Dynamic method of stiffness identification in impacting systems for percussive drilling applications

Peer reviewedPostprin

Experimental verification of the percussive drilling model

Acknowledgements This paper is supported by National Natural Science Foundation of China (No. 51904018), Beijing Municipal Natural Science Foundation (No. 3204049), Fundamental Research Funds for the Central Universities (No. FRF-TP-18-054A1), and Interdisciplinary Research Project for Young Teachers of USTB (Fundamental Research Funds for the Central Universities) (No. FRF-IDRY-19-006).Peer reviewedPostprin

A novel molecular signature for predicting prognosis and immunotherapy response in osteosarcoma based on tumor-infiltrating cell marker genes

BackgroundTumor infiltrating lymphocytes (TILs), the main component in the tumor microenvironment, play a critical role in the antitumor immune response. Few studies have developed a prognostic model based on TILs in osteosarcoma.MethodsScRNA-seq data was obtained from our previous research and bulk RNA transcriptome data was from TARGET database. WGCNA was used to obtain the immune-related gene modules. Subsequently, we applied LASSO regression analysis and SVM algorithm to construct a prognostic model based on TILs marker genes. What’s more, the prognostic model was verified by external datasets and experiment in vitro. ResultsEleven cell clusters and 2044 TILs marker genes were identified. WGCNA results showed that 545 TILs marker genes were the most strongly related with immune. Subsequently, a risk model including 5 genes was developed. We found that the survival rate was higher in the low-risk group and the risk model could be used as an independent prognostic factor. Meanwhile, high-risk patients had a lower abundance of immune cell infiltration and many immune checkpoint genes were highly expressed in the low-risk group. The prognostic model was also demonstrated to be a good predictive capacity in external datasets. The result of RT-qPCR indicated that these 5 genes have differential expression which accorded with the predicting outcomes.ConclusionsThis study developed a new molecular signature based on TILs marker genes, which is very effective in predicting OS prognosis and immunotherapy response

Carrageenan-based hydrogels for the controlled delivery of PDGF-BB in bone tissue engineering applications

One of the major drawbacks found in most bone tissue engineering approaches developed so far consists in the lack of strategies to promote vascularisation. Some studies have addressed different issues that may enhance vascularisation in tissue engineered constructs, most of them involving the use of growth factors (GFs) that are involved in the restitution of the vascularity in a damaged zone. The use of sustained delivery systems might also play an important role in the re-establishment of angiogenesis. In this study, !-carrageenan, a naturally occurring polymer, was used to develop hydrogel beads with the ability to incorporate GFs with the purpose of establishing an effective angiogenesis mechanism. Some processing parameters were studied and their influence on the final bead properties was evaluated. Platelet derived growth factor (PDGF-BB) was selected as the angiogenic factor to incorporate in the developed beads, and the results demonstrate the achievement of an efficient encapsulation and controlled release profile matching those usually required for the development of a fully functional vascular network. In general, the obtained results demonstrate the potential of these systems for bone tissue engineering applications.This work was supported by the European NoE EXPERTISSUES (NMP3-CT-2004-500283), the European STREP HIPPOCRATES (NMP3-CT-2003-505758), and by the Portuguese Foundation for Science and Technology (FCT) through the project PTDC/FIS/68517/2006 and through the V. Espirito Santo's Ph.D. grant (SFRH/BD/39486/2007)

China’s Energy Consumption Rebound Effect Analysis Based on the Perspective of Technological Progress

Energy issues are the focus of global concern, and estimations of the energy rebound effect are very important for energy policy. Existing research has proved the existence of the energy rebound effect. This paper, based on the estimation of China’s capital stock in 1952, establishes three elements of the neoclassical production function to calculate the contribution rate of technological progress on economic growth. It then calculates China’s energy rebound effect over the past 20 years from the perspective of technological progress. The research results show that though China’s energy intensity has been declining from 1994 to 2017, the energy rebound effect each year is obviously different, with an average level of 54.4%. Technological progress leads to the improvement of energy efficiency, which reduces energy consumption, but the rebound effect makes energy savings less effective than expected. This paper proved the Granger causality between energy structure adjustment and the rebound effect. And the increase of coal consumption will enhance the rebound effect. So, upgrading the structure of energy consumption is considered helpful to reduce the energy rebound effect, which can promote energy conservation and emission reduction

Optimization of the Vibro-Impact Capsule System for Promoting Progression Speed

This paper studies the dynamics of vibro-impact capsule systems with one-sided and double-sided constraints under variations of control parameters, including frequency of excitation, mass ratio, and stiffness ratio. The aim of this study is to optimize the progression speed of the capsule system. Extensive comparisons reveal that the capsule system with one-sided constraint is better than the one with double-sided constraints in terms of progression speed. Moreover, the system’s period-one one-right-impact motion is proved as the ideal vibration condition due to its lowest energy consumption for impacts. According to the dynamic analyses of control parameters, an inner mass with its weight similar as the weight of capsule and a right constraint with a relative weak stiffness are beneficial for further accelerating the capsule system forwards

A deep learning-based sentiment analysis approach for online product ranking With probabilistic linguistic term sets

The probabilities linguistic term set (PLTS) is an efficient tool to represent sentimental intensities hidden in unstructured text reviews that are useful for multicriteria online product ranking. Traditional machine learning-based sentiment analysis methods adopted in existing studies to obtain PLTSs often result in unsatisfying prediction accuracy and, thus, inevitably affect product ranking results. To overcome this limitation, in this study, we propose a deep learning-based sentiment analysis approach to produce PLTSs from online product reviews to rank online products. A natural language processing-based method is first applied to extract product features and corresponding feature texts from online reviews. Then, state-of-the-art deep learning-based models are implemented to conduct the sentiment classification for online product/feature review texts. To ensure classification accuracy, we propose an experimental matching mechanism to identify the level of sentiment tendency for all rating labels of a review dataset and then match each label with the most appropriate linguistic term. The experimental results reveal that our matching mechanism can benefit the training of a text classification model to identify sentiment tendencies from review texts with high prediction accuracy and with the help of the trained classification model, our approach can predict sentimental intensities of the extracted features' texts in the form of PLTSs with competitive accuracy. A case study of applying PLTSs output from our approach to an online product decision-making problem is also provided to validate the applicability of our approach