In this paper, we propose autonomous cycles of data analysis tasks for the automation of the production chains aimed to improve the productivity of Micro, Small and Medium Enterprises (MSMEs) in the context of agroindustry. In the autonomous cycles of data analysis tasks, each task interacts with the others and has different functions, in order to reach the goal of the cycle. In this article, we identify three industrial-automation processes within the production chain, in which autonomous cycles can be applied. The first cycle is responsible to identify the type of input to be transformed—such as quantity, quality, time, and cost—based on information from the organization and its context. The second cycle selects the technological level used in the raw-material transformation, characterizing the platform of plant processing. The last cycle identifies the level of specialization of the generated product, such as the quality and value of the product. Finally, we apply the first autonomous cycle to define the type of input to be transformed in a coffee factory.

Currently, approaches to correcting misspelled words have problems when the words are complex or massive. This is even more serious in the case of Spanish, where there are very few studies in this regard. So, proposing new approaches to word recognition and correction remains a research topic of interest. In particular, an interesting approach is to computationally simulate the brain process for recognizing misspelled words and their automatic correction. Thus, this article presents an automatic recognition and correction system of misspelled words in Spanish texts, for the detection of misspelled words, and their automatic amendments, based on the systematic theory of pattern recognition of the mind (PRTM). The main innovation of the research is the use of the PRTM theory in this context. Particularly, a corrective system of misspelled words in Spanish based on this theory, called Ar2p-Text, was designed and built. Ar2p-Text carries out a recursive process of analysis of words by a disaggregation/integration mechanism, using specialized hierarchical recognition modules that define formal strategies to determine if a word is well or poorly written. A comparative evaluation shows that the precision and coverage of our Ar2p-Text model are competitive with other spell-checkers. In the experiments, the system achieves better performance than the three other systems. In general, Ar2p-Text obtains an F-measure of 83%, above the 73% achieved by the other spell-checkers. Our hierarchical approach reuses a lot of information, allowing for the improvement of the text analysis processes in both quality and efficiency. Preliminary results show that the above will allow for future developments of technologies for the correction of words inspired by this hierarchical approach.

In the present study, rainfall rate statistics for radio propagation applications collected during 1‐year measurement with a 1‐min integration time in three locations of Venezuela are presented and analyzed for the first time in the country. Specifically, the statistics of the worst‐month (WM) are assessed and compared with the Recommendation ITU‐R P.841‐7 prediction model. The results indicate that the model does not accurately predict the statistics of the WM in the country. Also, the performance of Recommendation ITU‐R P.837‐7 (Annex 1 and Annex 2) in estimating the rain rate for a 1‐min integration time at the three locations is examined, with the ITU‐R P.837‐7 Annex 1 exhibiting the best performance and providing results that encourage its use in the prediction of complementary cumulative distributions of rain rate in the country.

The rapid expansion of the IoT has led to increasing concerns about security, particularly in the early stages of communication where many IoT application-layer protocols, such as CoAP and MQTT, lack native support for secure key exchange. This absence exposes IoT systems to critical vulnerabilities, including dictionary attacks, session hijacking, and MitM threats, especially in resource-constrained environments. To address this challenge, this paper proposes the integration of OWL, a password-authenticated key exchange (PAKE) protocol, into existing IoT communication frameworks. OWL introduces a lightweight and secure mechanism for establishing high-entropy session keys from low-entropy credentials, without reliance on complex certificate infrastructures. Its one-round exchange model and resistance to both passive and active attacks make it particularly well-suited for constrained devices and dynamic network topologies. The originality of the proposal lies in embedding OWL directly into protocols like CoAP, enabling secure session establishment as a native feature rather than as an auxiliary security layer. Experimental results and formal analysis indicate that OWL achieves reduced authentication latency and lower computational overhead, while enhancing scalability, resilience, and protocol performance. The proposed solution provides an innovative, practical, and efficient framework for securing IoT communications from the foundational protocol level.

Predicting precipitation rate and rainfall attenuation are key aspects in planning and dimensioning of wireless communications systems operating at frequencies above 10 GHz, such as satellite communication systems at Ku and Ka bands. In this paper, contour maps of rainfall rate and rain attenuation are developed for the first time in Venezuela, based on 1-min rain rate statistics obtained from measurements carried out in Venezuela over at least 30 years period with a higher integration time and using Rice-Holmberg model, refined Moupfouma-Martin model and Recommendation ITU-R P.837-7, for rain rate estimation and Recommendation ITU-R P.618-13, Ramachandran-Kumar model, Yeo-Lee-Ong model and Rakshit-Adhikari-Maitra model, for rain attenuation prediction in “Simon Bolivar” satellite links in Venezuela. The overall results of both types of maps represent a useful tool for preliminary planning of those links in the country, specifically, in Ku and Ka bands