Search Results (136)

(1) Background: Cybersecurity has grown in scale and complexity, increasing the need for shared conceptual frameworks that enable consistent, interoperable, and machine-readable representations of security knowledge. Ontologies address this need by structuring core cybersecurity concepts, yet existing efforts vary widely in purpose and methodological rigour. Prior developments tend to follow either an instrumental path—prioritizing usability and rapid adoption—or a formal path, emphasising logical precision and reasoning capabilities. This divergence has resulted in a fragmented landscape lacking analytical synthesis. (2) Methods: To clarify current practices and uncover research opportunities, we conducted a systematic literature review of 93 cybersecurity ontologies published over the past decade. Following PRISMA guidelines, we analysed their conceptual coverage, development methods, validation strategies, and alignment with the NIST Cybersecurity Framework (CSF) 2.0. (3) Results: Despite heterogeneity in scope, the ontologies consistently model core entities such as Asset, Threat, Vulnerability, Attack, and Countermeasure. However, conceptual coverage remains uneven: most contributions focus on the Identify and Detect functions of the NIST CSF, while Respond and Recover are largely underrepresented. This reveals a prevailing emphasis on preventive security rather than resilience and highlights gaps in empirical validation and industrial deployment. (4) Conclusions: The field shows strong conceptual maturation but limited methodological consistency and operational impact. Advancing cybersecurity ontologies will require integrating pragmatic and formal modelling traditions, incorporating emerging techniques such as knowledge graphs and LLM-assisted ontology learning, and expanding coverage toward post-incident response and recovery. These steps are essential for developing a unified, explainable, and adaptive cybersecurity knowledge base capable of supporting real-world security operations. Full article

Cadmium (Cd) is one of the most toxic heavy metals in the environment, and it severely represses photosynthesis, growth, development and nutrient uptake in photosynthetic organisms. Excessive cadmium (Cd) taken up by plants seriously threatens global food security and human health. Therefore, designing an eco-friendly and sustainable strategy that can reduce the accumulation of Cd in plants is a major challenge. Phosphorus (P), as an essential nutrient for plant growth, has been shown to play a pivotal role in mediating Cd-induced stress response. However, the molecular mechanisms underlying the crosstalk between phosphate signaling and Cd stress response remain largely uncharacterized, especially the role of the core phosphate homeostasis regulator Phosphate Starvation Response 1 (PSR1). Here, we used the model green microalga Chlamydomonas reinhardtii to investigate the physiological and transcriptomic responses to Cd stress in wild type (WT, CC-125) and PSR1 loss-of-function mutant (Crpsr1, CC-4267). Our results showed that the Crpsr1 mutant exhibited significantly enhanced Cd tolerance compared with WT under P-sufficient conditions, with a better growth phenotype and a significantly lower Cd accumulation. Transcriptome analysis revealed distinct gene expression profiles between WT and the Crpsr1 mutant in response to Cd treatment. Gene Ontology (GO) enrichment analysis showed that differentially expressed genes (DEGs) were mainly involved in primary metabolism, protein kinase activity, ion binding and transmembrane transport, which are critical processes for mitigating Cd stress. Notably, key genes associated with iron uptake and homeostasis were significantly upregulated in the Crpsr1 mutant under Cd stress, indicating a potential regulatory link between PSR1, iron homeostasis and Cd tolerance. Taken together, our findings establish a functional association between the central phosphate signaling regulator PSR1 and Cd stress response in green microalgae, and provide novel candidate genes and regulatory networks for developing engineered microalgae with enhanced Cd phytoremediation capacity. Full article

