Large Eddy Simulation (LES) of Glass Fibre Dispersion in an Internally Spout-Fluidised Bed for Thermoplastic Composite Processing

Large eddy simulation (LES) has been conducted to investigate glass fibre dispersion in an internally spout-fluidised bed with draft tube and disk-baffle, which was used in the manufacture of long glass fibre reinforced thermoplastic composites. The LES results have demonstrated that the internally spout-fluidised bed with draft tube and disk-baffle can remarkably improve its hydrody-namic behaviour, which can effectively disperse fibre bundles and promote pre-impregnation with resin powder in manufacturing fibre reinforced thermoplastics. The hydrodynamics of the spout-fluidised bed has been investigated and reported in a previous paper (Hosseini et al., 2009). This study attempts to reveal important features of fibre dispersion and correlations between the fibre disper-sion and the characteristics of turbulence in the internally spout-fluidised bed using the LES modelling, focusing on the likely hydro-dynamic impact on fibre dispersion. The simulation has clearly indicated that there exists a strong interaction between the turbulent shear flow and transported fibres in the spout-fluidised bed. Fibre entrainment is strongly correlated with the local vorticity distribu-tion. The dispersion of fibres was modelled by a species transport equation in the LES simulation. The turbulent kinetic energy, Rey-nolds stress and strain rate were obtained by statistical analysis of the LES results. The LES results also clearly show that addition of the internals in the spout-fluidised bed can significantly change the turbulent flow features and local vorticity distribution, enhancing the capacity and efficiency of fibre flocs dispersion

Deep Learning on Abnormal Chromosome Segments: An Intelligent Copy Number Variants Detection System Design

Gene testing emerged as a business in the last two decades, and the testing cost has been reduced from 100 million to 1000 dollars for the development of technologies. Preimplantation genetic screening (PGS) is a popular genetic profiling of embryos prior to implantation in gene testing. Copy number variants (CNVs) detection is a key task in PGS which still needs the manual operation and evaluation. At the same time, deep learning technology earns a booming development and wide application in recent years for its strong computing and learning capability. This research redesigns the PGS workflow with the intelligent CNVs detection system, and proposes the corresponding system framework. Deep learning is selected as the proper technology in the system design for CNVs detection, which also fit the task of denoising. The evaluation is conducted on simulation dataset with high accuracy and low time cost, which may achieve the requirements of clinical application and reduce the workload of bioinformatics experts. Moreover, the redesigned process and proposed framework may enlighten the intelligent system design for gene testing in following work, and provide a guidance of deep learning application in AI healthcar

MiLMo:Minority Multilingual Pre-trained Language Model

Pre-trained language models are trained on large-scale unsupervised data, and they can fine-turn the model only on small-scale labeled datasets, and achieve good results. Multilingual pre-trained language models can be trained on multiple languages, and the model can understand multiple languages at the same time. At present, the search on pre-trained models mainly focuses on rich resources, while there is relatively little research on low-resource languages such as minority languages, and the public multilingual pre-trained language model can not work well for minority languages. Therefore, this paper constructs a multilingual pre-trained model named MiLMo that performs better on minority language tasks, including Mongolian, Tibetan, Uyghur, Kazakh and Korean. To solve the problem of scarcity of datasets on minority languages and verify the effectiveness of the MiLMo model, this paper constructs a minority multilingual text classification dataset named MiTC, and trains a word2vec model for each language. By comparing the word2vec model and the pre-trained model in the text classification task, this paper provides an optimal scheme for the downstream task research of minority languages. The final experimental results show that the performance of the pre-trained model is better than that of the word2vec model, and it has achieved the best results in minority multilingual text classification. The multilingual pre-trained model MiLMo, multilingual word2vec model and multilingual text classification dataset MiTC are published on http://milmo.cmli-nlp.com/

“Left on read” examining social media users’ lurking behavior: an integration of anxiety and social media fatigue

