Data and Metadata
type: channel
description: Data and Metadata
publisher: AG Editor (Argentina)
language: en
publication name: Data and Metadata
issn: 2953-4917
copyright: The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
items: Quantum Optimization for Intelligent User Data Allocation in Distributed Cloud Infrastructure
items: Presence of Ecuador in the Web of Science from open access in post-pandemic period 2019- 2021: A multivariate analysis
items: Analyzing the Impact of Self-Regulated Learning on Academic Performance and Student Engagement
items: Mathematical model based on Machine Learning Enabled IoT Sensing and Decision-Making in Farming
items: Methodology for Vulnerability Assessment in WSNs Using CVSS and NIST SP 800-30
items: Financial predictors of sme failure: variable selection with lasso versus random forest
items: Transformer guided and GAN augmented deep learning for medical image diagnostics
items: Validation of learning styles in Higher Education: A case study at the Faculty of Applied Sciences (FICA)
items: Development of IT Project Management Learning Media Using YOLO and Reinforcement Learning
items: AI-Driven Hybrid Defense Mechanisms for Enhancing Cybersecurity in Cyber-Physical Systems Through Packet Sniffing and Cyber Ranges
items: Improving Autism Detection Accuracy with an Optimized Local-Asymmetric Adaptive Hybrid GCN for EEG Data
items: IoT and AI for Smart Rural Fishing: System Architecture, TEK Integration, and Economic Viability Analysis
items: Artificial intelligence: prototype of an automated irrigation system for the cultivation of roses in Cotopaxi
items: Predicting saturation for a new fabric using artificial intelligence (fuzzy logic): experimental part
items: A proposed method for detecting network intrusion using an ensemble learning (stacking -voting) approach with unbalanced data
items: Management of operational activities at the Regional Human Milk Collection Center: an analysis from the perspective of local intervention strategies and information systems
items: Bridging the Gap to Success: A 50-Year Bibliometric Analysis of Preparatory Education and Academic Performance in Engineering and STEM Fields
items: Non-Invasive Blood Density Measurement using Photoplethysmography Technique
items: Simulated Annealing-Based Optimization of IEEE-33 Radial Distribution Networks with Integrated Auxiliary PV Sources Using PyPower
items: Development and Mechanical Evaluation of a Biocomposite Based on Abaca Fiber and Acrylic Resin
items: Bibliometric Analysis of Sports Running Socks Made from Bamboo, Cotton, and Acrylic
items: Intelligent Monitoring and Early Warning System Against Frosts for Sustainable Agriculture
items: Heterogeneous Computing for High-Complexity Problems: A Performance Study with DPC++ on HPC Platforms
items: Spectrum-Based Security Assessment of Electric Gates Using Offensive Security Methodology
items: Route Optimization for Urban Waste Collection via LoRa Communication and Deep Q-Network in a Controlled Simulation
items: Implantation of Information Systems in the Popular Financial Sector: Two Case Studies Based on the SImplE Framework
items: Binary classification of defects in multiple coffee beans using lightweight convolutional neural networks for embedded systems
items: Effects of altitude on On-Board Fuel Consumption and CO₂ Emissions of a Vehicle: An analysis based on experimental data
items: Gamification and Formative Feedback with Kahoot! in Programming Fundamentals: A TAM–SDT–UCD Approach with Mechatronics Engineering Students
items: Exploring user perceptions at public transport stops in a SEM approach to Accessibility and Safety

Quantum Optimization for Intelligent User Data Allocation in Distributed Cloud Infrastructure
type: item
description: In a modern environment in which cloud computing is distributed globally, optimizing the placements of user data in the different cloud locations would ensure that the data can be accessed with minimum latency and fast access. Traditional heuristic and machine-learning methods are often prohibitively expensive to scale and have a hard time adjusting to a dynamic cloud environment. Scalable and adaptive optimization strategies are required when user demands and data volumes grow very fast. There is a fair likelihood that quantum-based methods, particularly, the metaheuristic methods, are an alternative that can effectively react to dynamic conditions. The proposed model will be based on Quantum Particle Swarm Optimization when moving user data to the most suitable places within distributed cloud canters. Through quantum-inspired probabilistic search, the algorithm becomes more adaptive and more efficient than traditional ones. Experiments, based on these simulations of the user request in the case of a geo-distributed cloud, have shown a significant reduction in latency up to 28% and better load balancing compared to the traditional approaches. Altogether, these results highlight the prospects of quantum computing when it comes to improving the efficiency and responsiveness of cloud infrastructure. The primary strength of the QPSO is that it can be easily modified to facilitate the rapid response to the rapidly changing environment to allow access to the distributed cloud systems in an efficient way and with a low latency.
subject: Original
  topic: section
subject: Quantum optimization
  topic: keywords
subject: Data placement
  topic: keywords
subject: Distributed cloud computing
  topic: keywords
subject: Latency reduction
  topic: keywords
subject: Quantum-inspired algorithms
  topic: keywords
creator: Viji C
creator: Rajkumar N
creator: Stephen R
creator: Gobinath R
creator: Balusamy Nachiappan
creator: Prabhu Shankar B
rights: Copyright (c) 2025 Viji C, Rajkumar N, Stephen R, Gobinath R, Balusamy Nachiappan, Prabhu Shankar B (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-12
publication date: 2025-12-12
starting page: 1328
ending page: 1328
doi: 10.56294/dm20251328

