Determination of torasemide in human plasma and its bioequivalence study by high-performance liquid chromatography with electrospray ionization tandem mass spectrometry

A sensitive and selective method using high-performance liquid chromatography coupled with electrospray ionization tandem mass spectrometry (HPLC–ESI–MS) to determine the concentration of torasemide in human plasma samples was developed and validated. Tolbutamide was chosen as the internal standard (IS). The chromatography was performed on a Gl Sciences Inertsil ODS-3 column (100 mm×2.1 mm i.d., 5.0 µm) within 5 min, using methanol with 10 mM ammonium formate (60:40, v/v) as mobile phase at a flow rate of 0.2 mL/min. The targeted compound was detected in negative ionization at m/z 347.00 for torasemide and 269.00 for IS. The linearity range of this method was found to be within the concentration range of 1–2500 ng/mL (r=0.9984) for torasemide in human plasma. The accuracy of this measurement was between 94.05% and 103.86%. The extracted recovery efficiency was from 84.20% to 86.47% at three concentration levels. This method was also successfully applied in pharmacokinetics and bioequivalence studies in Chinese volunteers

Modeling and mapping the current and future distribution of Pseudomonas syringae pv. actinidiae under climate change in China.

Bacterial canker of kiwifruit caused by Pseudomonas syringae pv. actinidiae (Psa) is a major threat to the kiwifruit industry throughout the world and accounts for substantial economic losses in China. The aim of the present study was to test and explore the possibility of using MaxEnt (maximum entropy models) to predict and analyze the future large-scale distribution of Psa in China.Based on the current environmental factors, three future climate scenarios, which were suggested by the fifth IPCC report, and the current distribution sites of Psa, MaxEnt combined with ArcGIS was applied to predict the potential suitable areas and the changing trend of Psa in China. The jackknife test and correlation analysis were used to choose dominant climatic factors. The receiver operating characteristic curve (ROC) drawn by MaxEnt was used to evaluate the accuracy of the simulation.The results showed that under current climatic conditions, the area from latitude 25° to 36°N and from longitude 101° to 122°E is the primary potential suitable area of Psa in China. The highly suitable area (with suitability between 66 and 100) was mainly concentrated in Northeast Sichuan, South Shaanxi, most of Chongqing, West Hubei and Southwest Gansu and occupied 4.94% of land in China. Under different future emission scenarios, both the areas and the centers of the suitable areas all showed differences compared with the current situation. Four climatic variables, i.e., maximum April temperature (19%), mean temperature of the coldest quarter (14%), precipitation in May (11.5%) and minimum temperature in October (10.8%), had the largest impact on the distribution of Psa.The MaxEnt model is potentially useful for forecasting the future adaptive distribution of Psa under climate change, and it provides important guidance for comprehensive management

Energy-Saving Testing System for a Coal Mine Emulsion Pump Using the Pressure Differential Flow Characteristics of Digital Relief Valves

Most energy-saving testing methods for plunger pumps use hydraulic motors. The loading test of coal mine emulsion pumps generally uses an overflow valve as the loading unit, which is characterized by high energy consumption. The coal mine emulsion pump uses emulsion as the transmission medium, and the viscosity and lubricity of the emulsion are much lower than those of hydraulic oil, which creates great difficulties in the development of high water-based hydraulic products. The nominal flow rate of the emulsion motor is much smaller than that of the emulsion pump, and there is no mature and reliable water-based flow control valve. Based on the above reasons, traditional energy-saving testing methods cannot be utilized for the testing process of emulsion pumps. The loading test of emulsion pumps generally uses an overflow valve as the loading unit, and during the testing process, all electrical energy is converted into internal energy, resulting in very high energy consumption. This article proposes an energy-saving testing system for emulsion pumps based on multiple emulsion motors in parallel. In order to solve the flow regulation problem of each parallel branch, a flow-intelligent control algorithm is proposed that utilizes the pressure difference flow characteristics of digital relief valves combined with artificial neural network predictive control. Firstly, the feasibility of the proposed system and method is theoretically verified through the analysis of the mathematical model of the digital relief valve. Secondly, further verification is carried out by establishing simulation and testing platforms. The simulation results show that the energy recovery efficiency of the system exceeds 53%. The experimental results show that the proposed testing system has a pressure control error of less than 1%, a flow control error of about 5%, and a maximum overshoot of about 9 L/min relative to the steady-state flow rate. The control accuracy and system stability are high

Reinforcement Learning Control of Hydraulic Servo System Based on TD3 Algorithm

This paper aims at the characteristics of nonlinear, time-varying and parameter coupling in a hydraulic servo system. An intelligent control method is designed that uses self-learning without a model or prior knowledge, in order to achieve certain control effects. The control quantity can be obtained at the current moment through the continuous iteration of a strategy–value network, and the online self-tuning of parameters can be realized. Taking the hydraulic servo system as the experimental object, a twin delayed deep deterministic (TD3) policy gradient was used to reinforce the learning of the system. Additionally, the parameter setting was compared using a deep deterministic policy gradient (DDPG) and a linear–quadratic–Gaussian (LQG) based on linear quadratic Gaussian objective function. To compile the reinforcement learning algorithm and deploy it to the test platform controller for testing, we used the Speedgoat prototype target machine as the controller to build the fast prototype control test platform. MATLAB/Coder and compute unified device architecture (CUDA) were used to generate an S-function. The results show that, compared with other parameter tuning methods, the proposed algorithm can effectively optimize the controller parameters and improve the dynamic response of the system when tracking signals

Reinforcement Learning Control of Hydraulic Servo System Based on TD3 Algorithm

This paper aims at the characteristics of nonlinear, time-varying and parameter coupling in a hydraulic servo system. An intelligent control method is designed that uses self-learning without a model or prior knowledge, in order to achieve certain control effects. The control quantity can be obtained at the current moment through the continuous iteration of a strategy–value network, and the online self-tuning of parameters can be realized. Taking the hydraulic servo system as the experimental object, a twin delayed deep deterministic (TD3) policy gradient was used to reinforce the learning of the system. Additionally, the parameter setting was compared using a deep deterministic policy gradient (DDPG) and a linear–quadratic–Gaussian (LQG) based on linear quadratic Gaussian objective function. To compile the reinforcement learning algorithm and deploy it to the test platform controller for testing, we used the Speedgoat prototype target machine as the controller to build the fast prototype control test platform. MATLAB/Coder and compute unified device architecture (CUDA) were used to generate an S-function. The results show that, compared with other parameter tuning methods, the proposed algorithm can effectively optimize the controller parameters and improve the dynamic response of the system when tracking signals

Predicted suitable areas for Psa under current and future climatic conditions.

<p>Predicted suitable areas for Psa under current and future climatic conditions.</p

Center displacement of highly suitable areas during different periods.

<p>Center displacement of highly suitable areas during different periods.</p

Shifts in distance and direction of the mean centers of highly suitable areas in different periods.

<p>Shifts in distance and direction of the mean centers of highly suitable areas in different periods.</p