The rachises of extant feathers, composed of dense cortex and spongy internal medulla, are flexible and light, yet stiff enough to withstand the load required for flight, among other functions. Incomplete knowledge of early feathers prevents a full understanding of how cylindrical rachises have evolved. Bizarre feathers with unusually wide and flattened rachises, known as “rachis‐dominated feathers” (RDFs), have been observed in fossil nonavian and avian theropods. Newly discovered RDFs embedded in early Late Cretaceous Burmese ambers (about 99 million year ago) suggest the unusually wide and flattened rachises mainly consist of a dorsal cortex, lacking a medulla and a ventral cortex. Coupled with findings on extant feather morphogenesis, known fossil RDFs were categorized into three morphotypes based on their rachidial configurations. For each morphotype, potential developmental scenarios were depicted by referring to the rachidial development in chickens, and relative stiffness of each morphotype was estimated through functional simulations. The results suggest rachises of RDFs are developmentally equivalent to a variety of immature stages of cylindrical rachises. Similar rachidial morphotypes documented in extant penguins suggest that the RDFs are not unique to Mesozoic theropods, although they are likely to have evolved independently in extant penguins.
This paper proposes a reinforcement learning (RL) algorithm for the security problem of state estimation of cyber-physical system (CPS) under denial-of-service (DoS) attacks. The security of CPS will inevitably decline when faced with malicious cyber attacks. In order to analyze the impact of cyber attacks on CPS performance, a Kalman filter, as an adaptive state estimation technology, is combined with an RL method to evaluate the issue of system security, where estimation performance is adopted as an evaluation criterion. Then, the transition of estimation error covariance under a DoS attack is described as a Markov decision process, and the RL algorithm could be applied to resolve the optimal countermeasures. Meanwhile, the interactive combat between defender and attacker could be regarded as a two-player zero-sum game, where the Nash equilibrium policy exists but needs to be solved. Considering the energy constraints, the action selection of both sides will be restricted by setting certain cost functions. The proposed RL approach is designed from three different perspectives, including the defender, the attacker and the interactive game of two opposite sides. In addition, the framework of Q-learning and state–action–reward–state–action (SARSA) methods are investigated separately in this paper to analyze the influence of different RL algorithms. The results show that both algorithms obtain the corresponding optimal policy and the Nash equilibrium policy of the zero-sum interactive game. Through comparative analysis of two algorithms, it is verified that the differences between Q-Learning and SARSA could be applied effectively into the secure state estimation in CPS.
Forests represent the greatest carbon reservoir in terrestrial ecosystems. Climate change drives the changes in forest vegetation growth, which in turn influences carbon sequestration capability. Exploring the dynamic response of forest vegetation to climate change is thus one of the most important scientific questions to be addressed in the precise monitoring of forest resources. This paper explores the relationship between climate factors and vegetation growth in typical forest ecosystems in China from 2007 to 2019 based on long−term meteorological monitoring data from six forest field stations in different subtropical ecological zones in China. The time−varying parameter vector autoregressive model (TVP−VAR) was used to analyze the temporal and spatial differences of the time−lag effects of climate factors, and the impact of climate change on vegetation was predicted. The enhanced vegetation index (EVI) was used to measure vegetation growth. Monthly meteorological observations and solar radiation data, including precipitation, air temperature, relative humidity, and photosynthetic effective radiation, were provided by the resource sharing service platform of the national ecological research data center. It was revealed that the time−lag effect of climate factors on the EVI vanished after a half year, and the lag accumulation tended to be steady over time. The TVP−VAR model was found to be more suitable than the vector autoregressive model (VAR). The predicted EVI values using the TVP−VAR model were close to the true values with the root mean squares error (RMSE) < 0.05. On average, each site improved its prediction accuracy by 14.81%. Therefore, the TVP−VAR model can be used to analyze the relationship of climate factors and forest EVI as well as the time−lag effect of climate factors on vegetation growth in subtropical China. The results can be used to improve the predictability of the EVI for forests and to encourage the development of intensive forest management.
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