Presence of Ecuador in the Web of Science from open access in post-pandemic period 2019- 2021: A multivariate analysis
type: item
description: Introduction: The COVID-19 pandemic has profoundly transformed scholarly communication, accelerating the global adoption of open access (OA). In this context, it is relevant to analyze how Ecuador positioned itself in these dynamics in the post-pandemic period. Objective: To evaluate Ecuador’s presence in the Web of Science (WoS) database from 2019 to 2021 and identify the impact of the pandemic on scientific production and the adoption of open-access models. Method: A total of 9085 articles indexed in WoS under the affiliation “Ecuador” were retrieved. Data analysis was performed in R using multivariate statistical techniques and visualization tools (HJ-Biplot), complemented by Bonferroni tests at the 95 % confidence level to compare citation differences between OA and subscription publications. Results: Of the total publications, 52 % corresponded to open access. These articles received more citations on average than subscription-based articles, with statistically significant differences. Private universities accounted for 43 % of publications, public universities for 42 %, and collaborative works for 15 %. A progressive shift toward OA was evident, especially after 2020, with the green route predominating over the gold, bronze, and hybrid pathways. Conclusions: Ecuador has notably transitioned toward open access, enhancing the visibility and impact of its scientific production. However, challenges remain, related to the lack of national policies and limited inter-institutional collaboration. Strengthening OA strategies is recommended to democratize knowledge and improve the international positioning of Ecuador’s scientific output.
subject: Original
  topic: section
subject: Scientific Journals
  topic: keywords
subject: WoS
  topic: keywords
subject: Scientific quality
  topic: keywords
subject: Knowledge
  topic: keywords
subject: Democratization
  topic: keywords
subject: Citations
  topic: keywords
creator: Patricio Álvarez Muñoz
creator: Fernando Erasmo Pacheco Olea
creator: Dennis Alfredo Peralta-Gamboa
rights: Copyright (c) 2026 Patricio Álvarez Muñoz, Fernando Erasmo Pacheco Olea, Dennis Alfredo Peralta-Gamboa (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-01
publication date: 2026-01-01
starting page: 1170
ending page: 1170
doi: 10.56294/dm20261170

Analyzing the Impact of Self-Regulated Learning on Academic Performance and Student Engagement
type: item
description: Self-Regulated Learning (SRL) refers to students’ ability to manage and control their own learning processes, and it has been widely recognized as a key predictor of academic achievement and student engagement. Despite the growing volume of SRL research, a Scopus-based analysis indicates that studies specifically examining the relationship between SRL, achievement, and engagement remain limited. This gap restricts the development of more comprehensive educational strategies that maximize student learning outcomes. This study aims to analyze trends in SRL research within the 2020–2024 period, particularly focusing on how SRL relates to academic achievement and student engagement, and to identify underexplored themes that require further investigation. Results: The analysis reveals that although SRL has been extensively studied, research explicitly linking SRL to academic achievement and student engagement remains scarce. Network visualization shows that Cluster 5 contains the least-developed themes, including achievement goals, learning strategies, metacognition, and motivation indicating that these topics require deeper exploration. Overlay visualization further demonstrates that the peak of SRL-related research occurred in mid-2021 and throughout 2022, followed by a noticeable decline in 2023 and 2024. Density visualization confirms that while SRL is a widely researched construct, studies investigating its direct impact on achievement and engagement are still relatively limited.
subject: Review
  topic: section
subject: Self-regulated learning
  topic: keywords
subject: Learning
  topic: keywords
subject: Students
  topic: keywords
subject: achievement
  topic: keywords
creator: Fany Rifqoh
creator: Muhammad Nur Wangid
creator: Suwarjo Suwarjo
creator: Yulia Azriya
rights: Copyright (c) 2026 Fany Rifqoh, Muhammad Nur Wangid, Suwarjo, Yulia Azriya (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-01
publication date: 2026-01-01
starting page: 1335
ending page: 1335
doi: 10.56294/dm20261335

Mathematical model based on Machine Learning Enabled IoT Sensing and Decision-Making in Farming
type: item
description: Introduction: The rapid expansion of global food demand, combined with unpredictable climate variability and resource scarcity, necessitates intelligent solutions for sustainable agriculture. Method: This study introduces an IoT-driven intelligent greenhouse monitoring and decision-making framework that integrates advanced machine learning (ML) models with heterogeneous environmental data. Using multi-source sensor networks and edge-cloud collaboration, the framework dynamically regulates greenhouse environments while providing yield forecasting and disease detection capabilities. Results: Experimental results demonstrate that the proposed system achieves high detection accuracy (F1 = 96.8%), low yield prediction error (RMSE = 0.40 tons/ha), and efficient energy usage (0.46 J per inference). Reinforcement learning controllers further optimize climate regulation, reducing temperature RMSE to 0.72 °C and achieving energy savings of up to 20% compared to traditional PID systems. The hybrid CNN-Transformer disease detection model outperforms benchmarks, attaining 97.9% accuracy with improved calibration reliability. Conclusion: Collectively, these findings confirm that the proposed IoT–ML framework not only improves productivity and sustainability but also ensures scalability for large-scale deployments in diverse agricultural environments.
subject: Original
  topic: section
subject: Agriculture 4.0
  topic: keywords
subject: Attention Mechanism
  topic: keywords
subject: Crop Disease Detection
  topic: keywords
subject: Data Fusion
  topic: keywords
subject: Edge Computing
  topic: keywords
subject: Smart farming
  topic: keywords
creator: Mosleh Hmoud Al-Adhaileh
rights: Copyright (c) 2026 Mosleh Hmoud Al-Adhaileh (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-01
publication date: 2026-01-01
starting page: 1345
ending page: 1345
doi: 10.56294/dm20261345

