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Selenium (Se) shows promise as a cathode candidate for all‐solid‐state lithium (Li) batteries due to its impressive theoretical volumetric energy density, much higher electronic conductivity, and improved safety in comparison to those for sulfur (S). An active cathode additive, lithium iodide (LiI) is demonstrated, to address the major challenge for all‐solid‐state Li–Se batteries, namely the sluggish redox kinetics resulting from the huge solid‐state conversion barrier. The LiI additive enhances Li+ transport and provides catalytic sites for Se cathode, thus endowing the batteries with accelerated reaction kinetics and extra capacity. DFT calculation and experimental analysis clearly reveal that LiI additive efficiently accelerates the conversion between polyselenide intermediates and Li2Se. With the above advantages, the battery with LiI using Li6PS5Br electrolyte gives an outstanding capacity of 862 mAh gSe−1 beyond the theoretical specific capacity of Se and a superlong life over 1800 cycles at 1C under room temperature. This work offers a simple strategy to facilitate the kinetics of all‐solid‐state Se cathodes and paves the way for the practicality of high‐capacity and long‐life all‐solid‐state Li–Se batteries. [ABSTRACT FROM AUTHOR]

With the rapid advancements in data analysis and the increasing complexity of high-dimensional datasets, traditional dimensionality reduction techniques like Local Linear Embedding (LLE) often face challenges in maintaining accuracy and efficiency. This research aims to overcome the limitations of LLE, specifically its reliance on the nearest neighbor concept, its inability to distinguish differences among manifold points, and its underutilization of data discrimination information. To address these issues, we propose an advanced LLE algorithm that integrates decision tree-based neighbor recognition with Gaussian kernel density estimation. Decision trees accurately determine neighboring relationships, which are then optimized using Gaussian kernel density estimation to better reflect the distribution of sample points on the manifold. The algorithm also incorporates data discrimination information to enhance classification accuracy and efficiency. Evaluations in facial recognition tasks using SVM classifiers demonstrate significant improvements. Integrating decision trees (LLE-DT) yielded accuracy gains, with LFW at 98.75%, CFP 96.10%, and Olivetti 92.18%. Gaussian density estimation (LLE-GDE) achieved further enhancements, especially in LFW (99.13%), with CFP at 96.85%, and Olivetti at 91.82%. Combining both methods (LLE-DT-GDE) led to substantial improvements: LFW 99.61%, CFP 97.23%, and Olivetti 93.56%, highlighting the synergy between decision trees and Gaussian estimation. This advanced LLE algorithm effectively addresses the limitations of traditional approaches, showing promising results in complex data processing tasks such as facial recognition. These findings suggest its potential for broader applications in fields requiring robust data analysis and classification. [ABSTRACT FROM AUTHOR]

This study addresses the challenges of setting segmentation points in the membership function and determining appropriate weights for different types of information within a fuzzy logic algorithm for map matching. We use linear fitting to derive an empirical formula for setting segmentation points for the information membership function. Furthermore, we evaluate the effects of various weights for direction and distance information in parallel and cross-road scenarios. The research identified the optimal distance that achieves the highest matching accuracy and provided insights into how the weights of connection, direction, and distance information affect this accuracy. The simulations confirmed the critical importance of precise segmentation point settings and weight determinations in enhancing the accuracy of fuzzy logic algorithms for map matching. The results underscore the potency of our tailored parameter-setting strategy and contribute to knowledge of symmetry, offering practical insights for implementing fuzzy logic in map matching with a particular emphasis on the principle of symmetry in algorithm design and information processing. [ABSTRACT FROM AUTHOR]

For over 50 years, various ion beam facilities have been utilized to simulate the irradiation effect of neutrons on relevant nuclear materials. However, due to the limitation of single ion beam irradiation, the injection of H2+ and He+ into the sample coaxially is necessary to observe the synergy of hydrogen and helium displacement damage. This study presents a beam line design that is capable of separating ions with different charge mass ratios, screening out unwanted particles, and finally combining the separated beams again. Through this design, H+, H2+, and He+ beams can be completely separated with an axis deviation of less than 0.5 mm after beam combination. Furthermore, the proportion of H2+ and He+ can be adjusted by regulating the inlet air of the ion source, down to the accuracy of a single ion. An accelerating tube is employed to adjust the energy of implanted ions within 10–50 keV. This device also has the capability to efficiently transmit a single type of ion while filtering out extraneous ions (such as O+ beam). The fine-beam optical design not only makes it easy to adjust beam parameters but also eliminates the need for a high-precision power supply. The goal of this work is to have an analytical tool to understand the effect of each electromagnetic component on the ion trajectories. [ABSTRACT FROM AUTHOR]

Based on Gaussian distribution, a method for modelling targets is proposed. The trajectories of infrared targets are predicted by Kalman filtering. The Munkres version of the Hungarian algorithm is utilized to find the optimum assignment between the trajectories and the positions of real targets. Finally, based on the open dataset, we prove that the proposed algorithm is able to find and track infrared dim targets more effectively, while satisfying the requirements of real-time detection. [ABSTRACT FROM AUTHOR]