The gas generation features of coals at different maturities were studied by the anhydrous pyrolysis of Jurassic coal from the Minhe Basin in sealed gold tubes at 50 MPa. The gas component yields (C 1 , C 2 , C 3 , i-C 4 , n-C 4 , i-C 5 , n-C 5 , and CO 2); the d 13 C of C 1 , C 2 , C 3 , and CO 2 ; and the mass of the liquid hydrocarbons (C 6?) were measured. On the basis of these data, the stage changes of d 13 C 1 , d 13 C 2 , d 13 C 3 , and d 13 CO 2 were calculated. The diagrams of d 13 C 1-d 13 C 2 vs ln (C 1 / C 2) and d 13 C 2-d 13 C 1 vs d 13 C 3-d 13 C 2 were used to evaluate the gas generation features of the coal maturity stages. At the high maturity evolution stage (T [ 527.6°C at 2°C/h), the stage change of d 13 C 1 and the CH 4 yield are much higher than that of CO 2 , suggesting that high maturity coal could still generate methane. When T \ 455°C, CO 2 is generated by breaking bonds between carbons and heteroatoms. The reaction between different sources of coke and water may be the reason for the complicated stage change in d 13 C CO 2 when the temperature was higher than 455°C. With increasing pyrolysis temperature, d 13 C 1-d 13 C 2 vs ln (C 1 /C 2) has four evolution stages corresponding to the early stage of breaking bonds between carbon and hetero atoms, the later stage of breaking bonds between carbon and hetero atoms, the cracking of C 6? and coal demethylation, and the cracking of C 2-5. The d 13 C 2-d 13 C 1 vs d 13 C 3-d 13 C 2 has three evolution stages corresponding to the breaking bonds between carbon and hetero atoms, demethylation and cracking of C 6? , and cracking of C 2-5 .

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We consider how to privately determine whether a private set owned by Bob is a subset of another private set owned by Alice. This problem has many applications in online collaboration. We first propose an encoding method to encode a set to a vector, which can reduce a set computation problem to a vector computation. Based on this encoding scheme and two different homomorphic encryption schemes, we present two efficient protocols for private subset problem in case where both private sets are subsets of a known universal set. These protocols are secure both in the semi‐honest model and in the malicious model. We then show how to use these protocols to privately determine whether a private number is a factor of another private number. Copyright © 2017 John Wiley & Sons, Ltd.

A single sensor is prone to decline recognition accuracy in the face of a complex environment, while the existing multi-sensor evidence theory fusion methods do not comprehensively consider the impact of evidence conflict and fuzziness. In this paper, a new evidence weight combination and probability allocation method is proposed, which calculated the degree of evidence fuzziness through the maximum entropy principle, and also considered the impact of evidence conflict on fusing results. The two impact factors were combined to calculate the trusted discount and reallocate the probability function. Finally, Dempster’s combination rule was used to fuse every piece of evidence. On this basis, experiments were first conducted to prove that the existing weight combination methods produce results contrary to common sense when handling high-conflicting and high-clarity evidence, and then comparative experiments were conducted to prove the effectiveness of the proposed evidence weight combination and probability allocation method. Moreover, it was verified, on the PAMAP2 data set, that the proposed method can obtain higher fusing accuracy and more reliable fusing results in all kinds of behavior recognition. Compared with the traditional methods and the existing improved methods, the weight allocation method proposed in this paper dynamically adjusts the weight of fuzziness and conflict in the fusing process and improves the fusing accuracy by about 3.3% and 1.7% respectively which solved the limitations of the existing weight combination methods.

Rockburst hazards pose a severe threat to mine safety. To accurately predict the risk level of rockburst, a LightGBM−TCN−RF prediction model is proposed in this paper. The correlation coefficient heat map combined with the LightGBM feature selection algorithm is used to screen the rockburst characteristic variables and establish rockburst predicted characteristic variables. Then, the TCN prediction model with a better prediction performance is selected to predict the rockburst characteristic variables at time t + 1. The RF classification model of rockburst risk level with a better classification effect is used to classify the risk level of rockburst characteristic variables at time t + 1. The comparison experiments show that the rockburst characteristic variables after screening allow a more accurate prediction. The overall RMSE and MAE of the TCN prediction model are 0.124 and 0.079, which are better than those of RNN, LSTM, and GRU by about 0.1–2.5%. The accuracy of the RF classification model for the rockburst risk level is 96.17%, which is about 20% higher than that of KNN and SVM, and the model accuracy is improved by 1.62% after parameter tuning by the PSO algorithm. The experimental results show that the LightGBM−TCN−RF model can better classify and predict rockburst risk levels at future moments, which has a certain reference value for rockburst monitoring and early warning.