This paper introduces a novel bio-inspired metaheuristic algorithm, named the sharpbelly fish optimizer (SFO), inspired by the collective ecological behaviors of the sharpbelly fish. The algorithm integrates four biologically motivated strategies—(1) fitness-driven fast swimming, (2) convergence-guided gathering, (3) stagnation-triggered dispersal, and (4) disturbance-induced escape—which synergistically enhance the balance between global exploration and local exploitation. To assess its performance, the proposed SFO is evaluated on the CEC2022 benchmark suite under various dimensions. The experimental results demonstrate that SFO consistently achieves competitive or superior optimization accuracy and convergence speed compared to seven state-of-the-art metaheuristic algorithms. Furthermore, the algorithm is applied to three classical constrained engineering design problems: pressure vessel, speed reducer, and gear train design. In these applications, SFO exhibits strong robustness and solution quality, validating its potential as a general-purpose optimization tool for complex real-world problems. These findings highlight SFO’s effectiveness in tackling nonlinear, constrained, and multimodal optimization tasks, with promising applicability in diverse engineering scenarios.

As hydrocarbon exploration advances toward deep and complex reservoirs, the identification accuracy of traditional time–frequency (TF) analysis is constrained by strongly heterogeneous geological conditions. Concurrently, while deep learning has shown great potential, mainstream supervised models commonly face the dual challenges of scarce labeled samples and the lack of interpretability in their black-box decision-making processes. To address these challenges, this study proposes an innovative, intelligent prediction framework integrating high-precision TF analysis, manifold-regularized semi-supervised generative adversarial networks, and SHapley Additive exPlanations (SHAP) for interpretability. First, the Fourier-based synchrosqueezing transform was employed to extract two-dimensional TF features with superior energy concentration, effectively overcoming the resolution limits imposed by the Heisenberg uncertainty principle. Subsequently, the manifold-regularized semi-supervised generative adversarial network was developed. By incorporating manifold regularization constraints, the discriminator captures the intrinsic topological structure of large-scale unlabeled samples, effectively leveraging data geometry to significantly enhance generalization capability under sparse-label conditions. Finally, the SHAP method was utilized to conduct a post hoc interpretation. Experimental results on the Marmousi II model demonstrate a remarkable testing accuracy of 98.4%. In a real-world application to deep marine reservoirs in the Sichuan Basin, the framework achieved an 85.0% testing accuracy using only 5% labeled samples. Compared to baseline models, the semi-supervised strategy and manifold regularization contributed accuracy gains of 18.8% and 5.0%, respectively. SHAP analysis further confirms the model’s adaptive capability to extract geophysical features, enabling it to deconstruct the tuning-effect patterns in synthetic data and the low-frequency enhancement/high-frequency attenuation patterns in real data, respectively. This validation of geophysical consistency provides a theoretical foundation for the application of artificial intelligence in complex hydrocarbon exploration.

The rapid development of end-edge-cloud networks significantly improves data storage and sharing efficiency by offloading computational tasks from the central cloud to the network edge. However, this architecture also introduces new security and privacy challenges. To address these issues, this paper proposes a hierarchical user online/offline revocable encryption (HUOORE) scheme that enhances both system security and computational efficiency. A trust level (TL) is defined to enforce fine-grained access control, allowing only authorized users to decrypt data based on their trust levels. The proposed HUOORE scheme divides encryption and decryption into online and offline phases, where most complex pairing operations are executed offline, leaving only lightweight computations for the user side. Moreover, an efficient key revocation mechanism is designed to prevent unauthorized access and simplify key management. Formal security analysis demonstrates that HUOORE achieves CPA security under the q-parallel Bilinear Diffie-Hellman Exponent (BDHE) assumption and ensures privacy-preserving access policies. Experimental results demonstrate that the proposed scheme significantly reduces user-side computation overhead with acceptable storage cost, making it suitable for secure data sharing in resource-constrained end-edge-cloud environments.

The rapid development of end-edge-cloud networks significantly improves data storage and sharing efficiency by offloading computational tasks from the cent