Modelling heterogeneity of concrete using 2D lattice network for concrete fracture and comparison with AE study

In this paper, numerical modelling of fracture in concrete using two-dimensional lattice model is presented and also a few issues related to lattice modelling technique applicable to concrete fracture are reviewed. A comparison is made with acoustic emission (AE) events with the number of fractured elements. To implement the heterogeneity of the plain concrete, two methods namely, by generating grain structure of the concrete using Fuller's distribution and the concrete material properties are randomly distributed following Gaussian distribution are used. In the first method, the modelling of the concrete at meso level is carried out following the existing methods available in literature. The shape of the aggregates present in the concrete are assumed as perfect spheres and shape of the same in two-dimensional lattice network is circular. A three-point bend (TPB) specimen is tested in the experiment under crack mouth opening displacement (CMOD) control at a rate of 0.0004 mm/sec and the fracture process in the same TPB specimen is modelled using regular triangular 2D lattice network. Load versus crack mouth opening isplacement (CMOD) plots thus obtained by using both the methods are compared with experimental results. It was observed that the number of fractured elements increases near the peak load and beyond the peak load. That is once the crack starts to propagate. AE hits also increase rapidly beyond the peak load. It is compulsory here to mention that although the lattice modelling of concrete fracture used in this present study is very similar to those already available in literature, the present work brings out certain finer details which are not available explicitly in the earlier works.

Latency, Power and Performance Trade-offs in Network-on-Chips by Link Microarchitecture Exploration

This paper presents a power, latency and throughput trade-off study on NoCs by varying microarchitectural (e.g. pipelining) and circuit level (e.g. frequency and voltage) parameters. We change pipelining depth, operating frequency and supply voltage for 3 example NoCs - 16 node 2D Torus, Tree network and Reduced 2D Torus. We use an in-house NoC exploration framework capable of topology generation and comparison using parameterized models of Routers and links developed in SystemC. The framework utilizes interconnect power and delay models from a low-level modelling tool called Intacte[1]1. We find that increased pipelining can actually reduce latency. We also find that there exists an optimal degree of pipelining which is the most energy efficient in terms of minimizing energy-delay product.

A novel neural network design for long range prediction of rainfall pattern

The importance of long-range prediction of rainfall pattern for devising and planning agricultural strategies cannot be overemphasized. However, the prediction of rainfall pattern remains a difficult problem and the desired level of accuracy has not been reached. The conventional methods for prediction of rainfall use either dynamical or statistical modelling. In this article we report the results of a new modelling technique using artificial neural networks. Artificial neural networks are especially useful where the dynamical processes and their interrelations for a given phenomenon are not known with sufficient accuracy. Since conventional neural networks were found to be unsuitable for simulating and predicting rainfall patterns, a generalized structure of a neural network was then explored and found to provide consistent prediction (hindcast) of all-India annual mean rainfall with good accuracy. Performance and consistency of this network are evaluated and compared with those of other (conventional) neural networks. It is shown that the generalized network can make consistently good prediction of annual mean rainfall. Immediate application and potential of such a prediction system are discussed.

Machine learning modelling for predicting soil liquefaction susceptibility

This study describes two machine learning techniques applied to predict liquefaction susceptibility of soil based on the standard penetration test (SPT) data from the 1999 Chi-Chi, Taiwan earthquake. The first machine learning technique which uses Artificial Neural Network (ANN) based on multi-layer perceptions (MLP) that are trained with Levenberg-Marquardt backpropagation algorithm. The second machine learning technique uses the Support Vector machine (SVM) that is firmly based on the theory of statistical learning theory, uses classification technique. ANN and SVM have been developed to predict liquefaction susceptibility using corrected SPT (N-1)(60)] and cyclic stress ratio (CSR). Further, an attempt has been made to simplify the models, requiring only the two parameters (N-1)(60) and peck ground acceleration (a(max)/g)], for the prediction of liquefaction susceptibility. The developed ANN and SVM models have also been applied to different case histories available globally. The paper also highlights the capability of the SVM over the ANN models.

