Review on Applications of X-ray computed tomography for coal characterization : recent progress and perspectives

This research was funded by the National Natural Science Foundation of China (grant nos. 42130806, 41830427, 41922016 and 42102227).Peer reviewedPostprin

Characterization, phylogeny, alternative splicing and expression of Sox30 gene

<p>Abstract</p> <p>Background</p> <p>Members of the Sox gene family isolated from both vertebrates and invertebrates have been proved to participate in a wide variety of developmental processes, including sex determination and differentiation. Among these members, <it>Sox30 </it>had been considered to exist only in mammals since its discovery, and its exact function remains unclear.</p> <p>Results</p> <p><it>Sox30 </it>cDNA was cloned from the Nile tilapia by RT-PCR and RACE. Screening of available genome and EST databases and phylogenetic analysis showed that <it>Sox30 </it>also exists in non-mammalian vertebrates and invertebrates, which was further supported by synteny analyses. Tissue expression in human, mouse and tilapia suggested that <it>Sox30 </it>was probably a gonad-specific gene, which was also supported by the fact that <it>Sox30 </it>EST sequences were obtained from gonads of the animal species. In addition, four alternatively spliced isoforms were isolated from tilapia gonad. Their temporal and spatial expression patterns during normal and sex reversed gonadal development were investigated by RT-PCR and <it>in situ </it>hybridization. Our data suggest that expressions of <it>Sox30 </it>isoforms are related to stage and phenotypic-sex, observed in the germ cells of male gonad and in somatic cells of the female gonad.</p> <p>Conclusions</p> <p><it>Sox30 </it>is not a gene only existed in mammals, but exists widely throughout the animal kingdom as supported by our bioinformatic, phylogenetic and syntenic analyses. It is very likely that <it>Sox30 </it>is expressed exclusively in gonads. Expression analyses revealed that <it>Sox30 </it>may be involved in female and male gonadal development at different stages by alternative splicing.</p

Intelligent classification of coal structure using multinomial logistic regression, random forest and fully connected neural network with multisource geophysical logging data

Acknowledgments This research was funded by the National Natural Science Foundation of China (grant nos. 42130806, 41922016 and 41830427).Peer reviewe

Frequency-Domain Transient Analysis of Multitime Partial Differential Equation Systems

Abstract-Multitime partial differential equations (MPDEs) provide an efficient method to simulate circuits with widely separated rates of inputs. This paper proposes a fast and accurate frequency-domain multitime transient analysis method for MPDE systems, which fills in the gap for the lack of general frequency-domain solver for MPDE systems. A blockpulse function-based multidimensional inverse Laplace transform strategy is adopted. The method can be applied to discrete input systems. Numerical examples then confirm its superior accuracy, under similar efficiency, over time-domain solvers

Virtual Reality-Integrated Immersion-Based Teaching to English Language Learning Outcome.

Globalization and informatization are reshaping human life and social behaviors. The purpose is to explore the worldwide strategies to cultivate international talents with a global vision. As a global language with the largest population, English, and especially its learning effect, have always been the major concerns of scholars and educators. This work innovatively studies the combination of immersion-based English teaching with virtual reality (VR) technology. Then, based on the experimental design mode, 106 students from a Chinese school were selected for a quasi-experimental study for 16 weeks (3 h a week, and 48 h in total). The collected data were analyzed by computer statistical software, and hypotheses are verified. The results showed that there is a significantly positive correlation between VR and immersion-based language teaching (0.851, p < 0.01). There is a significantly positive correlation between immersion-based language teaching and academic achievement (0.824, p < 0.01), and VR is positively correlated with learning outcome (LO) (0.836, p < 0.01). Compared with other state-of-art research methods, this work modifies the students' oral test through the analysis and comparison with the system database, and the students' learning effect is greatly improved. Finally, some suggestions are put forward according to the research results to provide an experimental reference for English teachers and future linguistics teaching

A Novel Double Cluster and Principal Component Analysis-Based Optimization Method for the Orbit Design of Earth Observation Satellites

