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The square-wave voltage signal injection method with the proportional-integral (PI) observer is commonly adopted to estimate the rotor position of interior permanent magnet synchronous motor (IPMSM). However, the saliency estimation method with PI observer is a continuous injection method and the polarity identification method is a discrete one, which causes extra transition time and complexity. To solve this problem, a fast unipolar sequence-pulse injection method is proposed to unify the process of saliency and polarity identification and the complexity of the algorithm as well as the identification time are reduced. At the same time, a four-stage search algorithm is proposed to ensure the fast convergence and improve the accuracy by reducing the number of the pulse voltage signals. The experiment on motor drive platform verifies the effectiveness of the proposed method to IPMSMs with different saliency ratios.

OBJECTIVE To perform therapeutic drug monitoring of total and free plasma valproic acid (VPA) concentrations in clinical samples and to analyze the related factors. METHODS The total VPA concentration in plasma was determined by ultrahigh-performance liquid chromatography with precolumn derivatization with α-bromoacetophenone, and the free VPA concentration was determined by liquid chromatography-tandem mass spectrometry after the plasma was treated by hollow fiber centrifugal ultrafiltration. Regression analysis was performed to examine the associations between free plasma VPA, total plasma VPA, and the plasma protein binding rate. The impact of individual situations, outpatient or inpatient factors, and drug combinations on VPA concentrations were examined. RESULTS Of the 569 clinical samples, 268 were inpatients and 301 were outpatients, and the total VPA concentration in 138 cases (24.2%) was lower than the effective treatment concentration range; the total and free VPA concentrations in outpatient samples were 11.0% and 26.1% higher than those of inpatients, respectively. There was no linear relationship between the free and total VPA concentrations. The relationship equation between the plasma protein binding rate and free VPA concentrations was as follows: Y = 0.0255X2 - 1.1357X + 97.429 (r = 0.8011). The total and free VPA concentrations were significantly decreased after the coadministration of phenobarbital (83.7% and 64.3% of the control group, P < 0.05) or carbapenem antibiotics (32.0% and 32.7% of the control group, P < 0.05). CONCLUSIONS The total VPA concentrations in patients with epilepsy at our hospital was lower than the effective treatment concentration range, which was inadequate for epilepsy control; the total VPA concentrations of outpatients were higher than those of inpatients; as phenobarbital affects VPA metabolism, therapeutic drug monitoring is recommended. Carbapenem antibiotic coadministration with VPA should be avoided because carbapenem antibiotics can lead to the failure of VPA antiepileptic treatment.

Ciprofol is a novel compound that was independently developed in China. According to the Chinese product instructions approved by the China National Medical Products Administration and the information of official website, indications for ciprofol include sedation and anesthesia during the surgical/procedure of nontracheal intubation, induction and maintenance of general anesthesia, and sedation during intensive care. Ciprofol is a short-acting intravenous sedative based on the structural modification of propofol. Ciprofol has high efficacy, good selectivity, and fewer adverse reactions, indicating good clinical application potential. A series of clinical studies have been conducted to evaluate the sedative effect of ciprofol in various procedures and settings, including gastroscopy and colonoscopy, fiber-optic bronchoscopy, general anesthesia in elective surgeries, and mechanical ventilation in intensive care units. This review summarizes the chemical structure, pharmacodynamics, and pharmacokinetic properties of ciprofol. We also assessed the efficacy and safety of ciprofol by synthesizing the relevant clinical trial data.

Identification of the stator d -axis and q -axis inductances is essential for the interior permanent magnet synchronous motor (IPMSM) sensorless control system. The signal injection method is an effective way to identify the inductance parameters; however, it requires the rotor position to ensure the accuracy of the injection reference position. To overcome the dependence on the rotor position, a sequence-pulse voltage signal injection method is presented. The matrix least-squares fitting algorithm is utilized to fit the current responses for the inductance identification. Based on the fitting method, the discrete standard orthogonal polynomial with look-up table is introduced to reduce the computation complexity. To reduce the disturbance of the injected signal to the rotor position during the identification process, a double-direction injection position trajectory planning is presented. In addition, the saturation effect on the identification method is also analyzed. The proposed method is verified via a 3.0 kW IPMSM motor drive platform. The identification results are compared with those measured by a standard method and experiment results show that the proposed method can identify the inductance parameters with acceptable accuracy without obtaining the rotor position information.

This paper presents a quadrilateral mesh element based magnetic equivalent circuit framework (MECF) for an axial flux permanent magnet synchronous machine (AFPMSM). Firstly, the modelling process of quadrilateral mesh element is introduced. Then, the axial and circumferential air gap flux densities of the AFPMSM are calculated by two kinds of air gap mesh methods respectively. The proposed MECF method can not only consider the material nonlinearity, leakage flux and rotor movement, but also predict the locations where magnetic saturation occurs and small fluctuations of the air gap flux densities. Finally, the MECF results are compared with finite element analysis (FEA).

In the conventional stator resistance measurement method, the introduction of the dead-time results in the distortion of the output voltage, which will affect the accuracy of the identification results. This paper presents an offline stator resistance identification method, which can avoid the influence of the dead-time effect during the resistance identification process. Firstly, a short pulse voltage signal is injected into the d-axis reference position to generate the d-axis current. Then the zero voltage vector is used to turn on the low-side MOSFETs to release energy stored in the inductor by the stator resistor. By measuring the current response, the stator resistance can be identified. Experiments at different exciting currents are carried out and the experimental results show that the proposed method can overcome the dead-time effect and achieve nice identification accuracy and numerical stability at different exciting currents.

The genus Eimeria belongs to the phylum Apicomplexa, which includes many obligate intra-cellular protozoan parasites of man and livestock. E. tenella is one of seven species that infect the domestic chicken and cause the intestinal disease coccidiosis which is economy important for poultry industry. E. tenella is highly pathogenic and is often used as a model species for the Eimeria biology studies. In this PhD thesis, a comprehensive annotation system named as \WAGA" (Workflow-based Automatically Genome Annotation) was built and applied to the E. tenella genome. InforSense KDE, and its BioSense plug-in (products of the InforSense Company), were the core softwares used to build the workflows. Workflows were made by integrating individual bioinformatics tools into a single platform. Each workflow was designed to provide a standalone service for a particular task. Three major workflows were developed based on the genomic resources currently available for E. tenella. These were of ESTs-based gene construction, HMM-based gene prediction and protein-based annotation. Finally, a combining workflow was built to sit above the individual ones to generate a set of automatic annotations using all of the available information. The overall system and its three major components were deployed as web servers that are fully tuneable and reusable for end users. WAGA does not require users to have programming skills or knowledge of the underlying algorithms or mechanisms of its low level components. E. tenella was the target genome here and all the results obtained were displayed by GBrowse. A sample of the results is selected for experimental validation. For evaluation purpose, WAGA was also applied to another Apicomplexa parasite, Plasmodium falciparum, the causative agent of human malaria, which has been extensively annotated. The results obtained were compared with gene predictions of PHAT, a gene finder designed for and used in the P. falciparum genome project.