Xu Wang’s research while affiliated with Jilin University and other places

In the looming post-quantum era, traditional cryptographic systems are increasingly vulnerable to quantum computing attacks that can compromise their mathematical foundations. To address this critical challenge, we propose crypto-ncRNA-a bio-convergent cryptographic framework that leverages the dynamic folding properties of non-coding RNA (ncRNA) to generate high-entropy, quantum-resistant keys and produce unpredictable ciphertexts. The framework employs a novel, multi-stage process: encoding plaintext into RNA sequences, predicting and manipulating RNA secondary structures using advanced algorithms, and deriving cryptographic keys through the intrinsic physical unclonability of RNA molecules. Experimental evaluations indicate that, although crypto-ncRNA's encryption speed is marginally lower than that of AES, it significantly outperforms RSA in terms of efficiency and scalability while achieving a 100% pass rate on the NIST SP 800-22 randomness tests. These results demonstrate that crypto-ncRNA offers a promising and robust approach for securing digital infrastructures against the evolving threats posed by quantum computing.

This paper introduces Crypto-ncRNA, a encryption framework leveraging the dynamic folding mechanisms of non-coding RNA (ncRNA) to address post-quantum cryptography challenges. Capitalizing on ncRNA's intrinsic physical unclonable functions (PUFs) and high conformational entropy, Crypto-ncRNA generates highly stochastic cryptographic keys and ciphertexts. The scheme implements a four-tiered architecture encompassing RNA sequence generation, structural folding, dynamic key derivation, and ciphertext packaging. Experimental results demonstrate Crypto-ncRNA's robust performance in efficiency , throughput, ciphertext randomness, and operational reliability, validated by NIST SP 800-22 randomness tests. Compared to classical algorithms, Crypto-ncRNA exhibits superior entropy and robust security. This study highlights the potential of bio-convergent cryptography for constructing next-generation secure infrastructures.

Language serves as an irreplaceable bridge for cultural communication, yet its origins and mechanisms remain largely uncharted territory. The emergence of intelligent agents empowered by LLM and NLP technology offers fresh approaches to investigate language understanding and generation across civilizations. This study constructs "Adam" and "Eve" agents that evolve through multi-scenario dialogues and iterative Q&A strategy learning, thereby elucidating novel language acquisition processes.Our framework reveals fundamental mechanisms of linguistic emergence, offering novel insights into intelligent interaction patterns during language development.

The rapid evolution of artificial intelligence has revolutionized digital art creation, enabling the development of novel methodologies that integrate artistic synthesis with robust intellectual property protection. In this study, we propose an integrated framework that combines Generative Adversarial Networks (GANs), fractal analysis, and wavelet-based turbulence modeling to generate abstract artworks inspired by Jackson Pollock's drip paintings. Beyond emulating Pollock's dynamic style via neural style transfer, our approach quantitatively characterizes the artworks' intrinsic complexity using fractal dimension and turbulence power spectrum metrics. Importantly, we introduce a comprehensive watermark robustness testing protocol that embeds imperceptible digital watermarks into the generated images and rigorously assesses their resilience against common perturbations-including Gaussian noise, JPEG compression, and spatial distortions. By merging these watermarks with NFT metadata, our framework ensures secure provenance and immutability of digital assets. Experimental results demonstrate the feasibility and efficacy of this multifaceted approach in advancing both artistic innovation and reliable digital copyright protection.

In order to enhance students' initiative and participation in MOOC learning, this study constructed a multi-level network model based on Social Network Analysis (SNA). The model makes use of data pertaining to nearly 40,000 users and tens of thousands of courses from various higher education MOOC platforms. Furthermore, an AI-based assistant has been developed which utilises the collected data to provide personalised recommendations regarding courses and study groups for students. The objective is to examine the relationship between students' course selection preferences and their academic interest levels. Based on the results of the relationship analysis, the AI assistant employs technologies such as GNN to recommend suitable courses and study groups to students. This study offers new insights into the potential of personalised teaching on MOOC platforms, demonstrating the value of data-driven and AI-assisted methods in improving the quality of online learning experiences, increasing student engagement, and enhancing learning outcomes.

This paper introduces a novel bionic intelligent optimisation algorithm, Octopus Inspired Optimization (OIO) algorithm, which is inspired by the neural structure of octopus, especially its hierarchical and decentralised interaction properties. By simulating the sensory, decision-making, and executive abilities of octopuses, the OIO algorithm adopts a multi-level hierarchical strategy, including tentacles, suckers, individuals and groups, to achieve an effective combination of global and local search. This hierarchical design not only enhances the flexibility and efficiency of the algorithm, but also significantly improves its search efficiency and adaptability. In performance evaluations, including comparisons with existing mainstream intelligent optimisation algorithms, OIO shows faster convergence and higher accuracy, especially when dealing with multimodal functions and high-dimensional optimisation problems. This advantage is even more pronounced as the required minimum accuracy is higher, with the OIO algorithm showing an average speedup of 2.27 times that of conventional particle swarm optimisation (PSO) and 9.63 times that of differential evolution (DE) on multimodal functions. In particular, when dealing with high-dimensional optimisation problems, OIO achieves an average speed of 10.39 times that of DE, demonstrating its superior computational efficiency. In addition, the OIO algorithm also shows a reduction of about $5\%$ in CPU usage efficiency compared to PSO, which is reflected in the efficiency of CPU resource usage also shows its efficiency. These features make the OIO algorithm show great potential in complex optimisation problems, and it is especially suitable for application scenarios that require fast, efficient and robust optimisation methods, such as robot path planning, supply chain management optimisation, and energy system management.

The cartoon image of Cinnamoroll achieves flight by synchronous flapping of both ears. Although this setting is exaggerated and anthropomorphic, it is worthwhile to analyse its flight mechanism from the perspective of physics. In this paper, we investigate the lift force generated by the synchronised flapping of both ears through aerodynamic and hydrodynamic theories and numerical simulations, and evaluate the feasibility of flight. Using Python programming language, this paper simulates the relationship between the lift force and the airflow velocity, flapping frequency, airflow time variation and other factors. The simulation results show that binaural synchronised flapping can generate sufficient lift to support flight for a specific flapping frequency and airflow velocity. This study provides scientific support for the flight mechanism of fictional organisms and bionic insights for the design of micro air vehicles.