Flexible sensors are revolutionizing wearable and implantable devices, with conductive hydrogels emerging as key materials due to their biomimetic structure, biocompatibility, tunable transparency, and stimuli-responsive electrical properties. However, their fragility and limited durability pose significant challenges for broader applications. Drawing inspiration from the self-healing capabilities of natural organisms like mussels, researchers are embedding self-repair mechanisms into hydrogels to improve their reliability and lifespan. This review highlights recent advances in self-healing (SH) conductive hydrogels, focusing on synthesis methods, healing mechanisms, and strategies to enhance multifunctionality. It also explores their wide-ranging applications, including in vivo signal monitoring, wearable biochemical sensors, supercapacitors, flexible displays, triboelectric nanogenerators, and implantable bioelectronics. While progress has been made, challenges remain in balancing self-healing efficiency, mechanical strength, and sensing performance. This review offers insights into overcoming these obstacles and discusses future research directions for advancing SH hydrogel-based bioelectronics, aiming to pave the way for durable, high-performance devices in next-generation wearable and implantable technologies.

Traditional computational fluid dynamics (CFD) methods are usually used to obtain information about the flow field over an airfoil by solving the Navier–Stokes equations for the mesh with boundary conditions. These methods are usually costly and time-consuming. In this study, the pix2pix method, which utilizes conditional generative adversarial networks (cGANs) for image-to-image translation, and a deep neural network (DNN) method were used to predict the airfoil flow field and aerodynamic performance for a wind turbine blade with various shapes, Reynolds numbers, and angles of attack. Pix2pix is a universal solution to the image-to-image translation problem that utilizes cGANs. It was successfully implemented to predict the airfoil flow field using fully implicit high-resolution scheme-based compressible CFD codes with genetic algorithms. The results showed that the vortical flow fields of the thick airfoils could be predicted well using the pix2pix method as a result of deep learning.

The global automobile industry faces mounting pressure to develop air conditioning (AC) systems that align with stringent environmental regulations while enhancing vehicle efficiency. Traditional vapor compression refrigeration (VCR) systems are energy-intensive and contribute significantly to greenhouse gas emissions and increased fuel consumption. This paper reviews emerging technologies for automotive air conditioning, emphasizing sustainable alternatives categorized into active, passive, and hybrid systems. Novel developments in thermoelectric cooling, vapor absorption, solar-assisted systems, and hybrid designs are explored. Additionally, the review introduces a comparative analysis of these systems’ performance, focusing on energy consumption, coefficient of performance (COP), and environmental impact. By synthesizing recent research and proposing innovative frameworks, this study offers insights into optimizing sustainable AC technologies for both conventional and electric vehicles, ensuring enhanced passenger comfort and compliance with global environmental standards.