With the growing complexity of integrated circuits and heterogeneous microsystem integration, effective thermal management and intelligent design are crucial for preventing thermal failures. This study proposes an adaptive deep reinforcement learning-based (ADRL) optimization method for pin-fin microchannel heat sinks, enhancing iterative optimization efficiency with a hybrid neural network (HNN).

Magnetic Resonance-guided Focused Ultrasound (MRgFUS) is a promising non-invasive thermal ablation technique widely applied in tumor treatment and functional neurological interventions. However, images are often affected by noise and artifacts during treatment, which reduces the accuracy of thermal monitoring and may lead to misjudgments of therapeutic efficacy or incorrect energy delivery.

This paper proposes an end-to-end framework integrating advanced cloud computing and machine learning for NDVI time series analysis and environmental change detection. The framework achieves efficient multi-source data access, quality control, and fusion analysis through modular design, and improves the accuracy and noise robustness of change identification by combining self-attention and recursive hybrid models.

Prostate cancer (PCa) is a major global health concern for men and the ability to detect it in its early stages is important. While imaging modalities such as Transrectal Ultrasound (TRUS) constitutes a critical role in diagnosis, challenges such as noise and limited specificity hinder their effectiveness especially when features are extracted from the images which may be used for classification of cancer.

This study proposes an automated segmentation of prostate cancer in transrectal ultrasound images using different preprocessing methods to enhance the segmentation accuracy. We propose the use of image intensity normalization and despeckle filtering, individually and in combination, as preprocessing techniques to improve the performance of a deep learning segmentation model (DeepLabv3 +) in ultrasound images of prostate cancer.