Sistema Regional de Información
en línea para Revistas Científicas de América Latina,
el Caribe, España y Portugal

Agriculture involves the cultivation and domestication of plants, but dif ferent agricultural practices worldwide have led to the intensive use of natural resources, synthetic fertilizers and pesticides, as well as the use of heavy machinery and irrigation technologies. This has had negative consequences, mainly through the reduction of biodiversity, the appearance of resistant pests, imbalances in agroecosystems and harmful environmental ef fects. In view of this, research has focused on sustainable agriculture, whose production is economically, socially and environmentally acceptable. To achieve this, the application of bio-inputs formulated with biotechnological techniques from microorganisms, plants, compounds and extracts thereof, capable of improving crop yield and health, has been proposed. Therefore, the objective of this chapter is to show the advances in the production of bio-inputs, their characteristics and application results since they can gradually replace the use of synthetic inputs, reducing the pollutants generated by this sector, in addition to taking advantage of waste from various sectors to incorporate them into the circular economy. It is necessary to continue with research to integrate potential species in the formulation of bioproducts and to deepen in metaomics and bioinformatics with impact and improvement of agricultural activities.

The productive and economic performance of experimental aquaponic and hydroponic (NFT Technique) systems for the production of tilapia (Oreochromis niloticus), lettuce (Lactuca sativa) and cucumber (Cucumis sativus) are evaluated. Two plant production cycles and a single fish production cycle were analyzed. The systems were compared regarding production and economic benefits. Production of 21.33 kg m-3 of fish, 9.75 pieces of lettuce m-2, and 0.0083 kg m-2 of cucumber was obtained in the aquaponic system. In the hydroponic system, a production of 10.49 pieces of lettuce m-2 and 0.94 kg m-2 of cucumber fruit was obtained. The benefit-cost ratios were 1.50 in the aquaponic system and 2.18 in the hydroponic system, while the aquaponic system was superior regarding net profit (US$ 36 693.32 ha-1) than the hydroponic system (US$ 24 313.42 ha-1). It is concluded that the hydroponic system had higher plant production; however, aquaponics is more profitable due to the production of fish and vegetables.

Rice (Oryza sativa L.) is one of the most widely consumed cereals worldwide and holds high socioeconomic importance in Ecuador, making it a key crop. Insect pests represent a significant threat to rice cultivation, and their identification is essential for developing more effective management strategies. The objective of this research was to identify insect pest species and evaluate their incidence, richness and abundance in rice crop in Nobol canton, Guayas province. Seven pest species were identified: Hydrellia sp., Tagosodes orizicolus, Spodoptera frugiperda, Rupella albinella and Diatraea saccharalis, Tibraca limbativentris and Oebalus sp. The highest incidence was recorded during the vegetative stage of the crop (40 to 70 days after transplanting). The Menhinick diversity index (0.40) showed low specific richness, while the Simpson dominance index (0.23) indicated an even distribution among species, with no dominant pest present. These findings provide a basis for implementing phytosanitary monitoring and control programs tailored to the phenological dynamics of the crop.

Payment delinquency represents a structural barrier to the financial sustainability of public electricity distribution companies; however, unlike the financial sector, predictive models based on machine learning have not yet been adapted to this context. In particular, CNEL EP faces challenges in anticipating customer defaults, which limits the effectiveness of its commercial management. This study aims to identify the most accurate machine learning model to predict delinquency risk in the Bolívar Business Unit. A methodological approach based on Design Science Research and CRISP-DM was adopted, incorporating a systematic literature review (PRISMA), the analysis of 72,483 historical records, and the application of techniques such as PCA, SMOTE, and ensemble models (RandomForest, Gradient Boosting, AdaBoost, and VotingClassifier). Gradient Boosting and VotingClassifier achieved near-perfect performance metrics (Accuracy: 0.9982; F1 Macro: 0.9957; AUC ROC: 1.000) and (Accuracy: 0.9983; F1 Macro: 0.9959; AUC ROC: 1.000), even under stress scenarios involving noise, imbalance, and data loss. Furthermore, the integration of SHAP and LIME enabled transparent interpretation of predictions for non-technical users. The findings show that the proposed solution is robust, replicable, and applicable in practice. This study makes a significant contribution by demonstrating that machine learning models can enhance portfolio management in Ecuador's public electricity sector.

The teaching of Language and Literature is essential in the education of upper elementary students, particularly during early adolescence (ages 9 to 12), a key stage for the development of communication and literary skills. However, the diversity in students’ performance levels requires the identification of pedagogical practices that effectively address learning needs. The main objective of this study was to analyze the impact of different teaching strategies used by Language and Literature teachers on students’ reading comprehension and written expression. A quantitative methodology with a descriptive design was employed through a structured survey applied to thirty teachers at a public educational institution. The results showed that the use of read-alouds, creative writing, digital tools, and intercultural activities significantly enhanced students’ motivation and critical thinking. Additionally, differences in skill development were identified among students from various socioeconomic backgrounds, highlighting the importance of inclusive pedagogical approaches. This study concludes that a combination of participatory strategies, the appropriate use of technology, and the encouragement of critical reflection through literature can significantly improve learning in this area. The research offers practical recommendations that can help strengthen the teaching of Language and Literature, promoting more equitable, meaningful, and contextualized education.

