The telecommunications services market faces essential challenges in an increasingly flexible and customer-adaptable environment. Research has highlighted that the monopolization of the spectrum by one operator reduces competition and negatively impacts users and the general dynamics of the sector. This article aims to present a proposal to predict the number of users, the level of traffic, and the operators’ income in the telecommunications market using artificial intelligence. Deep Learning (DL) is implemented through a Long-Short Term Memory (LSTM) as a prediction technique. The database used corresponds to the users, revenues, and traffic of 15 network operators obtained from the Communications Regulation Commission of the Republic of Colombia. The ability of LSTMs to handle temporal sequences, long-term dependencies, adaptability to changes, and complex data management makes them an excellent strategy for predicting and forecasting the telecom market. Various works involve LSTM and telecommunications. However, many questions remain in prediction. Various strategies can be proposed, and continued research should focus on providing cognitive engines to address further challenges. MATLAB is used for the design and subsequent implementation. The low Root Mean Squared Error (RMSE) values and the acceptable levels of Mean Absolute Percentage Error (MAPE), especially in an environment characterized by high variability in the number of users, support the conclusion that the implemented model exhibits excellent performance in terms of precision in the prediction process in both open-loop and closed-loop.

Combining physical, social, and economic elements, urban planning plays a critical role in creating sustainable, resilient, and livable urban environments. It encompasses the regulation of land use, infrastructure, transportation systems, and environmental resources, with a focus on sustainable urban design and green infrastructure. While progress has been made, there are still areas that have not been fully explored, including the integration of renewable energy sources and the development of urban environments that are resilient to environmental stresses. This study aims to analyze the direction and scope of urban planning research and to identify research gaps in this area. The method used is bibliometrics by analyzing data obtained from the Scopus database in January 2024. The results of this study showed that Yufeng Zhang, a professor at Wuhan University, China, was the most productive author in producing publications, namely 22 documents. In addition, the article produced by Qianqian Zhou is also influential in this research topic because it gets a number of citations, as high as 204 citations. Additionally, the results indicate the current focus of research on sustainability, adaptation to climate change, and technology in urban planning. These findings can guide future research, direct policy, and ensure an interdisciplinary approach to modern urban and regional challenges.

The objective of this study was to evaluate the growth of four lettuce cultivars in Southern Piauí to recommend the best ones for the region. The experiment was conducted in a greenhouse with randomized blocks, with evaluation in subdivided time plots, evaluated in six seasons (20, 24, 28, 32, 36, 40 days after sowing—DAS) and with treatments corresponding to four cultivars (Americana Rafaela^®, Grand Rapids TBR^®, Crespa Repolhuda^® and Repolhuda Todo ano^®) with five repetitions. Leaf area, number of leaves, collar diameter, aboveground fresh mass, aboveground dry mass, root dry mass and total and the physiological indices of growth analysis were evaluated. The lettuce cultivars interfered significantly in the studied parameters, being that Americana Rafaela^® and Repolhuda todo ano^®, in the conditions that they were submitted, presented better performances and bigger morphophysiological indexes, cultivated in pot. The cultivars Americana Rafaela^® and Repolhuda todo ano^® can be produced under the conditions of the south of Piauí.

The agronomic use of mushroom post-harvest substrates (SPCHs) in horticultural seedbeds could be an interesting alternative for the reuse of these wastes in line with the European circular economy strategy. This work evaluates the potential use of four treatments with different SPCHs, mushroom (-Ch), mushroom (-St), mushroom compost (-CO), and a mixture (SPCH-Ch and SPCH-St) as substrates for lettuce and chili pepper seed germination. The trial was carried out in a germination chamber using commercial compost as a control treatment. The evaluation was based on its chemical (salinity, N and C content), physical (bulk and real density, porosity and water retention) and plant effect (germination and biomass) characteristics. Of the chemical properties studied, the high salinity in SPCH-Ch and SPCH-CO was a limiting factor for the development of the horticultural species evaluated (electrical conductivity 1:2.5; p/v; ~11 dS m^-1), and low germination percentages were observed. Regarding physical properties, porosity and water retention, the SPCH-CO, SPCH-St and mixture treatments presented some values outside the optimal range established for germination substrates. In the case of SPCH-St, its high C/N ratio could be a limitation for supplying N to the crop. In relation to biomass production (aerial and root) of lettuce and chili pepper, all the treatments evaluated obtained similar values to the control treatment. The mixed treatment presented the highest biomass values, significantly higher in the lettuce crop. In general, the mixed treatment proved to be the best alternative for use in the seedbed.