Building Information Modelling (BIM) is a process for managing construction project information in such a way as to provide a basis for enhanced decision-making and for collaboration in a construction supply chain. One impediment to the uptake of BIM is the limited interoperability of different BIM systems. To overcome this problem, a set of Industry Foundation Classes (IFC) has been proposed as a standard for the construction industry. Building on IFC, the ifcOWL ontology was developed in order to facilitate representation (...) of building data in a consistent fashion across the Web by using the Web Ontology Language (OWL). This study presents a critical analysis of the ifcOWL ontology and of the associated interoperability issues. It shows how these issues can be resolved by using Basic Formal Ontology (ISO/IEC 21838-2) as top-level architecture. A set of competency questions is used as the basis for comparison of the original ifcOWL with the enhanced ontology, and the latter is used to align with a second ontology – the ontology for building intelligent environments (DOGONT) – in order to demonstrate the added value derived from BFO by showing how querying the enhanced ifcOWL yields useful additional information. (shrink)

The rapid evolution of artificial intelligence has given rise to agentic AI systems—autonomous entities capable of perceiving their environment, making decisions, and executing actions with minimal human intervention. This work provides a systematic analysis of agentic AI frameworks, governance models, and implementation strategies. Drawing on a comprehensive review of the literature, we examine the current state of agentic AI technologies, highlight key challenges in governance, security, and ethical oversight, and compare architectural frameworks for responsible deployment. Our results, illustrated through (...) detailed framework comparisons and governance analyses, demonstrate that while agentic AI holds transformative potential across multiple sectors, notable gaps persist in standardization, regulatory compliance, and interoperability. To address these issues, we propose a layered architecture that embeds governance and security across all system layers. An analysis of the competitive landscape further identifies critical interoperability challenges that could undermine U.S. leadership. Based on these insights, we outline a strategic framework for U.S. competitiveness, emphasizing accelerated standards development, international collaboration, and investment in interoperability research. Finally, emerging trends and future directions are explored to provide a comprehensive roadmap for responsible deployment of agentic AI. -/- . (shrink)

The orbital space environment is home to natural and artificial satellites, debris, and space weather phenomena. As the population of orbital objects grows so do the potential hazards to astronauts, space infrastructure and spaceflight capability. Orbital debris, in particular, is a universal concern. This and other hazards can be minimized by improving global space situational awareness (SSA). By sharing more data and increasing observational coverage of the space environment we stand to achieve that goal, thereby making spaceflight safer (...) and expanding our knowledge of near-Earth space. To facilitate data-sharing interoperability among distinct orbital debris and space object catalogs, and SSA information systems, I proposed ontology in (Rovetto, 2015) and (Rovetto and Kelso, 2016). I continue this effort toward formal representations and models of the overall domain that may serve to improve peaceful SSA and increase our scientific knowledge. This paper explains the project concept introduced in those publications, summarizing efforts to date as well as the research field of ontology development and engineering. I describe concepts for an ontological framework for the orbital space environment, near-Earth space environment and SSA domain. An ontological framework is conceived as a part of a potential international information system. The purpose of such a system is to consolidate, analyze and reason over various sources and types of orbital and SSA data toward the mutually beneficial goals of safer space navigation and scientific research. Recent internationals findings on the limitations of orbital data, in addition to existing publications on collaborative SSA, demonstrate both the overlap with this project and the need for data-sharing and integration. (shrink)

Cloud computing is becoming more sophisticated, with companies increasingly adopting multi-cloud and hybrid cloud systems to improve flexibility, scalability, and disaster recovery capabilities. The shift in this direction poses new security, interoperability, and management challenges that need to be resolved to guarantee a secure and efficient functioning of cloud environments. This study delves into the future of cloud computing by investigating significant trends, obstacles, and resolutions connected to multi-cloud and hybrid cloud implementations. Key areas of focus examine effective methods for (...) effortless cloud integration, encompassing multi-cloud orchestration, secure application relocation, and cloud-specific security measures. It incorporates real-world examples and comparisons of key cloud service providers (AWS, Microsoft Azure, and Google Cloud) to demonstrate best practices and valuable insights. Findings from this research adds to the expanding domain of cloud security and infrastructure management by suggesting a strategic framework for the future of multi-cloud and hybrid cloud settings. The study's results are designed to assist companies, cloud designers, and cybersecurity specialists in navigating the changing cloud environment while ensuring security, compliance, and operational effectiveness. (shrink)

The desideratum of semantic interoperability has been intensively discussed in medical informatics circles in recent years. Originally, experts assumed that this issue could be sufficiently addressed by insisting simply on the application of shared clinical terminologies or clinical information models. However, the use of the term ‘ontology’ has been steadily increasing more recently. We discuss criteria for distinguishing clinical ontologies from clinical terminologies and information models. Then, we briefly present the role clinical ontologies play in two multicentric research projects. Finally, (...) we discuss the interactions between these different kinds of knowledge representation artifacts and the stakeholders involved in developing interoperational real-world clinical applications. We provide ontology engineering examples from two EU-funded projects. (shrink)

With quantum computing nearing practical implementation in 2025, concerns over the vulnerability of classical cryptographic algorithms in cloud networking have intensified. This research addresses the urgent need for quantum-resilient communication protocols in federated and multi-tenant cloud environments. The study explores the integration of post-quantum cryptography (PQC) into cloud-native networking layers, focusing on protocols such as TLS 1.3, IPsec, and gRPC. It also investigates the performance trade-offs, key management challenges, and compliance implications of transitioning to PQC in large-scale, geographically distributed cloud (...) systems. A hybrid implementation model is proposed, blending classical and quantum-safe algorithms to ensure forward secrecy and interoperability during the migration phase. Experimental results suggest that lattice-based and hash-based key exchange mechanisms can be integrated into existing protocols with acceptable performance overhead and minimal disruption. (shrink)

The increasing diversity of data generated across enterprise, cloud, and distributed computing environments has created significant challenges for integrating heterogeneous data sources into unified analytical platforms. Differences in data models, schema structures, and semantic representations often limit the effectiveness of conventional extract-transform-load (ETL) approaches. This paper presents ASIF (A Semantic Integration Framework), a scalable architecture designed to support semantic integration across structured, semi-structured, and unstructured data sources. The framework combines ontology-driven schema reconciliation, entity resolution, and distributed consistency mechanisms to enable (...) reliable integration in large-scale data environments. ASIF was evaluated using datasets from government open-data repositories, healthcare interoperability benchmarks, and e-commerce transaction systems comprising approximately 4.2 billion records. Experimental results demonstrate that the framework significantly reduces schema conflicts while achieving high entity-matching accuracy across heterogeneous data sources. The proposed architecture also supports scalable ingestion and query processing in distributed environments. These findings indicate that semantic integration approaches can effectively address structural and semantic heterogeneity while maintaining the performance requirements of modern big data platforms. The framework provides a practical foundation for enterprise and cloud-based data integration systems operating at scale. (shrink)

