Narrow Results

Consider a convex set in R^d and a piecewise polynomial concave function . Let be an algorithm that given a point x ∈ R^d computes F(x) if , or returns a concave polynomial p such that p(x) < 0 but for any , p(y) ≥ 0. We assume that d is fixed and that all comparisons in depend on the sign of polynomial functions of the input point. We show that under these conditions, one can find in time which is polynomial in the number of arithmetic operations of . Using our method we give the first strongly polynomial algorithms for many non-linear parametric problems in fixed dimension, such as the parametric max flow problem, the parametric minimum s-t distance, the parametric spanning tree problem and other problems. We also present an efficient algorithm for a very general convex programming problem in fixed dimension.

The occurrence of botnets over the network is crucial as it shows advent effect on various applications like finance, cyber-security, and healthcare application. Botnets are refined and more dangerous in their functionality over the network model. Most of the prevailing models and flow- and rule-based models feel challenging to predict the bot functionalities in a preventive manner. Therefore, the modeling of efficient and automated botnet detection approaches is highly essential. This research concentrates on modeling a novel botnet detection approach based on the recursively analyzing the flow of features of the network nodes spatially and temporally where the attack samples are intra-dependent time-series data. The hierarchical structural design of the network helps to integrate various levels of feature information and learns the spatial and temporal information automatically among the adjacent network connection. This process is carried out by the proposed architectural model known as Recursively Learning Long Short-Term Memory over spatial and temporal (${\mathrm{\text{RL}}}^2\text{TM-st})$. Thus, the bot activities are detected by recursively analyzing the limited number of nodes. The ${\mathrm{\text{RL}}}^2\text{TM-st})$ model is modeled to improve the efficiency of the network by eliminating unnecessary activities. The proposed model is validated using the online accessible CTU-13 dataset and benchmarked against the prevailing classification approaches for botnet detection. The simulation is done in a MATLAB environment, and the outcomes work efficiently and evaluated with prevailing models to project the significance of the ${\mathrm{\text{RL}}}^2\text{TM-st}$ model.

Bogie is one of the most major mechanical part of railway train. Its security and reliability are of paramount importance. Since research in this field is still on the early stage, which focus on either mechanical structure without condition or binary coherent systems. A multistate network flow model has been proposed in this paper with consideration of components degradation level and functional interaction between them. Firstly, the structure and function of the bogie for CRH3 were made a detailed introduction. Then transmission paths of three types force on bogie were study to determine the network strcture. Different from other papers, arcs represent the components and nodes are the transitive relation. Arc capacity tends to be confirmed easily with utilization of performance deterioration of elements on bogie involved in force tranferring. Flow rate of each arc depends on both component' health status and the task it undertakes. Furthermore, the minimal paths (MPs) method and the recursive sum of disjoint products (RSDP) with ordering heuristics are used for system reliability calculation; and the relative probability importance of each basic component and system reliability with and without forehead information are given at last. The results show that the network flow model works well on CRH3 bogie, and can support as guidance of bogie system design, daily system operation and predictive maintenance.

The maximum dynamic contraflow problem in transportation networks seeks to maximize the flow from a source to a sink within a given time horizon with a possibility of arc reversals. This may result into blockage of paths of desired length from some node of the network towards the source. In some cases such as the evacuation planning, we may require a path towards the source to move some facilities, for example, emergency vehicles. In this work, we model the problem of saving such a path as a bicriteria optimization problem which minimizes the length of the path and maximizes the dynamic flow with arc reversals. We use the $𝜖$-constraint approach to solve the problem and propose a procedure that gives the set of all Pareto optimal solutions in a single-source-single-sink network with integer inputs. We also present computational performance of the algorithm on a road network of Kathmandu city, and on randomly generated networks. The results are of both theoretical and practical importance.