Chaotic systems are hard to synchronize, and no general solution exists. The presence of hidden attractors makes finding a solution particularly elusive. Successful synchronization critically depends on the control strategy, which must be carefully chosen considering system features such as the presence of hidden attractors. We studied the feasibility of fuzzy control for synchronizing chaotic systems with hidden attractors and employed a special numerical integration method that takes advantage of the oscillatory characteristic of chaotic systems. We hypothesized that fuzzy synchronization and the chosen numerical integration method can successfully deal with this case of synchronization. We tested two synchronization schemes: complete synchronization, which leverages linearization, and projective synchronization, capitalizing on parallel distributed compensation (PDC). We applied the proposal to a set of known chaotic systems of integer order with hidden attractors. Our results indicated that fuzzy control strategies combined with the special numerical integration method are effective tools to synchronize chaotic systems with hidden attractors. In addition, for projective synchronization, we propose a new strategy to optimize error convergence. Furthermore, we tested and compared different Takagi–Sugeno (T–S) fuzzy models obtained by tensor product (TP) model transformation. We found an effect of the fuzzy model of the chaotic system on the synchronization performance.

In the rapidly evolving landscape of the Internet of Things (IoT), managing the vast volumes of data generated by connected devices presents significant challenges, particularly in B5G IoT environments. One key issue is data redundancy, where identical data is stored several times because it is captured by multiple sensors. To address this, we introduce “FODIT”, a filter-based module designed to optimize data storage in IoT systems. FODIT leverages probabilistic data structures, specifically filters, to improve storage efficiency and query performance. We hypothesize that applying these structures can significantly reduce redundancy and accelerate data access in resource-constrained IoT deployments. We validate our hypothesis through targeted simulations under a specific and rare configuration: high-frequency and high-redundancy environments, with controlled duplication rates between 4% and 8%. These experiments involve data storage in local databases, cloud-based systems, and distributed ledger technologies (DLTs). The results demonstrate FODIT’s ability to reduce storage requirements and improve query responsiveness under these stress-test conditions. Furthermore, the proposed approach has broader applicability, particularly in DLT-based environments such as blockchain, where efficient querying remains a critical challenge. Nonetheless, some limitations remain, especially regarding the current data structure used to maintain consistency with the DLT, and the need for further adaptation to real-world contexts with dynamic workloads. This research highlights the potential of filter-based techniques to improve data management in IoT and blockchain systems, contributing to the development of more scalable and responsive infrastructures.

Traditional academic degrees often fail to capture the full range of competencies students acquire throughout their university education, particularly those developed through laboratory activities, internships, volunteering, and other extra-curricular experiences. This limitation hinders students’ ability to differentiate themselves in increasingly competitive labor markets and complicates employers’ identification of candidates with balanced technical and transversal competencies. To address this challenge, this paper presents a design-oriented research study proposing a Self-Sovereign Identity (SSI)-based framework for the decentralized issuance and verification of academic micro-credentials. The proposed approach combines a structured methodology for generating micro-credentials with a decentralized architecture supported by a prototype implementation based on SSI technologies. The framework enables universities, lecturers, and other trusted entities to issue verifiable and tamper-resistant credentials that students can securely manage, control, and share through SSI wallets. Unlike existing approaches, which typically focus either on secure credential infrastructures or on the pedagogical value of micro-credentials, the proposed framework integrates both technological and educational perspectives while explicitly supporting the certification of extra-curricular and soft skills. The system supports the creation of granular and portable competency profiles while enhancing transparency, authenticity, interoperability, and trust in credential management. Furthermore, the paper discusses key challenges associated with large-scale adoption, including trust management, governance, scalability, interoperability, and issuer credibility. The results suggest that SSI-based micro-credentialing represents a promising approach for improving the recognition of both technical and transversal competencies, contributing to better alignment between higher education outcomes and evolving labor market demands.