Mathematics

We propose MUSE++, an advanced and lightweight speech enhancement (SE) framework that builds upon the original MUSE architecture by introducing three key improvements: a Mamba-based state space model, dynamic SNR-driven data augmentation, and an augmented multi-objective loss function. First, we replace the original multi-path enhanced Taylor (MET) transformer block with the Mamba architecture, enabling substantial reductions in model complexity and parameter count while maintaining robust enhancement capability. Second, we adopt a dynamic training strategy that varies the signal-to-noise ratios (SNRs) across diverse speech samples, promoting improved generalization to real-world acoustic scenarios. Third, we expand the model’s loss framework with additional objective measures, allowing the model to be empirically tuned towards both perceptual and objective SE metrics. Comprehensive experiments conducted on the VoiceBank-DEMAND dataset demonstrate that MUSE++ delivers consistently superior performance across standard evaluation metrics, including PESQ, CSIG, CBAK, COVL, SSNR, and STOI, while reducing the number of model parameters by over 65% compared to the baseline. These results highlight MUSE++ as a highly efficient and effective solution for speech enhancement, particularly in resource-constrained and real-time deployment scenarios.

JPEG images are widely used in multimedia transmission, such as on social media platforms, owing to their efficiency for reducing storage and transmission requirements. However, because such images may contain sensitive information, encryption is essential to ensure data privacy. Traditional image encryption schemes face challenges when applied to JPEG images, as maintaining compatibility with the JPEG structure and managing the effects of lossy compression can distort encrypted data. Existing JPEG-compatible encryption methods, such as Encryption-then-Compression (EtC) and Compression-then-Encryption (CtE), typically employ a single encryption stage, either before or after compression, and often involve trade-offs between security, storage efficiency, and visual quality. In this work, an Encryption–Compression–Encryption algorithm is presented that preserves full JPEG compatibility while combining the advantages of both EtC and CtE schemes. In the proposed method, pixel-block encryption is first applied prior to JPEG compression, followed by selective coefficient encryption after compression, in which the quantized DC coefficient differences are permuted. Experimental results indicate that the second encryption stage enhances the entropy achieved in the first stage, with both stages complementing each other in terms of resistance to attacks. The addition of this second layer does not significantly impact storage efficiency or the visual quality of the decompressed image; however, it introduces a moderate increase in computational time due to the two-stage encryption process.

Current treatments for hepatocellular carcinoma (HCC) include partial hepatectomy (PH), where two-thirds of the liver is removed. However, when some cancer remains after PH, competition for resources and survival occurs between healthy and cancerous cells. Recent studies suggest that hyperactivation of yes-associated protein (YAP) could be a non-invasive treatment option for HCC. In this study, we propose two simple ordinary differential equation models of resource competition in HCC livers after PH, one capturing the natural dynamics of resource competition between healthy and cancer cells and the other incorporating YAP hyperactivation therapy. We perform full qualitative and quantitative analyses of these models and validate them on experimental data. Our numerical simulations reveal that treatment schedules must be prescribed based on a patient’s tumor aggression. We also found that a high dose of treatment is necessary to completely clear a tumor in all patients, leading us to suggest prescribing YAP hyperactivation therapy in lower doses with other treatments for HCC for the best patient outcomes.