The Assisted Living Environments Research Area–AAL (Ambient Assisted Living), focuses on generating innovative technology, products, and services to assist, medical care and rehabilitation to older adults, to increase the time in which these people can live. independently, whether they suffer from neurodegenerative diseases or some disability. This important area is responsible for the development of activity recognition systems—ARS (Activity Recognition Systems), which is a valuable tool when it comes to identifying the type of activity carried out by older adults, to provide them with assistance. that allows you to carry out your daily activities with complete normality. This article aims to show the review of the literature and the evolution of the different techniques for processing this type of data from supervised, unsupervised, ensembled learning, deep learning, reinforcement learning, transfer learning, and metaheuristics approach applied to this sector of science. health, showing the metrics of recent experiments for researchers in this area of knowledge. As a result of this article, it can be identified that models based on reinforcement or transfer learning constitute a good line of work for the processing and analysis of human recognition activities.

The use of wireless sensor networks (WSN) for monitoring variables in agricultural environments and natural forests has been increasing in recent years. However, the sizing of these systems is affected by the inaccuracy of the radio wave propagation models used, leading to possible increased costs and measurement errors. This systematic literature review (SLR) aims to identify propagation models widely used in WSN deployments in agricultural or naturally vegetated environments and their effectiveness in estimating signal losses. We also identified today’s wireless technologies most used in precision agriculture (PA) system implementations. In addition, the results of studies focused on the development of new propagation models for different environments are evaluated. Scientific and technical analysis is presented based on articles consulted in different specialized databases, which were selected according to different combinations of criteria. The results show that, in most of the application cases, vegetative models present high error values when estimating attenuation.

Background and objectives: This article analyzes the comprehensive assessment and enhancement of the sustainable management of Agricultural Production Units (APUs) in the departments of Atlántico, César, and Magdalena in the Caribbean region of Colombia. The main objective is to identify challenges and opportunities for enhancing agricultural sustainability and propose appropriate strategies within a local context. Methods: The research employed a mixed methodological approach, incorporating APU diagnosis; strengths, weaknesses, opportunities, and threats analysis; and structured surveys administered to agricultural producers in each department. Moreover, secondary sources were employed to assess sustainable agricultural practices and key environmental variables. Findings: APUs in the Caribbean region face significant challenges, including limited access to technology, inefficient soil management, and scarce water resources. In Atlántico, 40% of producers lack education in soil studies. In Cesar, 30% of soils are degraded. In Magdalena, 70% are affected by salinization. These results emphasize the need to adopt agroecological technologies and strategies adjusted to the local context to ensure agricultural sustainability and resilience. Conclusion: This study highlights the urgency of implementing comprehensive strategies that consider regional specific

Avicennia germinans, a representative of the marine coastal mangrove ecosystem, vital in the Colombian Caribbean, harbors a unique microbial diversity that could contain microorganisms with the potential to promote plant growth of agricultural species such as maize. The objective of this research was to evaluate A. germinans endophytes at different sampling sites and in diverse plant organs in order to identify the growth-promoting role of the most sodium chloride-tolerant endophyte found. These were then inoculated in maize seeds under drought stress conditions simulated by polyethylene glycol (PEG) in vitro. To this end, samples of adult A. germinans plants were collected from four mangrove ecosystems in the Colombian Caribbean. Several isolates were able to tolerate up to 15% NaCl (w/v), produce indole-3-acetic acid (IAA), show proteolytic activity, and inhibit phytopathogenic fungi. The best-performing strain, C1T-KM1901-B, was genomically identified as Bacillus paralicheniformis and evaluated as a bioinoculant in maize seeds under PEG-induced drought stress. Inoculation with B. paralicheniformis significantly increased germination potential and germination index of drought-resistant seeds compared to non-inoculated controls under severe drought stress conditions (40% PEG). In addition, inoculated seedlings exhibited significantly higher roots and shoot fresh and dry biomass at moderate to severe drought stress levels (15% and 20% PEG). These results are position B. paralicheniformis C1T-KM1901-B, isolated from Avicennia germinans, as a promising bioinoculant to enhance maize establishment under drought conditions.