Resumo O objetivo deste trabalho foi estabelecer e validar um modelo de previsão de distribuição de massa de matéria seca e de rendimento, com base no tempo de desenvolvimento fisiológico, para comparar as diferenças entre o modelo de índice de distribuição de matéria seca e o modelo de coeficiente de distribuição de matéria seca, para a simulação da massa de matéria seca da espiga e para melhorar a precisão de modelos de crescimento do milho para a previsão de rendimento. Os experimentos foram realizados em três locais (Longchuan, Mangshi e Ruili), na região tropical da província de Yunnan, China. O NRMS da massa de matéria seca e o rendimento da espiga foram geralmente menores que 10. O método do índice de distribuição da massa de matéria seca (NRMS = 5,44% e RMSE = 807,22 kg ha-1 para massa de matéria seca da espiga; e o NRMS = 7,32% e RMSE = 707,67 kg ha-1 para rendimento de grãos) é melhor do que o método do coeficiente de distribuição de massa de matéria seca (NRMS = 7,52% e RMSE = 1115,31 kg ha-1 para massa de matéria seca de espiga; NRMS = 8,6% e RMSE = 830,76 kg ha-1 para rendimento de grãos) para a simulação da massa de matéria seca de espiga e o rendimento de grãos de milho. O modelo do índice de distribuição melhora a precisão do modelo, o que é valioso para o futura produção de milho e seu manejo em Yunnan. fisiológico Longchuan, Longchuan (Longchuan Ruili, Ruili , Ruili) Yunnan China 10 544 5 44 5,44 80722 807 22 807,2 ha1 ha 1 ha- 732 7 32 7,32 70767 707 67 707,6 752 52 7,52 111531 1115 31 1115,3 86 8 6 8,6 83076 830 76 830,7 54 4 5,4 8072 80 2 807, 73 3 7,3 7076 70 707, 75 7,5 11153 111 1115, 8, 8307 83 830, 5, 7, 11

Abstract The objective of this work was to establish and validate the dry matter distribution and yield prediction models based on physiological developmental timing, to compare the differences between the dry mass distribution index model and the dry mass distribution coefficient model, for the simulation of ear dry mass and to improve the accuracy of maize growth models for predicting yield. The experiments were conducted in three tropical sites (Longchuan, Mangshi, and Ruili) in the tropical region of Yunnan Province, China. The NRMS of ear dry mass and yield were generally less than 10. The dry mass distribution index method (NRMS = 5.44% and RMSE = 807.22 kg ha-1 for ear dry mass; and NRMS = 7.32% and RMSE = 707.67 kg ha-1 for grain yield) is better than the dry mass distribution coefficient method (NRMS = 7.52% and RMSE = 1115.31 kg ha-1 for ear dry mass; NRMS = 8.6% and RMSE = 830.76 kgha-1 for grain yield) to simulate maize ear dry mass and grain yield. The distribution index model improves the accuracy of the model, which is valuable for future maize production and management in Yunnan. timing Longchuan, Longchuan (Longchuan Mangshi Ruili Province China 10 544 5 44 5.44 80722 807 22 807.2 ha1 ha 1 ha- 732 7 32 7.32 70767 707 67 707.6 752 52 7.52 111531 1115 31 1115.3 86 8 6 8.6 83076 830 76 830.7 kgha1 kgha kgha- 54 4 5.4 8072 80 2 807. 73 3 7.3 7076 70 707. 75 7.5 11153 111 1115. 8. 8307 83 830. 5. 7. 11

ABSTRACT In order to estimate the adaptability of Agricultural Production Systems Simulator (APSIM)-Maize model under the special climate environment in tropic, 10 field experiments were conducted at different seasons in three sites (Longchuan, Mangshi and Ruili) of tropic in Yunnan Province, China. The parameters of APSIM model were calibrated and its adaptability was validated. The results showed that the days from sowing to flowering and sowing to maturity were predicted accurately for all sites with mean errors of 2.0 ± 0.4, and 3.2 ± 0.7 d respectively. The normalized root mean square errors (NRMSE) of the model for yield prediction were 2%, 3% and 5% for each of three sites, respectively, which indicated that the APSIM model had good accuracy and sensitivity in predicting phenological phases and yield of maize (Zea mays L.) grown in different seasons, and the model had good adaptability in the tropic of Southwest China. This study provided the basis and technical support for evaluating maize production potential based on the model.

ABSTRACT The constant strain rate loading and dynamic stress equilibrium are prerequisites for ensuring the experimental data of the Split Hopkinson Pressure Bar (SHPB). In order to achieve this requirement, a proper pulse shaper dimensions were used in the experiment. For this purpose, the pulse shaper diameter, thickness and strength and the strike bar length and its velocity on the incident pulse are studied by ∅80mm SHPB apparatus. Then, the parameters affecting the two key inflection points and three loading areas of the incident pulse are discussed. In addition, the improved methods for two typical non-constant strain rate waveforms for the concrete experiments are obtained based on the regular pulse shaping. Moreover, SHPB experiments are conducted for concrete by employing different pulse shaper dimensions. The results show that more valid data can be obtained by employing the pulse shaper with large thickness when the strain rate is non-constant during the experiment. The conclusions and method provide guidance for selecting pulse shaper for concrete SHPB experiments.

Abstract China is the largest royal jelly producer and exporter in the world, and high royal jelly-yielding strains have been bred in the country for approximately three decades. However, information on the molecular mechanism underlying high royal jelly production is scarce. Here, a cDNA microarray was used to screen and identify differentially expressed genes (DEGs) to obtain an overview on the changes in gene expression levels between high and low royal jelly producing bees. We developed a honey bee gene chip that covered 11,689 genes, and this chip was hybridised with cDNA generated from RNA isolated from heads of nursing bees. A total of 369 DEGs were identified between high and low royal jelly producing bees. Amongst these DEGs, 201 (54.47%) genes were up-regulated, whereas 168 (45.53%) were down-regulated in high royal jelly-yielding bees. Gene ontology (GO) analyses showed that they are mainly involved in four key biological processes, and pathway analyses revealed that they belong to a total of 46 biological pathways. These results provide a genetic basis for further studies on the molecular mechanisms involved in high royal jelly production.