Understanding the adaptive dynamics of social-ecological systems (SESs) is critical for regional sustainability as human-environment interactions intensify. However, existing indicator-based research frequently lacks a clear theoretical framework and methodological clarity when analyzing SES adaptation. Using complex adaptive system (CAS) theory as an interpretive lens, this research creates a social-ecological system (SES) adaptability evaluation framework that incorporates the pressure-state-response (PSR) model from a CAS perspective. This study examines the Huaihe River Ecological and Economic Belt (HREEB) as a case study, combining remote sensing (RS) and geographic information system (GIS) data from 28 prefecture-level cities from 2005 to 2020. The entropy-weight approach is used to create a composite adaptability index, and obstacle-degree analysis is used to identify key limiting factors, followed by an examination of spatiotemporal evolution patterns. The study found that: (1) SES adaptability in the HREEB increased steadily (mean annual growth rate: 3.97%), with the social subsystem exhibiting a larger connection with the overall trend and the ecological subsystem displaying greater volatility; (2) there was significant spatial heterogeneity, forming a "high in the east and west, low in the center" pattern (supported by a global Moran's I = 0.535, p < 0.05); (3) obstacle degree analysis identified per capita afforestation area (ecological response), per capita GDP (social state), and population density (ecological pressure) as persistent key constraints.
Distributed drive electric vehicles (DDEVs), characterized by compact structure, efficient transmission, and flexible control, have gradually become the mainstream in the development of new energy electric vehicles. This study focuses on DDEVs and employs a hierarchical control strategy. At the upper level, a sliding mode controller is used for vehicle yaw stability control, while torque distribution is performed at the lower level. Addressing the shortcomings of conventional average and load-based distribution methods in terms of energy consumption, this paper proposes a multi-objective torque distribution strategy that optimizes tire load ratio, torque deviation, and motor energy consumption. The strategy integrates objective functions using weighting coefficients and imposes constraints based on road adhesion and motor output capabilities. To tackle this optimization problem, the Dung Beetle Optimizer (DBO) algorithm is introduced, known for its efficient global search capabilities and adaptability. By applying the DBO algorithm, the optimal torque distribution scheme under constraints is determined. Finally, through joint simulations using Carsim and Matlab/Simulink, comparative experiments are conducted under different conditions to evaluate the simulation results of average distribution, load-based distribution, and DBO multi-objective optimization distribution. The experimental findings demonstrate that the proposed multi-objective torque distribution strategy effectively balances tire load ratio, torque deviation, and motor energy consumption, thereby enhancing the overall performance of distributed drive electric vehicles.
This study investigates the impact of ultrasonic shot peening (USP) treatment on the microstructure and fatigue behavior of Inconel 718 alloy. The results of research show that the USP treatment can increase the fatigue life of Inconel 718 alloy. This is mainly due to the fact that USP treatment not only eliminates mechanical grooves that are prone to the formation of crack sources, but also creates pits with smooth transitions, which effectively slows down stress concentrations. In addition, the generation of residual compressive stress in USP-treated sample makes the crack source transfer from the surface to the interior and effectively inhibits the growth rate of fatigue cracks.
In this paper, a variable speed-displacement control with virtual load sensing structure is proposed to reduce the energy loss caused by the difference between supply and demand while ensuring that the engine is at its optimal economic operating point. The virtual load sensing structure is implemented by the controller to distribute the output of the positive flow pumps to adjust the operating point of the engine and maintain the original maneuverability of the excavator. The energy-saving control parameters of the positive flow excavator are determined based on the experiment and the mathematical models are built to analyze the energy coupling between critical components. The simulation and the experimental results show that the proposed control strategy can maximize the energy efficiency without adding any energy storage devices, which reduces the energy consumption by 4.8%-12.19% under different load pressure conditions for the XCMG XE215 type positive flow excavator.
Lithium-ion battery charge dynamics is consisted of several discipline theories. This is particularly challenging for putting all kinds of discipline theories into a dynamics model, for which the difference between the electricity and the chemistry is very significant. In this paper, a voltage dynamics model of lithium-ion battery is formulated by fusing current-integral principle and voltage charge curve into equivalent circuit model. For low computing-power and practical engineering-application, an extended sliding mode observer is designed to estimate the state-of-charge, and five parameters of the voltage dynamics model are also observed online in real-time. Then, the extended sliding mode observer algorithm is translated into a battery management system, which is validated in a battery electric vehicle. Among extended sliding mode observer, current integral method, and extended Kalman filter, some experiments are tested in a same driving cycle. Based on experimental results and statistical analyses, it can be concluded that the extended sliding mode observer is a good candidate for state-of-charge estimation by the voltage dynamics model.
