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Machining surface topography is a key factor affecting the properties of optical materials.It is generally accepted that the fracture mode tends to dominate practical concerns onmachined parts and the elastic recovery area decreases with the increase of normal load.However, material removal rate is low for ductile zone processing of brittle materials. In thiscase, pile-up and elastic recovery are key factors for surface quality. In this study, an accuratescratching and ploughing hardness model with consideration of both pile-up and elasticrecovery was established based on a series of continuous and constant nanoscratch tests.The hardness evolution mechanism under different nanoscratch deformation modes wasthen investigated. It was found that, in different modes, hardness values exhibited differentcharacteristics due to the change of elastic recovery rate and the intersection of elastic andplastic states. Further, the mapping relationship between hardness dispersion and surfacemorphology characteristics was also investigated. The results indicated that high degree ofhardness dispersion usually corresponded to modes I and II while stable hardness valuerepresented a steady plastic stage. Based on the intrinsic relationship between evolution ofhardness and deformation modes, predicting hardness distribution by in-situ testing dataand then adjusting deformation mode in real time would be helpful in optimizing surfacequality.

In the context of global climate governance, as the biggest carbon emitter, China bears momentous responsibility for mitigating emissions. Especially after the carbon neutrality target is proposed, it is urgent for China to seek a feasible pathway to achieve net-zero carbon dioxide (CO 2 ) emissions by 2060. With the aims of exploring the net-zero emission pathways, an integrated prediction model incorporating the extreme learning machine (ELM) network, the Aquila optimizer (AO) technique, and the Elastic Net (EN) regression method is constructed. Then the prediction model is employed to project CO 2 emissions and forest carbon sinks during 2021-2060 under the nine designed scenarios. The simulation results reveal that China has the potential to achieve net-zero CO 2 emissions by 2060 under the combined effects of reducing emissions and increasing forest carbon sinks. Specifically, the total CO 2 emissions will be peaked at 11441 million tons CO 2 (MtCO 2 ) in 2029. The post-peak carbon reduction rate should be 8% per year, and the average annual forest carbon sink is required to be 209.45 TgC/year during 2021-2060. In addition, in accordance with the optimal carbon neutrality pathway, the GDP per capita growth rate should be maintained at 5.5% during the period of 2021-2030, China's urbanization rate should be increased to 72% in 2030, and the total energy consumption should be limited to a peak value of 6000 million tons of coal equivalent (Mtce) in 2030., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)