The weighted sum and genetic algorithm-based hybrid method (WSGA-based HM), which has been applied to multiobjective orbit optimizations, is negatively influenced by human factors through the artificial choice of the weight coefficients in weighted sum method and the slow convergence of GA. To address these two problems, a cluster and principal component analysis-based optimization method (CPC-based OM) is proposed, in which many candidate orbits are gradually randomly generated until the optimal orbit is obtained using a data mining method, that is, cluster analysis based on principal components. Then, the second cluster analysis of the orbital elements is introduced into CPC-based OM to improve the convergence, developing a novel double cluster and principal component analysis-based optimization method (DCPC-based OM). In DCPC-based OM, the cluster analysis based on principal components has the advantage of reducing the human influences, and the cluster analysis based on six orbital elements can reduce the search space to effectively accelerate convergence. The test results from a multiobjective numerical benchmark function and the orbit design results of an Earth observation satellite show that DCPC-based OM converges more efficiently than WSGA-based HM. And DCPC-based OM, to some degree, reduces the influence of human factors presented in WSGA-based HM

Ultrafast field-driven monochromatic photoemission from carbon nanotubes

Ultrafast electron pulses, combined with laser-pump and electron-probe technologies, allow for various forms of ultrafast microscopy and spectroscopy to elucidate otherwise challenging to observe physical and chemical transitions. However, the pursuit of simultaneous ultimate spatial and temporal resolution has been largely subdued by the low monochromaticity of the electron pulses and their poor phase synchronization to the optical excitation pulses. State-of-the-art photon-driven sources have good monochromaticity but poor phase synchronization. In contrast, field-driven photoemission has much higher light phase synchronization, due to the intrinsic sub-cycle emission dynamics, but poor monochromaticity. Such sources suffer from larger electron energy spreads (3 - 100 eV) attributed to the relatively low field enhancement of the conventional metal tips which necessitates long pump wavelengths (> 800 nm) in order to gain sufficient ponderomotive potential to access the field-driven regime. In this work, field-driven photoemission from ~1 nm radius carbon nanotubes excited by a femtosecond laser at a short wavelength of 410 nm has been realized. The energy spread of field-driven electrons is effectively compressed to 0.25 eV outperforming all conventional ultrafast electron sources. Our new nanotube-based ultrafast electron source opens exciting prospects for attosecond imaging and emerging light-wave electronics

On the combination of adaptive neuro-fuzzy inference system and deep residual network for improving detection rates on intrusion detection

Deep Residual Networks (ResNets) are prone to overfitting in problems with uncertainty, such as intrusion detection problems. To alleviate this problem, we proposed a method that combines the Adaptive Neuro-fuzzy Inference System (ANFIS) and the ResNet algorithm. This method can make use of the advantages of both the ANFIS and ResNet, and alleviate the overfitting problem of ResNet. Compared with the original ResNet algorithm, the proposed method provides overlapped intervals of continuous attributes and fuzzy rules to ResNet, improving the fuzziness of ResNet. To evaluate the performance of the proposed method, the proposed method is realized and evaluated on the benchmark NSL-KDD dataset. Also, the performance of the proposed method is compared with the original ResNet algorithm and other deep learning-based and ANFIS-based methods. The experimental results demonstrate that the proposed method is better than that of the original ResNet and other existing methods on various metrics, reaching a 98.88% detection rate and 1.11% false alarm rate on the KDDTrain+ datase

Room temperature all-solid-state lithium batteries based on a soluble organic cage ionic conductor

All solid-state lithium batteries (SSLBs) are poised to have higher energy density and better safety than current liquid-based Li-ion batteries, but a central requirement is effective ionic conduction pathways throughout the entire cell. Here we develop a catholyte based on an emerging class of porous materials, porous organic cages (POCs). A key feature of these Li(+) conducting POCs is their solution-processibility. They can be dissolved in a cathode slurry, which allows the fabrication of solid-state cathodes using the conventional slurry coating method. These Li(+) conducting cages recrystallize and grow on the surface of the cathode particles during the coating process and are therefore dispersed uniformly in the slurry-coated cathodes to form a highly effective ion-conducting network. This catholyte is shown to be compatible with cathode active materials such as LiFePO(4), LiCoO(2) and LiNi(0.5)Co(0.2)Mn(0.3)O(2), and results in SSLBs with decent electrochemical performance at room temperature