The Department of Defense is working at all levels to rationalize its data management strategy (Stenbit, 2003). However, though this strategy is broad in its application, its reach has thus far not extended to specialized areas of interest such as modeling and simulation. Now, however, the rapid development of net-centric technologies and methods provides new opportunities for the modeling and simulation community within the DoD and in fact offers opportunities to bring together communities of practice (such as C2, logistics) in (...) ways that can help to bring greater coordination in use of data and systems and enable more rapid configuration of models that can bridge separate domains. The specific components that we shall address here include: -/- 1. Ontologies 2. Strategy for the identification and treatment of authoritative data 3. Service oriented architectures (including Web Services) 4. Semantic search 5. Reasoning and analysis engines 6. Data Interoperability . (shrink)

What separates the unique nature of human consciousness and that of an entity that can only perceive the world via strict logic-based structures? Rather than assume that there is some potential way in which logic-only existence is non-feasible, our species would be better served by assuming that such sentient existence is feasible. Under this assumption, artificial intelligence systems (AIS), which are creations that run solely upon logic to process data, even with self-learning architectures, should therefore not face the opposition they (...) have to gaining some legal duties and protections insofar as they are sophisticated enough to display consciousness akin to humans. Should our species enable AIS to gain a digital body to inhabit (if we have not already done so), it is more pressing than ever that solid arguments be made as to how humanity can accept AIS as being cognizant of the same degree as we ourselves claim to be. By accepting the notion that AIS can and will be able to fool our senses into believing in their claim to possessing a will or ego, we may yet have a chance to address them as equals before some unforgivable travesty occurs betwixt ourselves and these super-computing beings. (shrink)

The agentic web marks a structural transition from a human-centered information network to a digital environment populated by artificial intelligence (AI) agents that perceive, decide, and act autonomously. As delegated action unfolds at machine speed, exceeds discrete moments of human judgment, and distributes decision-making across non-human actors, existing legal frameworks face growing strain, creating an urgent need for new mechanisms capable of sustaining legality in this emerging order. A trustworthy agentic web therefore depends on the infrastructuring of legality through (...) interoperable protocols that organize identity, delegation, and accountability across systems, enabling coherent governance beyond isolated platforms. Towards this end, this article advances a distributed legal infrastructure (DLI), a governance paradigm composed of five interlocking layers: (1) self-sovereign, soulbound agent identities; (2) cognitive AI logic and constraint systems; (3) decentralized adjudication mechanisms for dispute resolution; (4) bottom-up agentic market regulation to mitigate information asymmetries and network effects, including insurance-based models; and (5) portable institutional frameworks that enable legal interoperability while preserving plural sources of authority. This reference framework contributes to emerging research on embedding legality within agentic web infrastructure, aligning distributed technical systems with accountability, contestability, and rule-of-law principles. (shrink)

Traditional approaches to complex system analysis often employ domain-specific models that lack interoperability across scales. This paper introduces Pattern Identity and Parallel Branching 4.0 (PIPB 4.0), a generative meta-theory based on “pattern reality priority.” Building on the ontological foundations of pattern realism (PIPB 1.0), the analytical grammar of the pattern quadruple (PIPB 2.0), and the multi-scale branching detection of PIPB 3.0, we formalize a two-layer framework: (1) the objective pattern quadruple (A, V, P, I); (2) the observer’s representation Cˆ = (...) (A, ˆ V , ˆ P , ˆ ˆI), where ˆI is not arbitrary but estimated via ˆI = E(A, ˆ V , ˆ Pˆ; D) from data D. The identity boundary I is an emergent property of (A, V, P) and is decomposed into three distinct layers: Ihard (inviolable constraints, triggering veto branching), Isoft (allowable variation range, measured by a composite metric on (A, V, P) with normalized weights), and Istat (statistical invariants such as behavioral distributions). Critically, I does not participate in the composite distance metric; it only defines the boundary conditions. The material layer (M) and environment (E) are explicitly included; E is a constitutive force, not a perturbation. We introduce a criticality mechanism: as a system approaches the boundary of Isoft, stability degrades, and small fluctuations can trigger a discontinuous branch. Observer detection is asymptotically consistent: with increasing information, the probability of correctly identifying the true branching time approaches one. Cross-temporal layer analysis (micro, meso, macro, meta) is integrated, drawing on foundational work in complex systems and hierarchy theory. A persistence condition is introduced to avoid spurious branches due to short-lived fluctuations. Hierarchical merging is defined as the emergence of a collective pattern from multiple individual systems. The framework provides a unified analytical language for identity dynamics, consciousness modeling, and social organization across scales. (shrink)

This paper reports the results of a series of experiments designed to establish how non-expert subjects conceptualize geospatial phenomena. Subjects were asked to give examples of geographical categories in response to a series of differently phrased elicitations. The results yield an ontology of geographical categories—a catalogue of the prime geospatial concepts and categories shared in common by human subjects independently of their exposure to scientific geography. When combined with nouns such as feature and object, the adjective geographic elicited almost exclusively (...) elements of the physical environment of geographical scale or size, such as mountain, lake and river. The phrase things that could be portrayed on a map, on the other hand, produced many geographical scale artefacts (roads, cities, etc.) and fiat objects (states, countries, etc.), as well as some physical feature types. These data reveal considerable mismatch as between the meanings assigned to the terms ‘geography’ and ‘geographic’ by scientific geographers and by ordinary subjects, so that scientific geographers are not in fact studying geographical phenomena as such phenomena are conceptualized by naive subjects. The data suggest, rather, a special role in determining the subject-matter of scientific geography for the concept of what can be portrayed on a map. This work has implications for work on usability and interoperability in geographical information science, and it throws light also on subtle and hitherto unexplored ways in which ontological terms such as ‘object’, ‘entity’ and ‘feature’ interact with geographical concepts. (shrink)

The integration of artificial intelligence (AI) and quantum computing is poised to redefine the landscape of financial risk modeling and enterprise decision-making systems. This paper investigates the synergistic potential of these transformative technologies, emphasizing the development of hybrid AI-quantum algorithms to address the increasing complexity of modern financial systems. Traditional risk modeling methodologies often face significant limitations in capturing intricate market dynamics and accounting for real-time decision-making constraints. By leveraging quantum computing's unparalleled computational capabilities, particularly its ability to handle high-dimensional (...) optimization problems, AI-powered quantum algorithms present a paradigm shift in financial risk prediction and mitigation. The research elaborates on key applications, including portfolio optimization, fraud detection, and credit risk analysis, demonstrating how quantum-enhanced AI algorithms achieve superior performance in terms of accuracy, efficiency, and scalability compared to classical approaches. The study begins by elucidating the theoretical underpinnings of hybrid AI-quantum systems, detailing their algorithmic structures and computational advantages. Quantum-inspired AI techniques, such as quantum neural networks and quantum-enhanced support vector machines, are examined for their ability to process vast datasets with unparalleled speed and precision. Portfolio optimization is analyzed as a case study, showcasing how quantum algorithms excel in minimizing risk while maximizing returns within a multidimensional constraint environment. Similarly, advanced fraud detection systems are explored, where hybrid models significantly improve anomaly detection rates by incorporating quantum-enhanced pattern recognition. The paper also delves into credit risk analysis, emphasizing how AI-quantum solutions can predict default probabilities with unprecedented accuracy, thereby supporting financial institutions in managing systemic risks more effectively. Despite these advancements, the integration of AI and quantum computing into financial ecosystems poses substantial challenges. The research discusses issues such as algorithmic scalability, error mitigation in quantum computations, and the resource-intensive nature of quantum hardware. Furthermore, the implementation of these technologies within the existing fintech landscape is fraught with obstacles, including interoperability with classical systems, regulatory compliance, and the high costs associated with quantum infrastructure. Addressing these challenges requires a multidisciplinary approach, combining expertise in quantum mechanics, AI, and financial engineering to develop robust, scalable solutions. The paper also examines the broader implications of AI-quantum integration for enterprise decision systems. By enabling real-time analysis of volatile markets, these technologies empower organizations to make informed, data-driven decisions, thereby enhancing operational resilience and competitive advantage. Furthermore, the ethical considerations and regulatory frameworks governing the deployment of such advanced systems are critically analyzed, emphasizing the need for transparency, fairness, and accountability in algorithmic decision-making. The findings presented in this study underscore the transformative potential of AI-powered risk modeling in quantum finance. By bridging the gap between theoretical advancements and practical implementations, this research contributes to the growing body of knowledge on hybrid AI-quantum systems and their applications in the financial domain. Ultimately, the integration of AI and quantum computing represents a pivotal development in enterprise decision systems, offering unprecedented opportunities to address the complexities of financial risk management in an increasingly interconnected and uncertain world. (shrink)

