Accurate assessment of battery State of Health (SOH) is crucial for the safe and efficient operation of electric vehicles (EVs), which play a significant role in reducing reliance on non-renewable energy sources. This study introduces a novel SOH estimation method combining Kolmogorov–Arnold Networks (KAN) and Long Short-Term Memory (LSTM) networks. The method is based on fully charged battery characteristics, extracting key parameters such as voltage, temperature, and charging data collected during cycles. Validation was conducted under a temperature range of 10 °C to 30 °C and different charge–discharge current rates. Notably, temperature variations were primarily caused by seasonal changes, enabling the experiments to more realistically simulate the battery’s performance in real-world applications. By enhancing dynamic modeling capabilities and capturing long-term temporal associations, experimental results demonstrate that the method achieves highly accurate SOH estimation under various charging conditions, with low mean absolute error (MAE) and root mean square error (RMSE) values and a coefficient of determination ($R^2$) exceeding 97%, significantly improving prediction accuracy and efficiency.

For the airborne pod strapdown inertial navigation system, it is necessary to use the host aircraft's inertial navigation system for the transfer alignment as quickly and accurately as possible in the flight process of the aircraft. The purpose of this paper is to propose an adaptive transfer alignment method based on the observability analysis for the strapdown inertial navigation system, which is able to meet the practical need of maintaining the navigation accuracy of the airborne pod. The observability of each state variable is obtained by observability analysis of system state variables. According to the weight of the observability, a transfer alignment filter algorithm based on adaptive adjustment factor is constructed to reduce the influence of weak observability state variables on the whole filter, which can improve the estimation accuracy of transfer alignment. Simulations and experiment tests of the airborne pod and the master strapdown inertial navigation systems show that the adaptive transfer alignment method based on the observability analysis can overcome the shortage of the weak observability state variables, so as to improve the alignment and the navigation performance in practical applications, thus improving the adaptability of the airborne pod.

A terahertz (THz) frequency‐modulated continuous wave (FMCW) imaging radar system is developed for high‐resolution 3D imaging recently. Aiming at the problems of long data acquisition periods and large sample sizes for the developed imaging system, an algorithm based on compressed sensing is proposed for THz FMCW radar 3D imaging in this paper. Firstly, the FMCW radar signal model is built, and the conventional range migration algorithm is introduced for THz FMCW radar imaging. Then, compressed sensing is extended for THz FMCW radar 3D imaging, and the Newton smooth L0‐norm (NSL0) algorithm is presented for sparse measurement data reconstruction. Both simulation and measurement experiments demonstrate the feasibility of reconstructing THz images from measurements even at the sparsity rate of 20%.

Urban agglomerations are playing an increasingly important role in regional economic development, and economic externalities are the key factors in the formation and development of urban agglomerations. According to different mechanisms, agglomeration externalities can be divided into pecuniary externality and technological externality, but the literature has not paid enough attention to the differences between them. Based on the case of China’s five representative urban agglomerations, this paper analyzes and compares the origins, mechanisms, and factors of the two types of agglomeration externality. The results indicated that the pecuniary externality of urban agglomerations originates from the intercity flow and allocation of production factors, and its mechanisms include the specialized production brought by industrial division and the cost reduction caused by scale economy. While the technological externality originates from technological spillovers between cities, its mechanisms include knowledge sharing and technology cooperation. Among China’s five representative urban agglomerations, the key factor affecting their pecuniary externality is market size, and the key factor affecting their technological externality is economic density. In other words, the pursuit of a larger market and higher economic density are the two main driving forces for the formation of urban agglomerations in China. By distinguishing core cities from peripheral cities in China’s five representative urban agglomerations, we also find that there is no significant difference in their pecuniary externality. However, their technological externality presents complex differences. There is still much room to improve the externalities of agglomeration in China’s urban agglomerations. For example, the flow of capital does not show a shift to more productive cities. R&D activities are still mainly concentrated within a city, not intercity, in urban agglomerations.

In this paper, a new backstepping-based adaptive stabilization of a fractional-order system with unknown parameters is investigated. We assume that the controlled system is perturbed by external disturbance, the bound of external disturbance to be unknown in advance. Moreover, the effects of sector and dead-zone nonlinear inputs both are taken into account. A fractional-order auxiliary system is established to generate the necessary signals for compensation the nonlinear inputs. Meantime, in order to deal with these unknown parameters, some fractional-order adaption laws are given. The frequency-distributed model is used so that the indirect Lyapunov theory is available in designing controllers. Finally, simulation results are presented to verify the effectiveness and robustness of the proposed control strategy.