Methodology for Vulnerability Assessment in WSNs Using CVSS and NIST SP 800-30
type: item
description: Wireless Sensor Networks (WSNs) play a vital role in applications where protecting data is critical. This study presents a six-step methodology for performing intrusive security audits on IEEE 802.15.4-based WSNs, focusing on identifying and evaluating vulnerabilities that compromise confidentiality, integrity, and availability. The approach combines the Offensive Security framework, the NIST SP 800-30 risk assessment guidelines, and the CVSS scoring system to quantify vulnerabilities. Two experimental setups were used: one with temperature sensors, and another with both temperature and CO₂ sensors. Attacks including sniffing, spoofing, data tampering, and denial-of-service were executed using ZBOSS Sniffer, Wireshark, and a Zigbee CC emulator. Key vulnerabilities involved network tracking, unauthorized data interception, and manipulation of traffic flows. Results showed that sniffing was the most effective technique, achieving the highest CVSS scores, particularly in the dual-sensor scenario. The methodology proved effective in uncovering security weaknesses and highlights the need for tailored mitigation strategies (e.g., stronger commissioning, authenticated encryption, and anomaly detection) to improve WSN resilience.
subject: Original
  topic: section
subject: Wireless Sensor Networks
  topic: keywords
subject: vulnerability assessment
  topic: keywords
subject: penetration Testing
  topic: keywords
subject: risk assessment
  topic: keywords
subject: cybersecurity
  topic: keywords
creator: Fabián Cuzme-Rodríguez
creator: Kevin Oñate-Pozo
creator: Henry Farinango-Endara
creator: Luis Suárez-Zambrano
creator: Edgar Jaramillo-Vinueza
creator: Jorge Benalcázar-Gómez
rights: Copyright (c) 2025 Fabián Cuzme-Rodríguez, Kevin Oñate-Pozo, Henry Farinango-Endara, Luis Suárez-Zambrano, Edgar Jaramillo-Vinueza, Jorge Benalcázar-Gómez (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-25
publication date: 2025-11-25
starting page: 836
ending page: 836
doi: 10.56294/dm2025836

Financial predictors of sme failure: variable selection with lasso versus random forest
type: item
description: Introduction: The study aimed to identify the financial variables that best predicted the failure of small and medium-sized enterprises (SMEs). It addressed the need for reliable financial indicators capable of signaling early distress and supporting risk-management practices. Methods: A quantitative methodology was adopted within a hypothetico-deductive framework. Two complementary variable-selection techniques were applied. First, the LASSO regression method introduced a regularization constraint to eliminate variables with weak explanatory power. Second, the Random Forest algorithm assessed the relative importance of financial variables in overall model performance. The two approaches were compared to determine their effectiveness in identifying the most relevant predictors of SME failure. Results: The LASSO model produced a negative coefficient of determination (R² = –1.2179), demonstrating performance inferior to a simple mean-based prediction and indicating that LASSO was not suitable in this context. In contrast, the Random Forest model achieved a very high R² value (0.9571), reflecting strong predictive accuracy and robustness. Based on the Random Forest results, six key financial predictors of SME failure were identified: financial structure, return on assets, return on sales, return on equity, liquidity, and solvency. Conclusions: The study demonstrated that Random Forest outperformed LASSO in selecting meaningful financial predictors of SME failure. The six identified variables offered a reliable analytical framework for understanding and anticipating financial distress. These findings provided valuable insights for academic research and practical applications in risk assessment and early warning systems for SMEs.
subject: Original
  topic: section
subject: Prediction SME failure
  topic: keywords
subject: Variable selection
  topic: keywords
subject: Financial ratios
  topic: keywords
subject: LASSO regression
  topic: keywords
subject: Random Forest
  topic: keywords
creator: Amina Guennoun
creator: Siham Ammari
creator: Saad Saadouni
creator: Souad Habbani
rights: Copyright (c) 2026 Amina Guennoun, Siham Ammari , Saad Saadouni , Souad Habbani (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-01
publication date: 2026-01-01
starting page: 1289
ending page: 1289
doi: 10.56294/dm20261289

