A Hybrid Structure to Improve Electrochemical Performance of SiO Anode Materials in Lithium-Ion Battery.

The wide utilization of lithium-ion batteries (LIBs) prompts extensive research on the anode materials with large capacity and excellent stability. Despite the attractive electrochemical properties of pure Si anodes outperforming other Si-based materials, its unsafety caused by huge volumetric expansion is commonly admitted. Silicon monoxide (SiO) anode is advantageous in mild volume fluctuation, and would be a proper alternative if the low initial columbic efficiency and conductivity can be ameliorated. Herein, a hybrid structure composed of active material SiO particles and carbon nanofibers (SiO/CNFs) is proposed as a solution. CNFs, through electrospun processes, serve as a conductive skeleton for SiO nanoparticles and enable SiO nanoparticles to be uniformly embedded in. As a result, the SiO/CNF electrochemical performance reaches a peak at 20% the mass ratio of SiO, where the retention rate reaches 73.9% after 400 cycles at a current density of 100 mA g-1, and the discharge capacity after stabilization and 100 cycles are 1.47 and 1.84 times higher than that of pure SiO, respectively. A fast lithium-ion transport rate during cycling is also demonstrated as the corresponding diffusion coefficient of the SiO/CNF reaches ~8 × 10-15 cm2 s-1. This SiO/CNF hybrid structure provides a flexible and cost-effective solution for LIBs and sheds light on alternative anode choices for industrial battery assembly.

Size characterization of x-ray tube source with sphere encoded imaging method.

In x-ray imaging, the size of the x-ray tube light source significantly impacts image quality. However, existing methods for characterizing the size of the x-ray tube light source do not meet measurement requirements due to limitations in processing accuracy and mechanical precision. In this study, we introduce a novel method for accurately characterizing the size of the x-ray tube light source using spherical encoded imaging technology. This method effectively mitigates blurring caused by system tilting, making system alignment and assembly more manageable. We employ the Richardson-Lucy algorithm to iteratively deconvolve the image and recover spatial information about the x-ray tube source. Unlike traditional coded imaging methods, spherical coded imaging employs high-Z material spheres as coding elements, replacing the coded holes used in traditional approaches. This innovation effectively mitigates blurring caused by system tilting, making system alignment and assembly more manageable. In addition, the mean square error is reduced to 0.008. Our results demonstrate that spherical encoded imaging technology accurately characterizes the size of the x-ray tube light source. This method holds significant promise for enhancing image quality in x-ray imaging.

Attention-enhanced architecture for improved pneumonia detection in chest X-ray images.

In this paper, we propose an attention-enhanced architecture for improved pneumonia detection in chest X-ray images. A unique attention mechanism is integrated with ResNet to highlight salient features crucial for pneumonia detection. Rigorous evaluation demonstrates that our attention mechanism significantly enhances pneumonia detection accuracy, achieving a satisfactory result of 96% accuracy. To address the issue of imbalanced training samples, we integrate an enhanced focal loss into our architecture. This approach assigns higher weights to minority classes during training, effectively mitigating data imbalance. Our model's performance significantly improves, surpassing that of traditional approaches such as the pretrained ResNet-50 model. Our attention-enhanced architecture thus presents a powerful solution for pneumonia detection in chest X-ray images, achieving an accuracy of 98%. By integrating enhanced focal loss, our approach effectively addresses imbalanced training sample. Comparative analysis underscores the positive impact of our model's spatial and channel attention modules. Overall, our study advances pneumonia detection in medical imaging and underscores the potential of attention-enhanced architectures for improved diagnostic accuracy and patient outcomes. Our findings offer valuable insights into image diagnosis and pneumonia prevention, contributing to future research in medical imaging and machine learning.

Simulation of a micron resolution capillary liquid scintillation detector for 14 MeV fusion neutrons.

Aiming to improve the spatial resolution of a neutron imaging system (NIS) for 14 MeV fusion neutrons, an ideal micron resolution capillary detector filled with a high optical index liquid scintillator was simulated. A threshold for each capillary pixel and a threshold for each cluster were applied to suppress the gamma-induced background. In addition, by using a pattern recognition algorithm and an optimized Hough transform, the accuracy of determining the neutron impinging positions and the dynamic range of this detector were enhanced. For an ideal capillary array detector, the spatial resolution is expected as one capillary size of 20µm. The dynamic range of ∼1000 is reachable while the accuracy of neutron impinging position determination keeps better than 85%. The ionization quenching, light sharing and energy resolution of the detector were applied to the simulated data to understand the capillary array detector.