Search Results (263)

Contemporary artificial intelligence is dominated by generative systems that excel at extracting patterns but fail to grasp meaning, sense, context, and experiential temporality. This limitation highlights the need for new computational wisdom that combines philosophical insights with advanced models to produce AI systems capable of authentic understanding. Sophimatics, as elaborated upon in this article, is introduced as a science of computational wisdom that rejects the purely syntactic manipulation of symbols characteristic of classical physical symbolic systems and addresses the shortcomings of generative statistical approaches. Building on philosophical foundations of dynamic ontology, intentionality and dialectical reasoning, Sophimatics integrates complex temporality, multidimensional semantic modeling, hybrid symbolic–connectionist logic, and layered memory structures to that the AI can perceive, remember, reason, and act in ethically grounded ways. This article, which is part of a set of papers, summarizes the theoretical framework underlying Sophimatics and outlines the conceptual results of the materials and methods, illustrating the potential of this approach to improve interpretability, contextual adaptation, and ethical deliberation compared to basic generative models. This is followed by a methodology and a complete formal model for translating philosophical categories into an operational model and specific architecture. This article represents Phase 1 of a six-phase research program, providing mathematical foundations for the architectural implementation and empirical validation presented in companion publications. Following this, several use cases are outlined, and then the Discussion Section anticipates the main results and perspectives for post-generative AI solutions within the Sophimatic paradigm. Full article

Natural heritage digitization has evolved beyond simple 3D representation. Contemporary approaches require transparent documentation integrating biological, heritage, and digitization standards, yet existing frameworks operate in isolated domains without semantic interoperability. Current digitization frameworks fail to integrate biological standards (Darwin Core, ABCD), heritage standards (CIDOC-CRM), and digitization standards (CRMdig, PROV-O) into a unified semantic architecture, limiting transparent documentation of natural heritage data across its entire lifecycle—from physical observation through digital reconstruction to knowledge reasoning. This study proposes an integrated semantic framework comprising three components: (1) the E-DNH ontology, which adopts a triple-layer architecture (data–metadata–paradata) and a triple-module structure (nature–heritage–digital), bridging Darwin Core, CIDOC-CRM, CRMdig, and PROV-O; (2) the HR3D workflow, which establishes a standardized high-precision 3D data acquisition protocol that systematically documents paradata; and (3) the C-EDNH platform, which implements a Neo4j-based knowledge graph with semantic search capabilities, AI-driven quality assessment, and persistent identifiers (NSId/DOI). The framework was validated through digitization of 197 natural heritage specimens (68.5% avian, 24.9% insects, 5.1% mammals, 1.5% reptiles), demonstrating high geometric accuracy (RMS 0.18 ± 0.09 mm), visual fidelity (SSIM 0.92 ± 0.03), and color accuracy (ΔE₀₀ 2.1 ± 0.7). The resulting knowledge graph comprises 15,000+ nodes and 45,000+ semantic relationships, enabling cross-domain federated queries and reasoning. Unlike conventional approaches that treat digitization as mere data preservation, this framework positions digitization as an interpretive reconstruction process. By systematically documenting paradata, it establishes a foundation for knowledge discovery, reproducibility, and critical reassessment of digital natural heritage. Full article

As prosthetic technologies become increasingly data-rich and embedded in care systems, traditional human-centred approaches often fall short of addressing evolving use realities. This paper contributes an applied computing framework that enables semantic reasoning and data-driven adaptation within prosthesis aftercare. We present an ontology-driven, cyber–physical prosthesis service system designed to enable personalised and adaptive care. Implemented through the Adaptive Prosthesis Life-Cycle Service System (adProLiSS) framework and demonstrated via a smart prosthesis prototype, the system treats the prosthesis as a semi-autonomous actor within an emotionally responsive and semantically mediated ecosystem. The proposed architecture integrates sensor data acquisition, ontology-based knowledge representation, and semantic reasoning to enable context-aware decision support and adaptive personalisation. A layered cyber–physical infrastructure, comprising embedded sensors, semantic reasoning, and user feedback through a digital twin interface, supports personalised aftercare, cross-disciplinary collaboration, and reflective design engagement. Evaluation with 26 participants across clinical, engineering, and user groups confirmed the system’s value in enhancing functionality, reducing downtime, and supporting emotional well-being. By positioning ontologies as both computational enablers and design support mechanisms, this research contributes a practical and scalable model for prosthetic service systems that adapt across bodily, emotional, and ecological dimensions, advancing more responsive and consequence-aware care practices. Full article

