This article focuses on the development of a system based on the long-range network (LoRa), which is used for monitoring the agricultural sector and is implemented in areas of the Andean region of Ecuador. The LoRa network is applied for the analysis of climatic parameters by monitoring temperature, relative humidity, soil moisture and ultraviolet radiation. It consists of two transmitter nodes and one receiver node, a LoRa Gateway with two communication channels for data reception and one for data transmission, and an IoT server. In addition, a graphical user interface has been developed in Thinger.io to monitor the crops and remotely control the actuators. The research conducted contains useful information for the deployment of a LoRa network in agricultural crops located in mountainous areas above 2910 m.a.s.l., where there are terrains with irregular orography, reaching a coverage of 50 hectares and a range distance of 875 m to the farthest point in the community of Chirinche Bajo, Ecuador. An average RSSI of the radio link of −122 dBm was obtained in areas with a 15% slope and 130 m difference in height according to the Gateway, where the presence of vegetation, eucalyptus trees and no line-of-sight generated interference to the radio signal. The success rate of PDR packet delivery with an SF of nine, had a better performance, with values of no less than 76% and 92% in uplink and downlink respectively. Finally, the technological gap is reduced, since the network reaches places where traditional technologies do not exist, allowing farmers to make timely decisions in the production process in the face of adverse weather events.

The increasing integration of artificial intelligence into educational processes offers new opportunities to address critical issues in higher education, such as student dropout, academic underperformance, and the need for personalized tutoring. This scoping review aims to map the scientific literature on the use of AI techniques to predict academic performance, risk of dropout, and the need for academic advising, with an emphasis on e-learning or technology-mediated environments. The study follows the Joanna Briggs Institute PCC strategy, and the review was reported following the PRISMA-ScR checklist for search reporting. A total of 63 peer-reviewed empirical studies (2019–2025) were included after systematic filtering from the Scopus and Web of Science databases. The findings reveal that supervised machine learning models, such as decision trees, random forests, and neural networks, dominate the field, with an emerging interest in deep learning, transfer learning, and explainable AI. Academic, behavioral, emotional, and contextual variables are integrated into increasingly complex and interpretable models. Most studies focus on undergraduate students in digital and hybrid learning contexts, particularly in regions with high dropout rates. The review highlights the potential of AI to enable early intervention and improve the effectiveness of tutoring systems, while noting limitations such as lack of model generalization and ethical concerns. Recommendations are provided for future research and institutional integration.

Neural network quantization has become established as a key strategy for transitioning medical imaging models from research environments to clinical devices and resource-constrained edge platforms; however, the available evidence remains fragmented and focused on highly heterogeneous use cases. This study presents a systematic review of 72 studies on quantization applied to medical images, following PRISMA guidelines, with the aim of characterizing the relationship among quantization technique, network architecture, imaging modality, and execution environment, as well as their impact on latency, memory footprint, and clinical deployment. Based on a structured variable matrix, we analyze—through tailored visualizations—usage patterns of Post-Training Quantization (PTQ), Quantization-Aware Training (QAT), mixed precision, and binary/low-bit schemes across frameworks such as PyTorch V 2.6.0, TensorFlow 2.19.0, and TensorFlow Lite, executed on server-class GPUs, edge/embedded devices, and specialized hardware. The results reveal a strong concentration of evidence in PyTorch/TensorFlow pipelines using INT8 or mixed precision on GPUs and edge platforms, contrasted with limited attention to PACS/RIS interoperability, model lifecycle management, energy consumption, cost, and regulatory traceability. We conclude that, although quantization can approximate real-time performance and reduce memory footprint, its clinical adoption remains constrained by integration challenges, model governance requirements, and the maturity of the hardware–software ecosystem.