Transformer guided and GAN augmented deep learning for medical image diagnostics
type: item
description: Introduction: Medical imaging serves as a crucial tool for disease diagnosis but current image analysis techniques fail to handle noisy data and insufficient annotations and different imaging modalities. Deep learning techniques have transformed medical imaging but achieving high diagnostic accuracy alongside computational efficiency remains a key challenge in clinical deployment. Objective: The research proposes a single deep learning system which combines CNNs with RNNs and GANs to enhance automated disease detection from medical images through improved accuracy, better interpretability and faster processing times. Method: The proposed Transformer-guided hybrid model uses CNNs to extract spatial features and RNNs to detect temporal patterns while GANs perform data augmentation and anomaly detection. Use consistent passive or active voice. The model was trained, validated on multimodal datasets and subsequently evaluated against ten baseline models, including SVM, transfer learning, and attention-based architectures. The evaluation metrics consisted of accuracy and precision and sensitivity and ROC-AUC. Results: The integrated framework achieved superior diagnostic performance with 90% accuracy, 88% precision, 86% sensitivity and 0.95 ROC-AUC which outperformed all baseline models. The system delivered achieved faster processing without sacrificing diagnostic accuracy across imaging modalities without compromising its diagnostic accuracy for different imaging techniques. Conclusions: The research developed an AI diagnostic system which uses CNN, RNN and GAN technologies to achieve efficient and ethical medical image analysis. The system enhances precision and speed while ensuring patient data security and transparent clinical reporting, enabling scalable AI-driven diagnostics.
subject: Original
  topic: section
subject: Attention Mechanisms
  topic: keywords
subject: Capsule Networks
  topic: keywords
subject: Convolutional Neural Networks
  topic: keywords
subject: Disease Detection
  topic: keywords
subject: Generative Adversarial Networks
  topic: keywords
subject: Medical Imaging
  topic: keywords
subject: Predictive Modeling
  topic: keywords
subject: Recurrent Neural Networks
  topic: keywords
subject: Resource Optimization
  topic: keywords
subject: Transfer Learning
  topic: keywords
creator: Ahmed A. F Osman
creator: Rajit Nair
creator: Theyazn H. H Aldhyani
creator: Sultan Ahmad
creator: Mosleh Hmoud Al-Adhaileh
creator: Hikmat A. M. Abdeljaber
creator: Mohammed Ataelfadiel
rights: Copyright (c) 2026 Ahmed A.F Osman, Rajit Nair, Theyazn H.H Aldhyani, Sultan Ahmad, Mosleh Hmoud Al-Adhaileh, Hikmat A. M. Abdeljaber, Mohammed Ataelfadiel (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-01
publication date: 2026-01-01
starting page: 1301
ending page: 1301
doi: 10.56294/dm20261301

Validation of learning styles in Higher Education: A case study at the Faculty of Applied Sciences (FICA)
type: item
description: The identification of learning styles and the adaptation of pedagogical strategies constitutes a determining factor in university academic success, especially in technical programs where dropout rates can reach up to 40% during the first years. This study presents the development, implementation, and validation of an innovative web application designed to analyze learning styles in university students. The platform integrates the theoretical models of Kolb, Herrmann, and Sperry, developed with React and Flask technologies under a service-oriented architecture that ensures scalability and ease of maintenance. Validation was carried out using the DeLone & McLean model, applying a structured questionnaire to a stratified sample of 89 students from the Faculty of Engineering in Applied Sciences. The results demonstrated high system acceptance, with favorability levels exceeding 80% across all dimensions. The statistical analysis, with a Cronbach’s alpha of 0.867, confirmed the reliability of the instrument and the robustness of the results. Likewise, the implementation of the platform enabled the early identification of learning patterns and facilitated the personalization of educational strategies, improving the student experience through an accessible and user-friendly interface. The total effect of 0.6 reflected a significant global impact on academic performance, by strengthening the process of adaptation to learning styles. The findings showed a predominance of the accommodator style according to Kolb, a realistic thinking preference in the Herrmann test, and greater left-hemisphere activity according to Sperry, highlighting the importance of designing adaptive pedagogical strategies.
subject: Original
  topic: section
subject: Learning styles
  topic: keywords
subject: web development
  topic: keywords
subject: educational analysis
  topic: keywords
subject: educational technology
  topic: keywords
subject: React and Flask
  topic: keywords
subject: higher education
  topic: keywords
creator: Silvia Arciniega Hidrobo
creator: José Jácome León
creator: Stefany Flores
rights: Copyright (c) 2025 Silvia Arciniega Hidrobo, José Jácome León, Stefany Flores (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-26
publication date: 2025-11-26
starting page: 824
ending page: 824
doi: 10.56294/dm2025824

