The number of domestic studies on "variational pragmatics" (Ren Yuxin, Chen Xinren, 2012) is very limited. The research scopes are also relatively limited, which has not yet attracted the attention of more researchers. Therefore, based on this book The Routledge Handbook of Second Language Acquisition and Pragmatics, this paper aims to sort out and summarize the development trend of pragmatics from the meaning, goal and theory of variational pragmatics, and then put forward suggestions for future research.

With the purpose of identifying the characteristics of variation in fruit size and seed production (potential and efficiency) of Cedrela odorata L. between sites and progenies established in the ejido La Balsa, municipality of Emiliano Zapata, Veracruz, fruits were harvested from 20 trees in February 2013, preserving the identity of each one. Fruit length and width were measured, seed was extracted and developed and aborted seeds were counted to calculate Seed Production Potential (SPP) and Seed Efficiency (SE). The results showed significant differences between sites and between progenies and for fruit length between sites. The mean values found were: 32.52 mm (fruit length), 18.73 mm (fruit width), 39.9 seeds per fruit (SPP) and 57.51% (SE). The seed of this species for its use should be selected taking into account the production characteristics of crops and outstanding individual trees, in addition, due to the current regulatory restrictions on seed collection, the establishment of trials and plantations for germplasm production is a viable option for forest management of the species.

Accurate demand forecasting is key for companies to optimize inventory management and satisfy customer demand efficiently. This paper aims to Investigate on the application of generative AI models in demand forecasting. Two models were used: Long Short-Term Memory (LSTM) networks and Variational Autoencoder (VAE), and results were compared to select the optimal model in terms of performance and forecasting accuracy. The difference of actual and predicted demand values also ascertain LSTM’s ability to identify latent features and basic trends in the data. Further, some of the research works were focused on computational efficiency and scalability of the proposed methods for providing the guidelines to the companies for the implementation of the complicated techniques in demand forecasting. Based on these results, LSTM networks have a promising application in enhancing the demand forecasting and consequently helpful for the decision-making process regarding inventory control and other resource allocation.