The releasing of transgenic soybeans (Glycine max (L.) Merr.) into farming systems raises concerns that transgenes might escape from the soybeans via pollen into their endemic wild relatives, the wild soybean (Glycine soja Sieb. et Zucc.). The fitness of F1 hybrids obtained from 10 wild soybean populations collected from China and transgenic glyphosate-resistant soybean was measured without weed competition, as well as one JLBC-1 F1 hybrid under weed competition. All crossed seeds emerged at a lower rate from 13.33–63.33%. Compared with those of their wild progenitors, most F1 hybrids were shorter, smaller, and with decreased aboveground dry biomass, pod number, and 100-seed weight. All F1 hybrids had lower pollen viability and filled seeds per plant. Finally, the composite fitness of nine F1 hybrids was significantly lower. One exceptional F1 hybrid was IMBT F1, in which the composite fitness was 1.28, which was similar to that of its wild progenitor due to the similarities in pod number, increased aboveground dry biomass, and 100-seed weight. Under weed competition, plant height, aboveground dry biomass, pod number per plant, filled seed number per plant, and 100-seed weight of JLBC-1 F1 were lower than those of the wild progenitor JLBC-1. JLBC-1 F1 hybrids produced 60 filled seeds per plant. Therefore, F1 hybrids could emerge and produce offspring. Thus, effective measures should be taken to prevent gene flow from transgenic soybean to wild soybean to avoid the production F1 hybrids when releasing transgenic soybean in fields in the future.

For the non-linear function extremum optimization, this paper draws on the ideology of mutation in genetic algorithm and introduces the mutation operation in the standard particle swarm algorithm to increase the possibility of the algorithm to search the optimal value; the LDWPSO linearly decreasing weight particle swarm optimization) is adopted to balance the global search and local search ability of the algorithm. By the optimization test for the multi-peak function, the improved algorithm is compared with the standard particle swarm optimization, which demonstrates that the former one owns better global optimization ability and higher convergence rate.

Aiming at the load requirements, positioning accuracy and workflow of building panels installed robot, developed a 6 DOF of a series-parallel hybrid structure installed robot. According to the structural features of parallel mechanism to the installation manipulator, designed a robot attitude sensing system based on the inclination and laser ranging sensor information feedback; according to the characteristics of the building environment, proposed the position detection method of panels to be installed based on the structured light vision; constructed the control system of the installation manipulator, and used teaching mode to achieve robot control and system calibration. The experimental data prove that, the control and sensing systems of panels installed robot, overcome the complexity of the building environment and the diversity of installation object, and can achieve the automated installation of building panels.

Chaos is a non-linear phenomenon that widely exists in the nature. Due to the ease of implementation and its special ability to avoid being trapped in local optima, chaos has been a novel optimization technique and chaos-based searching algorithms have aroused intense interests. Many real world optimization problems are dynamic in which global optimum and local optima change over time. Particle swarm optimization has performed well to find and track optima in static environments. When the particle swarm optimization (PSO) algorithm is used in dynamic multi-objective problems, there exist some problems, such as easily falling into prematurely, having slow convergence rate and so on. To solve above problems, a hybrid PSO algorithm based on chaos algorithm is brought forward. The hybrid PSO algorithm not only has the efficient parallelism but also increases the diversity of population because of the chaos algorithm. The simulation result shows that the new algorithm is prior to traditional PSO algorithm, having stronger adaptability and convergence, solving better the question on moving peaks benchmark.