Redroot pigweed (Amaranthus retroflexus L.) is a troublesome dicot weed species widely distributed across China. A population of A. retroflexus that survived the recommended label rate of thifensulfuron-methyl was collected from the main soybean [Glycine max (L.) Merr.] production area in China. Whole-plant dose–response assays indicated that the resistant (R) population was highly resistant (61.80-fold) to thifensulfuron-methyl compared with the susceptible (S1 and S2) populations. In vitro acetolactate synthase (ALS) activity experiments showed that the thifensulfuron-methyl I50 value for the R population was 40.17 times higher than that for the S1 population. A preliminary malathion treatment study indicated that the R population might have cytochrome P450–mediated metabolic resistance. The R population exhibited a high level of cross-resistance to representative ALS herbicides (imazethapyr, flumetsulam, and bispyribac-sodium) and multiple resistance to the commonly used protoporphyrinogen oxidase (PPO)-inhibiting herbicides lactofen and fomesafen. Two common mutations, Trp-574-Leu in ALS and Arg-128-Gly in PPO2, were identified within the R population. This study identified possible enhanced metabolism of thifensulfuron-methyl coexisting with target-site mutations in both ALS and PPO2 in a multiple-resistant A. retroflexus population.

In laser-pointing-related applications, when only the centroid of a laser spot is considered, then the position and angular errors of the laser beam are often coupled together. In this study, the decoupling of the position and angular errors is achieved from one single spot image by utilizing a neural network technique. In particular, the successful application of the neural network technique relies on novel experimental procedures, including using an appropriate small-focal-length lens and tilting the detector, to physically enlarge the contrast of different spots. This technique, with the corresponding new system design, may prove to be instructive in the future design of laser-pointing-related systems.

The safety of maritime transportation has become increasingly important in recent decades. In this paper, a decision model (a multi-objective and multi-layer fuzzy optimisation model) for navigation risk in different sea areas is established. This is done according to the evaluation index system based on relative data extracted and analysed from Automatic Identification Systems (AIS) information and the multi-objective and multi-layer fuzzy optimisation theory. Then, sorted by an optimal relative membership degree vector and calculated from lower layer to higher layer, the sea areas which have higher navigation risk are selected. Finally, the decision model is shown to be scientific and practical since the results from it are basically consistent with real traffic in Chengshantou waters and the results from the fuzzy comprehensive evaluation model. With the decision model, navigation risk judgments in different sea areas can be offered. It can also provide decision making references to the design of ship routing systems, the layout of search and rescue sites, the configuration of rescue forces and the administration of navigation safety.