Letrozole is one of the third generation aromatase inhibitors. It is suitable for the treatment of postmenopausal patients with advanced breast cancer and early treatment of breast cancer. It is necessary to develop a rapid, reliable, selective and sensitive LC–MS/MS assay to determine letrozole in human plasma to evaluate the clinical efficacy and adverse reactions with clinical pharmacokinetic and therapeutic drug monitoring. Separation was carried out on a Kromasil-C18 column using acetonitrile-water (55: 45, v/v) as mobile phase. Detection was carried out by multiple reaction monitoring on a 3200Qtrap mass spectrometry. The method needed one-step protein precipitation procedure only, and the cycle time was 2.5 min allowing 500–550 samples per day. It was linear within 0.30–50.00 ng/mL for plasma with the limit of detection (LOD) of 0.030 ng/mL. The intra- and inter-day RSD were 5.51–8.63%, 2.28–9.95% and the RE was 0.18–1.65%. The recovery rates of letrozole and internal standard for plasma were 89.30–98.55%. Letrozole was stable under all the conditions in the study. The method was sensitive enough to quantitate letrozole over a period of 288 h after giving a single oral dose of 2.5 mg–24 Chinese healthy volunteers. The absorption of letrozole was rapid with small individual difference, the tissue distribution of letrozole was more than that in blood, and the clearance was slow. Letrozole was similar to three-compartment model in vivo. Due to metabolism and excretion, the AUCs of letrozole varied greatly among individuals.
As one of the most important decision-making problems in fully mechanised mining, the corresponding mining technology pattern is the technical foundation of the working face. Characterised by complexity in a thin seam fully mechanised mining system, there are different kinds of patterns. In this paper, the classification strategy of the patterns in China is put forward. Moreover, the corresponding theoretical model using neural networks applied for patterns decision-making is designed. Based on the above, optimal selection of these patterns under given conditions is achieved. Lastly, the phased implementation plan for automatic mining pattern is designed. As a result of the industrial test, automatic mining for panel 22204 in Guoerzhuang Coal Mine is realised.
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Cortisol and cortisone are 2 important glucocorticoids produced in the human hypothalamus–pituitary–adrenal (HPA) axis that respond to stress. An analytical method to determinate cortisol and cortisone in serum and saliva using high-performance liquid chromatography–tandem mass spectrometry following a supported liquid extraction (SLE) was developed. Serum and saliva samples of 0.2 mL were extracted by SLE three times using 0.4 mL of methyl tert-butyl ether each time. The chromatographic separation was obtained on an Agilent Poroshell column using a 0.01% formic acid buffer and acetonitrile (60:40, v/v) as the solvent with a flow rate of 0.3 mL/min. Optimized quantitative mass transitions for cortisol, cortisone, and cortisone d-4 were 363.2/121.0 (m/z), 361.2/163.1 (m/z), and 367.1/270.7 (m/z), respectively. The method validation was achieved according to regulatory guidance. The lower limit of quantification (LLOQ) in serum were 2 ng/mL for cortisol and 1 ng/mL for cortisone, and the LLOQ in saliva were 0.1 ng/mL for cortisol and 0.2 ng/mL for cortisone. The developed method showed convenient and efficient extraction, a lower LLOQ, and a short running time. Modest correlations between serum and saliva cortisol and cortisone concentrations were found. The method was successfully applied in assessing the HPA condition of patients with depressive disorders.
This paper presents a novel low-complexity soft demapping algorithm for two-dimensional non-uniform spaced constellations (2D-NUCs) and massive order one-dimensional NUCs (1D-NUCs). NUCs have been implemented in a wide range of new broadcasting systems to approach the Shannon limit further, such as DVB-NGH, ATSC 3.0 and NGB-W. However, the soft demapping complexity is extreme due to the substantial distance calculations. In the proposed scheme, the demapping process is classified into four cases based on different quadrants. To deal with the complexity problem, four groups of reduced subsets in terms of the quadrant for each bit are separately calculated and stored in advance. Analysis and simulation prove that the proposed demapper only introduces a small penalty under 0.02dB with respect to Max-Log-MAP demapper, whereas a significant complexity reduction ranging from 68.75% to 88.54% is obtained.
As one of the key techniques in the fully mechanized mining process, equipment selection and matching has a great effect on security, production and efficiency. The selection and matching of fully mechanized mining equipment in thin coal seam are restricted by many factors. In fully mechanized mining (FMM) faced in thin coal seams (TCS), to counter the problems existing in equipment selection, such as many the parameters concerned and low automation, an expert system (ES) of equipment selection for fully mechanized mining longwall face was established. A database for the equipment selection and matching expert system in thin coal seam, fully mechanized mining face has been established. Meanwhile, a decision-making software matching the ES was developed. Based on several real world examples, the reliability and technical risks of the results from the ES was discussed. Compared with the field applications, the shearer selection from the ES is reliable. However, some small deviations existed in the hydraulic support and scraper conveyor selection. Then, the ES was further improved. As a result, equipment selection in fully mechanized mining longwall face called 4301 in the Liangshuijing coal mine was carried out by the improved ES. Equipment selection results of the interface in the improved ES is consistent with the design proposal of the 4301 FMM working face. The reliability of the improved ES can meet the requirements of the engineering. It promotes the intelligent and efficient mining of coal resources in China.
PL
Dobór sprzętu, jako jedna z kluczowych technik w pełni zmechanizowanego procesu wydobycia, ma ogromny wpływ na bezpieczeństwo, produkcję i wydajność. Wybór i dopasowanie w pełni zmechanizowanego sprzętu górniczego w cienkim pokładzie węgla jest ograniczone przez wiele czynników. W przypadku całkowicie zmechanizowanej ściany wydobywczej węgla (FMM) w cienkich pokładach (TCS) przeciwdziałanie problemom związanym z wyborem sprzętu, takim jak m.in.: wielość rozpatrywanych parametrów i niska automatyzacja, ustanowiono system ekspercki (ES) doboru sprzętu do w pełni zmechanizowanej ściany wydobywczej. Utworzono bazę danych systemu doboru i dopasowania systemu eksperckiego w cienkich pokładach węgla w pełni zmechanizowanej ściany wydobywczej. Jednocześnie opracowano oprogramowanie do podejmowania decyzji, dopasowane do ES. Na podstawie kilku rzeczywistych przykładów omówiono wiarygodność i ryzyko techniczne związane z wynikami ES. W porównaniu z zastosowaniem obecnym, wybór kombajnu systemem eksperckim (ES) jest niezawodny. Wystąpiły jednak pewne niewielkie odchylenia w wyborze stojaków hydraulicznych i przenośnika zgarniającego, następnie ES został ulepszony. W rezultacie poprawiono wybór sprzętu w całkowicie zmechanizowanej ścianie wydobywczej o nazwie 4301 w kopalni Liangshuijing. Interfejs wyników wyboru sprzętu w ulepszonym ES jest zgodny z propozycją projektu 4301 FMM roboczej ściany wydobywczej. Niezawodność ulepszonego ES może spełniać wymagania inżynieryjne. Promuje inteligentne i wydajne wydobycie zasobów węgla w Chinach.
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