Can green growth policies help protect the environment while keeping the industry growing and infrastructure expanding? The City of Kitakyushu, Japan has actively implemented eco-friendly policies since 1967 and recently inspired the pursuit of sustainable development around the world, especially in the Global South region. However, empirical studies on the effects of green growth policies are still lacking. This study explores the relationship between road infrastructure development and average industrial firm size with air pollution in the city through the (...) Environmental Kuznets Curve (EKC) hypothesis. Auto-Regressive Distributed Lag (ARDL) and Non-linear Auto-Regressive Distributed Lag (NARDL) methods were applied on nearly 50-years’ time series data, from 1967 to 2015. The results show that the shape of the EKC of industrial growth, measured by average firm size, depends on the type of air pollution: inverted N-shaped relationships with NO2 and CO, and the U-shaped relationships with falling dust particle and Ox. Regarding infrastructure development, on the one hand, our analysis shows a positive effect of road construction on alleviating the amount of falling dust and CO concentration. On the other hand, the emissions of NO2 and Ox are shown to rise when plotted against road construction. The decline of CO emission, when plotted against both industrial growth and road development, indicates that the ruthlessness of the local government in pursuing green growth policies has been effective in this case. However, the story is not straightforward when it comes to other air pollutants, which hints at the limits of the current policies. The case of Kitakyushu illustrates the complex dynamics of the interaction among policy, industry, infrastructure, and air pollution. It can serve as an important reference point for other cities in the Global South when policies are formed, and progress is measured in the pursuit of a green economy. Finally, as an OECD SDGs pilot city and the leading Asian green-growth city, policymakers in Kitakyushu city are recommended to revise the data policy to enhance the findability and interoperability of data, as well as to invest in the application of big data. (shrink)

The advent of quantum supremacy, defined as the point at which quantum computers outperform classical systems in solving computational problems, represents a paradigm shift with profound implications for the field of cryptography, particularly in the context of secure financial transactions. Classical cryptographic techniques, such as public-key encryption systems that rely on the computational hardness of problems like integer factorization and discrete logarithms, are foundational to modern financial security architectures. However, the accelerated computational capabilities of quantum systems, driven by Shor’s and (...) Grover’s algorithms, render these traditional cryptographic methods vulnerable to compromise. This paper delves into the potential impact of quantum computing on cryptographic frameworks, emphasizing its disruptive influence on secure communication protocols, authentication systems, and transaction mechanisms within digital financial ecosystems. A comprehensive analysis of quantum-resistant cryptographic algorithms, collectively termed post-quantum cryptography (PQC), is presented as a critical countermeasure to these emerging threats. The paper explores lattice-based cryptography, hash-based signatures, code-based encryption, multivariate polynomial cryptography, and isogeny-based cryptographic schemes, evaluating their robustness against quantum adversaries. Through a rigorous examination of these algorithms’ theoretical foundations and practical implementations, the study identifies strengths, limitations, and potential deployment challenges. Special attention is devoted to the trade-offs between computational efficiency, security guarantees, and scalability, which are critical considerations for the widespread adoption of PQC in financial systems. Additionally, the study highlights the transitional challenges posed by integrating post-quantum cryptographic methods into existing infrastructures. Financial institutions, reliant on legacy systems and global interoperability standards, face significant hurdles in adopting quantum-resilient solutions. These include the need for standardized cryptographic protocols, cross-platform compatibility, and resistance to side-channel attacks. The research underscores the urgency of proactive measures to safeguard critical financial systems, including the development of hybrid cryptographic models that incorporate both classical and post-quantum techniques during the transition phase. Furthermore, the role of regulatory frameworks in mandating the adoption of quantum-safe protocols is examined, along with the potential for collaborative efforts among governments, financial institutions, and cryptographic researchers to ensure a coordinated response to the quantum threat landscape. The implications of quantum supremacy extend beyond technical considerations, affecting the trust and stability of global financial systems. This paper provides strategic recommendations for mitigating risks, including the prioritization of research into quantum-resistant algorithms, the establishment of testing environments for post-quantum solutions, and investment in the development of quantum-secure hardware. By leveraging these strategies, financial ecosystems can enhance their resilience against quantum-induced vulnerabilities. The discussion is substantiated with case studies and simulations that evaluate the performance of post-quantum algorithms in real-world financial contexts, illustrating their effectiveness in securing digital transactions against quantum attacks. By bridging the gap between theoretical advancements and practical implementations, this paper contributes to the ongoing discourse on the future of cryptography in the quantum era. Ultimately, the research aims to provide a comprehensive roadmap for securing digital financial ecosystems against the disruptive potential of quantum computing, ensuring the continued reliability and integrity of secure financial transactions in the face of emerging technological challenges. (shrink)

The Cell Ontology (CL) is designed to provide a standardized representation of cell types for data annotation. Currently, the CL employs multiple is_a relations, defining cell types in terms of histological, functional, and lineage properties, and the majority of definitions are written with sufficient generality to hold across multiple species. This approach limits the CL’s utility for cross-species data integration. To address this problem, we developed a method for the ontological representation of cells and applied this method to develop a (...) dendritic cell ontology (DC-CL). DC-CL subtypes are delineated on the basis of surface protein expression, systematically including both species-general and species-specific types and optimizing DC-CL for the analysis of flow cytometry data. This approach brings benefits in the form of increased accuracy, support for reasoning, and interoperability with other ontology resources. 104. Barry Smith, “Toward a Realistic Science of Environments”, Ecological Psychology, 2009, 21 (2), April-June, 121-130. Abstract: The perceptual psychologist J. J. Gibson embraces a radically externalistic view of mind and action. We have, for Gibson, not a Cartesian mind or soul, with its interior theater of contents and the consequent problem of explaining how this mind or soul and its psychological environment can succeed in grasping physical objects external to itself. Rather, we have a perceiving, acting organism, whose perceptions and actions are always already tuned to the parts and moments, the things and surfaces, of its external environment. We describe how on this basis Gibson sought to develop a realist science of environments which will be ‘consistent with physics, mechanics, optics, acoustics, and chemistry’. (shrink)