In recent years, agricultural production activities have been advancing towards mechanization and intelligence to bridge the growing gap between the high labor intensity and time sensitivity of harvesting operations and the limited labor resources. As the component that directly interacts with target crops, the end-effector is a crucial part of agricultural harvesting robots. This paper first reviews their materials, number of fingers, actuation methods, and detachment techniques. Analysis reveals that three-fingered end-effectors, known for their stability and ease of control, are the most prevalent. Soft materials have gained significant attention due to their flexibility and low-damage characteristics, while the emergence of variable stiffness technology holds promise for addressing their issues of poor stability and fragility. The introduction of bionics and composite concepts offers potential for enhancing the performance of end-effectors. Subsequently, starting from an analysis of the biomechanical properties of fruits and vegetables, the relationship between mechanical damage and the intrinsic parameters of produce is elucidated. On the other hand, practical and efficient finite element analysis has been applied to various stages of end-effector research, such as structural design and grasping force estimation. Given the importance of compliance control, this paper explores the current research status of various control methods. It emphasizes that while hybrid force–position control often suffers from frequent controller switching, which directly affects real-time performance, active admittance control and impedance control directly convert external forces or torques into the robot’s reference position and velocity, resulting in more stable and flexible external control. To enable a unified comparison of end-effector performance, this review proposes a progressive comparison framework centered on control philosophy, comprising the ontological characteristic layer, physical interaction layer, feedback optimization layer, and task layer. Additionally, in response to the current lack of scientific rigor and systematization in performance evaluation systems for end-effectors, performance evaluation criteria (harvest success rate, harvest time, and damage rate) are defined to standardize the characterization of end-effector performance. Finally, this paper summarizes the challenges faced in the development of end-effectors and analyzes their causes. It highlights how emerging technologies, such as digital twin technology, can improve the control accuracy and flexibility of end-effectors. Full article

A landform is a physical feature of the Earth’s surface with its own recognizable shape. Most current automated landform identification methods use Object-Based Image Analysis (OBIA) techniques. Such methods segment the terrain into landform elements and assemble them into topographic objects and landforms. Usually, these methods are specific to the landform to be identified. However, geomorphologist experts can contextually recognize any landform on the Earth’s surface in relation to its environment. They have a holistic view of the landscape, adopting a physiographic approach for the interpretation of the observed regions, the objects that they contain and their relationships. Moreover, geomorphological processes leave marks on the Earth’s surface that enable geomorphologists to identify homogeneous regions by recognizing features known as structural elements. In this paper, we show that the physiographic approach can be formalized and that the context of appearance of a landform and its association with other types of landforms can be represented as a landsystem. We propose a conceptual model that organizes the main concepts and relationships characterizing the physiographic approach: they are used to formalize landsystems, landforms and structural elements. The approach is illustrated using a case study of the identification of landsystems characteristic of mountainous glacial valleys. We developed a software to automatically identify landsystems, in a way that is compatible with the geomorphologists’ physiographic approach. The core of this system is a knowledge base implemented as a Neo4j graph database. We also provide details about the logical transformation of the conceptual model and the corresponding ontologies in Noe4j structures. The tool automates the identification of landsystems in accordance with geomorphological practices, facilitating the integration of expert knowledge in the computational workflows. Full article

The mathematical universe of the quantum topos, which is formulated on the basis of classical Boolean snapshots, delivers a neo-realist description of quantum mechanics that preserves realism. The main contribution of this article is developing formal objectivity in physical theories beyond quantum mechanics in the topos-theory approach. It will be shown that neo-realist responses to non-perturbative structures of quantum field theory do not preserve realism. In this regard, the method of Feynman graphons is applied to reframe the task of describing objectivity in quantum field theory in terms of replacing the standard Hilbert-space/operator-algebra ontology with a new context category built from a certain family of topological Hopf subalgebras of the topological Hopf algebra of renormalization as algebraic/combinatorial data tied to non-perturbative structures. This topological-Hopf-algebra ontology, which is independent of instrumentalist probabilities, enables us to reconstruct gauge field theories on the basis of the mathematical universe of the non-perturbative topos. The non-Boolean logic of the non-perturbative topos cannot be recovered by classical Boolean snapshots, which is in contrast to the quantum-topos reformulation of quantum mechanics. The article formulates a universal version of the non-perturbative topos to show that quantum field theory is a globally and locally neo-realist theory which can be reconstructed independent of the standard Hilbert-space/operator-algebra ontology. Formal objectivity of the universal non-perturbative topos offers a new route to build objective semantics for non-perturbative structures. Full article

