Province as an example, the risk associated with the typhoon disaster chain is assessed, verifying the effectiveness of the method. This study provides a scientific basis for enhancing government emergency response capabilities and achieving sustainable regional development. Full article

One of the issues in Natural Language Processing (NLP) and Artificial Intelligence (AI) is language representation and modeling, aiming to manage its structure and find solutions to linguistic issues. With the pursuit of the most efficient capture of knowledge about the Modern Greek language and, given the scientifically certified usability of the ontological structuring of data in the field of the semantic web and cognitive computing, a new ontology of the Modern Greek language at the level of structure and vocabulary is presented in this paper, using the Protégé platform. With the specific logical and structured form of knowledge representation to express, this research processes and exploits in an easy and useful way the distributed semantics of linguistic information. Full article

The rapid growth of biomedical literature makes it challenging for researchers to stay current. Integrating knowledge from various sources is crucial for studying complex biological systems. Traditional text-mining methods often have limited accuracy because they don’t capture semantic and contextual nuances. Deep-learning models can be computationally expensive and typically have low interpretability, though efforts in explainable AI aim to mitigate this. Furthermore, transformer-based models have a tendency to produce false or made-up information—a problem known as hallucination—which is especially prevalent in large language models (LLMs). This study proposes a hybrid approach combining text-mining techniques with graph neural networks (GNNs) and fine-tuned large language models (LLMs) to extend biomedical knowledge graphs and interpret predicted edges based on published literature. An LLM is used to validate predictions and provide explanations. Evaluated on a corpus of experimentally confirmed protein interactions, the approach achieved a Matthews correlation coefficient (MCC) of 0.772. Applied to insomnia, the approach identified 25 interactions between 32 human proteins absent in known knowledge bases, including regulatory interactions between MAOA and 5-HT2C, binding between ADAM22 and 14-3-3 proteins, which is implicated in neurological diseases, and a circadian regulatory loop involving RORB and NR1D1. The hybrid GNN-LLM method analyzes biomedical literature efficiency to uncover potential molecular interactions for complex disorders. It can accelerate therapeutic target discovery by focusing expert verification on the most relevant automatically extracted information. Full article

The practice of science appears to involve “model-talk”. Scientists, one thinks, are in the business of giving accounts of reality. Scientists, in the process of furnishing such accounts, talk about what they call “models”. Philosophers of science have inspected what this talk of models suggests about how scientific theories manage to represent reality. There are, it seems, at least three distinct philosophical views on the role of scientific models in science’s portrayal of reality: the abstractionist view, the indirect fictionalist view, and the direct fictionalist view. In this essay, I try to articulate a question about what makes a scientific model more or less appropriate for a specific domain of reality. More precisely, I ask, “What accounts for the fact that given a determinate target domain, some scientific models, but not others, are thought to be “appropriate” for that domain?” I then consider whether and the degree to which each of the mentioned views on scientific models institutes a satisfactory response to this question. I conclude that, amongst those views, the direct fictionalist view seems to have the most promising response. I then utilize this argument to develop a more precise account of the problem of differential importability, and ultimately offer a more general and less presumptive argument that the problem seems to be optimally solved by justifying comparative evaluation of model-importabilities solely in terms of comparative evaluations of what I characterize as models’ “holistic” predictive success. Full article

The incorporation of Artificial Intelligence (AI) models into various optimization systems is on the rise. However, addressing complex urban and environmental management challenges often demands deep expertise in domain science and informatics. This expertise is essential for deriving data and simulation-driven insights that support informed decision-making. In this context, we investigate the potential of leveraging the pre-trained Large Language Models (LLMs) to create knowledge representations for supporting operations research. By adopting ChatGPT-4 API as the reasoning core, we outline an applied workflow that encompasses natural language processing, Methontology-based prompt tuning, and Generative Pre-trained Transformer (GPT), to automate the construction of scenario-based ontologies using existing research articles and technical manuals of urban datasets and simulations. From these ontologies, knowledge graphs can be derived using widely adopted formats and protocols, guiding various tasks towards data-informed decision support. The performance of our methodology is evaluated through a comparative analysis that contrasts our AI-generated ontology with the widely recognized pizza ontology, commonly used in tutorials for popular ontology software. We conclude with a real-world case study on optimizing the complex system of multi-modal freight transportation. Our approach advances urban decision support systems by enhancing data and metadata modeling, improving data integration and simulation coupling, and guiding the development of decision support strategies and essential software components. Full article

The museum system is exposed to a high risk of seismic hazards. However, it is difficult to carry out seismic hazard prevention to protect cultural relics in collections due to the lack of real data and diverse types of seismic hazards. To address this problem, we developed a deep-learning-based multi-source feature-fusion method to assess the data on seismic damage caused by collected cultural relics. Firstly, a multi-source data-processing strategy was developed according to the needs of seismic impact analysis of the cultural relics in the collection, and a seismic event-ontology model of cultural relics was constructed. Additionally, a seismic damage data-classification acquisition method and empirical calculation model were designed. Secondly, we proposed a deep learning-based multi-source feature-fusion matching method for cultural relics. By constructing a damage state assessment model of cultural relics using superpixel map convolutional fusion and an automatic data-matching model, the quality and processing efficiency of seismic damage data of the cultural relics in the collection were improved. Finally, we formed a dataset oriented to the seismic damage risk analysis of the cultural relics in the collection. The experimental results show that the accuracy of this method reaches 93.6%, and the accuracy of cultural relics label matching is as high as 82.6% compared with many kinds of earthquake damage state assessment models. This method can provide more accurate and efficient data support, along with a scientific basis for subsequent research on the impact analysis of seismic damage to cultural relics in collections. Full article