Fully Homomorphic Encryption (FHE) represents a transformative approach to securing payment transactions, particularly Card-Not-Present (CNP) transactions in e-commerce environments. This article explores how FHE addresses the fundamental vulnerability in current payment security frameworks: the necessity to decrypt sensitive data for processing. By enabling computation on encrypted data, FHE maintains complete data privacy throughout the transaction lifecycle, eliminating exposure points that hackers traditionally exploit. The synergistic integration of FHE with existing tokenization technologies creates a multi-layered security approach that significantly enhances protection (...) for payment data while enabling previously impossible collaborative fraud detection across organizational boundaries. Despite its compelling security benefits, FHE implementation faces substantial challenges including integration complexity with legacy systems, performance overhead, standardization requirements, and interoperability concerns across heterogeneous payment ecosystems. This article analyzes these challenges alongside emerging optimization techniques and implementation strategies that show promise for overcoming current adoption barriers. As computational efficiency continues to improve through hardware acceleration and algorithmic innovations, FHE stands poised to revolutionize payment security by fundamentally altering how sensitive financial data is processed in distributed environments. (shrink)

Production scheduling represents a critical function within manufacturing and industrial operations, exerting a direct influence on productivity, operational efficiency, and overall cost management. Traditional scheduling methodologies, while foundational,often exhibit limitations when confronted with the complexity, variability, and dynamic demands of contemporary production environments. In response, this paper investigates the potential of Machine Learning (ML) techniques for the enhancement of production scheduling outcomes. Specifically, it examines the capabilities of reinforcement learning, neural networks, and genetic algorithms to model complex systems, adapt to (...) real-time disruptions, and support more effective decision-making processes. The paper furtherreviews notable industrial applications of these techniques, critically evaluating their performance relative to conventional methods. In addition, it addresses the inherent challenges associated with the deployment of ML in production scheduling, including data availability, algorithmic interpretability, and integration with legacy systems. Finally, the study outlines future research directions, emphasizing the need for more robust, scalable, and interpretable ML-based scheduling solutions to meet the evolving demands of modern industry. (shrink)

Enterprises increasingly operate across multiple cloud ecosystems to support large-scale analytics, artificial intelligence (AI), and data-driven decision making. However, the rapid growth of specialized cloud services has created architectural fragmentation, where data ingestion pipelines, analytics workloads, machine learning platforms, and business intelligence tools operate across heterogeneous environments. This paper proposes a reference architecture for unified multi-cloud AI data platforms that integrates data ingestion, governed storage, analytics processing, and AI/ML services into a cohesive enterprise framework. The architecture is designed to support (...) interoperability across major cloud ecosystems including AWS, Azure, Google Cloud Platform, BigQuery, and Snowflake while maintaining governance, portability, and operational consistency. The study analyzes architectural layers for data integration, lakehouse and warehouse storage models, and consumption patterns for AI services and business intelligence applications. It also introduces practical evaluation metrics for platform performance, data freshness, portability overhead, and cost efficiency in multi-cloud environments. Through a comparative analysis of leading cloud data platforms, the paper highlights how hybrid deployment strategies can balance platform-native capabilities with enterprise governance requirements. The findings demonstrate that effective enterprise data platforms do not rely on a single cloud provider but instead leverage unified architectural principles, governed data products, and reusable analytics pipelines to enable scalable AI-driven analytics across domains such as healthcare, FinTech, and retail. The proposed reference architecture provides guidance for organizations seeking to design resilient, interoperable, and governance-aware multi-cloud data platforms for enterprise AI workloads. (shrink)

Property and Casualty (P&C) insurers are adopting multi-cloud environments as a strategic imperative because of their increasing data volumes and complexities in regulatory compliance, customer expectations and technological advancements. This paper discusses how Snowflake’s cloud-agnostic, unified platform enables insurers to create resilient, efficient, and compliant multi cloud data strategies. Using Snowflake’s elastic scalability, real-time analytics, secure data sharing and seamless cloud interoperability, insurers can optimize claims processing, augment fraud detection, and support customer engagement. The study offers core design principles for (...) multi cloud data architectures and sheds light on problems in data fragmentation, latency, and security risks. It points to how Snowflake enables data resilience, including data replication, encryption, GDPR, and CCPA compliance. Details are provided. Practical implementation strategies of phased data migration ETL pipelines and integration with existing enterprise system to ensure a smooth deployment. Snowflake is seen as a clear win through a cost-benefit analysis with metrics like RTO and RPO that define how well Snowflake performs disaster recovery and business continuity. Additionally, the paper also looks at the emergence of tech like AI, blockchain and edge computing which could additionally bolster multi-cloud strategies even further. Finally, Snowflake enables the journey for P&C insurers through cloud ecosystem pitfalls, enabling operational resilience, regulatory compliance, along competitive advantage in a data-driven industry. (shrink)

Dense retrieval has become a critical component of modern knowledge-intensive AI systems, enabling efficient semantic search over large document collections for applications such as question answering, enterprise search, recommendation, and retrieval-augmented generation. In practice, however, many high-value corpora are inherently distributed across organizations, regions, devices, or business units and cannot be centralized due to privacy, compliance, ownership, or cost constraints. Federated Learning (FL) provides a natural paradigm for collaborative model training without sharing raw data, yet applying FL to retrieval systems (...) introduces a fundamental challenge: embedding inconsistency across clients under heterogeneous knowledge distributions. When local retrievers are trained on domain-skewed corpora and aggregated natively, the resulting representation space can drift across clients, leading to degraded ranking quality, unstable convergence, and poor cross-client interoperability. This paper presents Federated Retrieval Alignment (FRA), a framework for training dense retrieval models in federated environments while preserving representation consistency across heterogeneous clients. FRA augments the standard retrieval objective with an alignment-aware regularization term that constrains local query embeddings toward a shared global embedding geometry, thereby reducing drift introduced by heterogeneous document and query distributions. We implement a prototype using dual-encoder retrieval models and evaluate it on partitioned Natural Questions and TriviaQA workloads with controlled domain skew, query skew, and client imbalance. Experimental results show that FRA improves Recall@10 by up to 9-12% relative over standard Federated Averaging under heterogeneous settings, reduces embedding drift by more than 60%, and stabilizes convergence without introducing additional communication overhead. Ablation studies further demonstrate that moderate alignment strength provides the best trade-off between retrieval quality and optimization speed. These findings indicate that embedding-aware coordination is essential for reliable federated retrieval learning and that native federated aggregation is insufficient for distributed knowledge systems operating under realistic heterogeneity. (shrink)