Recovery from DoS Attacks in MIPv6: Modelling and Validation

Denial-of-service (DoS) attacks form a very important category of security threats that are prevalent in MIPv6 (mobile internet protocol version 6) today. Many schemes have been proposed to alleviate such threats, including one of our own [9]. However, reasoning about the correctness of such protocols is not trivial. In addition, new solutions to mitigate attacks may need to be deployed in the network on a frequent basis as and when attacks are detected, as it is practically impossible to anticipate all attacks and provide solutions in advance. This makes it necessary to validate the solutions in a timely manner before deployment in the real network. However, threshold schemes needed in group protocols make analysis complex. Model checking threshold-based group protocols that employ cryptography have not been successful so far. Here, we propose a new simulation based approach for validation using a tool called FRAMOGR that supports executable specification of group protocols that use cryptography. FRAMOGR allows one to specify attackers and track probability distributions of values or paths. We believe that infrastructure such as FRAMOGR would be required in future for validating new group based threshold protocols that may be needed for making MIPv6 more robust.

Modelling and analysis of new coolstreaming for P2P IPTV

Peer to peer networks are being used extensively nowadays for file sharing, video on demand and live streaming. For IPTV, delay deadlines are more stringent compared to file sharing. Coolstreaming was the first P2P IPTV system. In this paper, we model New Coolstreaming (newer version of Coolstreaming) via a queueing network. We use two time scale decomposition of Markov chains to compute the stationary distribution of number of peers and the expected number of substreams in the overlay which are not being received at the required rate due to parent overloading. We also characterize the end-to-end delay encountered by a video packet received by a user and originated at the server. Three factors contribute towards the delay. The first factor is the mean shortest path length between any two overlay peers in terms of overlay hops of the partnership graph which is shown to be O (log n) where n is the number of peers in the overlay. The second factor is the mean number of routers between any two overlay neighbours which is seen to be at most O (log N-I) where N-I is the number of routers in the internet. Third factor is the mean delay at a router in the internet. We provide an approximation of this mean delay E W]. Thus, the mean end to end delay in New Coolstreaming is shown to be upper bounded by O (log E N]) (log N-I) E (W)] where E N] is the mean number of peers at a channel.

Emergent properties of the interferon-signalling network may underlie the success of hepatitis C treatment

Current interferon alpha-based treatment of hepatitis C virus (HCV) infection fails to cure a sizeable fraction of patients treated. The cause of this treatment failure remains unknown. Here using mathematical modelling, we predict treatment failure to be a consequence of the emergent properties of the interferon-signalling network. HCV induces bistability in the network, creating a new steady state where it can persist. Cells that admit the new steady state alone are refractory to interferon. Using a model of viral kinetics, we show that when the fraction of cells refractory to interferon in a patient exceeds a critical value, treatment fails. Direct-acting antivirals that suppress HCV replication can eliminate the new steady state, restoring interferon sensitivity and improving treatment response. Our study thus presents a new conceptual basis of HCV persistence and treatment response, elucidates the origin of the synergy between interferon and direct-acting antivirals, and facilitates rational treatment optimization.

Social insect colony as a biological regulatory system: modelling information flow in dominance networks

Social insects provide an excellent platform to investigate flow of information in regulatory systems since their successful social organization is essentially achieved by effective information transfer through complex connectivity patterns among the colony members. Network representation of such behavioural interactions offers a powerful tool for structural as well as dynamical analysis of the underlying regulatory systems. In this paper, we focus on the dominance interaction networks in the tropical social wasp Ropalidia marginata-a species where behavioural observations indicate that such interactions are principally responsible for the transfer of information between individuals about their colony needs, resulting in a regulation of their own activities. Our research reveals that the dominance networks of R. marginata are structurally similar to a class of naturally evolved information processing networks, a fact confirmed also by the predominance of a specific substructure-the `feed-forward loop'-a key functional component in many other information transfer networks. The dynamical analysis through Boolean modelling confirms that the networks are sufficiently stable under small fluctuations and yet capable of more efficient information transfer compared to their randomized counterparts. Our results suggest the involvement of a common structural design principle in different biological regulatory systems and a possible similarity with respect to the effect of selection on the organization levels of such systems. The findings are also consistent with the hypothesis that dominance behaviour has been shaped by natural selection to co-opt the information transfer process in such social insect species, in addition to its primal function of mediation of reproductive competition in the colony.