This study proposes an empirical methodology to identify the most valued dimensions of teaching practices in higher education through a mixed-method approach that integrates qualitative and quantitative analyses. Over 89,000 student comments collected during teacher evaluations at the University of Cuenca (2024–2025) were analyzed. Advanced natural language processing techniques, specifically sentiment analysis and Structural Topic Modeling (STM), were used to process an extensive textual corpus. The analysis focused on positive comments aimed at teachers recognized through a prior comprehensive evaluation, aiming to uncover recurring patterns in student perceptions. Four key dimensions of student evaluation emerged from the analysis: disciplinary knowledge, communication management, socio-emotional skills, and teaching methodology. These dimensions reflect perceived teaching strengths and exhibit significant variability across disciplinary contexts and academic levels. For example, disciplinary knowledge is prioritized in technical areas, while socio-emotional skills are more valued in social sciences and health disciplines. These findings underscore that student evaluations are an essential source of qualitative information, overcoming limitations of traditional numeric metrics by offering deeper, contextual insights. Ultimately, the proposed methodology provides higher education institutions with a practical tool to identify specific faculty strengths and design evidence-based, personalized professional development programs, thus fostering continuous and contextualized improvements in educational quality.

This study aims to evaluate the potential of blockchain technology in higher education, with a particular focus on its benefits and challenges in academic processes. A systematic literature review was conducted, analyzing 42 key studies selected from a total of 533 academic publications over the past six years. The results indicate that blockchain applications are primarily concentrated in secure certificate issuance, the enhancement of teaching and learning environments, and the transparent management of academic credits and scholarships. The main contribution of this research is the presentation of a comprehensive and up-to-date overview of blockchain integration in the educational domain. Furthermore, it introduces the UCE-Camp model as a sustainable framework designed to improve the efficiency, transparency, and quality of academic operations. The study also identifies existing gaps and challenges, outlining future research directions for the broader adoption of blockchain in higher education.

Currently, according to the Cisco Annual Internet Report, between 80% and 85% of internet data traffic is generated by video content, making it imperative to develop methods or algorithms that enable the efficient transmission of multimedia content over the network. In this context, this article proposes an optimized algorithm for video traffic control in an SDN environment, allowing for adaptive real-time transmission. The methodology consists of the following stages: first, the video is transmitted at a minimum encoded bitrate; then, the available network bandwidth is measured, based on which the encoding rate and video quality are adjusted; and finally, the video is played on the client side. The results obtained using the control algorithm were compared with those obtained using common routing protocols such as RIP (Routing Information Protocol) and OSPF (Open Shortest Path First). The use of the control algorithm resulted in an 18.1% lower delay compared to RIP and a 94.8% lower delay compared to OSPF. Under background traffic conditions, SDN demonstrated a 27.23% lower delay than RIP and a 54.1% lower delay than OSPF. The main contribution of this work is, through the implementation of a testbed, the analysis of a mechanism that improves quality of service (QoS) and quality of experience (QoE) by optimizing real-time video transmission through the selection of the best route and/or the dynamic adjustment of video bitrate according to network conditions.

The objective of this work focuses on the design and implementation of a low-cost intelligent system for the early detection of forest fires, using IoT (Internet of Things) and AI (Artificial Intelligence) technologies. The designed methodology involved the development and comparative evaluation of multiple algorithms, divided into two approaches: techniques based on the analysis of color spaces (RGB, YCbCr, HSI, HSV, and PJF) and AI models (CNN, YOLOv8, and Haar Cascade). From this analysis, a hybrid architecture was selected that integrates the two highest-performing methods: an object detector based on YOLOv8 (Method 9) and a chromatic algorithm that fuses the PJF, RGB, and YCbCr spaces (Method 12). This visual system is complemented by a PM2.5 particle sensor to validate the presence of smoke and GPS/4G modules to issue georeferenced alerts. As a key result, the final prototype validated under controlled conditions achieved outstanding metrics such as 99.82% accuracy, 99.64% sensitivity, and 100.00% specificity under high illumination conditions. It also demonstrated energy efficiency and thermal stability through continuous monitoring of CPU, RAM, and current consumption. The main contribution of this work consists of a validated field solution, whose hybrid architecture proves to be accurate, efficient, and adaptable, confirming its feasibility for implementation in emergency contexts.

This study proposes a novel hybrid framework for knowledge representation in emergencies, integrating Natural Language Processing (NLP), OWL ontologies, and SWRL rules to process unstructured data from Ecuador's Integrated Security Service (ECU 911). The key contribution lies in the unique combination of advanced NLP models such as BERT for Named Entity Recognition and XLM-RoBERTa for zero-shot semantic classification, with a formally validated ontological model developed in Protégé and a parallel logical implementation in Prolog using the Object-Attribute-Value paradigm. Unlike prior works, this approach specifically addresses the challenge of transforming raw emergency call transcripts into actionable knowledge by (1) automating entity extraction (locations, persons) and semantic categorization of incidents, (2) generating interpretable decision rules via decision trees, and (3) enabling cross-paradigm interoperability through synchronized OWL/SWRL and Prolog inference engines. Experimental validation with SPARQL/SQWRL queries and the Pellet reasoner demonstrated 96.7% accuracy in inferring emergency priorities such as medical emergencies, outperforming standalone NLP or ontology-based methods. This work advances semantic AI for emergency response by bridging unstructured text analysis with formal reasoning, offering a scalable solution for real-time decision support in critical scenarios.