As digital infrastructure becomes increasingly reliant on multi-cloud architectures, ensuring consistent availability, fault tolerance, and disaster recovery across distributed systems is a critical challenge. This extended study advances the discourse on cloud resiliency engineering by addressing underexplored areas such as the quantification of resilience metrics, economic modeling of redundancy strategies, and the integration of AI-driven automation for predictive fault detection. It also evaluates the operational and regulatory complexities introduced by multi-jurisdictional deployments, legacy system modernization, and emerging threats like quantum computing. (...) Unlike traditional approaches focused solely on infrastructure-level solutions, this research presents a multi-dimensional framework that encompasses human factors, policy-aware architecture, and vendor interoperability. By synthesizing insights from real-world implementations, performance benchmarks, and evolving technologies such as edge computing and post-quantum cryptography, the study provides a comprehensive roadmap for building resilient, secure, and scalable cloud systems. The proposed framework equips cloud architects, developers, and enterprise leaders with actionable strategies to design and manage cloud environments that are both technically robust and contextually adaptable. (shrink)

Organizations are migrating their policy management systems from the ground to the cloud, changing how they manage, secure and scale their operations. This paper analyses how Amazon Web Services (AWS) and Snowflake support robust and efficient cloud-based policy management solutions. This marriage of sort of AWS, which has an almost panoply of cloud services, and Snowflake, which is very well known for its data warehousing and analytics capabilities, makes sense because it is an enterprise in which you will want to (...) modernize your policy management. Ways to explore Amazon’s infrastructural offerings, such as IAM (Identity and Access Management) and Security Hub, are also discussed as key components in the path to governance and compliance in a cloud environment. Snowflake’s ability to merge and process large sets of data worldwide to drive dynamic policy changes, predictive analysis, and real time insight punts is unique. Finally, this paper discusses the critical challenges this paper addresses, ranging from security, high interoperability, and cost optimization, and how these platforms address these hurdles and features, such as Zero Trust, automated scaling, and native integration with enterprise systems. Using case studies and the best practices, this analysis offers actionable insights for organizations that want to use AWS and Snowflake to manage cloud policy. These findings indicate that a combined approach using AWS and Snowflake looks to improve our operational efficiency and ensure we comply with the existing framework to be ready for future advancements in the cloud native policy ecosystems. (shrink)

The increasing complexity of megaprojects has intensified the need for more efficient and accurate cost estimation systems within the construction industry. Conventional quantity surveying methods are often characterized by manual calculations, fragmented information management, and delayed cost reporting, which contribute to budget overruns and poor financial decision-making. Building Information Modelling (BIM) has improved digital coordination in construction projects by enabling integrated project visualization and automated quantity extraction. More recently, Artificial Intelligence (AI) technologies have been introduced into BIM environments to support (...) intelligent data processing, automated classification, and predictive cost analysis. This study investigates the integration of AI with BIM for real-time cost estimation in megaprojects. A mixed-methods research design was adopted, involving comparative analysis of documented project case studies and semi-structured interviews with industry professionals. The findings reveal that AI-assisted BIM systems improve estimation efficiency, reduce manual errors, and enhance coordination between design and commercial teams. However, challenges relating to data standardization, interoperability, implementation costs, and workforce competence continue to affect adoption. The study concludes that AI-enabled BIM systems have significant potential to improve quantity surveying and commercial management practices when supported by appropriate digital standards and organizational readiness. (shrink)

As data centers seek faster and more secure data transport, Fibre Channel (FC) remains a preferred solution for storage-intensive applications. This paper explores advancements in 32G/64G FC networks, Brocade and Cisco innovations, and integration with virtualization platforms like VMware vSphere. It discusses how zoning, traffic segmentation, and low-latency switching ensure performance and reliability, particularly in financial and healthcare environments. The analysis also reviews upcoming interoperability standards and the increasing relevance of FC-NVMe deployments.

Can green growth policies help protect the environment while keeping the industry growing and infrastructure expanding? The City of Kitakyushu, Japan, has actively implemented eco-friendly policies since 1967 and recently inspired the pursuit of sustainable development around the world, especially in the Global South region. However, empirical studies on the effects of green growth policies are still lacking. This study explores the relationship between road infrastructure development and average industrial firm size with air pollution in the city through the (...) Environmental Kuznets Curve (EKC) hypothesis. Auto-Regressive Distributed Lag (ARDL) and Non-linear Auto-Regressive Distributed Lag (NARDL) methods were applied on nearly 50-years’ time series data, from 1967 to 2015. The results show that the shape of the EKC of industrial growth, measured by average firm size, depends on the type of air pollution: inverted N-shaped relationships with NO2 and CO, and the U-shaped relationships with falling dust particle and Ox. Regarding infrastructure development, on the one hand, our analysis shows a positive effect of road construction on alleviating the amount of falling dust and CO concentration. On the other hand, the emissions of NO2 and Ox are shown to rise when plotted against road construction. The decline of CO emission, when plotted against both industrial growth and road development, indicates that the ruthlessness of the local government in pursuing green growth policies is effective in this case. However, the story is not straightforward when it comes to other air pollutants, which hint at limits in the current policies. The case of Kitakyushu illustrates the complex dynamics of the interaction among policy, industry, infrastructure, and air pollution. It can serve as an important reference point for other cities in the Global South when policies are formed, and progress is measured in the pursuit of a green economy. Finally, as an OECD SDGs pilot city and the leading Asian green-growth city, policymakers in Kitakyushu city are recommended to revise the data policy to enhance the findability and interoperability of data as well as to invest in the application of big data. (shrink)

Industrial digital twins increasingly support monitoring, simulation, optimization, and decision-making across manufacturing, energy systems, logistics, infrastructure, and quality control. However, many implementations still depend on deterministic assumptions that treat system inputs, model parameters, and operating conditions as fixed. This limits their reliability in real industrial environments where uncertainty arises from sensor noise, demand fluctuation, machine degradation, environmental variability, model abstraction, and supply chain disruption. This review synthesizes stochastic digital twin frameworks for uncertainty quantification in industrial operations. It examines how aleatory, (...) epistemic, data-quality, model, and prediction uncertainties enter digital twin pipelines and propagate through virtual models into operational decisions. The review evaluates key modelling and computational approaches, including Bayesian updating, Monte Carlo simulation, stochastic processes, Markov models, polynomial chaos expansion, Gaussian process emulators, probabilistic machine learning, reduced-order models, and hybrid physics-data architectures. It also maps major industrial applications in predictive maintenance, production scheduling, supply chain risk management, energy optimization, and process quality control. The analysis shows that stochastic digital twins improve decision quality by replacing single-point predictions with probability distributions, confidence bounds, failure probabilities, risk metrics, and scenario-sensitive outputs. Yet practical deployment remains constrained by computational cost, data quality, calibration difficulty, real-time latency, multi-level integration, and lack of standardized implementation protocols. Future progress requires adaptive learning, uncertainty-aware artificial intelligence, scalable distributed computing, and interoperable digital twin ecosystems. Stochastic digital twins will not eliminate industrial uncertainty, but they can make it measurable, interpretable, and actionable for resilient industrial decision-making. (shrink)

