We compared the volatiles of JA-treated plants of six rice varieties and then determined, in the laboratory and field, if they differed in attractiveness to Anagrus nilaparavate Pand et Wang, an egg parasitoid of rice planthoppers. Analyses of volatiles revealed significant differences among varieties, both in total quantity and quality of the blends emitted. On the basis of these differences, the six varieties could be roughly divided into three groups. In a Y-tube olfactometer, female wasps preferred odors from two groups. These preferences corresponded to observed parasitism rates in a field experiment. A comparison of the volatiles with results from behavioral assays and field experiments indicates that the quality (composition) of the blends is more important for attraction than the total amount emitted. The results imply that the foraging success of natural enemies of pests can be enhanced by breeding for crop varieties that release specific volatiles.

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Based on the ESPRIT-like and polynomial rooting methods, a high-performance and low computational cost localization algorithm for the mixed near-field sources (NFS) and far-field sources (FFS) is proposed using the uniform linear sensor array. First, we combine the steering vectors of the two subarrays to eliminate the range parameters and then yield a new steering vector, which only contains direction-ofarrival (DOA) information. Second, based on the ESPRIT-like and polynomial rooting methods, the DOAs of all NFSs and FFSs are obtained from the new steering vector. Third, with the DOA estimates, the range parameters are estimated depending on the polynomial rooting method again; and further according to the number of the closest to the unit circle roots, we can determine the number of sources at the same DOA direction. Finally, based on the size of the range parameters, the types (NFS or FFS) of sources can be confirmed. In addition, the proposed algorithm does not require the high-order statistics or any 1-or 2-D search and thus has low computational cost. Meanwhile, it makes full use of the array aperture and obtains outstanding estimation performance for both the DOA and range parameters. Moreover, the proposed algorithm avoids parameter match procedure. Numerical experiments show the performance of the proposed algorithm in this paper.

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Detailed knowledge of the in vivo loading and kinematics in the knee joint is essential to understand its normal functions and the aetiology of osteoarthritis. Computer models provide a viable non-invasive solution for estimating joint loading and kinematics during different physiological activities. However, the joint loading and kinematics of the tibiofemoral and patellofemoral joints during a gait cycle were not typically investigated concurrently in previous computational simulations. In this study, a natural knee architecture was incorporated into a lower extremity musculoskeletal multibody dynamics model based on a force-dependent kinematics approach to investigate the contact mechanics and kinematics of a natural knee joint during a walking cycle. Specifically, the contact forces between the femoral/tibial articular cartilages and menisci and between the femoral and tibial/patellar articular cartilages were quantified. The contact forces and kinematics of the tibiofemoral and patellofemoral joints and the muscle activations and ligament forces were predicted simultaneously with a reasonable level of accuracy. The developed musculoskeletal multibody dynamics model with a natural knee architecture can serve as a potential platform for assisting clinical decision-making and postoperative rehabilitation planning.

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