The concept of digital twins has been studied for over two decades and the core tenet lies in it being a “digital representation of a connected physical object”. Utilization of digital twins promises to enable superior decision-making, enhanced operational understanding and future predictions to enable levels of Condition Based Maintenance (CBM) through Integrated Vehicle Health Management (IVHM) which exceeds existing capabilities. Digital twins are being embraced by many industries, including aviation, and are often depicted as electronic images of an asset of interest. However, in a less visually appealing manner, they can also be described simply as a collection of data in an organized and easily accessible format from across the lifecycle which describes a feature that addresses a specific use case. This review demonstrates how the creation and maintenance of digital twins will play a critical role in enhancing IVHM to enable CBM within the aerospace industry. Through a literature review, this paper demonstrates the need for digital twins, of a sufficient level of fidelity, to facilitate the transition to being condition based through deeper levels of operational and component understanding. It emphasizes how detailed knowledge, represented through ontologies, regarding component design, manufacturing, and operational history aid in achieving the desired fidelity levels. By synthesizing insights from various industries with a focus on aerospace applications, this paper aims to provide a comprehensive understanding, focused on the aviation industry, of digital twin definitions, their creation processes, fidelity measurement, and their implications for CBM, while acknowledging the limitations of the current research landscape. Full article

This paper introduces Contextual Object Grouping (COG), a specific computer vision framework that enables automatic interpretation of technical security diagrams through dynamic legend learning for intelligent sensing applications. Unlike traditional object detection approaches that rely on post-processing heuristics to establish relationships between the detected elements, COG embeds contextual understanding directly into the detection process by treating spatially and functionally related objects as unified semantic entities. We demonstrate this approach in the context of Cyber-Physical Security Systems (CPPS) assessment, where the same symbol may represent different security devices across different designers and projects. Our proof-of-concept implementation using YOLOv8 achieves robust detection of legend components (mAP50 ≈ 0.99, mAP50–95 ≈ 0.81) and successfully establishes symbol–label relationships for automated security asset identification. The framework introduces a new ontological class—the contextual COG class that bridges atomic object detection and semantic interpretation, enabling intelligent sensing systems to perceive context rather than infer it through post-processing reasoning. This proof-of-concept appears to validate the COG hypothesis and suggests new research directions for structured visual understanding in smart sensing environments, with applications potentially extending to building automation and cyber-physical security assessment. Full article

Background: Extracellular vesicles, particularly exosomes, play a crucial role in cell–cell communication and as carriers of biomarkers. However, their use in clinical settings is limited due to a lack of standardized isolation and characterization. Ultracentrifugation (UC) is considered a gold standard for exosome isolation but presents several limitations. Size-exclusion chromatography (SEC) has recently gained attention as a superior method, which offers better yield, purity, and protection of exosome physical properties. This study focused on optimizing the SEC method for isolation of exosomes from seminal plasma and comparing yield, quality, and proteome profiles with those obtained by UC. Methods: In this SEC method, seminal plasma (0.5 mL) was loaded onto a SEC column and collected in 13 fractions of 0.4 mL each. The physical and molecular characterization of exosomes was carried out using a ZetaView analyzer and Western blot, respectively. Further, SEC-isolated exosomes were used for proteomic profiling and functional bioinformatic analysis. Results: The second and third fractions had the highest concentration of exosomes with uniform size and strong expression of exosome markers. Also, comparative proteomic analysis identified 3315 proteins in SEC-isolated exosomes and 931 in UC-isolated exosomes, with 709 proteins in common. SEC-isolated exosomes showed greater overlap with Vesiclepedia’s and ExoCarta’s top 100 lists than UC-isolated exosomes (Vesiclepedia: 91 vs. 77 proteins, ExoCarta: 94 vs. 79). Proteins from SEC- and UC-isolated exosomes showed similar enrichment profiles across all three gene ontology categories. Conclusions: Overall, this optimized SEC protocol is a reliable alternative method to isolate seminal exosomes with high purity, supporting its potential applications in clinical and basic research. Full article

Precision manufacturing requires handling multi-physics coupling during processing, where digital twin and AI technologies enable rapid robot programming under customized requirements. However, heterogeneous data sources, diverse domain models, and rapidly changing demands pose significant challenges to digital twin system integration. To overcome these limitations, this paper proposes a digital twin modeling strategy based on a metamodel and a virtual–real fusion architecture, which unifies models between the virtual and physical domains. Within this framework, subsystems achieve rapid integration through ontology-driven knowledge configuration, while ROS provides the execution environment for establishing robot manufacturing digital twin scenarios. A case study of a robot shaping system demonstrates that the proposed architecture effectively addresses heterogeneous data association, model interaction, and application customization, thereby enhancing the adaptability and intelligence of precision manufacturing processes. Full article