As Intelligent Transportation Systems (ITS) transition towards automated ecosystems, the deployment of advanced wireless charging technologies becomes a critical infrastructure requirement. Central to the management of these networks is the Open Charge Point Protocol (OCPP), which ensures interoperability across diverse hardware vendors. However, the reliance on digital communication for power transfer introduces significant cybersecurity vulnerabilities. This paper presents a methodology for evaluating the impact of cyber-threats on urban transport services, with a specific focus on the communication layers that support these Advanced Wireless Power Transfer (WPT) environments. Utilising Stochastic Petri net (SPN) ontology, we model the operational states of an Electric Vehicle (EV) service—including the activation and the arrival phases—to quantify how protocol-level vulnerabilities affect service reliability. We introduce an Extended Vulnerability List (EVL) and analyse two distinct scenarios: a public transport service and a weather forecasting integration. Our results demonstrate that as wireless charging moves towards standardization, the security of the OCPP-based backbone is a fundamental necessity for preventing service disruption. The proposed assessment framework provides a roadmap for securing the next generation of dynamic wireless charging infrastructures against evolving cyber-physical threats. Full article

Digital identity is increasingly treated as foundational infrastructure for digital economies and public services, yet national approaches remain fragmented and difficult to compare. This study presents a PRISMA-guided systematic artefact-based review of government digital identity programmes, using programme-relevant government artefacts as the review corpus, including strategies, trust frameworks, guidance, service documentation, and identity-enabled public-service materials. Adapting an NLP pipeline for large-scale digital identity text analysis, the study identifies recurring themes, constructs comparative programme profiles, and operationalises three artefact-based measures: alignment, transparency, and maturity. Rather than assessing innovation performance or operational system quality directly, it examines the documentary layer through which programmes are described, justified, and made comparable. The analysis reveals substantial variation in how highly digitalised societies articulate governance, trust, interoperability, security, privacy, and service delivery. The review contributes a repeatable artefact-based framework for cross-jurisdictional comparison and provides a baseline for ontology development and future triangulation against citizen perception, expert assessment, and technical evaluation. Full article

In Norway, children are entitled to all individual and collective rights under the UN Convention on the Rights of the Child (CRC), while parents play an important role in facilitating access to these rights. However, conflicts may arise when a teenager’s right to freedom of association clashes with their parents’ religious beliefs and identity. This article studies the ontological (in)security challenge faced by Muslim parents in Norway when their teenage children choose to participate in confirmation rites. The article explores how Muslim parents navigate the tension between their responsibility to pass on religious beliefs and identity to their children and their children’s assertion of the right to freedom of association with peer groups. The study is based on a pilot survey of Muslim parents whose children participated in confirmation rites at the Norwegian Humanist Association (NHA). Full article

Zero Trust Network Access (ZTNA) has emerged as a fundamental paradigm for securing cloud-native and distributed computing environments. However, existing ZTNA implementations remain largely limited by static policy enforcement and opaque machine-learning-based anomaly detection mechanisms, which often lack contextual adaptability, policy awareness, and interpretable decision-making capabilities. These limitations create significant challenges in dynamic multi-cloud environments where access behavior continuously evolves and security decisions must be both accurate and explainable. To address these challenges, this study proposes Cognitive ZTNA framework, a unified neuro-symbolic trust enforcement framework that integrates transformer-based behavioral trust modeling with ontology-guided symbolic reasoning. The proposed architecture enables continuous trust evaluation by combining behavioral access patterns with explicit policy semantics through a hybrid trust fusion mechanism. This design allows the system to capture long-range behavioral dependencies while maintaining policy-compliant and interpretable access control decisions. The framework is evaluated using the CloudZT-Bench-2025 dataset, comprising 4.2 million cross-platform access events derived from enterprise security telemetry, AWS CloudTrail logs, and simulated adversarial scenarios. Experimental results demonstrate that Cognitive ZTNA achieves Precision = 0.96, Recall = 0.93, and F1-score = 0.95, significantly outperforming rule-based and machine-learning baselines while reducing the false positive rate to 0.03. In addition, the system maintains real-time feasibility with an average decision latency of 24 ms and explanation latency below 5 ms, while achieving 92% analyst-rated explanation sufficiency. These findings demonstrate that integrating behavioral intelligence with symbolic policy reasoning enables adaptive, interpretable, and policy-aware Zero Trust enforcement. The proposed framework therefore provides a practical foundation for next-generation ZTNA systems capable of supporting secure, transparent, and context-aware access control in modern cloud environments. Full article

