A forest-fire model for technology risk transfer

Based on the transmission characteristics of networks and the systematization of technical risks in national major science and technology projects, the risk transmission mechanism of national major science and technology projects is deeply discussed herein. Firstly, the system of systems (SoS) engineering process model of national major science and technology projects is constructed, including two levels and three stages. Secondly, the hierarchical structure of national major science and technology projects is analyzed, and the risks are divided into different levels, such as SoS, system, subsystem, equipment, module, and components. Finally, we describe the risk transfer mechanism using the forest-fire model and outline a risk control strategy. The results show that the findings are helpful for recognizing the essence and transmission mechanism of technical risks of national major science and technology projects and for reducing the risk from the source. The research results can be applied to project management in the transportation field.

Forest Fire Simulation

In this chapter a heuristic forest fire model based on cellular automata is presented and realized for efficiency reasons with the DataFlow programming approach. Real-world images taken by satellites are analyzed and used as the basis for simulations. In the presented forest fire model, natural influences like wind strength and direction, burning behavior, as well as different levels of inflammability are considered. The DataFlow implementation on an FPGA-based Maxeler MAX3 Vectis card was compared to a sequential C version executed on an Intel Xeon E5-2650 2.0 GHz CPU. The author obtained speedups of up to 70 for a strong wind situation and 46 for a random wind setting while reducing energy consumption.

Drivers and distribution of forest fires in Sikkim Himalaya: A maximum entropy-based approach to spatial modelling.

The recent episodes of forest fire in Brazil and Australia of 2019 are tragic reminders of the hazards of the forest fire. Globally incidents of forest fire events are in the rise due to human encroachment into wilderness and climate change. Sikkim with a forest cover of more than 47%, suffers seasonal instances of frequent forest fire during the dry winter months. To address this issue, a GIS-aided and MaxEnt machine learning-based forest fire prediction map has been prepared using forest fire inventory database and maps of environmental features. The study indicates that amongst the environmental features, climatic conditions and proximity to roads are the major determinants of the forest fire. Model validation criteria like ROC curve, correlation coefficient and Cohen’s Kappa show a good predictive capability (AUC = 0.95, COR = 0.78, κ = 0.78). The outcomes of this study in the form of a forest fire prediction map can aid the stakeholders of the forest in taking informed mitigation measures.

A New Evolution Model for Weighted Directed Networks

The most of the recent models of directed weighted network evolution capture the growth process based on two conventional assumptions: constant average degree assumption and slowly growing diameter assumption. Such evolution models cannot fully support and reflect the dense power law and diameter shrinkage in the process of evolution of real networks. In this paper, a new evolution model, called BBVd, is proposed for directed weighted networks by extending BBV model with the idea of the Forest Fire model. In BBVd, new directed edges are established with probabilities computed based on in/our-strength of nodes, with dynamical evolution of weights for local directed edges. The experimental result shows that the generated networks using BBVd display power-law behavior for the node strength distributions, and moreover, it satisfies the densification power laws and has shrinking diameter.