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While the relationship among land price, transportation cost, and urban sprawl under the condition of local governments monopolizing land supply has been rarely discussed in the literature, this paper addresses this gap by hypothesizing based on the monocentric city model. The hypothesis suggests that due to increasing land prices and decreasing transportation costs, urban fringes are more likely to be developed, leading residents to move away from the city center and altering the demographic pattern of urban sprawl. An empirical analysis is conducted using panel data from 104 cities in China, including remote sensing data of urban land construction areas. The results show that high land prices and low transportation costs drive urban sprawl, with varying impacts across cities. The coupling coordination analysis indicates increasing influence of urban sprawl on land prices and transportation costs. These findings suggest the government should address the effects of unplanned urban expansion on these factors. The findings suggest that the government should pay more attention to the effects of unplanned urban expansion on land prices and transportation costs. Understanding the role of local governments in urban planning is crucial for comprehending China's urbanization strategy and its implications on social exclusion, gentrification, high housing prices, and other urban issues.

Orthogonal frequency division multiplexing (OFDM) has been widely used as radar, communication, and jamming shared waveform. To improve the power utilization and efficiency of radar, communication, and jamming integrated system, we propose an adaptive OFDM shared waveform design method. Considering the problem of jamming path propagation loss and sensitivity of frequency response error of radar and communication channels, we propose a robust OFDM shared waveform design method. Firstly, the suppressed jamming entropy model, the detection probability (Pd) of the stationary point target model, the data information rate (DIR) model with a given bit error rate (BER) are formulated. With the constraints on total power and BER, the adaptive and the robust OFDM optimization problems are formulated. Then we derive the Karush-Kuhn-Tucker (KKT) conditions of the two optimization problems and an improved greedy algorithm is proposed to solve the optimization problems, and the optimal power and bit allocation schemes for the two problems are obtained. Finally, we present several numerical results to demonstrate the effectiveness of the proposed method.

The development of integrated avionics systems and electromagnetic spectrum technology has attracted widespread attention. It has further increased the performance requirements for modulation signal recognition technology in complex electromagnetic environments. Therefore, this paper proposes a deep joint learning technique, including deep representation and low-dimensionality discrimination, to enhance feature stability and environmental adaptability. Specifically, we design a feature learning network based on AlexNet to extract in-depth features and optimize it through parameter-based transfer learning techniques, promote multi-level representation capabilities of features and reduce the sample size requirements. Moreover, we propose a classification algorithm based on kernel collaborative representation and discriminative projection to enhance the ability of low-dimensionality representation and between-class discrimination, which optimized using the mini-batch random gradient descent method. As shown in the simulation, the overall average recognition success rate of this method aiming at twelve radar signal modulation types reaches 97.58% at SNR of -6dB. The results of simulation and analysis demonstrate the superiority of the proposed model in terms of robustness, timeliness, and adaptability to small samples.

A calmness condition for a general multiobjective optimization problem with equilibrium constraints is proposed. Some exact penalization properties for two classes of multiobjective penalty problems are established and shown to be equivalent to the calmness condition. Subsequently, a Mordukhovich stationary necessary optimality condition based on the exact penalization results is obtained. Moreover, some applications to a multiobjective optimization problem with complementarity constraints and a multiobjective optimization problem with weak vector variational inequality constraints are given.