Yield-related traits of rice (Oryza sativa L.) are pivotal to safeguarding global food security. As a powerful and efficient strategy, genome-wide association study (GWAS) has identified numerous genes for yield-related traits in rice over recent decades, providing critical resources for germplasm improvement. Most yield-related traits are complex quantitative traits controlled by multiple genes with diverse effect sizes, and traditional GWAS approaches have limited power to detect small-effect loci. In this study, we employed Fast3VmrMLM, a compressed mixed linear model integrating genome-wide scanning and machine learning, to perform GWAS for 10 key yield-related traits using a panel of 529 rice accessions and 4,945,006 single-nucleotide polymorphisms (SNPs). The traits included heading date, plant height, panicle number, effective panicle number, yield per plant, spikelet length, grain length, grain width, grain weight, and grain thickness. We detected 141 significant quantitative trait nucleotides (QTNs) associated with target traits and identified 92 previously validated genes located near these QTNs. As a key environmental regulator, photoperiod directly controls flowering and indirectly modulates yield-related traits, and we further identified 182 photoperiod-responsive candidate genes via differential expression and Gene Ontology (GO) enrichment analysis. Through tissue-specific expression analysis, homology analysis with Arabidopsis genes, and haplotype-phenotype differential analysis, six pleiotropic candidate genes were confirmed; notably, LOC_Os02g02210 appears to contribute substantially to grain width and yield-related traits. In conclusion, Fast3VmrMLM proved effective for dissecting the genetic basis of yield-related traits, especially in detecting small-effect loci. These results not only establish a potential genetic link between photoperiod regulation and rice yield formation but also provide high-confidence candidate genes and loci that will accelerate functional genomic studies and precision molecular breeding for high-yield rice. Full article

Outbreaks of stored grain pests can pose significant threats to food security. In-depth analyses of sudden outbreaks are key to achieving effective prevention and control. To address the issue of models’ insufficient reasoning capability arising from complex causal relationships in stored grain pest events, this study proposes an Event Patterns Enhancing Causal Reasoning (EPECR) method incorporating category theory. Specifically, we focus on common pests such as Sitophilus zeamais (maize weevil) and Sitotroga cerealella (Angoumois grain moth). We formally map the domain ontology—including entities like environmental factors (e.g., temperature, humidity) and control measures (e.g., fumigation)—to categories, and represent their inter-relationships (e.g., inhibition, promotion) as functors. To handle complex scenarios, we model multi-cause events (e.g., high temperature and humidity jointly accelerating pest reproduction) using functor products, and represent multi-hop events (e.g., environmental changes leading to pest outbreak and subsequent grain loss) through functor compositions. This formal expression enables Large Language Models (LLMs) to extract reliable event patterns. Based on these patterns, this study constructed 1440 structured datasets and adopted the Low-Rank Adaptation (LoRA) strategy to fine-tune the LLMs. Experiments on the domain-specific Stored Grain Pest Events Dataset (SGPE) demonstrate that EPECR achieves a reasoning accuracy of 85.9% on in-distribution data and 79.9% on out-of-distribution data, effectively identifying correct causal chains for pest logic. This method significantly outperforms the state-of-the-art domain method-Naive Augmentations (NA)-by 4.9%, providing precise decision support for the early warning and control of specific pest incidents. Full article

The article is based on On the Move a holistic, playful movement intervention with children in Red Cross asylum centers in Denmark. Children in asylum centers in Denmark have diverse backgrounds, challenges, and resources. Common challenges due to their life situations can include potential trauma stemming from flight, migration, and/or war experienced by the children and their parents. Furthermore, they live with uncertainty regarding future relocation. These conditions may induce a state of alert, as the children’s foundations feel insecure. These circumstances can also affect the children’s emotional, cognitive, motor, and relational developmental processes. On the Move is a practice-based research project focused on examining how participation in a long-term holistic, playful movement intervention can support children in asylum centers regarding connectedness. The research project is inspired by a phenomenological understanding of body and movement, hermeneutic–phenomenological research, practitioner research, and Arts-Based Research. The data presented here is derived from scenic descriptions and interviews collected during the research project. The theoretical framework is based on the concepts of ontological security, movement philosophy and movement psychology. The article illuminates one of the main practice-based thematic findings from the research project: “Children sway—movement processes”. The article highlights challenges faced by the children due to their life situations and shows how teachers can support the children’s participation in the intervention. The article focuses both on the children’s life situations viewed by professionals and on the children’s movement processes during the intervention. In the movement processes, the children can enter a state in which they are described as being in harmony with the movements, with themselves, and with others. In this way, participating in a holistic, playful movement intervention can support the connectedness of children in asylum centers. Full article

