In today’s information age, how to accurately identify a person’s identity and protect information security has become a hot topic of people from all walks of life. At present, a more convenient and secure solution to identity identification is undoubtedly biometric identification, but a single biometric identification cannot support increasingly complex and diversified authentication scenarios. Using multimodal biometric technology can improve the accuracy and safety of identification. This paper proposes a biometric method based on finger vein and face bimodal feature layer fusion, which uses a convolutional neural network (CNN), and the fusion occurs in the feature layer. The self-attention mechanism is used to obtain the weights of the two biometrics, and combined with the RESNET residual structure, the self-attention weight feature is cascaded with the bimodal fusion feature channel Concat. To prove the high efficiency of bimodal feature layer fusion, AlexNet and VGG-19 network models were selected in the experimental part for extracting finger vein and face image features as inputs to the feature fusion module. The extensive experiments show that the recognition accuracy of both models exceeds 98.4%, demonstrating the high efficiency of the bimodal feature fusion.

Linking the ferrite fraction in a dual-phase (DP) steel microstructure and its electromagnetic properties is critical in the effort to develop on-line measurement techniques for phase transformation using electromagnetic (EM) sensors. This paper developed a seamlessly integrated method for generating 3D microstructures and evaluating their equivalent permeability values. Both the generation of 3D microstructures and evaluation of equivalent permeability have been achieved through custom modelling packages developed by the authors. Voronoi modelling based on the random close packing of spheres (RCPS-VM) was used to precisely control the ferrite fraction in DP steel microstructure, and an equivalent uniform field method for 3D finite element simulation was developed for efficient analysis.

A Pd-decorated V₂O₅/porous silicon (Pd-V₂O₅/PSi) composite was synthesized via magnetron sputtering for enhanced NO₂ gas sensing. The material’s morphology and composition were systematically characterized, and its gas sensing performance was evaluated through comprehensive experimental measurements and first-principles calculations. The decoration of Pd nanoparticles significantly improved the sensing capabilities of the V₂O₅/PSi composite, particularly enhancing sensitivity and response/recovery characteristics. Experimental results revealed a 3.1-fold increase in response to specific NO₂ concentrations (ppm level) compared to the undecorated V₂O₅/PSi sensor. The composite exhibited rapid NO₂ response at room temperature with excellent selectivity, reproducibility, and long-term stability. First-principles calculations elucidated the structural, electronic, and adsorption properties of the Pd-V₂O₅/PSi composite, uncovering the gas sensing mechanism in NO₂ environments. This combined experimental and theoretical study provides valuable insights for developing advanced gas sensors and lays a foundation for optimizing metal oxide-based sensing materials.

Nowadays, defect detection technology based on deep learning continuously increases the surface quality requirements of hot-rolled strip steel. However, due to limitations in industrial production, defect datasets often suffer from insufficient training samples and imbalanced categories. This paper proposes effective solutions, namely the GT-CutMix offline data augmentation algorithm and lightweight small sample defect detection models. The GT-CutMix augmentation algorithm significantly improves defect utilization by accurately sampling defect locations and integrating them into the original data set. We design the S-deconvolutional single shot detector (DSSD) defect detection model by constructing a lightweight SI-MobileNet to replace the ResNet101 backbone of the DSSD network. This can reduce the resource parameters and consumption. At the same time, it can speed up training and inference. To further improve the detection accuracy, we integrate the pyramid split attention (PSA) mechanism into the prediction module of DSSD and construct the SA-DSSD model. Under the GT-CutMix augmentation algorithm, the mAP of S-DSSD and SA-DSSD models on X-SDD dataset are 76.83% and 78.63%, respectively. Meanwhile, the corresponding detection speeds are 45 FPS and 40 FPS, respectively. In addition, on the NEU-DET cross-dataset experiment, the mAP of the SA-DSSD model reaches 74.88%. Our methods are highly effective and generalized for small sample defect detection, which can provide selective solutions for specific needs such as high speed and precision in different industrial production scenarios.

Pd-WO₃ coatings on porous silicon (PSi) substrates are engineered to enhance interfacial charge transfer and surface reactivity through atomic-scale structural tailoring. This study combines first-principles calculations and experimental characterization to elucidate how Pd nanoparticles (NPs) optimize the coating’s electronic structure and environmental stability. The hierarchical PSi framework with uniform nanopores (200–500 nm) serves as a robust substrate for WO₃ nanorod growth (50–100 nm diameter), while Pd decoration (15%–20% surface coverage) strengthens Pd–O–W interfacial bonds, amplifying electron density at the Fermi level by 2.22-fold. Systematic computational analysis reveals that Pd-induced d-p orbital hybridization near the Fermi level (−2 to +1 eV) enhances charge delocalization, optimizing interfacial charge transfer. Experimentally, these modifications enhance the coating’s response to environmental degradation, showing less than 3% performance decay over 30 days under cyclic humidity (45 ± 3% RH). Although designed for gas sensing, the coating’s high surface-to-volume ratio and delocalized charge transport channels demonstrate broader applicability in catalytic and high-stress environments. This work provides a paradigm for designing multifunctional coatings through synergistic interface engineering.