Chenlin Wei

Conventional scholarship has long identified the late Qing period as the starting point of Chinese modernity, associating its emergence with the intellectual reforms of Kang Youwei and Liang Qichao and the dissemination of Western ideas through translated texts. This article challenges this preconception and argues that elements of Chinese modernism were in fact already present during the late Ming period, most prominently in contemporaneous Chinese fiction written in both the vernacular and classical prose. By focusing on these texts' attention to the subjective and psychological experience, as well as their experimental narrative forms, an argument will be made regarding the indigenous genealogy that situates Chinese modernism within the longer trajectory of China's own literary and cultural transformations versus its 20th‐century encounters with Western literary models. In doing so, this paper hopes to contribute to ongoing academic discussions on the historical and cultural coordinates of global modernism with a more plural and decentered understanding.

With the deteriorating environment and excessive consumption of primary energy, solar energy has become used in buildings worldwide for renewable energy. Due to the fluctuations of solar radiation, a solar photovoltaic (PV) power system is often combined with a storage battery to improve the stability of a building’s energy supply. In addition, the real-time energy consumption pattern of the residual houses fluctuates; a larger size for a PV and battery integrated system can offer more solar energy but also bring a higher equipment cost, and a smaller size for the integrated system may achieve an energy-saving effect. The traditional methods to size a PV and battery integrated system for a detached house are based on the experience method or the traversal algorithm. However, the experience method cannot consider the real-time fluctuating energy demand of a detached house, and the traversal algorithm costs too much computation time. Therefore, this study applies Nondominated Sorting Genetic Algorithm-II (NSGA-II) to size a PV and battery integrated system by optimizing total electricity cost and usage of the grid electricity simultaneously. By setting these two indicators as objectives separately, single-objective genetic algorithms (GAs) are also deployed to find the optimal size specifications of the PV and battery integrated system. The optimal solutions from NSGA-II and single-objective GAs are mutually verified, showing the high accuracy of NSGA-II, and the rapid convergence process demonstrates the time-saving effect of all these deployed genetic algorithms. The robustness of the deployed NSGA-II to various grid electricity prices is also tested, and similar optimal solutions are obtained. Compared with the experience method, the final optimal solution from NSGA-II saves 68.3% of total electricity cost with slightly more grid electricity used. Compared with the traversal algorithm, NSGA-II saves 94% of the computation time and provides more accurate size specifications for the PV and battery integrated system. This study suggests that NSGA-II is suitable for sizing a PV and battery integrated system for a detached house.