IntroductionWith the widespread use of social media, the behavior and mindset of users have been transformed, leading to a gradual increase in lurking users, which can impede the sustainable development of social media platforms. In this study, we aim to investigate the impact of intrinsic and extrinsic motivational factors on social media users’ anxiety, social media fatigue, and lurking behavior.MethodologyFor the confirmation of these phenomena and to validate the theories, a structural equation model was constructed based on the SSO (Stressor-Strain-Outcome) theoretical framework. The model was then tested and validated with data from 836 valid online surveys. These data were analyzed using SPSS 27.0 and AMOS 24.0 software.ResultsThe results indicate that intrinsic motivations (such as social comparison and privacy concerns) and extrinsic motivations (including information overload, functional overload, and social overload) are positively associated with users’ lurking behavior through the mediating effects of social media fatigue and anxiety. Additionally, for the mediator variables, social media fatigue was found to be positively associated with anxiety.DiscussionThese findings underscore the importance of social media platforms considering both intrinsic and extrinsic motivational factors to mitigate user anxiety and social media fatigue. By addressing these factors, platforms can foster user satisfaction and increase engagement, ultimately contributing to the sustainable development of social media platforms

The combination of bioactive herbal compounds with biomaterials for regenerative medicine

Regenerative medicine aims to restore the function of diseased or damaged tissues and organs by cell therapy, gene therapy, and tissue engineering, along with the adjunctive application of bioactive molecules. Traditional bioactive molecules, such as growth factors and cytokines, have shown great potential in the regulation of cellular and tissue behavior, but have the disadvantages of limited source, high cost, short half-life, and side effects. In recent years, herbal compounds extracted from natural plants/herbs have gained increasing attention. This is not only because herbal compounds are easily obtained, inexpensive, mostly safe, and reliable, but also owing to their excellent effects, including anti-inflammatory, antibacterial, antioxidative, proangiogenic behavior and ability to promote stem cell differentiation. Such effects also play important roles in the processes related to tissue regeneration. Furthermore, the moieties of the herbal compounds can form physical or chemical bonds with the scaffolds, which contributes to improved mechanical strength and stability of the scaffolds. Thus, the incorporation of herbal compounds as bioactive molecules in biomaterials is a promising direction for future regenerative medicine applications. Herein, an overview on the use of bioactive herbal compounds combined with different biomaterial scaffolds for regenerative medicine application is presented. We first introduce the classification, structures, and properties of different herbal bioactive components and then provide a comprehensive survey on the use of bioactive herbal compounds to engineer scaffolds for tissue repair/regeneration of skin, cartilage, bone, neural, and heart tissues. Finally, we highlight the challenges and prospects for the future development of herbal scaffolds toward clinical translation. Overall, it is believed that the combination of bioactive herbal compounds with biomaterials could be a promising perspective for the next generation of regenerative medicine

A new post-frac evaluation method for shale gas wells based on fracturing curves

AbstractPost-fracturing evaluation by using limited data is of great significance to continuous improvement of the fracturing programs. In this paper, a fracturing curve was divided into two stages (i.e., prepad fluid injection and main fracturing) so as to further understand the parameters of reservoirs and artificial fractures. The brittleness and plasticity of formations were qualitatively identified by use of the statistics of formation fracture frequency, and average pressure dropping range and rate during the prepad fluid injection. The composite brittleness index was quantitatively calculated by using the energy zones in the process of fracturing. It is shown from the large-scale true triaxial physical simulation results that the complexity of fractures is reflected by the pressure fluctuation frequency and amplitude in the main fracturing curve, and combined with the brittleness and plasticity of formations, the fracture morphology far away from the well can be diagnosed. Well P, a shale gas well in SE Chongqing, was taken as an example for post-fracturing evaluation. It is shown that the shale beds are of stronger heterogeneity along the extension directions of horizontal wells, and with GR 260 API as the dividing line between brittleness and plasticity in this area, complex fracture systems tend to form in brittleness-prone formations. In Well P, half of the fractures are single fractures, so it is necessary to carry out fine subsection and turnaround fracturing so as to improve development effects. This paper provides a theoretical basis for improving the fracturing well design and increasing the effective stimulated volume in this area

