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Based on the theoretical and empirical foundations of the gravity model, this article systematically investigates the determinants of international tourist arrivals to China. Various origin–destination (O-D) linked factors accounting for the economic, political and social/culture preferences between China and its tourist origins are particularly explored. Utilizing a panel data set of tourist arrivals to China from 21 countries from 1995 to 2014, the results suggest that the basic gravity determinants all have significant effects on tourist arrivals to China. Furthermore, four O-D linked factors are found to be significant in explaining the number of international tourists and have a greater tourist-enhancing effect. In particular, overseas Chinese, proxying for social/culture-preferential relationships, have caused significant increases in tourism arrivals to China of approximately 120% when the percentage of ethnic Chinese in the origin countries’ population is more than 1%. For China to be chosen as a manufacturing and processing centre by international enterprises, various O-D relationships are important but neglected factors of Chinese tourism patterns, with important policy implications.

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Burley tobacco is a genotype of chloroplast-deficient mutant with accumulates high levels of tobacco-specific nitrosamines (TSNAs) which would induce malignant tumors in animals. Nitrate is a principle precursor of tobacco-specific nitrosamines. Nitrate content in burley tobacco was significantly higher than that in flue-cured tobacco. The present study investigated differences between the two tobacco types to explore the mechanisms of nitrate accumulation in burley tobacco. transcripts (3079) related to the nitrogen and carbon metabolism were observed. Expression of genes involved in carbon fixation, glucose and starch biosynthesis, nitrate translocation and assimilation were significantly low in burley tobacco than flue-cured tobacco. Being relative to flue-cured tobacco, burley tobacco was significantly lower at total nitrogen and carbohydrate content, nitrate reductase and glutamine synthetase activities, chlorophyll content and photosynthetic rate (Pn), but higher nitrate content. Burley tobacco required six-fold more nitrogen fertilizers than flue-cured tobacco, but both tobaccos had a similar leaf biomass. Reduced chlorophyll content and photosynthetic rate (Pn) might result in low carbohydrate formation, and low capacity of nitrogen assimilation and translocation might lead to nitrate accumulation in burley tobacco.

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Traditional multiobjective evolutionary algorithms (MOEAs) consider multiple objectives as a whole when solving multiobjective optimization problems (MOPs). However, this consideration may cause difficulty to assign fitness to individuals because different objectives often conflict with each other. In order to avoid this difficulty, this paper proposes a novel coevolutionary technique named multiple populations for multiple objectives (MPMO) when developing MOEAs. The novelty of MPMO is that it provides a simple and straightforward way to solve MOPs by letting each population correspond with only one objective. This way, the fitness assignment problem can be addressed because the individuals' fitness in each population can be assigned by the corresponding objective. MPMO is a general technique that each population can use existing optimization algorithms. In this paper, particle swarm optimization (PSO) is adopted for each population, and coevolutionary multiswarm PSO (CMPSO) is developed based on the MPMO technique. Furthermore, CMPSO is novel and effective by using an external shared archive for different populations to exchange search information and by using two novel designs to enhance the performance. One design is to modify the velocity update equation to use the search information found by different populations to approximate the whole Pareto front (PF) fast. The other design is to use an elitist learning strategy for the archive update to bring in diversity to avoid local PFs. CMPSO is comprehensively tested on different sets of benchmark problems with different characteristics and is compared with some state-of-the-art algorithms. The results show that CMPSO has superior performance in solving these different sets of MOPs.