In this study, ethylene glycol diglycidyl ether (EGDE) and melamine (ME) were used to prepare a spherical composite material CMC-LTO-EGDE-ME through the strategy of cross-linking reaction of biodegradable sodium carboxymethyl cellulose (CMC) with Li2TiO3 (LTO) to improve the cycle stability of the adsorption-desorption for lithium recovery. In the geothermal water at 333.15°K, the adsorption capacity of the spherical composite adsorbent for lithium-ion is 12.02 mg/g, and the adsorption equilibrium time is 8 h. There is a good selectivity of Li+ (Kd = 3.7 × 103) and high separation factors between Li+ and Na+, K+, Cs+ and Ca2+ (between 39.17 and 181.97). Studies on adsorption kinetics and adsorption isotherm showed that composite material’s adsorption process was obtained from the pseudosecond-order kinetics and the molecular diffusion model. It was found that the composite material has broad applications in lithium recovery from geothermal water.
An effective strategy for accelerating the calculation of convex hulls is to filter the input points by discarding interior points. In this paper, we present such a straightforward preprocessing approach by discarding the points locating in a convex polygon formed by 16 extreme points.Extreme points of a planar point set do not alter when all points are rotated with the same angle in the plane. Four groups of four extreme points with min or max x or y coordinates can be found for the original point set and three rotated point sets. These 16 extreme points are used to form a planar convex polygon. We discard those points locating in the convex polygon and calculate the desired convex hull of the remaining points. The proposed preprocessing algorithm is evaluated on two computational platforms. Experiments show that, when employing the proposed preprocessing algorithm on the computational platform 1, it achieves speedups of approximately 4 × ∼ 5× on average and 5 × ∼ 6× in the best cases over the cases where the proposed approach is not used, while on the computational platform 2, the speedups are approximately 6 × ∼ 9× on average and 9 × ∼ 14× in the best cases. Moreover, more than 99% input points can be discarded in most cases.
Traffic congestion is becoming a critical problem in urban traffic planning. Intelligent transportation systems can help expand the capacity of urban roads to alleviate traffic congestion. As a key concept in intelligent transportation systems, urban traffic networks, especially dynamic traffic networks, can serve as potential solutions for traffic congestion, based on the complex network theory. In this paper, we build a traffic flow network model to investigate traffic congestion problems through taxi GPS trajectories. Moreover, to verify the effectiveness of the traffic flow network, an actual case of identifying the congestion areas is considered. The results indicate that the traffic flow network is reliable. Finally, several key problems related to traffic flow networks are discussed. The proposed traffic flow network can provide a methodological reference for traffic planning, especially to solve traffic congestion problems.
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