Spilocaea oleagina is a common and dangerous fungal disease in olive trees that significantly reduces olive production. The early and accurate detection of this disease is essential for implementing effective control measures. In this study, we propose the creation of a new meta-architecture for identifying peacock spots on olive leaves. This new meta-architecture integrates Xception and VGG16 as the basis for the methodology employed in this study. Furthermore, a machine learning approach was used to pretrain the model in an unsupervised manner, thereby improving its generalization capacity. Metrics such as Kappa (K), True Skill Statistic (TSS), Proportion of Expected Success (Pe), Threat Index (Ts), and the Heidke Skill Score (HSS) were used to validate the model results. Based on these indicators, we evaluated the robustness, accuracy, and ability of the model to identify peacock spots. The testing results showed that the suggested meta-architecture, named SSL-XceVNet, substantially outperformed the baseline XceVNet model (88.24%) in detecting peacock spots, with an accuracy of 95.22%. The validation measurement results (K, 73.57%; TSS, 72.66%; Pe, 55.06%; Ts, 90.71%; HSS, 90.44%) attested to the efficacy and resilience of the suggested model, underscoring its ability to generalize and produce accurate predictions under a variety of circumstances. In conclusion, the combination of Xception and VGG16 into a new meta-architecture added the strengths of both models, and self-supervised learning helped learn meaningful representations without the need for explicit labels.

Image segmentation enables accurate object identification, a key requirement in computer vision applications. In aquaculture, this technology is essential for monitoring and management of species such as Oncorhynchus mykiss. In the Peruvian highlands, where trout farming is a vital economic activity, robust computational models are needed to automate the estimation of fish size and weight and to optimize the sustainability of production systems. This research proposes ATrUNet, a U-Net-based architecture optimized for accurate segmentation of Oncorhynchus mykiss. ATrUNet improves the information flow between the encoding and decoding layers by incorporating convolutional layers, batch normalization, and activation functions. A dataset of 1166 images was constructed, processed using LabelMe with JSON annotations, and converted into binary masks. Evaluation was conducted using loss, accuracy, and IoU. As a result, ATrUNet showed higher performance than U-Net, achieving a 27.55% reduction in loss, a 0.40% increase in accuracy (0.992), and improvements in overlap metrics such as IoU and GIoU by 2.70% and 8.75%, respectively. Future work includes expanding dataset diversity, exploring instance segmentation, and optimizing for embedded deployment. This research contributes to the application of computer vision to aquaculture, with the possibility of extending it to different species and various research contexts.

Despite recent advances in multilingual artificial intelligence, Indigenous languages of the Americas remain underrepresented in natural language processing (NLP) because of limited corpora, annotations, benchmarks, and reusable resources. This systematic review examines AI-based NLP research on low-resource Indigenous languages published within the 2022–2026 review window. Following PRISMA 2020 guidance and an expanded source-verification and supplementary-search process, the final analytical corpus comprised 283 unique records. Machine translation was the most represented application domain (58.3%), followed by language documentation (51.2%), Indigenous-language education (30.4%), and speech technologies (28.6%). Transformer-related architectures (54.1%) and BERT-based models (34.6%) showed the greatest literature visibility, although verified implementation/evaluation was substantially lower than terminology-level occurrence. Quechua (39.2%) and Guarani (33.6%) were the most represented languages. Multilingual training and fine-tuning were the most frequently identified adaptation strategies, while Accuracy, BLEU, and chrF predominated among evaluation metrics. Overall, the evidence reveals a developing but structurally uneven and methodologically heterogeneous field. Persistent challenges include data scarcity, linguistic complexity, orthographic and dialectal variation, limited benchmark availability, uneven community participation, and underreported Indigenous data-governance practices.

In the developed research we present a local production network designed and implemented with Multipath TCP, to improve reliability through redundant links. In particular, the MultiPath protocol was evaluated, which improved the performance and increased the solidity against possible faults in one of the routes. MutiPath TCP conventional and MultiPath TCP were implemented with integration of name-based sockets. The capture procedure and the analysis fields were: data flow, bit rate, protocol type, source IP address, destination IP address, sent bytes, received bytes, start time, source port and destination port. The Argus software was used to monitor network traffic on the server and the client. The implementation of the MultiPath server was performed on the Ubuntu 15.04 Linux platform, and the Wireshark tool to capture protocol data units. For the implementation of conventional MultiPath TCP an average transfer rate of 54.86Mbps was obtained and when it was integrated into the MultiPath TCP communication with name-based socket integration, an average transfer rate of 60.89Mbps was obtained, so optimization for the second case is around 10%. It has been proven that the production network works correctly and with better performance.

Technology Rain Journal ISSN 2953-464X, indexada en Latindex Catálogo 2.0, es una Revista enfocada en el área de Ciencias Informáticas e Ingeniería. Posee una periodicidad Semestral con un volumen anual de dos números . Nuestra revista es editada por la editorial Kolibri, auspiciada por La Red de Investigacion en Ingenieria e Informatica  Ri3, el Instituto de Investigaciones Informáticas y Forenses del Ecuador,  y otras entidades sin fines de lucro.  La Revista publica artículos originales e inéditos, y evaluado en doble ciego. Revista de acceso abierto Licencia Creative Commons Attribution 4.0 International (CC BY 4.0) Recepción permanente de artículos científicos