Cyber adversaries are becoming more sophisticated, creating complex security challenges as digital services expand. The reliability of the firewall is of the utmost importance in the context of network security since it serves as the first line of protection. Penetration testing is an approach used to evaluate the reliability of a firewall and improve security by uncovering exploitable flaws. Frequently, penetration testing solutions are developed using machine learning, and it is of the utmost importance to explain the obtained results during the penetration testing. The emergence of explainable AI (XAI) addresses transparency in ML models, which is essential for informed cybersecurity decisions. Additionally, effective penetration testing reports are crucial for organizations, helping them comprehend and address vulnerabilities with tailored mitigation strategies. This study contributes to firewall security by developing an explainable penetration testing method, which includes two machine learning classification models: a binary model for detecting attacks and a multiclass model for identifying attack types with an explainability feature. This research introduces a novel explainability method that emphasizes significant features related to attack types based on multiclass predictions and proposes an approach using the extended System Security Assurance Ontology (SSAO) to clarify vulnerabilities and suggest alternative mitigation strategies. After evaluating numerous ML algorithms for the CIC-IDS2017 dataset, the Fine Tree model was considered to have the greatest performance. For the binary model, it achieved a validation accuracy of 99.7%, while for the multiclass model, it achieved a validation accuracy of 99.6%. Both models were used to test the firewall for vulnerabilities. Firewall penetration testing using the binary model achieves an accuracy of 82.1%, while the multiclass model achieves an accuracy of 78.7%. Full article

Phase 6 of the Sophimatics framework represents the culmination of a comprehensive research program integrating philosophical wisdom with computational sophistication to address fundamental challenges in artificial intelligence systems. Building upon the Complex-Time Recursive Model established in Phase 5, this phase introduces a human-in-the-loop iterative refinement methodology specifically designed for security-critical applications. Through systematic validation across real-world cybersecurity datasets, including NSL-KDD and CICIDS2017, alongside healthcare privacy scenarios using MIMIC-III derived data, we demonstrate that collaborative human–AI co-creation significantly enhances system performance across multiple dimensions, including interpretive accuracy, contextual fidelity, and ethical consistency. The proposed architecture implements three complementary feedback mechanisms: symbolic knowledge base refinement through expert-provided ontological corrections, neural parameter optimization guided by human evaluation of ethical alignment, and dynamic weight adjustment for value-system integration. Experimental results show substantial improvements over baseline approaches, with intrusion detection accuracy reaching 98.7% on NSL-KDD while maintaining 94.3% privacy preservation scores as measured by differential privacy guarantees. The healthcare privacy experiments demonstrate 97.2% sensitive attribute protection with only 2.1% utility loss compared to non-private baselines. Critical analysis reveals that human oversight mechanisms reduce false positive rates in ethical constraint violations by 67% compared to purely automated systems, while convergence analysis indicates stable performance after approximately 12–15 iterations across diverse application domains. These findings establish Phase 6 as an essential bridge between theoretical Sophimatics foundations and practical deployment in privacy-sensitive contexts, demonstrating that philosophically grounded AI architectures can achieve superior performance when augmented with structured human feedback loops. The work contributes both methodological innovations in human–AI collaboration and empirical validation, demonstrating the viability of Sophimatics principles for addressing contemporary challenges in data protection and cybersecurity. Full article

