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Traffic flow prediction plays an important role in intelligent transportation applications, such as traffic control, navigation, path planning, etc., which are closely related to people’s daily life. In the last twenty years, many traffic flow prediction approaches have been proposed. However, some of these approaches use the regression based mechanisms, which cannot achieve accurate short-term traffic flow predication. While, other approaches use the neural network based mechanisms, which cannot work well with limited amount of training data. To this end, a light weight tensor-based traffic flow prediction approach is proposed, which can achieve efficient and accurate short-term traffic flow prediction with continuous traffic flow data in a limited period of time. In the proposed approach, first, a tensor-based traffic flow model is proposed to establish the multi-dimensional relationships for traffic flow values in continuous time intervals. Then, a CANDECOMP/PARAFAC decomposition based algorithm is employed to complete the missing values in the constructed tensor. Finally, the completed tensor can be directly used to achieve efficient and accurate traffic flow prediction. The experiments on the real dataset indicate that the proposed approach outperforms many current approaches on traffic flow prediction with limited amount of traffic flow data.

With the growing consumption of oil, the production of p-xylene(PX) from renewable biomass has gained significant attention recently. This work demonstrated that cellulose, a main component in lignocellulosic biomass, was directly converted into PX over the Mg/P surface-modified zeolites. The catalysts modified by the incorporation of P2O5 and MgO into HZSM-5(HZ) promoted the isomerization of m-/o-xylenes to p-xylene. The PX selectivity was greatly enhanced using the modified zeolites due to the deactivation of external surface and the adjustment of pore entrance. In addition, the addition of methanol to cellulose was beneficial to increase the selectivity of xylenes due to the alkylation reactions and the Diels-Alder reactions between cellulose-derived furans and methanol-derived olefins. The highest PX yield of 10.7%(molar fraction) with a high PX selectivity in xylenes(97.1%) was obtained over the 10%Mg/5%P/HZ catalyst. The reaction pathway for the formation of p-xylene was addressed according to the study of the key reactions and the characterization of catalysts.

This paper proposes a novel multicycle spectrum sensing method. The method first divides the received data samples into several segments, and then gets cyclic autocorrelation estimates from these segments, constructs a test statistic from these estimates, and finally makes an F-test to determine whether there is a primary signal or not. The method can use multiple cyclic frequencies simultaneously; hence, it is a multicycle detector. The method is also nonparametric, and thus it is robust to noise uncertainty. Compared with existing cyclic spectrum sensing methods, the proposed method can reduce computational complexity significantly at the cost of a little performance loss. Simulation results are given to show the validity and the superiority of the proposed method.