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Since bus prioritization policies can help mitigate urban traffic jams, the planning of bus lanes has drawn considerable attention. Existing methods suffer from a common limitation, which is that the limited spatial adaptability resulting from certain road condition information cannot be directly specified. Many bus GPS trajectories have been accumulated and can be contiguously gathered if needed. This paper proposes a trajectory-based bus lane planning method. First, we formulize the bus lane planning problem as a multiobjective optimization problem in which the road conditions, traffic flow, connectivity of bus lanes, and construction cost are organized as four constraints, and road utilization and bus punctuality are modeled as two objectives. Then, an evolutionary algorithm-based method is presented to solve the problem. We tested the model in the Nanshan District, Shenzhen City, China. Through a comparison with existing survey-based methods, the parameters associated with road conditions in this method are directly extracted from GPS trajectories, and this method is more effectively deployed than other methods. Since GPS trajectories can cover a wide area if needed, and because the proposed method can be effectively executed, this method can be adapted to large urban scales. [ABSTRACT FROM AUTHOR]

Creating appropriate colour schemes is challenging for both novice and experienced cartographers. This paper introduces an artificial bee colony (ABC) algorithm to automatically create various colour schemes. Colour scheme creation is treated as a constrained search problem in a continuous colour space. We considered the gamut of the target device and a series of cartographic rules, such as convention, discrimination, contrast, perceptual uniformity and brightness mirror, in the ABC algorithm and presented detailed initialization, fitness definition, local exploration, and global exploration methods for creating qualitative, sequential and diverging colour schemes. The proposed method is evaluated with a case study, and the results indicate that compared with the brute force search method, the proposed method can create satisfying colour schemes of similar quality but significantly improved efficiency. [ABSTRACT FROM AUTHOR]

Anoplophora glabripennis (Motschulsky) has an advanced and complicated olfactory system to identify hosts, mates and spawning locations, and odorant-binding proteins (OBPs) play a key role by binding to volatile materials from different hosts. The full-length cDNA sequence of an OBP, AglaOBP, was cloned by RACE from an antenna cDNA library, and the protein structure and function were predicted by bioinformatics analysis. Gene temporal and spatial expression was detected by real-time qPCR. AglaOBP had distinctive sequence, location and expression profiles compared with other OBPs of A. glabripennis, as it was found in different tissues, and the highest expression was in the elytrums. The possible physiological functions of this OBP were discussed. These findings help elucidate the physiology of this pest and provide a new potential target for pest control. [ABSTRACT FROM AUTHOR]