In order to analyze the effect of the correlation between the last-mile delivery and time slot existing in the customer choice procedure of urban distribution service on the operational decisions, such as reception box locating, time slot allocating and vehicle routing, a nested Logit model was used to quantify the customer's choice of delivery service options, and a two-tier nested Logit selection model for urban delivery was proposed. Then a multi-objective optimization model integrated with reception box locating, time slot allocating and vehicle routing was constructed in the purpose of maximizing the delivery amount and minimizing the delivery cost. At last, a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm was constructed to solve this model based on non-dominance sorting, adaptive grid and crowding distance sorting. The analysis shows that, as the attended-home-delivery independence parameter is gradually increased, the substitution of customer demand in different time slots is smaller, the optimal solutions tend to improve the delivery punctuality with increasing the delivery amount, whether it is to minimize the cost or to maximize the number of delivery; on the contrary, with the increase of the reception-box independence parameter, the optimal solutions will decrease the delivery punctuality with reducing the delivery amount.

In order to alleviate the problem of inaccurate noise identifying and blurred restoration in image edges and details, a novel algorithm based on weighted Spatial Local Outlier Measure (SLOM) was proposed for removing random-valued impulse noise, namely WSLOM-EPR. Based on optimized spatial distance difference, the mean and standard deviation of neighborhood were introduced to set up a noise detection method for reflecting local characters in image edges, which could improve the precision of noise identification in edges. According to the precision detection results, the Edge-Preserving Regularization (EPR) function was optimized to improve the computation efficiency and preserving capability of edges and details. The simulation results showed that, with 40% to 60% noisy level, the overall performance in noise points detection was better than that of the contrast detection algorithms, which can maintain a good balance in false detection and miss detection of noise. The Peak Signal-to-Noise Ratios (PSNR) of WSLOM-EPR was better than that of the most of the contrast algorithms, and the restoring image had clear and continuous edges. Experimental results show that WSLOM-EPR can improve detection precision and preserve more edges and details information.

The newly developed botnet defense technologies pose a severe challenge to botnet survivability. In order to improve the survivability of the botnet, from an attackers perspective, this article proposed a new anti-strike mechanism based on regroup, which was suitable for semi-distributed botnet. In the case that semi-distributed botnet suffered a severe blow, which caused topology broken, this mechanism could perceive the state of botnet, detect survival nodes, recover survival node and reassemble them into a new botnet. The experiments verify the effectiveness of the mechanism to effectively enhance the survivability of the semi-distributed botnet.

At present there are many kinds of technologies which can track, detect and counter botnet effectively, which are serious threats to botnet. In order to improve the survivability of botnets, with the analysis on the existing anti-botnet technology, the paper proposd a new P2P-botnet based on Kademlia protocol from an attacker's prospective. A communication encryption and node authentication mechanism was designed. The theoretical analysis shows that the mechanism can effectively address improper command attack and sybil attack. Eventually, the experimental results verify that this botnet has high survivability.