Two-Layer Optimization Algorithm for Multi-UAV Conflict Resolution considering Individual Fairness

In order to solve the unfair individual payment costs problem in the low-altitude unmanned aerial vehicle (UAV) conflict resolution process, a multi-UAV conflict resolution algorithm based on the cooperative game concept “coalition complaint value” is proposed. Firstly, based on the low-altitude multi-UAV conflict scene characteristics, according to the “coalition complaint value” concept, the UAV conflict resolution payment matrix is established. Secondly, combined with the advantages of the artificial potential field (APF) method and the genetic algorithm (GA), a hybrid solution strategy for conflict resolution based on APF-GA is proposed. The final simulation results show that the APF-GA hybrid solution strategy has the best efficiency by combining the three evaluation indicators of calculation time, feasibility, and system efficiency. The reliability of the proposed algorithm is verified based on the Monte Carlo algorithm. The solution strategy based on the cooperative game “coalition complaint value” can improve individual fairness to a certain extent. At the same time, it can achieve the rapid planning goal with priority drones at the expense of a small amount of overall benefits.

Design Space Description through Adaptive Sampling and Symbolic Computation

In this paper, we propose a novel solution strategy to explicitly describe the design space in which no recourse is considered for the realization of the parameters. First, to smooth the boundary of the design space, the Kreisselmeier-Steinhauser (KS) function is applied to aggregate all inequality constraints, and project them into the design space. Next, for creating a surrogate polynomial model of the KS function, we focus on finding the sampling points on the boundary of KS space. After testing the feasibility of Latin hypercube sampling points, two methods are presented to efficiently extend the set of boundary points. Finally, a symbolic computation method, cylindrical algebraic decomposition, is applied to transform the surrogate model into a series of explicit and triangular subsystems that can be further converted to describe the KS space. Two case studies are considered to show the efficiency of the proposed algorithm.

"Part of the Solution:" Food Corporation Strategies for Regulatory Capture and Legitimacy

Background: For decades, the food industry has sought to deflect criticisms of its products and block public health legislation through a range of offensive and defensive strategies. More recently, food corporations have moved on to present themselves as "part of the solution" to the health problems their products cause. This strategic approach is characterised by appeasement, co-option and partnership, and involves incremental concessions and attempts to partner with health actors. This paper details how corporate practices have evolved and changed over the past two decades and gives some definition to what this new political economy signifies for the wider behaviours of corporations producing and selling harmful commodities. Methods: This paper draws on public health and political science literature to classify the food industry’s "part of the solution" strategy into three broad components: regulatory responses and capture; relationship building; and market strategies. We detail the key characteristics and consequences of each component. Results: The three components of the food industry’s "part of the solution" strategy all involve elements of appeasement and co-option. They also improve the political environment and resources of the food industry. Regulatory responses offer incremental concessions that seek to maintain corporate influence over governance processes and minimise the threat of regulations; relationship building fosters access to health and government stakeholders, and opportunities to acquire and maintain channels of direct influence; and market strategies to make products and portfolios healthier bolster the market share and revenue of food corporations while improving their public image. Conclusion: Rather being a signal of lost position and power, the food industry’s repositioning as "part of the solution" has created a highly profitable political economy of ‘healthy’ food production, alongside continued production of unhealthy commodities, a strategy in which it is also less burdensome and conflictual for corporations to exercise political power and influence.

An Adaptive Sequential Fully Implicit Domain-Decomposition Solver

Summary Modern reservoir simulation must handle complex compositional fluid behavior, orders-of-magnitude variations in rock properties, and large velocity contrasts. We investigate how one can use nonlinear domain-decomposition preconditioning to combine sequential and fully implicit (FI) solution strategies to devise robust and highly efficient nonlinear solvers. A full simulation model can be split into smaller subdomains that each can be solved independently, treating variables in all other subdomains as fixed. In subdomains with weaker coupling between flow and transport, we use a sequential fully implicit (SFI) solution strategy, whereas regions with stronger coupling are solved with an FI method. Convergence to the FI solution is ensured by a global update that efficiently resolves long-range interactions across subdomains. The result is a solution strategy that combines the efficiency of SFI and its ability to use specialized solvers for flow and transport with the robustness and correctness of FI. We demonstrate the efficacy of the proposed method through a range of test cases, including both contrived setups to test nonlinear solver performance and realistic field models with complex geology and fluid physics. For each case, we compare the results with those obtained using standard FI and SFI solvers. This paper is published as part of the 2021 Reservoir Simulation Conference Special Issue.

An Adaptive Weighted Phase Optimization Algorithm Based on the Sigmoid Model for Distributed Scatterers

Distributed scatterers (DSs) have been widely used in the time series interferometric synthetic aperture radar technique, which compensates for the insufficient density of persistent scatterers (PSs) in nonurban areas. In contrast to PS, DS is vulnerable to temporal and geometric decorrelation effects. Thus, phase optimization processing for DS is essential for reliable deformation parameter estimation. Advanced research has revealed that the application of all possible interferometric pairs will be more conducive to the reduction in phase biases. However, the low-coherence pixels will inevitably increase the difficulty of phase optimization and introduce unpredictable negative effects, which will reduce the effect of phase optimization. Therefore, this study proposed an advanced adaptive weighted phase optimization algorithm (AWPOA). In the AWPOA, the adaptive weighting strategy based on the sigmoid model was first proposed to assign more reasonable weights to pixels of different quality, which can efficiently reduce the negative influence of low-coherence pixels and improve the optimization performance. Moreover, coherence bias correction based on the second-kind statistics and an efficient solution strategy based on eigenvalue decomposition were derived and applied to achieve optimal phase series retrieval. The experimental results validated against both simulated and two sets of TerraSAR-X data demonstrated the overall superiority of the AWPOA over traditional phase optimization algorithms (POAs). Specifically, the processing efficiency of the eigenvalue decomposition solution strategy used in AWPOA was nearly 20 times faster than that of the PTA iterative solution strategy under the case without bias correction. Although bias correction increased the processing time, the optimization effect was significantly improved. Moreover, in terms of the quantitative evaluation indexes with the residual and the sum of the phase difference, the mean value of the improvement percentage of the AWPOA was increased by more than 12%, and the standard deviation was reduced by more than 1% over the traditional POAs, indicating its superior optimization performance and noise robustness.