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As an emerging class of spatial trajectory data, mobile user trajectory data can be used to analyze individual or group behavioral characteristics, hobbies and interests. Besides, the information extracted from original trajectory data is widely used in smart cities, transportation planning, and anti-terrorism maintenance. In order to identify the important locations of the target user from his trajectory data, a novel division method for preprocessing trajectory data is proposed, the feature points of original trajectory are extracted according to the change of trajectory structural, and then important locations are extracted by clustering the feature points, using an improved density peak clustering algorithm. Finally, in order to predict next location of mobile users, a multi-order fusion Markov model based on the Adaboost algorithm is proposed, the model order k is adaptively determined, and the weight coefficients of the 1~k-order models are given by the Adaboost algorithm according to the importance of various order models, a multi-order fusion Markov model is generated to predict next important location of the user. The experimental results on the real user trajectory dataset Geo-life show that the prediction performance of Adaboost-Markov model is better than the multi-order fusion Markov model with equal coefficient, and the universality and prediction performance of Adaboost-Markov model is better than the first to third order Markov models.

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Logistics service quality (LSQ) is one of the key influential factors in the success of an ecommerce business. In view of the complexity of the topic, this paper proposes a novel model for fresh ecommerce cold chain LSQ evaluation based on the catastrophe progression method. In the proposed methodology, first an index system for evaluating the fresh ecommerce cold chain LSQ is established from the perspective of service recipients. Then, the comprehensive weight of each evaluation index is determined using a combination weighting approach based on maximizing deviations and fuzzy set theory. The priority weights and the ranking of the indices are determined using the catastrophe progression method. Finally, the model is applied in a case study of two representative enterprises. The study demonstrates the validity and practical applicability of the proposed model. Also, based on the evaluation results and findings, some improvement suggestions are made for improving the cold chain LSQ of similar kinds of fresh ecommerce companies.

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In recent years, "Big Data" has attracted increasing attention. It has already proved its importance and value in several areas, such as aerospace research, biomedicine, and so on. In "Big Data" era, financial work which is dominated by transaction, business record, business accounting and predictions may spring to life. This paper makes an analysis about what change that "Big Data" brings to Accounting Data Processing, Comprehensive Budget Management, and Management Accounting through affecting the idea, function, mode, and method of financial management. Then the paper states the challenges that "Big Data" brings to enterprise aiming to illustrate that only through fostering strengths and circumventing weaknesses can an enterprise remain invincible in "Big Data" era.

Pesticide residue in mushrooms is less known. In this study, the risks of beta-cypermethrin, pyriproxyfen, avermectin, and diflubenzuron in oyster and shiitake mushrooms were evaluated using two different treatments: substrate mixture and surface spraying. Almost all the concentrations of these pesticides at day 90 were higher than 80% of the initial concentrations, while it was less than 45% for all cases within 35 days by spraying. For surface spraying, the residues of beta-cypermethrin were 0.0843-1.22 mg kg in shiitake mushrooms and below 0.005 mg kg in oyster mushrooms; the residues of pyriproxyfen, avermectin, and diflubenzuron were 0.122-4.84, 0.00501-0.111, and 0.0681-1.91 mg kg, respectively. The residues of beta-cypermethrin, pyriproxyfen, and diflubenzuron in oyster mushrooms (in shiitake mushrooms) at interval of 0, 3, 5 days (1, 5, 7 days) were below their MRLs in China or Japan. The residue of avermectin in both mushrooms was lower than its limit of detection. These results provide information to safe and proper use of the pesticides in oyster and shiitake mushrooms.