Development of IT Project Management Learning Media Using YOLO and Reinforcement Learning
type: item
description: This study addresses the gap in traditional project management education by developing a web-based learning platform for the Information Technology Project Management course. The platform integrates YOLO technology for real-time visual detection and Reinforcement Learning (RL) for automated decision-making. The development process adopted a Research and Development (R&D) approach using the ADDIE model, which consisted of five phases: Analysis, Design, Development, Implementation, and Evaluation. This research is significant as it offers a solution to the challenge of bridging the gap between theoretical knowledge and practical application in project management. The primary objective is to enhance students’ understanding of project dynamics and improve their decision-making skills through real-time project monitoring and data-driven recommendations. The development process adopted a Research and Development (R&D) approach using the ADDIE model, consisting of five phases: Analysis, Design, Development, Implementation, and Evaluation. Evaluation results demonstrated that the developed learning media achieved an average validity score of 87.7%, a practicality score of 85.7%, and an effectiveness score of 86.4%, confirming that the platform is valid, practical, and effective in improving student engagement and conceptual understanding of project management. The integration of YOLO and RL technologies proved effective in helping students comprehend project dynamics and support data-driven decision-making.
subject: Original
  topic: section
subject: learning media
  topic: keywords
subject: project management
  topic: keywords
subject: information technology
  topic: keywords
subject: YOLO
  topic: keywords
subject: reinforcement learning
  topic: keywords
creator: Anggun Fergina
creator: Lusiana Sani Parwati
creator: RR. Amanna Dzikrillah LAH
creator: Zaenal Alamsyah
creator: Muhammad Fadhli Nurdiansyah Rangkuti
creator: Axa Rajandrya
creator: Muhammad Rizki Akbar
rights: Copyright (c) 2026 Anggun Fergina, Lusiana Sani Parwati, RR. Amanna Dzikrillah LAH, Zaenal Alamsyah, Muhammad Fadhli Nurdiansyah Rangkuti, Axa Rajandrya, Muhammad Rizki Akbar (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-01
publication date: 2026-01-01
starting page: 1305
ending page: 1305
doi: 10.56294/dm20261305

AI-Driven Hybrid Defense Mechanisms for Enhancing Cybersecurity in Cyber-Physical Systems Through Packet Sniffing and Cyber Ranges
type: item
description: Introduction: Cyber-Physical Systems are the backbone of modern critical infrastructures but remain inherently vulnerable to cyberattacks due to their interconnected nature. This calls for more adaptive and intelligent intrusion detection solutions, as existing approaches often fall short in capturing the spatial-temporal complexity of CPS traffic. Methods: This work proposes a hybrid deep learning framework based on the integration of CNN and LSTM networks with an attention mechanism. The system exploits real-time packet sniffing for fine-grained traffic analysis and the use of cyber range simulations to evaluate its performance in different attack conditions. A structured preprocessing pipeline, covering normalization, time windowing, and controlled data augmentation, ensures high-quality feature extraction while maintaining spatial and temporal patterns. Results: The proposed model outperforms standalone CNN and LSTM architectures on a balanced multi-class CPS dataset with 99,08 % accuracy and very high precision, recall, and F1-scores across all attack types. Attention significantly enhances sensitivity by picking up important temporal features and provides better interpretability via packet-level relevance mapping. The model maintains an extremely low false-positive rate, further supporting its suitability for real-world deployment. Conclusions: These results position the hybrid CNN-LSTM-Attention architecture, combined with packet sniffing, as a robust and adaptive intrusion detection for CPS environments. Strong performance with low error rates accordingly underlines the potential to mitigate emerging threats. Future work will extend the evaluation to diverse datasets and will benchmark the system against state-of-the-art detection models in order to further validate generalizability.
subject: Original
  topic: section
subject: Cyber-Physical Systems
  topic: keywords
subject: Intrusion Detection
  topic: keywords
subject: Hybrid CNN–LSTM
  topic: keywords
subject: Attention Mechanism
  topic: keywords
subject: Packet Sniffing
  topic: keywords
subject: Real-Time Detection
  topic: keywords
creator: Deepa Singh Sisodiya
creator: Ritu Tiwari
creator: Priyank Jain
rights: Copyright (c) 2026 Deepa Singh Sisodiya, Ritu Tiwari, Priyank Jain (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-01
publication date: 2026-01-01
starting page: 1329
ending page: 1329
doi: 10.56294/dm20261329

Improving Autism Detection Accuracy with an Optimized Local-Asymmetric Adaptive Hybrid GCN for EEG Data
type: item
description: Autism spectrum disorder (ASD) is an intricate nervous disorder typically diagnosed through the use of electroencephalography (EEG). A novel model named Dual Encoder-Balanced Conditional Wasserstein Generative Adversarial Network with Resting-state EEG-based Hybrid Graph Convolutional Network (DEBCWGAN-Rest-HGCN) was made from this context. By fixing the class imbalance and making synthetic EEG samples, it was able to detect ASD with encouraging results. However, it ignores the dynamic brain patterns recorded by task-based EEG in favor of resting-state EEG. The Rest-HGCN model also cannot successfully capture the uneven spatial and temporal aspects of EEG signals, and its fixed hyperparameters might make it less accurate in detecting different types of EEG data. This article presents a new model for finding and diagnosing ASD called the Optimized Local-Asymmetric Adaptive Hybrid GCN (OLA2HGCN). This model uses both spatial and temporal information from resting-state and task-driven EEG signals. It is based on the way autism affects brain connections and a variation amid the left and right hemispheres. The LA2HGCN can efficiently collect discrete spatiotemporal EEG information through distinct areas and hemispheres by improving the HGCN model with hierarchical feature extraction and fusion approaches. This model has a time based feature extraction approach in the cognitive prior graph branch that picks up temporal characteristics inside and between brain areas. It also has an adaptive GCN for spatial feature extraction across non-Euclidean distributions of electrodes. An attention layer shows how each hemisphere helps with ASD classification. A new Quantum Artificial Gorilla Troops Optimizer (QGTO) is also presented to help the LA2HGCN model choose the best hyperparameters. The QGTO is based on the social intelligence of gorilla tribes. It rapidly traverses intricate search spaces and achieves an equilibrium between exploration and exploitation. By adding quantum mec…
subject: Original
  topic: section
subject: Autism spectrum disorder
  topic: keywords
subject: Resting-state EEG
  topic: keywords
subject: Task-based EEG
  topic: keywords
subject: DEBCWGAN
  topic: keywords
subject: GCN
  topic: keywords
subject: Quantum mechanics
  topic: keywords
subject: Artificial gorilla troops optimizer
  topic: keywords
creator: K. Lalli
creator: Senbagavalli M
rights: Copyright (c) 2026 K. Lalli, Senbagavalli. M (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-01
publication date: 2026-01-01
starting page: 1339
ending page: 1339
doi: 10.56294/dm20261339

