In this study, we have investigated the plant growth promoting effect of Bacillus mucilaginosus strain D(4)B(1), a rhizosphere soil organism, and its transgenic strain NKTS-3 on tobacco planting. The transgenic strain contains a phytase expression cassette that can express high active phytase extracellularly and hydrolyze phytate in the soil to liberate inorganic phosphorus for the growth of tobacco plants. Greenhouse study and field experiments showed that both wild-type B. mucilaginosus and the transgenic strain could promote tobacco plant growth. Moreover, the transgenic strain promoted tobacco plant growth (235% more than control in pot experiments and 125% more than control in field experiments) was higher than the wild-type B. mucilaginosus (183% more than control in pot experiments and 108% more than control in field experiments). In addition, the inoculation with transgenic rhizobacteria could significantly improve root acquisition of phosphorus and increase the phosphorus content of the plant.

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If the process‐induced distortions (PIDs) of asymmetrical laminates can be predicted accurately and tailored at the early design stage, the production of curved panels from flat molds could be an attractive technique in a cost‐driven production environment. A data‐driven computational methodology which integrates the finite element method (FEM) and artificial neural network (ANN) is presented to rapidly predict the maximum PID and to perform high‐throughput screening of thermosetting‐matrix composites of an asymmetrical laminate for a targeted maximum PID. We performed a grid search on ANN architectures and hyper‐parameters using cross‐validation and obtained a well‐trained ANN model with high generalization performance. For the forward problem, the ANN model was adopted to predict the maximum PIDs of CYCOM X850 and CYCOM 977‐2 prepregs, which were subsequently verified experimentally. For inverse design, a large‐scale screening method based on the ANN model was utilized to determine the candidates for a targeted maximum PID, with an experimental demonstration using one of these candidates. The well‐trained ANN model provides an alternative approach to faster computation with high accuracy for the maximum PID prediction and further guides the discovery of materials with desired distortion behaviors.

By considering the velocity variations of both terminals and moving scatterers, a general segment-based model for non-stationary vehicle-to-vehicle (V2V) channels is proposed in this paper. The time evolving channel parameters, i.e., Doppler frequencies, angles of arrival and departure, path delays, and path powers, are analyzed and simplified by the Taylor series expansions. The proposed model can be applied for realistic V2V communication environments and explicitly reveal the impact of velocity variations on the channels. In addition, the theoretical statistical properties, i.e., the temporal correlation function (TCF), Doppler power spectrum density (DPSD), level-crossing rate (LCR), and average fading duration (AFD) are also analyzed and derived. Simulation results under four typical V2V scenarios show that the theoretical results agree well with the simulated and measured ones, which verifies the effectiveness of both the proposed model and the derivations. INDEX TERMSVehicle-to-vehicle (V2V) channel, non-stationary channel model, velocity variation, statistical property. DAZHUAN XU received the B.S. degree from the Nanjing Institute of Technology, Nanjing, China, in 1983, and the M.S. and Ph.D. degrees in communication and information systems from the Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing, in 1986 and 2001, respectively. He has been with the College of Electronic and Information Engineering, NUAA, where he is currently a Full Professor. His research interests include digital communications, software radio, and coding theory. FEI BAI received the B.S. degree in information engineering from the Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2019, where she is currently pursuing the M.S. degree in electronics and communication engineering. Her research interests include the MIMO channel models and channel measurements.