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Biological networks are open systems that can utilize nutrients and energy from their environment for use in their metabolic processes, and produce metabolic products. System entropy is defined as the difference between input and output signal entropy, i.e., the net signal entropy of the biological system. System entropy is an important indicator for living or non-living biological systems, as biological systems can maintain or decrease their system entropy. In this study, system entropy is determined for the first time for stochastic biological networks, and a computation method is proposed to measure the system entropy of nonlinear stochastic biological networks that are subject to intrinsic random fluctuations and environmental disturbances. We find that intrinsic random fluctuations could increase the system entropy, and that the system entropy is inversely proportional to the robustness and stability of the biological networks. It is also determined that adding feedback loops to shift all eigenvalues to the farther left-hand plane of the complex s-domain could decrease the system entropy of a biological network. [ABSTRACT FROM AUTHOR]

To solve collaborative slot allocation with airport probability capacity, a stochastic collaborative slot allocation strategy was proposed. According to the form of airport capacity being updated, both static model and dynamic model were proposed. With the aim to minimize the expected total flight delay cost and expected average delay time of passengers, the principles of efficiency and equity were respectively established. On the basis of the constraints of effectiveness, slots were allocated to arrival flights. Then, multi-objective genetic algorithm NSGA-II was applied to solving the models. To test how well the models work in real world, a simulation test was performed based on the operation data of an airport. Test results show that, compared with typical models for stochastic ground delay policy, the models proposed could efficiently, equitably and effectively allocate slot with airport probability capacity. [ABSTRACT FROM AUTHOR]

Fault Current Indicators (FCIs) with communication interfaces have been widely used in distribution systems to reduce fault-finding time. The effectiveness of a Fault Management System (FMS) composed of FCIs greatly depends on the performance of the communication network deployed by the FCIs and the failure rates of distribution systems. The conventional techniques only focus on the issues of optimal number and location of FCIs or communication network deployment individually; therefore, the effectiveness of an FMS cannot be assessed realistically. A systematic effectiveness assessment methodology for FMS considering the performance of the communication network deployed by the FCIs and the failure rates of distribution systems is vital and is investigated in this paper. A communication evaluation platform is designed in this paper and used to acquire the field measurements of communication parameters. The communication parameters, especially the Packet Success Rate (PSR), between two adjacent FCIs are measured, and the Probability Density Function (PDF) of the PSR can be built accordingly. The effectiveness of the FMS is then assessed by stochastic analysis considering the failure rates of the distribution system and PSR PDFs between two adjacent FCIs. Due to the characteristics of easy installation, maintenance, longer battery life, lower cost, and so on of ZigBee, the ZigBee-based FCI is mainly discussed in this paper. In order to efficiently find the communication route when a fault occurs, a fast communication route tracking method is also proposed in this paper and its feasibility is demonstrated in an actual distribution system. Experimental and simulation results demonstrate the validity of the proposed systematic effectiveness assessment methodology for an FMS composed of FCIs. The proposed assessment methodology can more realistically react to the actual conditions of the FMS and therefore save on installation time and costs. [ABSTRACT FROM AUTHOR]