竞争战略与美国特朗普政府对华政策调整

The Competitive Strategy, which helped the US gain great advantages in competition with its major rivals, has played an important part in US foreign policy for a long time. The full use of it in the cold war with Soviet Union is the most significant case. In recent years, under the promotion of the academic circle and the strategic circle, the concept of a competitive strategy is rapidly returning into the US foreign strategy, becoming an important theoretical basis for the adjustment of the Trump Administration's China policy. Recently, the United States has taken multi-domain actions simultaneously to strengthen the strategic competition and containment policies against China, such as mobilizing public opinion, provoking China-related issues, creating new weaponized legislation and even increasingly making use of Taiwan and Hong Kong to contain China. Under this competition situation, China must balance the whole picture and the critical point, make proper adjustments accordingly while sticking to their bottom-line priorities. Only in this way can China relieve the pressure and gain the initiative to effectively manage competition with the US. During this course, China should make more effort in improving its capacity and level of governance, strengthening the domestic foundation of competition against the US. Meanwhile, China should also actively adapt itself to the changing world and make full use of the situation to guide the direction of the China-US relationship. Key Words: Strategic competition; China-US relationship; Competition management; Counterbalancing ability 竞争战略在美国对外政策中占有重要地位，尤其在和主要对手的较量中，美国善于运用这一战略并曾收到较大成效，其中最显著的案例就是在美苏冷战中的充分运用。在美国学界和战略界的推动下，竞争战略理念在美对外战略中快速回归，也成为特朗普政府对华政策调整的重要理论依据。近来美国不断强化对华战略竞争与遏制的舆论动员，设置涉华议题，为强化对华战略竞争与遏制提供法律依据，并在等多个领域出手，全面强化对华战略竞争压力，甚至更多地利用台湾、香港局势的发展变化来牵制中国。面对咄咄逼人的竞争态势，中国既需抓住关键也需把握全局，既要坚守底线也要进退有据，有效管控中美竞争，化解战略压力拓展战略主动；而在竞争中更须切实提升国家治理能力和水平，筑牢战略竞争的国内基础，同时也要积极顺应世界变局，因势利导努力牵引中美关系的发展方向。 【关键词】战略竞争 中美关系 竞争管控 制衡能力 因势利

Time-dependent energetic proton acceleration and scaling laws in ultra-intense laser pulses interactions with thin foils

A two-phase model, where the plasma expansion is an isothermal one when laser irradiates and a following adiabatic one after laser ends, has been proposed to predict the maximum energy of the proton beams induced in the ultra-intense laser-foil interactions. The hot-electron recirculation in the ultra-intense laser-solid interactions has been accounted in and described by the time-dependent hot-electron density continuously in this model. The dilution effect of electron density as electrons recirculate and spread laterally has been considered. With our model, the scaling laws of maximum ion energy have been achieved and the dependence of the scaling coefficients on laser intensity, pulse duration and target thickness have been obtained. Some interesting results have been predicted: the adiabatic expansion is an important process of the ion acceleration and cannot be neglected; the whole acceleration time is about 10-20 times of laser pulse duration; the larger the laser intensity, the more sensitive the maximum ion energy to the change of focus radius, and so on.Comment: 15 pages, 4 figures, submitted to PR

A Noise-Tolerant Zeroing Neural Network for Time-Dependent Complex Matrix Inversion Under Various Kinds of Noises

Complex-valued time-dependent matrix inversion (TDMI) is extensively exploited in practical industrial and engineering fields. Many current neural models are presented to find the inverse of a matrix in an ideal noise-free environment. However, the outer interferences are normally believed to be ubiquitous and avoidable in practice. If these neural models are applied to complex-valued TDMI in a noise environment, they need to take a lot of precious time to deal with outer noise disturbances in advance. Thus, a noise-suppression model is urgent to be proposed to address this problem. In this article, a complex-valued noise-tolerant zeroing neural network (CVNTZNN) on the basis of an integral-type design formula is established and investigated for finding complex-valued TDMI under a wide variety of noises. Furthermore, both convergence and robustness of the CVNTZNN model are carefully analyzed and rigorously proved. For comparison and verification purposes, the existing zeroing neural network (ZNN) and gradient neural network (GNN) have been presented to address the same problem under the same conditions. Numerical simulation consequences demonstrate the effectiveness and excellence of the proposed CVNTZNN model for complex-valued TDMI under various kinds of noises, by comparing the existing ZNN and GNN models