IoT and AI for Smart Rural Fishing: System Architecture, TEK Integration, and Economic Viability Analysis
type: item
description: Introduction: The integration of IoT and AI technologies presents opportunities for modernizing traditional fishing practices while preserving ecological knowledge. Objective: This research develops and evaluates an integrated IoT-AI system to enhance traditional Chinese fishing net operations in Kerala backwaters, India, with an emphasis on TEK integration and economic viability assessment. Method: We deployed an edge-cloud computing architecture integrating 15 environmental sensors, automated winch systems, and cloud-based AI analytics at Chathedam fishing site (9,9674°N, 76,2816°E) over six months (January–June 2025), documenting 1,000 fishing operations and validating 20 TEK rules through statistical analysis. Results: Nine high-confidence TEK rules (≥0,80) achieved 100 % validation success with 18–47 % catch improvements. AI-guided operations achieved 10 percentage point improvement in profit margin (70,4 % vs 60,4 %) primarily through cost reduction. System investment of $1,720 achieves payback in 1,8 months with 898 % ROI over 18 months. Conclusions: The validated edge-cloud architecture, TEK-AI integration framework, and demonstrated economic viability provide a replicable model for technology-enabled enhancement of traditional small-scale fisheries.
subject: Original
  topic: section
subject: Internet of Things (IoT)
  topic: keywords
subject: Edge computing
  topic: keywords
subject: Traditional Ecological Knowledge (TEK)
  topic: keywords
subject: Economic viability
  topic: keywords
subject: Human-AI collaboration
  topic: keywords
subject: Smart fisheries
  topic: keywords
subject: Chinese fishing nets
  topic: keywords
creator: Manoj Krishnan
creator: Dr. R. Karthik
rights: Copyright (c) 2026 Manoj Krishnan , Dr. R. Karthik (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-01
publication date: 2026-01-01
starting page: 1302
ending page: 1302
doi: 10.56294/dm20261302

Artificial intelligence: prototype of an automated irrigation system for the cultivation of roses in Cotopaxi
type: item
description: Implementing artificial intelligence in agriculture can improve efficiency, reduce pollution, and promote more effective agricultural production. Efficient irrigation management avoids wasting water and ensures that plants receive the right amount of water at the right time. The purpose of this research is to present an intelligent irrigation system based on neural networks and fuzzy logic, to avoid the presence of pests due to excess relative humidity in rose crops in Cotopaxi. A mixed methodology was used. The SCRUM methodology, Android Studio as an integrated development environment, a relational database management system and the Mobile-D method were used as software elements. For the prototype construction, the main hardware element that was used was the Arduino Board. The system for irrigating automated water using fuzzy logic took less time than manual irrigation. Training actions were proposed for employers and employees in the use and maintenance of the automated irrigation system, to maintain continuous improvement in the process
subject: Original
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Irrigation System
  topic: keywords
subject: Rose Cultivation
  topic: keywords
subject: Cotopaxi
  topic: keywords
creator: Manuel William Villa Quisphe
creator: José Augusto Cadena Moreano
creator: Juan Carlos Chancusig Chisag
rights: Copyright (c) 2024 Manuel William Villa Quisphe, José Augusto Cadena Moreano, Juan Carlos Chancusig Chisag (Author) https://creativecommons.org/licenses/by/4.0
date: 2024-07-05
publication date: 2024-07-05
starting page: 398
ending page: 398
doi: 10.56294/dm2024398

Predicting saturation for a new fabric using artificial intelligence (fuzzy logic): experimental part
type: item
description: Weaving saturation can have harmful consequences, such as problems with loom performance, accelerated wear of mechanical parts and loss of raw materials. To avoid these problems, when designing and creating new fabrics, the densities and yarn qualities must be carefully matched with the weaves to ensure successful testing. To facilitate this task, this study focuses on the development of a practical fuzzy logic model for predicting the saturation of new fabrics. An experimental part was carried out to validate this fuzzy model. The fabric samples used in this study came from three different types of weaves, namely plain, twill and satin. These samples also included five weft counts (Nm) and eight different densities. The results obtained using the fuzzy logic model developed were compared with experimental values. The prediction results were satisfactory and precise, demonstrating the effectiveness of the fuzzy logic model developed. The mean absolute error of the calculated fuzzy model was 1,97 %. It was therefore confirmed that this fuzzy model was both fast and reliable for predicting the saturation of new fabric
subject: Original
  topic: section
subject: Weaving
  topic: keywords
subject: Fuzzy logic
  topic: keywords
subject: Modelling
  topic: keywords
subject: Saturation Index
  topic: keywords
subject: Weaveability
  topic: keywords
creator: Mhammed El Bakkali
creator: Redouane Messnaoui
creator: Mustapha Elkhaoudi
creator: Omar Cherkaoui
creator: Aziz Soulhi
rights: Copyright (c) 2024 Mhammed El Bakkali, Redouane Messnaoui, Mustapha Elkhaoudi, Omar Cherkaoui, Aziz Soulhi (Author) https://creativecommons.org/licenses/by/4.0
date: 2024-01-01
publication date: 2024-01-01
starting page: 251
ending page: 251
doi: 10.56294/dm2024251

