Aero-engine rotor-static rubbing characteristic analysis based on casing acceleration signal

The rotor experiment rig of aero-engine was used to simulate rubbing faults in different rotational speeds, rubbing intensities, rubbing positions and casing thickness. The casing acceleration signal was collected and subjected to the analysis by auto-correlation function frequency spectrum. The result indicates that the auto-correlation function frequency spectrum shows significant characteristic frequency in rubbing frequency (product between blade number and rotating frequency) and its integer multiple. The location of each characteristic frequency is characterized by band-frequency characteristic with rotating frequency as interval. The characteristic is not affected by sensor installed position, rotational speed, rubbing position and casing thickness

Reformulating Sequential Recommendation: Learning Dynamic User Interest with Content-enriched Language Modeling

Recommender systems are essential for online applications, and sequential recommendation has enjoyed significant prevalence due to its expressive ability to capture dynamic user interests. However, previous sequential modeling methods still have limitations in capturing contextual information. The primary reason for this issue is that language models often lack an understanding of domain-specific knowledge and item-related textual content. To address this issue, we adopt a new sequential recommendation paradigm and propose LANCER, which leverages the semantic understanding capabilities of pre-trained language models to generate personalized recommendations. Our approach bridges the gap between language models and recommender systems, resulting in more human-like recommendations. We demonstrate the effectiveness of our approach through experiments on several benchmark datasets, showing promising results and providing valuable insights into the influence of our model on sequential recommendation tasks. Furthermore, our experimental codes are publicly available

Nested Named Entity Recognition from Medical Texts: An Adaptive Shared Network Architecture with Attentive CRF

Recognizing useful named entities plays a vital role in medical information processing, which helps drive the development of medical area research. Deep learning methods have achieved good results in medical named entity recognition (NER). However, we find that existing methods face great challenges when dealing with the nested named entities. In this work, we propose a novel method, referred to as ASAC, to solve the dilemma caused by the nested phenomenon, in which the core idea is to model the dependency between different categories of entity recognition. The proposed method contains two key modules: the adaptive shared (AS) part and the attentive conditional random field (ACRF) module. The former part automatically assigns adaptive weights across each task to achieve optimal recognition accuracy in the multi-layer network. The latter module employs the attention operation to model the dependency between different entities. In this way, our model could learn better entity representations by capturing the implicit distinctions and relationships between different categories of entities. Extensive experiments on public datasets verify the effectiveness of our method. Besides, we also perform ablation analyses to deeply understand our methods

Condition trend prediction of aero-generator based on particle swarm optimization and fuzzy integral

In order to improve and enhance the prediction accuracy and efficiency of aero-generator running trend, grasp its running condition, and avoid accidents happening, in this paper, auto-regressive and moving average model (ARMA) and least squares support vector machine (LSSVM) which are used to predict its running trend have been optimized using particle swarm optimization (PSO) based on using features found in real aero-generator life test, which lasts a long period of time on specialized test platform and collects mass data that reflects aero-generator characteristics, to build new models of PSO-ARMA and PSO-LSSVM. And we use fuzzy integral methodology to carry out decision fusion of the predicted results of these two new models. The research shows that the prediction accuracy of PSO-ARMA and PSO-LSSVM has been much improved on that of ARMA and LSSVM, and the results of decision fusion based on fuzzy integral methodology show further substantial improvement in accuracy than each particle swarm optimized model. Conclusion can be drawn that the optimized model and the decision fusion method presented in this paper are available in aero-generator condition trend prediction and have great value of engineering application

Testing model transformation programs using metamorphic testing

Model transformations are crucial for the success of Model Driven Engineering. Testing is a prevailing technique of verifying the correctness of model transformation programs. A major challenge in model transformation testing is the oracle problem, which refers to the difficulty or high cost in determining the correctness of the output models. Metamorphic Testing alleviates the oracle problem by making use of the relationships among the inputs and outputs of multiple executions of the target function. This paper investigates the effectiveness and feasibility of metamorphic testing in testing model transformation programs. Empirical results show that metamorphic testing is an effective testing method for model transformation programs

Cytotoxicity of hydroxydihydrobovolide and its pharmacokinetic studies in Portulaca oleracea L. extract

Hydroxydihydrobovolide (HDB) was for the first time isolated from Portulaca oleracea L. and then its cytotoxicity against SH-SYTY cells was studied. Moreover, a rapid and sensitive ultra-high performance liquid chromatographic (UHPLC) method with bergapten as internal standard (IS) was developed and validated to investigate the pharmacokinetics of HDB in rats after intravenous and oral administrations of extract (POE). The UHPLC analysis was performed on a Diamonsil C18 analytical column, using acetonitrile-water (35:65, v/v) as the mobile phase with UV detection at 220 nm. The calibration curve was linear over the range of 0.2-25 µg/mL in rat plasma. The average extraction recovery was from 90.1 to 98.9%, and the relative standard deviations (RSDs) of the intra- and inter-day precisions were less than 4.7 and 4.1%, respectively. The results showed that 50 µM HDB had significant cytotoxicity on the SH-SY5Y cells, which was rapidly distributed with a Tmax of 11 min after oral administration and presented a low absolute bioavailability, 4.12%

Engineering a ratiometric fluorescent sensor membrane containing carbon dots for efficient fluoride detection and removal

Fluoride anion pollution is one of the main problems that needs to be addressed in contaminated water. Herein, we have developed a novel sensing platform using a pyrene boronic acid and carbon dots (CDs) for the selective detection and removal of fluoride (F−) ion at environmentally relevant levels. The probe consists of pyrene-boronic acid (PyB) moieties immobilized on to the surface of water-soluble CDs. The pyrene-boronic acid-based CDs (CDs-PyB) result in a sensor whose response is linear for F− concentrations over a range from 0 to 200 µM (R2 = 0.996) with a detection limit of 5.9 × 10−5 M and display high selectivity for F− over other anions. In addition, an amino-modified cellulose membrane containing CDs-PyB has been prepared for practical sensing and removal of F−. The cellulose membrane-based sensor shows great potential for the detection of F− with a high sensitivity, and excellent F− adsorption and removal efficiency of 90.2%. Moreover, an MTT assay for the membrane demonstrates high cell proliferation ca 400% after 5 days culture, indicating excellent cytocompatibility. Our approach offers a promising direction for the construction of other sensors by simply swapping the current probe with suitable replacements for a variety of relevant applications using biocompatible and abundant naturally based materials.</p