Peak-shaving is a very efficient and practical strategy for a day-ahead hydropower scheduling in power systems, usually aiming to appropriately schedule hourly (or in less time interval) power generations of individual plants so as to smooth the load curve while enforcing the energy production target of each plant. Nowadays, the power marketization and booming development of renewable energy resources are complicating the constraints and diversifying the objectives, bringing challenges for the peak-shaving method to be more flexible and efficient. Without a pre-set or fixed peak-shaving order of plants, this paper formulates a new peak-shaving model based on the mixed integer linear programming (MILP) to solve the scheduling problem in an optimization way. Compared with the traditional peak-shaving methods that need to determine the order of plants to peak-shave the load curve one by one, the present model has better flexibility as it can handle the plant-based operating zones and prioritize the constraints and objectives more easily. With application to six cascaded hydropower reservoirs on the Lancang River in China, the model is tested efficient and practical in engineering perspective.

The Xin'anjiang model and the Sacramento model are two widely used short-term watershed rainfall-runoff forecasting models, each with their own unique model structure, strengths, weaknesses and applicability. This paper introduces a weight factor to integrate the two models into a combined model, and uses the cyclic coordinate method to calibrate the weight factor and the parameters of the two models to explore the possibility of the complementarity between the two models. With application to the Yuxiakou watershed in Qingjiang River, it is verified that the cyclic coordinate method, although simple, can converge rapidly to a satisfactory calibration accuracy, mostly after two iterations. Also, the results in case studies show that the forecast accuracy of the new combined rainfall-runoff model can improve the forecast precision by 4.3% in a testing period, better in runoff process fitting than the Xin'anjiang model that performs better than the Sacramento model. HIGHLIGHT This paper introduces a weight factor to integrate the two models into a combined model, and uses the cyclic coordinate method to calibrate the weight factor. it is verified that the cyclic coordinate method can converge fast to a satisfactory calibration accuracy. The results show that the forecast accuracy of the new combined rainfall-runoff model can improve the forecast precision.

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Fodder, fish manure, and pond sludge will seriously affect the turbidity of the water body in aquaculture. How to quickly and online judge the turbidity of the water body is very important for realizing efficient, low-cost, and accurate control of aquaculture. In view of the shortcomings of traditional detection methods, a transfer learning method based on ResNet deep learning network model is proposed to realize water body turbidity classification, and two transfer learning methods of parameter partially frozen and completely unfrozen are designed based on ResNet18.Subsequently, the turbidity data set of aquaculture water quality was constructed, and the data set was enhanced by image cropping, image flipping, random scaling, and other methods. The experimental results show that when all parameters are not frozen, the transfer learning method can achieve the best class effect, and the accuracy rate is 0.9686, which can provide an effective method for online detection of aquaculture water body turbidity.

抽象的 粵東閩語揭陽方言的「著[tioʔ5]」具有多種功能，可以做動詞、情態動詞、動相補語（唯補詞）、準結果補語標記、主觀大量標記和主觀小量標記。本文詳細描寫「著」的各種功能，並構擬其語法化路徑。另外，「著」的多功能模式有向周邊客家方言輻射的趨勢，表現為「同源的複製」和「不同源的複製」。本文通過對多個方言點的調查和比較，推測「半山客」方言中「著」的情態動詞用法和「到/倒」的主觀小量標記用法可能是由於與潮汕方言的接觸而產生的。