A proposed method for detecting network intrusion using an ensemble learning (stacking -voting) approach with unbalanced data
type: item
description: The use of computer networks has become necessary in most human activities. However, these networks are exposed to potential threats affecting the confidentiality, integrity, and availability of data. Nowadays, the security of computer networks is based on tools and software such as antivirus software. Among the techniques used for machine protection, firewalls, data encryption, etc., were mentioned. These techniques constitute the first phase of computer network security. However, they remain limited and do not allow for full network protection. In this paper, a Network Intrusion Detection System (NIDS) was proposed for binary classification. This model was based on ensemble learning techniques, where the base models were carefully selected in a first layer. Several machine learning algorithms were individually studied to choose the best ones based on multiple metrics, including calculation speed. The SMOTE technique was used to balance the data, and cross-validation was employed to mitigate overfitting issues. Regarding the approaches used in this research, a stacking and voting model was employed, trained, and tested on a UNSW-NB15 dataset. The stacking classifier achieved a higher accuracy of 96 %, while the voting approach attained 95,6 %
subject: Original
  topic: section
subject: Binary Classification IDS
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Ensemble Learning
  topic: keywords
subject: Stacking model
  topic: keywords
subject: Voting model
  topic: keywords
subject: UNSW-NB15
  topic: keywords
creator: Anouar Bachar Anouar Bachar
creator: Omar EL Bannay Omar EL Bannay
rights: Copyright (c) 2024 Anouar Bachar, Omar EL Bannay (Author) https://creativecommons.org/licenses/by/4.0
date: 2024-01-01
publication date: 2024-01-01
starting page: 297
ending page: 297
doi: 10.56294/dm2024297

Management of operational activities at the Regional Human Milk Collection Center: an analysis from the perspective of local intervention strategies and information systems
type: item
description: The purpose of the creation of the Human Milk Donation Network is to function as a device for knowledge and interaction between actors at different provincial, regional and municipal levels, to revalue and empower the figure of the community promoter, administrative and childcare worker as part of the health team, each in his/her role, raising awareness among the population of the importance of milk donation, strengthening promotion and increasing the number of donors, jointly elaborating (central, regional and municipal teams) local intervention strategies and establishing and integrating the information system. The objective of this paper was to describe the experience on the implementation of the Human Milk Collection Center that contributes to increase the volume of human milk for pasteurization in the human milk bank, in the HZGA Prof. Dr. Ramon Carrillo of Ciudadela in the month January to March 2022. Between the two hospital institutions, the Human Milk Bank of the HZGA San Martin de La Plata and the Breastfeeding Center of the HZGA Prof. Dr. Ramon Carrillo de Ciudadela, an effective chain of support for breastfeeding has been activated, through the Health Service Network modality, with the objective of contributing to the increase of pasteurized human milk. With this purpose, safe strategies have been promoted, which promote donation and increase the number of human milk donors, a coordinated work has been propitiated that guarantees equity of access to human milk and equal opportunities for premature or critical newborns who require pasteurized milk, when there is not enough milk from the mother's own milk, donated milk is the best alternative
subject: Short communications
  topic: section
subject: Liaison Nursing
  topic: keywords
subject: Local Intervention
  topic: keywords
subject: Information Systems
  topic: keywords
subject: Breast Milk
  topic: keywords
subject: Human Milk Donation
  topic: keywords
creator: Matias Orsetti
creator: Yamila Bertolini
creator: Andrea Fabiana Villaalta
creator: Fernanda Creo
creator: Patricia Santillan
rights: Copyright (c) 2022 Matias Orsetti, Yamila Bertolini, Andrea Fabiana Villaalta, Fernanda Creo, Patricia Santillan (Author) https://creativecommons.org/licenses/by/4.0
date: 2022-12-10
publication date: 2022-12-10
starting page: 73
ending page: 73
doi: 10.56294/dm202273

