Distributed Cooperative Jamming with Neighborhood Selection Strategy for Unmanned Aerial Vehicle Swarms

In system science, a swarm possesses certain characteristics which the isolated parts and the sum do not have. In order to explore emergence mechanism of a large–scale electromagnetic agents (EAs), a neighborhood selection (NS) strategy–based electromagnetic agent cellular automata (EA–CA) model is proposed in this paper. The model describes the process of agent state transition, in which a neighbor with the smallest state difference in each sector area is selected for state transition. Meanwhile, the evolution rules of the traditional CA are improved, and performance of different evolution strategies are compared. An application scenario in which the emergence of multi–jammers suppresses the radar radiation source is designed to demonstrate the effect of the EA–CA model. Experimental results show that the convergence speed of NS strategy is better than those of the traditional CA evolution rules, and the system achieves effective jamming with the target after emergence. It verifies the effectiveness and prospects of the proposed model in the application of electronic countermeasures (ECM).

Computing Different Realizations of Linear Dynamical Systems with Embedding Eigenvalue Assignment

In this paper we investigate realizability of discrete time linear dynamical systems (LDSs) in fixed state space dimension. We examine whether there exist different Θ = (A,B,C,D) state space realizations of a given Markov parameter sequence Y with fixed B, C and D state space realization matrices. Full observation is assumed in terms of the invertibility of output mapping matrix C. We prove that the set of feasible state transition matrices associated to a Markov parameter sequence Y is convex, provided that the state space realization matrices B, C and D are known and fixed. Under the same conditions we also show that the set of feasible Metzler-type state transition matrices forms a convex subset. Regarding the set of Metzler-type state transition matrices we prove the existence of a structurally unique realization having maximal number of non-zero off-diagonal entries. Using an eigenvalue assignment procedure we propose linear programming based algorithms capable of computing different state space realizations. By using the convexity of the feasible set of Metzler-type state transition matrices and results from the theory of non-negative polynomial systems, we provide algorithms to determine structurally different realization. Computational examples are provided to illustrate structural non-uniqueness of network-based LDSs.

Artificial Electric Field Algorithm with Greedy State Transition Strategy for Spherical Multiple Traveling Salesmen Problem

The multiple traveling salesman problem (MTSP) is an extension of the traveling salesman problem (TSP). It is found that the MTSP problem on a three-dimensional sphere has more research value. In a spherical space, each city is located on the surface of the Earth. To solve this problem, an integer-serialized coding and decoding scheme was adopted, and artificial electric field algorithm (AEFA) was mixed with greedy strategy and state transition strategy, and an artificial electric field algorithm based on greedy state transition strategy (GSTAEFA) was proposed. Greedy state transition strategy provides state transition interference for AEFA, increases the diversity of population, and effectively improves the accuracy of the algorithm. Finally, we test the performance of GSTAEFA by optimizing examples with different numbers of cities. Experimental results show that GSTAEFA has better performance in solving SMTSP problems than other swarm intelligence algorithms.

Interplay between LHCSR proteins and state transitions governs the NPQ response in intact cells of Chlamydomonas during light fluctuations.

Photosynthetic organisms use sunlight as the primary energy source to fix CO2. However, in the environment, light energy fluctuates rapidly and often exceeds saturating levels for periods ranging from seconds to hours, which can lead to detrimental effects for cells. Safe dissipation of excess light energy occurs primarily by non-photochemical quenching (NPQ) processes. In the model green microalga Chlamydomonas reinhardtii, photoprotective NPQ is mostly mediated by pH-sensing light-harvesting complex stress-related (LHCSR) proteins and the redistribution of light-harvesting antenna proteins between the photosystems (state transition). Although each component underlying NPQ has been documented, their relative contributions to the dynamic functioning of NPQ under fluctuating light conditions remains unknown. Here, by monitoring NPQ throughout multiple high light-dark cycles with fluctuation periods ranging from 1 to 10 minutes, we show that the dynamics of NPQ depend on the frequency of light fluctuations. Mutants impaired in the accumulation of LHCSRs (npq4, lhcsr1, and npq4lhcsr1) showed significantly less quenching during illumination, demonstrating that LHCSR proteins are responsible for the majority of NPQ during repetitive exposure to high light fluctuations. Activation of NPQ was also observed during the dark phases of light fluctuations, and this was exacerbated in mutants lacking LHCSRs. By analyzing 77K chlorophyll fluorescence spectra and chlorophyll fluorescence lifetimes and yields in a mutant impaired in state transition, we show that this phenomenon arises from state transition. Finally, we quantified the contributions of LHCSRs and state transition to the overall NPQ amplitude and dynamics for all light periods tested and compared those with cell growth under various periods of fluctuating light. These results highlight the dynamic functioning of photoprotection under light fluctuations and open a new way to systematically characterize the photosynthetic response to an ever-changing light environment.

Evidence for surface effects on the intermolecular interactions in Fe(ii) spin crossover coordination polymers

From X-ray absorption spectroscopy (XAS) and X-ray photoemission spectroscopy (XPS) it is evident that the spin state transition behavior of Fe(ii) spin crossover coordination polymer crystallites at the surface differs from the bulk.

From Retrospective to Prospective: Integrated Value Representation in Frontal Cortex for Predictive Choice Behavior

Animals must flexibly estimate the value of their actions to successfully adapt in a changing environment. The brain is thought to estimate action-value from two different sources, namely the action-outcome history (retrospective value) and the knowledge of the environment (prospective value). How these two different estimates of action-value are reconciled to make a choice is not well understood. Here we show that as a mouse learns the state-transition structure of a decision-making task, retrospective and prospective values become jointly encoded in the preparatory activity of neurons in the frontal cortex. Suppressing this preparatory activity in expert mice returned their behavior to a naive state. These results reveal the neural circuit that integrates knowledge about the past and future to support predictive decision-making.