Security assurance aims to provide justified confidence that a system satisfies its security requirements under defined contextual conditions. In practice, assurance context is often handled implicitly, with assumptions, scope limitations, and boundary conditions embedded in documentation or expert judgment. This limits auditability, reproducibility, and governance, particularly in continuous assurance settings and workflows that rely on automation and AI-assisted reasoning. When reasoning operates over incomplete or underspecified context, implicit assumption formation can alter the basis of assurance conclusions. This paper introduces the Security Assurance Context Ontology (SACO), which reframes assurance context construction and evolution as explicit semantic and governance problems. SACO represents assurance-relevant context elements, their relationships, provenance, and epistemic status as authoritative semantic structures. Missing but required information is preserved as explicit semantic gaps that delimit when assurance claims may be authoritatively accepted. A strict separation between authoritative assurance context and advisory reasoning outputs constrains how automated or AI-assisted analysis may influence the assurance basis. The paper further presents a deterministic realization model for assurance context construction and evolution, where determinism applies to reconstructing authoritative context states from governed inputs. Full article

A universal approach to managing a person’s digital identity may be the single most important advancement to the Internet since its inception, promising the seamless flow of information, averting cybercrime, eliminating login credentials, and restoring privacy and trust through greater control of one’s identity online. However, this advancement brings significant risks, especially regarding personal privacy. It demands the meticulous development of digital identity infrastructure that balances robust data security measures with ethical handling of sensitive information, thereby safeguarding against misuse and unauthorised access. Currently, a consolidated vision for digital identity implementation remains unresolved, and aligning the different stakeholders’ motives and expectations is a challenging task. This article reviews and analyses the perspectives and expectations of four key stakeholder groups—government, business, academia, and consumers—regarding a digital identity ecosystem, aiming to increase trust in an eventual design framework. Using an online survey stratified across government, business, academia, and consumers, we identify areas of alignment and divergence regarding privacy, trust, usability, and governance expectations. We then encode these stakeholder expectations into a layered conceptual structure and illustrate its use as metadata for context-layered retrieval-augmented generation (RAG) in digital identity scenarios. Full article

Digital identity operates as the connective infrastructure of the digital age, linking individuals, organisations, and devices into networks through which services, rights, and responsibilities are transacted. Despite this centrality, the field remains fragmented, with technical solutions, disciplinary perspectives, and regulatory approaches often developing in parallel without interoperability. This paper presents a systematic survey of digital identity research, drawing on a Scopus-indexed baseline corpus of 2551 publications spanning full years 2005–2024, complemented by a recent stratum of 1241 publications (2023–2025) used to surface contemporary thematic structure and inform the ontology-oriented synthesis. The survey contributes in three ways. First, it provides an integrated overview of the digital identity landscape, tracing influential and widely cited works, historical developments, and recent scholarship across technical, legal, organisational, and cultural domains. Second, it applies natural language processing and subject metadata to identify thematic patterns, disciplinary emphases, and influential authors, exposing trends and cross-field connections difficult to capture through manual review. Third, it consolidates recurring concepts and relationships into ontological fragments (illustrative concept maps and subgraphs) that surface candidate entities, processes, and contexts as signals for future formalisation and alignment of fragmented approaches. By clarifying how digital identity has been conceptualised and where gaps remain, the study provides a foundation for progress toward a universal digital identity that is coherent, interoperable, and socially inclusive. Full article

Smart contracts are vulnerable to critical, design-level Business Logic Flaws (BLFs) that conventional analysis tools often fail to detect. To address this semantic gap, this study introduces a novel ontological framework that formally models the link between high-level architectural intent and low-level Sui Move code. The methodology employs a rigorous Linked Open Terms (LOT) approach to construct a comprehensive ontology, integrated with a library of secure design patterns and process-aware Object-Centric Dynamic Condition Response (OC-DCR) graphs. Qualitative validation was conducted on four canonical security patterns (Access Control, Circuit Breaker, Time Incentivization, Escapability) drawn from the official Sui Framework, confirming the framework’s representational adequacy and logical consistency. Ultimately, this work contributes the first machine-readable semantic layer for Sui Move, decoupling reasoning from raw code availability, and providing the essential semantic foundation for the future development of pattern-aware auditing tools. Full article