Caring for Alzheimer’s patients presents significant global challenges due to complex symptoms and the constant demand for care, which are further complicated by fragmented information and a lack of explicit integration between physical and computational worlds in existing support systems. This article details the construction and validation of OntoCaimer, an ontology designed to support Alzheimer’s patient care systems by acting as a comprehensive knowledge base that integrates disease recommendations with concepts from the physical world (sensors and actuators). Utilizing METHONTOLOGY and REFSENO formalisms, OntoCaimer was built as a modular ontology. Its validation through the FOCA method demonstrated a high quality score $(\hat{\mu }=0.99)$, confirming its robustness and suitability. Case studies showcased its functionality in automating recommendations, such as managing patient locations or environmental conditions, to provide proactive support. The main contribution of this work is OntoCaimer, a novel ontology that formally integrates clinical recommendations for Alzheimer’s care with concepts from cyber-physical systems (sensors and actuators). Its scientific novelty lies in bridging the gap between virtual knowledge and physical action, enabling direct and automated interventions in the patient’s environment. This approach significantly advances patient care systems beyond traditional monitoring and alerts, offering a tangible path to reducing caregiver burden. Full article

The global infrastructure industry is faced with increasing system complexity and requirements driven by the Sustainable Development Goals, technological advancements, and the shift from Industry 4.0 to human-centric 5.0 principles. Coupled with persistent infrastructure investment deficits, these pressures necessitate improved methods for efficient requirements management and validation. While digital twins promise transformative real-time decision-making, reliance on static unstructured data formats inhibits progress. This paper presents a novel framework that integrates Building Information Modelling (BIM) and Model-Based Systems Engineering (MBSE), using Linked Data principles to preserve semantic meaning during information exchange between physical abstractions and requirements. The proposed approach automates a step of compliance validation against regulatory standards explored through a case study, utilising requirements from a high-speed railway station fire safety system and a modified duplex apartment digital model. The workflow (i) digitises static documents into machine-readable MBSE formats, (ii) integrates structured data into dynamic digital models, and (iii) creates foundations for data exchange to enable compliance validation. These findings highlight the framework’s ability to enhance traceability, bridge static and dynamic data gaps, and provide decision-making support in digital twin environments. This study advances the application of Linked Data in infrastructure, enabling broader integration of ontologies required for dynamic decision-making trade-offs. Full article

The purpose of this experiment was to study the effects of heat stress on the performance and protein metabolism of skeletal muscle in meat rabbits. A total of 160 New Zealand White rabbits aged 80 days with mean initial body weights of 2359 ± 200 g were randomly divided into a control group and a heat stress group. The experiment duration was 20 days. Heat stress treatment reduced the growth performance and slaughter performance of the rabbits (p < 0.05) and increased muscle yellowness (b*, p < 0.05). In addition, heat stress treatment increased the concentrations of leptin, cholesterol, high-density lipoprotein (HDL) cholesterol, and low-density lipoprotein (LDL) cholesterol in serum (p < 0.05), and decreased the serum total protein and immunoglobulin (IgG, IgM, and IgA) contents of rabbits. Under the criteria fold-change ≥ 1.20 or ≤0.84 and p-value ≤ 0.05, 7 up-regulated proteins and 122 down-regulated proteins were screened. A gene ontology (GO) enrichment analysis of the differentially expressed proteins was performed. The most enriched specific GO terms among the differential proteins were response to stress, extracellular region, and protein binding in the biological process (BP), cellular component (CC), and molecular function (MF) categories, respectively, and the most enriched pathway was the PI3K/Akt signalling pathway. In conclusion, heat stress could reduce the carcass yield of meat rabbits, change the physical characteristics of the skeletal muscle, and influence protein metabolism by changing blood indices, potentially through the PI3K/Akt signalling pathway. Full article

This paper explores the formal structure and philosophical implications of polarization-path entanglement in quantum optics, where different degrees of freedom of a single photon become entangled. We examine the mathematical conditions under which coherence is preserved or lost, emphasizing the role of distinguishability and information flow. The analysis is situated within major interpretational frameworks (including Copenhagen, Many-Worlds, QBism, and Bohmian mechanics) to evaluate whether such entanglement reflects physical reality or epistemic constraints. Finally, we discuss experimental realizations, relevance to quantum information processing, and open conceptual questions regarding the ontological status of single-particle entanglement. Full article

We present a formal reconstruction of the conventional number systems, including integers, rationals, reals, and complex numbers, based on the principle of relational finitude over a finite field ${\mathbb{F}}_{\mathsf{p}}$. Rather than assuming actual infinity, we define arithmetic and algebra as observer-dependent constructs grounded in finite field symmetries. Consequently, we formulate relational analogues of the conventional number classes, expressed relationally with respect to a chosen reference frame. We define explicit mappings for each number class, preserving their algebraic and computational properties while eliminating ontological dependence on infinite structures. For example, relationally framed rational numbers emerge from dense grids generated by primitive roots of a finite field, enabling proportional reasoning without infinity, while scale-periodicity ensures invariance under zoom operations, approximating continuity in a bounded structure. The resultant framework—that we denote as Finite Ring Continuum—aims to establish a coherent foundation for mathematics, physics and formal logic in an ontologically finite paradox-free informational universe. Full article