The advancement of digital twins (DTws) has been instrumental in various scientific and industrial domains, facilitating real-time monitoring, analysis, and optimisation of complex systems. However, it remains difficult to describe precisely the architectural models and their characteristics of DTws and the aggregation of lower-level DTws to higher-level DTws. This article introduces two contributions with the goal of addressing challenges in describing DTws architectures and aggregating DTws. Firstly, it presents the development of “WoTDT” (WoT digital twin) ontology, an extension of the W3C Web of Things descriptions ontology, designed to semantically describe the five-dimensional model architecture of DTws. This ontology enhances data interoperability and accessibility across dimensions, promoting a deeper understanding of DTws. Secondly, it introduces the “DTAG” (digital twin aggregation) methodology for aggregating multiple DTws into an unified DTw aggregate (DTwA). This methodology considers whether the DTws contain semantics or not and employs the WoTDT ontology to conceptualise the architecture and features of the resulting DTwA. Finally, an example of WoTDT ontology together with the DTAG methodology is shown in the context of the European H2020 construction-related project COGITO. Full article

This paper presents a synoptic (“Two-Eyed Seeing”) approach to science transfer in Education for Sustainable Development (ESD), based on an ontological framework inspired by two related concepts from Western philosophy (Sellars’ synoptic view) and indigenous wisdom (Two-Eyed Seeing). It was tested and further developed in a participatory research process with first year student science teachers. The results show that this model can support a balanced approach between a scientific and a holistic perspective at each stage of the teaching process—preparation, implementation and assessment—and help to integrate sustainability issues consistently into science lessons. In the course of the research process, the model has developed into a viable educational tool that distinguishes between a person-oriented lifeworld image and a things-oriented scientific image and guides the systematic transfer between the two images. It promotes students’ reasoning and scientific practice as well as their identity formation and community interaction, two equally important issues in ESD of today. The pre-service teachers were careful to close the loop, as they put it, between the two images. They saw health and environmental issues as particularly helpful in realising scientific holism. The pre-service teachers interpreted the role of the teacher as a facilitator or mediator between the two images rather than as an expert and advocate of a one-sided scientific image of the world. The model may be of general interest to teachers and researchers who design, implement, evaluate and investigate ESD activities. The potential use of the scientific holism framework and the synoptic (“Two-Eyed Seeing”) tool for science transfer in public and political sustainability discourse is also discussed. Full article

Managing modern museum content and visitor data analytics to achieve higher levels of visitor experience and overall museum performance is a complex and multidimensional issue involving several scientific aspects, such as exhibits’ metadata management, visitor movement tracking and modelling, location/context-aware content provision, etc. In related prior research, most of the efforts have focused individually on some of these aspects and do not provide holistic approaches enhancing both museum performance and visitor experience. This paper proposes an integrated conceptualisation for improving these two aspects, involving four technological components. First, the adoption and parameterisation of four ontologies for the digital documentation and presentation of exhibits and their conservation methods, spatial management, and evaluation. Second, a tool for capturing visitor movement in near real-time, both anonymously (default) and eponymously (upon visitor consent). Third, a mobile application delivers personalised content to eponymous visitors based on static (e.g., demographic) and dynamic (e.g., visitor movement) data. Lastly, a platform assists museum administrators in managing visitor statistics and evaluating exhibits, collections, and routes based on visitors’ behaviour and interactions. Preliminary results from a pilot implementation of this holistic approach in a multi-space high-traffic museum (MELTOPENLAB project) indicate that a cost-efficient, fully functional solution is feasible, and achieving an optimal trade-off between technical performance and cost efficiency is possible for museum administrators seeking unfragmented approaches that add value to their cultural heritage organisations. Full article

In 2022, the authors of this paper were awarded with three years’ government funding to support seventy-five English schools and Further Education colleges with the running of their own Action Research for inclusion and special educational needs projects (ISEND). Based on the funder’s interest in the identification and scaling-up of the evidence-base for SEND practice, this reflective account analyzes the evidence-base drawn upon and created by the Action Researchers for ISEND and the efficacy of the approach. Adopting an interpretivist, qualitative approach to content analysis, this paper analyzes data from the first seven completed Action Research for ISEND projects. Aligned with Dewey’s scientific model of reflection, analysis shows the Action Researchers for ISEND draw upon a complex synthesis of contextualized understanding, broadened horizons (including collaborative working and study), deepened and/or reshaped understandings, and data analysis to form their theorizations of praxis. Bearing no relation to evidence-based practice, the Action Researchers for ISEND adopt a constructivist ontology towards the inclusion of children with SEND, which challenges positivistic paradigms of “what works” in SEND and embeds a praxis of democracy which frequently includes the voices of learners with disabilities in decision making processes. Full article