In the uncooled infrared imaging systems, owing to the non-uniformity of the amplifier in the readout circuit, the infrared image has obvious stripe noise, which greatly affects its quality. In this study, the generation mechanism of stripe noise is analyzed, and a new stripe correction algorithm based on wavelet analysis and gradient equalization is proposed, according to the single-direction distribution of the fixed image noise of infrared focal plane array. The raw infrared image is transformed by a wavelet transform, and the cumulative histogram of the vertical component is convolved by a Gaussian operator with a one-dimensional matrix, in order to achieve gradient equalization in the horizontal direction. In addition, the stripe noise is further separated from the edge texture by a guided filter. The algorithm is verified by simulating noised image and real infrared image, and the comparison experiment and qualitative and quantitative analysis with the current advanced algorithm show that the correction result of the algorithm in this paper is not only mild in visual effect, but also that the structural similarity (SSIM) and peak signal-to-noise ratio (PSNR) indexes can get the best result. It is shown that this algorithm can effectively remove stripe noise without losing details, and the correction performance of this method is better than the most advanced method.

Semantic segmentation of remote sensing images is an important technique for spatial analysis and geocomputation. It has important applications in the fields of military reconnaissance, urban planning, resource utilization and environmental monitoring. In order to accurately perform semantic segmentation of remote sensing images, we proposed a novel multi-scale deep features fusion and cost-sensitive loss function based segmentation network, named MFCSNet. To acquire the information of different levels in remote sensing images, we design a multi-scale feature encoding and decoding structure, which can fuse the low-level and high-level semantic information. Then a max-pooling indices up-sampling structure is designed to improve the recognition rate of the object edge and location information in the remote sensing image. In addition, the cost-sensitive loss function is designed to improve the classification accuracy of objects with fewer samples. The penalty coefficient of misclassification is designed to improve the robustness of the network model, and the batch normalization layer is also added to make the network converge faster. The experimental results show that the classification performance of MFCSNet outperforms U-Net and SegNet in classification accuracy, object details and prediction consistency.

Accurate prediction of thermophysical properties of hybrid nanofluids (HNFs) is a major challenge in thermodynamic applications. This research provides a computational framework to solve the complexity of heat transfer and flow physics in Al ₂ O ₃ –Cu/water HNFs. A reconstruction of the statistical microstructure of the fluid is constructed by the Karhunen–Loève expansion. The physical processes are modeled with a system of partial differential equations that model the interactions between the base fluid and dispersed nanoparticles. In the context of flow variables and entropy generation with sharp gradients, a Bayesian inference extension of the physics-informed neural network (PINN) is developed to improve prediction accuracy. Bayesian inference PINN (BIPINN) is the extension that infers posterior distributions of hidden thermal flow states, conditioned on prior physical laws. The framework is validated through many physical problems such as convective flow, thermal conduction, and fluctuation control. BIPINN shows improved accuracy over the finite element method while also showing improved generalization under sparse data while illustrating robustness of the framework while modeling complex thermal-fluid systems.

The development of highly efficient and economically viable bifunctional electrocatalysts is essential for overall water splitting in alkaline environments. Therefore, we synthesize V-doped NiCoP one-dimensional nanowire catalysts using hydrothermal and chemical vapor deposition methods. The as-obtained NCP-2 sample exhibits superior electrocatalytic performance, which might be attributed to the moderate vanadium doping that increases the density of electrochemically active sites and modulates the electronic structure of NiCoP. The NCP-2 sample shows an overpotential of 74.3 mV at 10 mA cm⁻² with a small Tafel slope of 83.8 mV dec⁻¹ during HER activity. In the OER process, NCP-2 exhibits an overpotential of 280 mV at 20 mA cm⁻² (79.2 mV dec⁻¹). Meanwhile, the NCP-2 nanowire arrays possess a low cell voltage of 1.55 V at 10 mA cm⁻². This material shows significant potential for applications in sustainable energy systems.