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Objective To evaluate clinical efficacy of artificial perfusion combined with interception basket in ureteral calculi lithotripsy. Methods Clinical data of 151 patients with ureteral calculi who underwent surgical treatment were retrospectively analyzed. They were divided into the observation group（artificial perfusion combined with interception basket）and control group（conventional perfusion pump）. The operation time，intraoperative blood loss，proportion of stone escape，stone re-intervention and complications were observed in two groups. Results There were no significant differences in operation time，intraoperative blood loss and incidence of complications between two groups（all P > 0.05）. The proportion of stone escape and stone re-intervention in the observation group were significantly lower than those in the control group，and the differences were statistically significant（both P < 0.05）. Conclusion Artificial low-pressure perfusion combined with interception basket in ureteral stone lithotripsy can reduce the proportion of stone escape and residual stone re-intervention and improve the stone clearance efficiency.

The vegetable sector is a vital pillar of society and an indispensable part of the national economic structure. As a significant segment of the agricultural market, accurately forecasting vegetable prices holds significant importance. Vegetable market pricing is subject to a myriad of complex influences, resulting in nonlinear patterns that conventional time series methodologies often struggle to decode. In this paper, we exploit the average daily price data of six distinct types of vegetables sourced from seven key wholesale markets in Beijing, spanning from 2009 to 2023. Upon training an LSTM model, we discovered that it exhibited exceptional performance on the test dataset. Demonstrating robust predictive performance across various vegetable categories, the LSTM model shows commendable generalization abilities. Moreover, LSTM model has a higher accuracy compared to several machine learning methods, including CNN-based time series forecasting approaches. With R2 score of 0.958 and MAE of 0.143, our LSTM model registers an enhancement of over 5% in forecast accuracy relative to conventional machine learning counterparts. Therefore, by predicting vegetable prices for the upcoming week, we envision this LSTM model application in real-world settings to aid growers, consumers, and policymakers in facilitating informed decision-making. The insights derived from this forecasting research could augment market transparency and optimize supply chain management. Furthermore, it contributes to the market stability and the balance of supply and demand, offering a valuable reference for the sustainable development of the vegetable industry. [ABSTRACT FROM AUTHOR]

Pumped storage plants (PSPs) play an important role in renewable energy consumption in power systems. Variable‐speed technology is a new and critical direction for the development of PSPs. In pump mode, variable‐speed pumped storage units (VSPSUs) have wider power regulation ranges and more flexible power responses than fixed‐speed pumped storage units (FSPSUs); however, the corresponding quantification study of VSPSUs is rare. Therefore, this study aims to quantify the static and dynamic power regulation characteristics of VSPSU in pump mode. First, the modeling of the fast speed control strategy of VSPSU in pump mode is improved. Then, the power regulation range of the VSPSU is explored. The factors (i.e., power command, governor, and converter) affecting the power response rapidity are quantified through the engineering case of a variable‐speed PSP adopting the doubly fed induction machine. Accordingly, suggestions on the settings of the parameters are provided. Quantification results show that parameter tuning based on this study effectively solves the problem of low regulation rate caused by great power fluctuations under typical power regulation cases in pump mode. This VSPSU has the fastest regulation rate of 13.75%/s when adjusting 40% power. This study could provide technical support to the practical operation of VSPSU. [ABSTRACT FROM AUTHOR]