While magnesium alloys have garnered attention for their lightweight properties across diverse applications, their susceptibility to corrosion presents a formidable challenge. Recent years have witnessed the emergence of machine learning (ML) as a formidable tool for predicting and augmenting material properties, notably corrosion resistance. This comprehensive review investigates the latest advancements and hurdles in utilizing ML techniques to investigate the corrosion behavior of magnesium alloys. This article delves into a spectrum of ML algorithms, encompassing artificial neural networks, support vector machines, and random forests, elucidating their roles in predicting corrosion rates, morphologies, and other corrosion-related characteristics in magnesium alloys. Furthermore, it underscores the pivotal challenges and opportunities within this field, such as data quality, model interpretability, and model transferability. Finally, it examines the potential of ML methods in the conception and enhancement of magnesium alloys endowed with superior corrosion resistance. This review aspires to offer valuable insights into harnessing ML’s potential for optimizing magnesium alloy designs with heightened corrosion resistance, a facet of paramount importance across diverse industries, including the automotive, aerospace, and biomedical sectors. By addressing the challenges inherent in using ML to forecast corrosion rates, including data limitations and the intricacies of corrosion mechanisms, ML stands poised to emerge as a potent instrument for advancing the development of corrosion-resistant materials.

As a non-contact method, stealth dicing provides an effective solution to the machining challenges of brittle 4H-SiC wafers that replace conventional cutting approaches. In this paper, the morphological evolution of 4H-SiC substrates during stealth dicing was investigated by using a picosecond laser. The results show that the pulse energy is a critical factor to determine the dimensions of the modified structure, with high pulse energy inducing a high aspect ratio laser self-focusing morphology inside the material. Microstructural analysis reveals three types of modified structures: clustered, filamentous, and blocklike structures, which are primarily influenced by the pulse energy and repetition rate. Cracks in the modified structure propagate from the interior of the material toward the cleavage direction, range of which along with the degree of modification are affected by the scanning times. By optimizing the scanning distance, the SiC sample is successfully separated under an applied force of 8.8 N without chipping. Raman spectroscopy analysis confirms that no phase transitions or chemical bond breakage occurred during laser processing. Instead, it confirms that the internal damage and crack propagation of the material are induced by compressive stress.

As one of China’s pivotal cash crops, cotton’s leaf health directly impacts the textile industry and agricultural economic growth, with leaf diseases emerging as a critical constraint on cotton yield. Traditional manual identification of cotton leaf diseases, plagued by high subjectivity, frequent errors, and inefficiency, fails to meet large-scale detection needs. With advances in deep learning and machine vision, image-based disease recognition has become an effective solution—and among such technologies, the YOLO series of object detection algorithms, renowned for their efficiency and accuracy, have shown remarkable potential in cotton leaf disease detection. While various YOLO-based improvements exist, detecting cotton leaf diseases in natural environments remains hindered by core challenges: complex background interference and difficulties in identifying small-target diseases. To address this, this study proposes a composite improved algorithm named MSAA-Smallhead-EMA_attention-YOLOv8n (abbreviated as M-S-E-YOLOv8n), optimized from the YOLOv8n model. Specifically, it integrates: (1) an MSAA module to enhance complex background processing and reduce interference; (2) a “Smallhead” detection head to improve small-target recognition; and (3) an EMA-attention module to strengthen feature representation while streamlining computations without losing channel information. Experimental results demonstrate that M-S-E-YOLOv8n outperforms the original YOLOv8n significantly: mAP@0.50:0.95 increases by 1.4%, precision by 3.7%, and recall by 2.9%. This effectively resolves detection challenges for cotton leaf diseases in complex environments, providing technical support for disease prevention and control in the cotton industry.