Determinants of 14-3-3σ dimerization and function in drug and radiation resistance

Many proteins exist and function as homodimers. Understanding the detailed mechanism driving the homodimerization is important and will impact future studies targeting the “undruggable” oncogenic protein dimers. In this study, we used 14-3-3σ as a model homodimeric protein and performed a systematic investigation of the potential roles of amino acid residues in the interface for homodimerization. Unlike other members of the conserved 14-3-3 protein family, 14-3-3σ prefers to form a homodimer with two subareas in the dimeric interface that has 180° symmetry. We found that both subareas of the dimeric interface are required to maintain full dimerization activity. Although the interfacial hydrophobic core residues Leu12 and Tyr84 play important roles in 14-3-3σ dimerization, the non-core residue Phe25 appears to be more important in controlling 14-3-3σ dimerization activity. Interestingly, a similar non-core residue (Val81) is less important than Phe25 in contributing to 14-3-3σ dimerization. Furthermore, dissociating dimeric 14-3-3σ into monomers by mutating the Leu12, Phe25, or Tyr84 dimerization residue individually diminished the function of 14-3-3σ in resisting drug-induced apoptosis and in arresting cells at G2/M phase in response to DNA-damaging treatment. Thus, dimerization appears to be required for the function of 14-3-3σ

Direct observation of the formation and stabilization of metallic nanoparticles on carbon supports

Direct formation of ultra-small nanoparticles on carbon supports by rapid high temperature synthesis method offers new opportunities for scalable nanomanufacturing and the synthesis of stable multi-elemental nanoparticles. However, the underlying mechanisms affecting the dispersion and stability of nanoparticles on the supports during high temperature processing remain enigmatic. In this work, we report the observation of metallic nanoparticles formation and stabilization on carbon supports through in situ Joule heating method. We find that the formation of metallic nanoparticles is associated with the simultaneous phase transition of amorphous carbon to a highly defective turbostratic graphite (T-graphite). Molecular dynamic (MD) simulations suggest that the defective T-graphite provide numerous nucleation sites for the nanoparticles to form. Furthermore, the nanoparticles partially intercalate and take root on edge planes, leading to high binding energy on support. This interaction between nanoparticles and T-graphite substrate strengthens the anchoring and provides excellent thermal stability to the nanoparticles. These findings provide mechanistic understanding of rapid high temperature synthesis of metal nanoparticles on carbon supports and the origin of their stability

Fault diagnosis of bearing vibration signals based on a reconstruction algorithm with multiple side Information and CEEMDAN method

When bearing vibration of instruments is monitored, a large number of data are produced. This requires a massive capacity of storage and high bandwidth of data transmission whereby costs and complex installation are concerned. In this study, we aim to propose an effective framework to address such the amount of bearing signals to which only meaningful information is extracted. Based on the compressed sensing (CS) theory. We proposed a reconstruction algorithm based on the multiple side information signal (RAMSI) with a purpose to effectively obtain important information from recorded bearing signals. In the process of sparse optimization, the RAMSI algorithm was implemented to solve the n-11 minimization problem with the weighting adaptive multiple side information signals. Wavelet basis and Hartley matrix were applied for the reconstruction process, for which the effective sparse optimization processing of bearing signals was able to adaptively computed. The performance of our RAMSI-based CS theory was compared with the basis pursuit (BP) which is based on the alternating direction method of multiplier (ADMM) and orthogonal matching pursuit (OMP). The error indices of the reconstruction algorithms were evaluated. This proves that the performance of the sparse optimization algorithm from our proposed framework is superior to the BP based on the ADMM and OMP algorithm. After recovering vibration signals, some strong noise caused by the incipient fault characteristic of the bearing. The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method was performed to extract the bearing fault component from such noise. In terms of performance, the CEEMDAN method was compared to the standard ensemble empirical mode decomposition (EEMD) method. The results show that the CEEMDAN method yields a better decomposition performance and is able to extract meaningful information of bearing fault characteristic