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Study on failure modes and calculation method of the cast steel joint with branches in treelike structure

Chen Feng, Sun Yun, Sun Shuxuan, Song Da, Liu Yangbing

Archives of Civil Engineering

2024

Vol. 70, nr 1

589--604

The treelike structure links members and transfers loads via its solitary cast steel joint with branches. Therefore, the joint’s bearing capacity significantly affects the treelike structure’s stability, security, and economics. This paper utilized experimental verification and numerical modeling to examine the mechanical behavior of cast-steel joints with branches in the treelike structure under various loading conditions. Then, researchers investigated the failure process and mechanism of joints, and the three most common failure modes were outlined. Furthermore, the researchers proposed the bearing capacity calculation formula based on the common failure modes. The results show that the three common failure modes of the cast-steel joints with branches under different loading conditions are the failure in the joint core area under the axial load, the failure in the main pipe compression side under eccentric load, and the failure in the compression side of the single branch pipe root when the single branch pipe is under the uneven load. The suggested empirical calculation method can serve as a reference point for similar engineering practices design.

LC-MS/MS assay for the therapeutic drug monitoring of perampanel in children with drug-resistant epilepsy

Dai Hao-Ran, Hu Ya-Hui, Long Jia-Yi, Xia Ying, Guo Hong-Li, Xu Jing, Ding Xuan-Sheng, Chen Jing, Lu Xiao-Peng, Chen Feng

Acta Chromatographica

2023

Vol. 35, no. 2

149--160

Perampanel (PER) is the first clinically available selective antagonist of α-amino-3-hydroxy-5-methylisoxazole-4-propionic acid (AMPA) receptor approved globally for the treatment of epilepsy. Studies have recently underlined the significant association between dose-exposure-effect-adverse events of PER in patients with epilepsy, so the therapeutic drug monitoring (TDM) of PER is critical in clinical practices, especially for pediatric patients with drug-resistant epilepsy. Due to several limits in previous published analytical methods, herein, we describe the development and validation of a novel liquid chromatography tandem mass spectrometry (LC-MS/MS) method for monitoring PER in human plasma samples. Protein precipitation method by acetonitrile containing PER-d5 as internal standard was applied for the sample clean-up. Formic acid (FA, 0.2 mM) in both aqueous water and acetonitrile were used as the mobile phases and the analyte was separated by an isocratic elution. Qualification and quantification were performed under positive electrospray ionization (ESI) mode using the m/z 350.3 → 219.1 and 355.3 → 220.0 ions pairs transitions for PER and PER-d5, respectively. Potential co-medicated anti-seizure medications (ASMs) have no interference to the analysis. Calibration curves were linear in the concentration range of 1.00–2,000 ng mL⁻¹ for PER. The intra- and inter-batch precision, accuracy, recovery, dilution integrity, and stability of the method were all within the acceptable criteria and no matrix effect or carryover was found. This method was then successfully implemented on the TDM of PER in Chinese children with drug-resistant epilepsy. We firstly confirmed the apparent inter- and intra-individual PER concentration variabilities and potential drug-drug interactions between PER and several concomitant ASMs occurred in Chinese pediatric patients, which were also in line with previous studies in patients of other race.

Forecasting short-term electric load using extreme learning machine with improved tree seed algorithm based on Lévy flight

Chen Xuan, Przystupa Krzysztof, Ye Zhiwei, Chen Feng, Wang Chunzhi, Liu Jinhang, Gao Rong, Wei Ming, Kochan Orest

Eksploatacja i Niezawodność

2022

Vol. 24, no. 1

153--162

In recent years, forecasting has received increasing attention since it provides an important basis for the effective operation of power systems. In this paper, a hybrid method, composed of kernel principal component analysis (KPCA), tree seed algorithm based on Lévy flight (LTSA) and extreme learning machine (ELM), is proposed for short-term load forecasting. Specifically, the randomly generated weights and biases of ELM have a significant impact on the stability of prediction results. Therefore, in order to solve this problem, LTSA is utilized to obtain the optimal parameters before the prediction process is executed by ELM, which is called LTSA-ELM. Meanwhile, the input data is extracted by KPCA considering the sparseness of the electric load data and used as the input of LTSA-ELM model. The proposed method is tested on the data from European network on intelligent technologies (EUNITE) and experimental results demonstrate the superiority of the proposed approaches compared to the other methods involved in the paper.