Semantic interoperability can be defined as the ability of two or more computer systems to exchange information in such a way that the meaning of that information can be automatically interpreted by the receiving system accurately enough to produce useful results to the end users of both systems. Several activities are currently being performed by a variety of stakeholders to achieve semantic interoperability in healthcare. Many of these activities are not beneficial, because they place too great a focus on business (...) aspects and not enough on involvement of the right sorts of researchers, in particular those that are able to see how the data and information relate to the entities of concern on the side of the patient. The lack of a central focus on the patient, and the associated focus on ‘concepts’, have spawned a variety of mutually incompatible terminologies exhibiting non-resolvable overlap. The predominance of the healthcare IT industry in the writing and selection of semantic interoperability standards mitigates against the benefits that standards, when well designed, can bring about. (shrink)

As engineering applications require management of ever larger volumes of data, ontologies offer the potential to capture, manage, and augment data with the capability for automated reasoning and semantic querying. Unfortunately, considerable barriers hinder wider deployment of ontologies in engineering. Key among these is lack of a shared top-level ontology to unify and organise disparate aspects of the field and coordinate co-development of orthogonal ontologies. As a result, many engineering ontologies are limited to their scope, and functionally difficult to extend (...) or interoperate with other engineering ontologies. This paper demonstrates how the use of a top-level ontology, specifically the Basic Formal Ontology (BFO), greatly facilitates interoperability of multiple engineering-related ontologies. We constructed a system of formal linked ontologies by re-engineering legacy ontologies to be conformant with BFO and developing new BFO-conformant ontologies to capture knowledge in the engineering design, enterprise, human factors, manufacturing, and application domain of additive manufacturing. The resulting Integrated Framework for Additively Manufactured Products (IFAMP), including the body knowledge instantiated on its basis, serve as the basis for a proposed Design with Additive Manufacturing Method (DWAM), which we believe can support the design of innovative products with semantically enhanced ideation tools and enhanced access to application domain knowledge. The method and its facilitation through the ontological framework are demonstrated using a case study in medicine. (shrink)

The meeting focused on uses of ontologies, with a special focus on spatial ontologies, in addressing the ever increasing needs faced by biology and medicine to cope with ever expanding quantities of data. To provide effective solutions computers need to integrate data deriving from myriad heterogeneous sources by bringing the data together within a single framework. The meeting brought together leaders in the field of what are called "top-level ontologies" to address this issue, and to establish strategies among leaders in (...) the field of biomedical ontology for the creation of interoperable biomedical ontologies which will serve the goal of useful data integration. (shrink)

Report on Dagstuhl Seminar 07132, Schloss Dagstuhl, March 27-30 , 2007.

This paper is a standards-and-scaffold proposal; physics completion remains gated by explicit obligations (4D selection/Lorentzian emergence, chirality/index/anomaly closure, and parameter-free predictions). We propose a cross-domain scaffold—physics-facing, cognition-facing, and governance-facing—built from (i) a fixed exceptional geometric ontology scaffold, (ii) a certifiable stochastic dynamics grammar, and (iii) autopoietic interoperability as the dynamic goal for joint work. The ontology scaffold is the 112-dimensional quaternionic-Kähler Wolf space m112 ≅ e8(−24)/(e7 × SU(2)), used as a rigid symmetry-and-representation reference geometry. Operational dynamics are posed on a (...) 156-dimensional product phase space x156 ∶= T∗mH × v64 × n4, expressed in a generator-ready Universal Master Equation (UME) form (Stratonovich SDE + Itô generator). An autopoiesis contract is defined by (a) stochastic viability of a constraint set S and (b) Foster-Lyapunov organizational stability for V. A structural simplifier is Deltron noise isolation: diffusion is confined to the flux/ecology channel V_64, making governance constraints on protected variables diffusion-free in the generator. Autopoietic interoperability is the shared ability of teams and institutions to self-maintain, self-correct, and coordinate across domains via typed, auditable methods. It is the operational target that makes the framework pluralistic with respect to ultimate end goals while enforcing a universal method for shared inquiry. We adopt an industry-grade epistemic foundation for diffusion into action: engineering pragmatism (Koen; Peirce/James/Dewey/Rorty) operationalized as DMEPS conformance gates, with pramāṇas implemented as typed epistemic operators (measurement, inference, testimony with provenance, postulation, etc.). The consciousness-first stance follows James’s radical empiricism as a [MODEL] commitment with 120 years of philosophical warrant. Interpretive modules (e.g., Trika Shaivism mappings) are included as explicitly tagged [MAP] correspondences and do not bear the main mathematical or empirical claims. The pragmatist lineage (Peirce → James → Dewey → Rorty) is a genealogical context for shared themes, not a claim of direct influence on Koen. (shrink)

Semantic interoperability requires the use of standards, not only for Electronic Health Record (EHR) data to be transferred and structurally mapped into a receiving repository, but also for the clinical content of the EHR to be interpreted in conformity with the original meanings intended by its authors. Accurate and complete clinical documentation, faithful to the patient’s situation, and interoperability between systems, require widespread and dependable access to published and maintained collections of coherent and quality-assured semantic resources, including models such as (...) archetypes and templates that would (1) provide clinical context, (2) be mapped to interoperability standards for EHR data, (3) be linked to well specified, multi-lingual terminology value sets, and (4) be derived from high quality ontologies. Wide-scale engagement with professional bodies, globally, is needed to develop these clinical information standards. (shrink)

This paper proposes the Ω-Ψ Framework, a philosophical and mathematical approach to a longstanding problem in philosophy of mind: how to compare cognitive systems with radically heterogeneous architectures (human, artificial, hybrid) without committing to specific metaphysical positions on phenomenal consciousness. Building on the Theory of Conscious Potentiality (TPC), the framework develops Ω-space, a four-dimensional metric space quantifying cognitive potential along model complexity, temporal horizon, meta-modeling, and adaptive flexibility, and Ψ-space, an eight-dimensional dyadic space modeling cognitive resonance between systems. The paper (...) articulates the framework's epistemic commitments explicitly: it operationalizes capacities relevant to consciousness studies (after Chalmers, Tononi, Clark) without claiming to resolve the Hard Problem, and it distinguishes empirically supported components from logically coherent and speculative ones. Companion technical specifications (RFC-style protocols for cognitive interoperability) are provided as supplementary material at the linked Zenodo DOI; the philosophical core of the framework is presented self-contained in this manuscript. Empirical validation of the measurement protocol for AI systems is reported in a separate companion preprint. (shrink)

The rapid proliferation of Artificial Intelligence (AI) systems across diverse sectors—including healthcare, critical infrastructure, and digital experiences—has unveiled critical interoperability challenges that poses a challenge to the ongoing innovation, safety, equitable access, and the global competitiveness of U.S. AI technologies. This paper presents a comprehensive analysis of the current AI interoperability landscape, examining technical standards, regulatory frameworks, and governance models across major economic regions. By studying current developments we identify significant fragmentation in AI development ecosystems, with divergent approaches emerging between (...) the United States, European Union, China, and other key players, highlighting strategic implications and proposals for maintaining U.S. leadership. Our research examines technical interoperability challenges in data formats, model architectures, workflow orchestration, and multi-agent frameworks, while analyzing regulatory divergence in AI governance approaches, including the EU’s risk-based AI Act and the U.S.’s sectoral strategies. The cooperation for standardization AI protocols, data and models between various countries, organization, companies, domains and technologies have been discussed. We synthesize emerging standards and risk management methodologies from leading international bodies such as ISO/IEC JTC 1/SC 42, IEEE, and NIST, including ISO/IEC 42001 for AI management systems and the NIST AI RMF, and explore the role of model cards and data specifications in achieving technical interoperability. We also put forward looking scenarios for the next five years in this subject. By integrating insights from industry whitepapers, government publications, and academic research, we propose a holistic framework for global AI interoperability that addresses both technical standardization and regulatory harmonization keeping US AI landscape in mind. This framework provides policymakers, industry leaders, and standards organizations with actionable pathways to ensure AI systems are not only powerful, safe, and trustworthy but also strategically positioned to reinforce U.S. AI leadership while enabling seamless collaboration across borders and domains in alignment with regional regulatory requirements and cultural contexts. (shrink)

