This study introduces a novel metaheuristic algorithm, Leaf in Wind Optimization, inspired by the natural phenomenon of falling leaves in the wind. The proposed method simulates the motion response of leaves in varying intensities of wind by establishing models for light wind-driven blades and strong wind-driven blades. The algorithm incorporates motion modes of linear translation and spiral rotation induced by wind, offering a hybrid search framework suitable for both strategies. This approach enables enhanced exploration and exploitation of the search space. The algorithm’s performance was evaluated using three challenging benchmark test sets, CEC 2017, CEC 2019 and CEC 2022, as well as an engineering practical problem. Its effectiveness was assessed through comparison with 10 random optimization algorithms, namely: Tree Seed Algorithm, Multi-Verse Optimizer, Salp Swarm Algorithm, Artificial Ecosystem-based Optimization, Hunger Games Search, Fox Optimizer, Spider Wasp Optimizer, AOBLMOA, Enhanced Snake Optimizer, and IbI Logic Algorithm. In the comprehensive testing conducted, the proposed algorithm consistently outperformed other optimizers in approximately 82% of comparisons. Through examination of convergence curves and statistical data, it is evident that Leaf in Wind Optimization demonstrates superior potential compared to the alternative optimizers under consideration.

Auxiliary information sources, a subset of target recognition data sources, play a significant role in target recognition. The reliability and importance of these sources can vary, thereby affecting the effectiveness of the data provided. Consequently, it is essential to integrate these auxiliary information sources prior to their utilization for identification. The Dempster-Shafer (DS) evidence theory, a well-established data-fusion method, offers distinct advantages in handling and combining uncertain information. In cases where conflicting evidence sources and minimal disparities in fundamental probability allocation are present, the implementation of DS evidence theory may demonstrate deficiencies. To address these concerns, this study refined DS evidence theory by introducing the notion of invalid evidence sources and determining the similarity weight of evidence sources through the Pearson correlation coefficient, reflecting the credibility of the evidence. The significance of evidence is characterized by entropy weights, taking into account the uncertainty of the evidence source. The proposed asymptotic adjustment compression function adjusts the basic probability allocation of evidence sources using comprehensive weights, leading to symmetric compression and control of the influence of evidence sources in data fusion. The simulation results and their application in ship target recognition demonstrate that the proposed method successfully incorporates basic probability allocation calculations for ship targets in various environments. In addition, the method effectively integrates data from multiple auxiliary information sources to produce accurate fusion results within an acceptable margin of error, thus validating its efficacy. The superiority of the proposed method is proved by comparing it with other methods that use the calculated weights to weight the basic probability allocation of the evidence sources.

Uncertainty quantification is an important issue in the field of radar cross section (RCS) research. To quantify the impact of specific uncertainty factor on RCS, a novel approach based on the feature selective validation (FSV) method combined with Monte Carlo (MC) method is proposed in this paper. MC method is applied as the basic framework for uncertainty analysis, and FSV is initially employed to compare the results derived from sufficient uncertainty simulations. To facilitate and enhance the massive data assessment, a novel single and direct indicator of FSV is proposed as a quantitative descriptor of data uncertainty. The feasibility of the proposed method in RCS uncertainty quantification is benchmarked through many RCS evaluation examples. The impact of attitude uncertainty on the target RCS, including the scene of dynamic flight, is also studied by the proposed method.

More and more electromagnetics analyses need more detailed-level data validation technologies, which can focus on the specific data region. The feature selective validation (FSV) method is a key electromagnetics validation tool, which was adopted in 2008 as the core of 1597.1 standard for validation of computational electromagnetics computer modeling and simulations. However, despite its successful performance for electromagnetic compatibility (EMC) data, it still comes to apply to the validation of narrowband radar data, which, differing from EMC data, is quite problematic. For this reason, this paper proposes a novel approach, which is called segment FSV (S-FSV), to deal with the narrowband radar data. This approach divides the data into several segments, extracts the peak and oscillation regions, allocates an individual importance degree to each segment, and generates validation results for all segments. Finally, the efficiency and feasibility of the proposed approach is corroborated by the statistical results in a narrowband radar data survey. The proposed S-FSV approach is shown to outperform the traditional FSV one in reflecting the expert opinions and to be more suitable for validation of such data.

The adoption of the flipped classroom in the lower grades of the university encounters several practical challenges within the context of the new engineering education objective. These challenges include the considerable student population, student reticence, and the facilitation of high-order cognitive skills. This study proposes a flipped project design mode and teaching activity design framework that addresses the practical challenges faced by the flipped classroom. Transforming the existing class discussion project into a student-led class project that possesses the capacity for generation, expansion, and exhibits a robust cross-disciplinary, creative, and dynamic nature. The enhancement of student involvement and participation can be greatly achieved through collaborative efforts between teachers and students. Four teaching interactions encompassing co-planning by the teacher and students, interaction between students within the group, interaction between students between groups and communication between the teacher and students are organically cross-integrated, resulting in the implementation of student-centered flipped classroom teaching through a dynamic competitive approach. The studies show that the flipped classroom approach, as implemented by the flipped project, helps actively engaged students develop high-order cognitive skills and improve learning efficiency. This educational strategy can motivate and inform passive learners to enhance refactoring skills, create a multi-dimensional knowledge tree system, and improve learning efficiency.