Tidal sea level variations in the Mediterranean basin, although altered and amplified by resonance phenomena in confined sub-basins (e.g., Adriatic Sea), are generally confined within 0.5 meters and exceptionally up to 1.5 meters. Here we explore the possibility of retrieving sea level measurements using data from GNSS antennas on duty for ground motion monitoring and analyze the spectral outcomes of such distinctive measurements. We estimate one year of GNSS data collected on the Mediterranean coasts in order to get reliable sea level data from all publicly available data and compare it with collocated tide gauges. A total of eleven stations were suitable for interferometric analysis (as of 2021), and all were able to supply centimeter-level sea level estimates. The spectra in the tidal frequency windows are remarkably similar to tide gauge data. We find that the O₁ and M₂ diurnal and semidiurnal tides and MK₃, MS₄ shallow sea water tides may be disturbed by aliasing effects.

Climate and vegetation are variables of the physical space that have a dynamic and interdependent relationship. Flora modifies climatic elements and gives rise to a microclimate whose characterization is a function of regional climatic conditions and vegetation structure. The objective of this work was to compare the climatic variations (inside and outside) of the Caldén Forest in the Parque Luro Provincial Reserve. Temperature, relative humidity, wind speed, wind direction and precipitation data from two meteorological stations for 2012 were analyzed and statistically compared. The influence of the forest on climatic parameters was demonstrated and it was found that the greatest variations were in wind speed, daily temperature and precipitation.

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.

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.