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Secondary structure plays an important role in determining the function of non-coding RNAs. Hence, identifying RNA secondary structures is of great value to research. Computational prediction is a mainstream approach for predicting RNA secondary structure. Unfortunately, even though new methods have been proposed over the past 40 years, the performance of computational prediction methods has stagnated in the last decade. Recently, with the increasing availability of RNA structure data, new methods based on machine-learning technologies, especially deep learning, have alleviated the issue. In this review, we provide a comprehensive overview of RNA secondary structure prediction methods based on machine-learning technologies and a tabularized summary of the most important methods in this field. The current pending issues in the field of RNA secondary structure prediction and future trends are also discussed., Comment: 25 pages, 5 figures, 1 table

Carbon Fibers (CFs) are the key solution for the future lightweight vehicle with enhanced fuel efficiency and reduced emissions owing to their ultrahigh strength to weight ratio. However, the high cost of the current dominant PAN-based CFs hinders their application. The use of low-cost alternative precursors may overcome this issue. Unfortunately, low-cost CFs derived from cheaper single component precursors suffer from poor mechanical properties. Developing composite CFs by adding nanoadditives is very promising for low-cost CFs. Therefore, a fundamental understanding of carbonization condition impacts and polymer/additives conversion mechanisms during whole CF production are essential to develop low-cost CFs. In this work, we have demonstrated how the carbonization temperature affects the PAN/graphene CFs properties by performing a series of ReaxFF based molecular dynamics simulations. We found that graphene edges along with the nitrogen and oxygen functional groups have a catalytic role and act as seeds for the graphitic structure growth. Our MD simulations unveil that the addition of the graphene to PAN precursor modifies all-carbon membered rings in CFs and enhances the alignments of 6-member carbon rings in carbonization which leads to superior mechanical properties compare to PAN-based CFs. These ReaxFF simulation results are validates by experimental structural and mechanical characterizations. Interestingly, mechanical characterizations indicate that PAN/graphene CFs carbonized at 1250 C demonstrate 90.9% increase in strength and 101.9% enhancement in Young's modulus compare to the PAN-based CFs carbonized at 1500 C. The superior mechanical properties of PAN/graphene CFs at lower carbonization temperatures offers a path to both energy savings and cost reduction by decreasing the carbonization temperature and could provide key insights for the development of low-cost CFs.

Cyclopentadiene (CPD) is an important intermediate in the combustion of fuel and the formation of aromatics. In the present study, the kinetics and thermodynamic properties for hydrogen abstraction and addition with CPD, and the related reactions including the isomerization and decomposition on the C5H7 potential energy surface were systematically investigated by theoretical calculations. High-level ab initio calculations were adopted to obtain the stationary points on the potential energy surfaces of CPD + H. Phenomenological rate coefficients for temperature- and pressure-dependent reactions in the full potential energy surface were calculated by solving the time-dependent multiple-well RRKM/master equation, and the hydrogen abstraction reactions were based on the conventional transition state theory. In terms of the hydrogen abstraction reactions, the hydrogen abstraction from the saturated carbon atom in CPD is found to be the dominant channel. For the hydrogen addition and the associated reactions on the C5H7 PES, the allylic and vinylic cyclopentenyl radicals and C2H2 + C3H5 were found to be the most important channels and reactivity-promoting products, respectively. The previously neglected role of open-chain intermediates in the evaluation of the reaction kinetics has been suggested and the corresponding rate constants have been recommended for inclusion in the modeling of the H+ c-C5H6 reaction. Results indicate that the transformation from to straight-chain C5H7 is kinetically unfavorable due to the high strain energy of the 3-membered ring structure of the isomerization transition state. Moreover, the thermodynamic data and the calculated rate coefficients for both H atom abstraction and addition were incorporated into the kinetics model to examine the impact of the computed pressure-dependent kinetics of C5H6 + H reactions on model predictions.

As a promising quantum key distribution (QKD), most of the existing round-robin differential-phase-shift quantum key distribution (RRDPS-QKD) protocols have adopted the decoy-state method and have assumed the source states are exactly controlled. However, the precise manipulation of source states is impossible for any practical experiment, and the RRDPS-QKD with source errors has an unignorable impact on the performance of the protocol. In the paper, we study the three-intensity decoy-state RRDPS-QKD protocol with source errors, formulate the secure generation key rate of the proposed protocol, do the numerical simulations to testify the deductions. The results show that our evaluation can estimate the influence of source errors., Comment: 12 pages, 3 figures