Purpose -To use distinct element simulation (PFC2D) to investigate the relationships between microparameters and macroproperties of the specimens that are modeled by bonded particles. To determine quantitative relationships between particle level parameters and mechanical properties of the specimens. Design/methodology/approach -A combined theoretical and numerical approach is used to achieve the objectives. First, theoretical formulations are proposed for the relationships between microparameters and macroproperties. Then numerical simulations are conducted to quantify the relationships. Findings -The Young's modulus is mainly determined by particle contact modulus and affected by particle stiffness ratio and slightly affected by particle size. The Poisson's ratio is mainly determined by particle stiffness ratio and slightly affected by particle size. The compressive strength can be scaled by either the bond shear strength or the bond normal strength depending on the ratio of the two quantities.Research limitations/implications -The quantitative relationships between microparameters and macroproperties for parallel-bonded PFC2D specimens are empirical in nature. Some modifications may be needed to model a specific material. The effects of the particle distribution and bond strength distribution of a PFC2D specimen are very important aspects that deserve further investigation. Practical implications -The results will provide guidance for people who use distinct element method, especially the PFC2D, to model brittle materials such as rocks and ceramics. Originality/value -This paper offers some new quantitative relationships between microparameters and macroproperties of a synthetic specimen created using bonded particle model.

We present TLR, a traffic-light-based intelligent routing strategy for NGEO satellite IP networks. In TLR, a set of traffic lights are used to indicate the congestion status at both the current node and the next node. When a packet travels along a pre-calculated route to the destination, it may adjust the route dynamically, according to the real-time color of traffic lights at each intermediate node. Through the combination of preliminary planning and real-time adjustment, each packet can eventually get an approximately optimal transmission path. The multi-path routing mechanism in TLR can help achieve a good distribution of traffics when the network traffic increases. The Public Waiting Queue scheme in TLR can fully utilize free spaces of the buffer queues and lower the packet drop rate.While the concept of TLR has many advantages, it may result in endless-loop of routing. To eliminate this phenomenon, a defense scheme is incorporated in the design of TLR. A set of simulations are conducted using the Network Simulator (version 2) to verify the good performance of TLR, in terms of lower packet drop rate, better distribution of traffics and higher throughput, over the entire satellite constellation.Index Terms-NGEO satellite network, traffic light, intelligent routing, packet drop rate, load balance.

In this paper, the authors show that structured social media data can act as an accurate predictor for wireless data demand patterns at a high spatial-temporal resolution. A casestudy is performed on Greater London covering a 5000km 2 area. The data used includes over 0.6 million geo-tagged Twitter data, over 1 million mobile phone data demand records, and UK census data. The analysis shows that social media activity (Tweets/s n) can accurately predict the long-term traffic demand for both the uplink and downlink channels. The relationship between social media activity and traffic demand obeys a power law and the model explains for over 71-79% of the variance in real traffic demand. This is a significant improvement over existing methods of long-term traffic prediction such as census population data (R 2 =0.57). The authors also show that social media data can also forward predict short-term traffic demand for up to 2 hours on the same day and for the same time in the following 2-3 days.