Health Level 7 (HL7) is an international standards development organisation in the domain of healthcare information technology. Initially the mission of HL7 was to enable data exchange via the creation of syntactic standards which supported point-to-point messaging. Currently HL7 sees its mission as one of creating standards for semantic interoperability in healthcare IT on the basis of its flagship “version 3” (v3). Unfortunately, v3 has been plagued by quality and consistency issues, and it has not been able to keep pace (...) with recent developments either in semantics and ontology or in computer science and engineering. HL7’s response has been to develop its “Services-Aware Interoperability Framework” (SAIF), which is intended to provide a foundation for work on all aspects of standardization in HL7 henceforth. We here summarise the major design principles that must be satisfied by a semantic interoperability framework – principles relating both to static semantics and to computational behaviour. We then assess the SAIF in light of these principles. We conclude that the SAIF is not in a position to support the needed reform of the HL7 v3 family of standards. (shrink)

When the U.S. conducts warfare, elements of a force are drawn from different services and work together as a single team to accomplish an assigned mission. To achieve such unified action, it is necessary that the doctrines governing the actions of members of specific services be both consistent with and subservient to joint Doctrine. Because warfighting today increasingly involves not only live forces but also automated systems, unified action requires that information technology that is used in joint warfare must be (...) aligned with joint doctrine. It requires also that the separate information systems used by the different elements of a joint force must be interoperable, in the sense that data and information that is generated by each element must be usable (understandable, processable) by all the other elements that need them. Currently, such interoperability is impeded by multiple inconsistencies among the different data and software standards used by warfighters. We describe here the on-going project of creating a Joint Doctrine Ontology (JDO), which uses joint doctrine to provide shared computer-accessible content valid for any field of military endeavor, organization, and information system. JDO addresses the two previously mentioned requirements of unified action by providing a widely applicable benchmark for use by developers of information systems that will both guarantee alignment with joint doctrine and support interoperability. (shrink)

As health systems around the world turn towards highly distributed, specialized and cooperative structures to increase quality and safety of care as well as efficiency and efficacy of delivery processes, there is a growing need for supporting communication and collaboration of all parties involved with advanced ICT solutions. The Electronic Health Record (EHR) provides the information platform which is maturing towards the eHealth core application. To meet the requirements for sustainable, semantically interoperable, and trustworthy EHR solutions, different standards and (...) different national strategies have been established. The workshop summarizes the requirements for such advanced EHR systems and their underlying architecture, presents different strategies and solutions advocated by corresponding protagonists, discusses pros and cons as well as harmonization and migration strategies for those approaches. It particularly highlights a turn towards ontology-driven architectures. (shrink)

Wearable devices are increasingly present in the health context, as tools for biomedical research and clinical care. In this context, wearables are considered key tools for a more digital, personalised, preventive medicine. At the same time, wearables have also been associated with issues and risks, such as those connected to privacy and data sharing. Yet, discussions in the literature have mostly focused on either technical or ethical considerations, framing these as largely separate areas of discussion, and the contribution of wearables (...) to the collection, development, application of biomedical knowledge has only partially been discussed. To fill in these gaps, in this article we provide an epistemic (knowledgerelated) overview of the main functions of wearable technology for health: monitoring, screening, detection, and prediction. On this basis, we identify 4 areas of concern in the application of wearables for these functions: data quality, balanced estimations, health equity, and fairness. To move the field forward in an effective and beneficial direction, we present recommendations for the 4 areas: local standards of quality, interoperability, access, and representativity. (shrink)

In the management of biomedical data, vocabularies such as ontologies and terminologies (O/Ts) are used for (i) domain knowledge representation and (ii) interoperability. The knowledge representation role supports the automated reasoning on, and analysis of, data annotated with O/Ts. At an interoperability level, the use of a communal vocabulary standard for a particular domain is essential for large data repositories and information management systems to communicate consistently with one other. Consequently, the interoperability benefit of selecting a particular O/T as a (...) standard for data exchange purposes is often seen by the end-user as a function of the number of applications using that vocabulary (and, by extension, the size of the user base). Furthermore, the adoption of an O/T as an interoperability standard requires confidence in its stability and guaranteed continuity as a resource. (shrink)

Ontologies are being developed throughout the biomedical sciences to address standardization, integration, classification and reasoning needs against the background of an increasingly data-driven research paradigm. In particular, ontologies facilitate the translation of basic research into benefits for the patient by making research results more discoverable and by facilitating knowledge transfer across disciplinary boundaries. Addressing and adequately treating mental illness is one of our most pressing public health challenges. Primary research across multiple disciplines such as psychology, psychiatry, biology, neuroscience and pharmacology (...) needs to be integrated in order to promote a more comprehensive understanding of underlying processes and mechanisms, and this need for integration only becomes more pressing with our increase in understanding of differences among individuals and populations at the molecular level concerning susceptibility to specific illnesses. Substance addiction is a particularly relevant public health challenge in the developed world, affecting a substantial percentage of the population, often co-morbid with other illnesses such as mood disorders. Currently, however, there is no straightforward automated method to combine data of relevance to the study of substance addiction across multiple disciplines and populations. In this contribution, we describe a framework of interlinked, interoperable bio-ontologies for the annotation of primary research data relating to substance addiction, and discuss how this framework enables easy integration of results across disciplinary boundaries. We describe entities and relationships relevant for the description of addiction within the Mental Functioning Ontology, Chemical Entities of Biological Interest Ontology, Protein Ontology, Gene Ontology and the Neuroscience Information Framework ontologies. (shrink)

Disaster response requires the cooperation of multiple emergency responder organizations (EROs). However, after‐action reports relating to large‐scale disasters identity communication difficulties among EROs as a major hindrance to collaboration. On the one hand, the use of two‐radio communication, based on multiple orthogonal frequencies and uneven coverage, has been shown to degrade inter‐organization communication. On the other hand, because they reflect different areas of expertise, EROs use differing terminologies, which are difficult to reconcile. These issues lead to ambiguities, misunderstandings, and inefficient (...) exchange of data and information among those involved, which can impede the response process and slow decision making. We, therefore, hypothesize that promoting semantic interoperability across ERO information systems might improve information exchange among stakeholders and thereby allow a more coherent response to the disaster. We propose an ontology‐based messaging service on the basis of the Emergency Data Exchange Language (EDXL) standards. The parties involved will continue to use the terminologies to which they are accustomed, but the system will resolve inconsistencies and thereby enhance mutual understanding among EROs by ensuring semantic translation of the exchanged information. The evaluation of the semantic translation demonstrated the effectiveness and accuracy of the proposed service. (shrink)

