The current chapter has examined the role of two specific business models, the Business Model Canvas and the Value Chain Analysis, in relation to the rapid development of cutting-edge technological tools, especially that of artificial intelligence (AI). The book chapter has identified that the incorporation of AI within organisations' business models requires the development of a comprehensive approach that will enable all relevant stakeholders to become more engaged in the overall process. The book chapter has also examined the McKinsey 7Sc model which focuses on both hard and soft skills. These skills have been identified to have a major impact on the adoption of AI in the business model, and therefore, they constitute a challenge for contemporary organisations and managers. Finally, the books chapter proposes a specific visual framework (Thrive Model) which attempts to confront possible challenges and obstacles in order to enable sustainability, innovation, and resilience.
Topological indices provide numerical descriptions of graph structure and support graph analysis in cheminformatics, network design, and graph mining. This study presents a reproducible computational framework that combines controlled cyclic-graph generation, structure-preserving transformations, exact computation of six classical topological indices, and multi-output graph neural network regression. The framework evaluates the Wiener, Merrifield–Simmons, Hosoya, first Zagreb, second Zagreb, and Randi’c indices for unicyclic and bicyclic graphs. It represents each graph using sparse connectivity and node-level features that encode degree, cycle membership, pendant connectivity, leaf status, and normalized eccentricity. A graph isomorphism network (GIN) jointly predicts the six indices and is compared with graph convolutional networks (GCNs), graph attention networks (GATs), and descriptor-based regression baselines. The controlled benchmark shows that nonlinear descriptor-based models achieve the lowest aggregate errors because the supplied graph-level descriptors contain strong prior information about graph size, degree structure, branching, and cycle complexity. Although GIN does not achieve the highest overall accuracy, it provides the strongest graph-native performance by learning directly from sparse connectivity and node-level features without requiring a fixed handcrafted graph-level descriptor vector. The proposed surrogate does not replace exact evaluation for isolated small graphs, where exact computation remains more appropriate. Instead, its practical value emerges through repeated evaluations of larger, more complex graph instances. To examine this setting, a computational stress experiment evaluates sparse multicyclic graphs under increasing cyclomatic complexity and measures exact computation time, timeout frequency, prediction accuracy, and the amortized break-even point. The results indicate that surrogate prediction becomes beneficial when combinatorial index computation becomes sufficiently expensive, and the trained model is reused across many structurally related graph queries. An external experiment on circulant graphs also demonstrates that the framework can extend beyond the original graph generators by modifying only the graph-construction stage.
In response to the growing need for coordinated intelligence in highly regulated and data-fragmented environments, this study develops a Federated Organizational Intelligence Capability Model that conceptualizes cross-silo federated learning as a distributed organizational capability. While existing research has primarily focused on algorithmic performance and privacy protection, limited attention has been given to how federated systems contribute to organizational capability development and long-term adaptation. Building on the Unified Theory of Acceptance and Use of Technology, Dynamic Capabilities Theory, and Privacy by Design, the study proposes a theoretically grounded framework that explains how federated infrastructures can be translated into higher-order organizational capabilities. Technology acceptance is conceptualized as enabling Federated Knowledge Integration, which supports the development of Federated Decision Intelligence, subsequently enhancing Organizational Agility and, over time, Organizational Adaptability. Privacy Governance Assurance is incorporated as a governance-enabling mechanism that conditions early-stage capability transitions by reinforcing trust, compliance, and collaboration under regulatory and data sovereignty constraints. While the framework is presented sequentially for analytical clarity, it acknowledges the potential for iterative dynamics in practice. Overall, the study advances existing literature by clarifying how cross-silo federated learning supports the emergence and coordination of distributed organizational capabilities, offering a structured and theory-driven basis for examining capability development under conditions of data fragmentation and governance constraints.
Learning generalizable video representations from unlabeled data remains a fundamental challenge in computer vision, as existing self-supervised learning approaches primarily rely on modeling statistical correlations rather than capturing structured temporal dependencies in video data. While recent methods such as masked video modeling and contrastive learning achieve strong empirical performance, they often fail to explicitly account for how changes in motion influence future states, limiting robustness and generalization under distribution shifts. In this work, we propose TRACE, a self-supervised framework for learning intervention-aware spatiotemporal representations by incorporating temporal interventions and counterfactual learning objectives. TRACE operates in latent space by decomposing video representations into content and motion components, enabling targeted perturbations of motion variables while preserving semantic structure. A temporal intervention module generates alternative trajectories by modifying motion dynamics, and a counterfactual prediction objective enforces consistency between predicted and intervention-induced future representations. This formulation encourages the model to learn representations that are sensitive to intervention-induced changes rather than relying on spurious correlations. Extensive experiments demonstrate that TRACE consistently outperforms state-of-the-art self-supervised video learning methods across multiple benchmarks. In particular, TRACE achieves 58.9% on Kinetics-400 and 27.6% on Something-Something V2 under linear evaluation, outperforming prior methods while also improving cross-dataset transfer performance and reducing robustness degradation under motion perturbations (from −9.1% to −4.8%). Furthermore, TRACE shows improved performance on intervention-based and counterfactual evaluation protocols, indicating stronger modeling of temporal dependencies under dynamic changes. These results suggest that incorporating intervention-based reasoning into self-supervised video learning provides a practical pathway toward more robust, transferable, and semantically meaningful video representations.
The integration of AI into cybersecurity represents a significant shift in the protection of digital infrastructures. AI enhances predictive threat detection, automated incident response, and real-time risk management, enabling more proactive security practices. However, the growing autonomy and opacity of AI systems introduce substantial legal, ethical, and governance challenges. This chapter examines these issues at the intersection of technological innovation and regulatory accountability, drawing on EU legal frameworks such as the AI Act and the AI Liability Directive. It analyses how algorithmic opacity and the “black box” problem undermine transparency, due process, and liability attribution, while highlighting persistent responsibility gaps and the dual-use nature of AI in defensive and offensive cyber operations. Emphasising explainable AI as a foundation for trustworthy governance, the chapter proposes a risk-based governance model that aligns technical explainability with legal accountability and human oversight to enhance digital trust and protect fundamental rights.