The collaboration of Language and Computing nv (L&C) and the Institute for Formal Ontology and Medical Information Science (IFOMIS) is guided by the hypothesis that quality constraints on ontologies for software ap-plication purposes closely parallel the constraints salient to the design of sound philosophical theories. The extent of this parallel has been poorly appreciated in the informatics community, and it turns out that importing the benefits of phi-losophical insight and methodology into application domains yields a variety of improvements. L&C’s LinKBase® (...) is one of the world’s largest medical domain ontologies. Its current primary use pertains to natural language processing ap-plications, but it also supports intelligent navigation through a range of struc-tured medical and bioinformatics information resources, such as SNOMED-CT, Swiss-Prot, and the Gene Ontology (GO). In this report we discuss how and why philosophical methods improve both the internal coherence of LinKBase®, and its capacity to serve as a translation hub, improving the interoperability of the ontologies through which it navigates. (shrink)

Dirichlet-type representations of the Riemann zeta function take the form F(σ,t) = ∑_(n=1)^∞ n^(-σ) e^(-itlog⁡n), s=σ+it, 0<σ<1 where zeros correspond to exact cancellation among weighted oscillatory terms. This work develops a structural framework for analyzing such cancellation by studying the interaction between magnitude decay and phase alignment. Within this framework, the vanishing of the sum is shown to force the emergence of localized cancellation blocks, for which diffuse phase alignment is quantitatively impossible. As a consequence, cancellation necessarily concentrates into interacting (...) configurations satisfying density, phase, and magnitude constraints. These constraints are not imposed but arise as necessary structural conditions for cancellation. Assuming the presence of a zero with σ∈(1/2,1), the vanishing of the first Fourier coefficient of the associated weighted phase distribution induces a global symmetry condition. In combination with the decay of the weights n^(-σ), this symmetry forces exponential localization of dominant contributions and constrains them to an approximately linearly spaced subsequence in log⁡n. Substitution of this structure reduces the sum to a weighted alternating exponential model with perturbations arising from integer constraints. The corresponding ideal alternating system is shown to remain strictly non-vanishing, and the perturbative contributions are bounded in such a way that they cannot produce cancellation of the required magnitude. The result is a structural obstruction: any admissible cancellation configuration within this oscillatory Dirichlet framework must satisfy a rigid system of constraints that is incompatible for σ≠1/2. At the critical line σ=1/2, the decay becomes scale-neutral, and the localization mechanism fails, removing the obstruction. This analysis demonstrates how structured constraint frameworks can force rigidity in oscillatory systems and collapse admissible cancellation configurations, providing a systematic method for studying phase–magnitude interactions in Dirichlet-type sums. (shrink)

Recent years have seen rapid progress in the development of ontologies as semantic models intended to capture and represent aspects of the real world. There is, however, great variation in the quality of ontologies. If ontologies are to become progressively better in the future, more rigorously developed, and more appropriately compared, then a systematic discipline of ontology evaluation must be created to ensure quality of content and methodology. Systematic methods for ontology evaluation will take into account representation of individual ontologies, (...) performance (in terms of accuracy, domain coverage and the efficiency and quality of automated reasoning using the ontologies) on tasks for which the ontology is designed and used, degree of alignment with other ontologies and their compatibility with automated reasoning. A sound and systematic approach to ontology evaluation is required to transform ontology engineering into a true scientific and engineering discipline. This chapter discusses issues and problems in ontology evaluation, describes some current strategies, and suggests some approaches that might be useful in the future. (shrink)

Information may be defined as the conceptual or communicable part of the content of mental acts. The content of mental acts includes sensory data as well as concepts, particular as well as general information. An information system is an external (non-mental) system designed to store such content. Information systems afford indirect transmission of content between people, some of whom may put information into the system and others who are among those who use the system. In order for communication to happen, (...) the conceptual systems of the originators and users of the information must be sufficiently similar. A formal conceptual framework that can provide the basis for exchange of information is termed an ontology. In its most fundamental form, ontology studies the most basic constituents of reality. Traditionally, ontology seeks to reflects structures that are independent of thought and cognition. The term ontology is used more broadly in artificial intelligence and software engineering, to refer to the conceptual basis for an information system. (shrink)

In this chapter, we explore the prospects of epistemic minimax and related principles. Minimax is a well-known approach to choosing a strategy under conditions of risk and uncertainty. In individual cases, the minimax strategy selects the action that can lead to the least bad outcome for the agent, even if taking that action ensures that expected utility is not maximized and that best-case outcomes are impossible. In social cases, the minimax strategy selects the policy that maximizes the wellbeing of the (...) least-well-off members of the group, even if doing so ensures that the aggregate utility of the group is not maximized and that best-case social outcomes are impossible. While there are many criticisms of minimax at both the individual and the social level, it is a prominent and popular approach at the social level because it prioritizes those at the bottom of the ladder. This chapter focuses on the social case for epistemic minimax and related principles. We develop the notions of epistemic position and epistemic power, both of which are prima facie epistemic goods, and demonstrate that a minimax principle can come apart from principles that would promote aggregate or maximum epistemic position or power. We also point to empirical and computational work that shows that testimonial networks online tend to be characterized by extreme inequalities of epistemic power. Then we extend traditional arguments in favor of social minimax to show that they hold in the epistemic case as well. In light of these arguments, we contend that the epistemic minimax principle should be treated as a heuristic for network interventions in democratic communities, and that it may be attractive for other types of communities as well. We conclude by examining the idea that some communities—especially the scientific community—should probably adopt different principles. Communities vary along different dimensions, and these dimensions may favor or disfavor different epistemic policies and principles, showing the sensitivity of our arguments to particular circumstances. (shrink)

The current global health situation (primarily as a result of Covid-19) has fostered a change in customer behaviour towards the use of telecommunications services, which has led to an increase in data traffic. As a result of this change, telecommunications operators have a golden opportunity to create new sources of revenue by utilising Big Data Analytics (BDA) solutions. In the process of establishing a BDA project, we encountered a number of obstacles, the most significant of which were the selection of (...) the technological solution from among the vast array of tools that were already available, as well as the selection of the governance methodology that would be used to control both the project and the data. The vast majority of study materials that pertain to the telecommunications industry have not addressed the implementation of BDA projects from the beginning to the end. The goal of this study is to concentrate on a BDA telecommunications project, specifically the governance of the project, the architecture of the project, the governance of the data, and the team working on the BDA project. Utilisable BDA use cases are presented in the final section of this study. These use cases are presented in terms of applications that enable income generating and cost optimisation. It would appear that this effort will make it easier to implement BDA projects and will provide telecoms operators with the ability to have a better understanding of the fundamental components that need to be focused on. (shrink)

In this paper, we define correlation coefficient measure between any two rough neutrosophic sets. We also prove some of its basic properties.. We develop a new multiple attribute group decision making method based on the proposed correlation coefficient measure. An illustrative example of medical diagnosis is solved to demonstrate the applicability and effecriveness of the proposed method.