Bridging the Gap to Success: A 50-Year Bibliometric Analysis of Preparatory Education and Academic Performance in Engineering and STEM Fields
type: item
description: The transition from secondary to higher education presents significant challenges for students in engineering and STEM fields, where academic preparedness is crucial for success. This study presents a comprehensive bibliometric analysis of global scientific production on preparatory education and its impact on academic performance, with a particular focus on engineering and STEM students. Using a dataset of 210 documents retrieved from the Scopus database (1974-2025), the analysis explores publication trends, influential sources, leading authors, thematic evolution, and citation patterns. The methodological framework integrates Biblioshiny (R-based Bibliometrix) and VOSviewer to perform descriptive, relational, and conceptual analyses. Results indicate a steady growth in publications, with a significant increase after 2010, and a moderate annual growth rate of 4.03%. The United States leads in productivity, followed by Australia and the United Kingdom, though contributions from emerging regions such as Latin America and the Middle East are gaining visibility. Keyword co-occurrence and thematic mapping reveal four major clusters: academic performance and educational measurement, engineering education and curricula, student transition and retention strategies, and emerging technologies such as machine learning and predictive analytics. Despite a rich thematic landscape, the field shows low international collaboration (4.76%) and a fragmented author network. This study contributes to the understanding of the intellectual structure and global dynamics of preparatory education research and offers insights for policymakers, educators, and researchers aiming to enhance student success in engineering and higher education. Future research should strengthen interdisciplinary collaborations and explore data-driven strategies for personalized academic support.
subject: Original
  topic: section
subject: Preparatory Education
  topic: keywords
subject: Academic Performance
  topic: keywords
subject: Engineering Education
  topic: keywords
subject: Transition to University
  topic: keywords
subject: Higher Education
  topic: keywords
subject: Academic Gap
  topic: keywords
subject: Network Analysis
  topic: keywords
subject: International Collaboration
  topic: keywords
creator: Julio Guerra
creator: Ana C. Umaquinga-Criollo
creator: Daisy E. Imbaquingo-Esparza
creator: Fernando R. Ramírez-Paredes
rights: Copyright (c) 2025 Julio Guerra, Ana C. Umaquinga-Criollo , Daisy E. Imbaquingo-Esparza, Fernando R. Ramírez-Paredes (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-27
publication date: 2025-11-27
starting page: 818
ending page: 818
doi: 10.56294/dm2025818

Non-Invasive Blood Density Measurement using Photoplethysmography Technique
type: item
description: Introduction: Blood density measurement is a key diagnostic indicator for assessing hematological and cardiovascular conditions. Conventional methods require blood extraction and laboratory equipment. In this context, a non-invasive biomedical device based on photoplethysmography (PPG) and the Beer–Lambert law was developed to estimate blood density through optical parameters. Methods: A prototype was designed using a microcontroller, an optical sensor, and an OLED display. The acquired signals were digitally filtered through FIR and IIR algorithms to separate pulsatile and non-pulsatile components. Twenty-one volunteers were recruited, and sixteen valid recordings remained after artifact removal. Laboratory reference values were compared using linear regression and Bland–Altman analysis to evaluate concordance between clinical and device-derived measurements. Results: The device achieved an average error of 2.0 % for blood-density estimation, 6.06 % for hematocrit, and 7.01 % for erythrocyte count. The limits of agreement remained within clinically acceptable ranges, with a slight underestimation bias at higher density values. Main limitations were related to the restricted spectral range of the red and infrared LEDs and to physiological variables such as peripheral perfusion and involuntary movements. Conclusions: The proposed system demonstrated accuracy and stability for non-invasive blood-density estimation, validating PPG as a portable, low-cost diagnostic tool. Future improvements should include a broader calibration dataset and multispectral light sources with higher sensitivity to enhance linearity and dynamic range.
subject: Original
  topic: section
subject: blood density
  topic: keywords
subject: photoplethysmography
  topic: keywords
subject: Beer–Lambert law
  topic: keywords
subject: non-invasive biomedical device
  topic: keywords
creator: Theo Marcelo Galindo Chicaiza
creator: Luz María Tobar Subía Contento
creator: Brizeida Nohemí Gámez Aparicio
creator: Marco Antonio Ciaccia Sortino
creator: Cosme Damián Mejía Echeverría
creator: Diego Luis Ortiz Morales
rights: Copyright (c) 2025 Theo Marcelo Galindo Chicaiza, Luz María Tobar Subía Contento , Brizeida Nohemí Gámez Aparicio , Marco Antonio Ciaccia Sortino, Cosme Damián Mejía Echeverría , Diego Luis Ortiz Morales (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-27
publication date: 2025-11-27
starting page: 819
ending page: 819
doi: 10.56294/dm2025819

Simulated Annealing-Based Optimization of IEEE-33 Radial Distribution Networks with Integrated Auxiliary PV Sources Using PyPower
type: item
description: The integration of distributed photovoltaic (PV) generation in radial distribution networks has been consolidated as a key strategy to reduce technical losses and improve voltage profiles. However, the optimal placement and sizing of these sources remain a challenge due to the nonlinear and multimodal nature of the problem. In this work, an approach based on simulated annealing with adaptive restarts, implemented in PyPower, is proposed to determine the optimal location of PV units in the IEEE 33-bus test system. The methodology considers the minimization of active power losses as the objective function, subject to operational constraints and voltage limits. The results show that the proposed strategy achieves a reduction in losses of up to 52% compared to the base scenario, in addition to improving minimum voltage profiles to values close to 0.98 pu. The comparison with non-optimized scenarios highlights the effectiveness of the method in balancing energy efficiency and quality of service. This study contributes to the literature by demonstrating the applicability of lightweight metaheuristics in distribution network planning problems and lays the groundwork for future research integrating storage and dynamic load and generation scenarios.
subject: Original
  topic: section
subject: Transformer optimization
  topic: keywords
subject: Simulated annealing
  topic: keywords
subject: Distribution networks
  topic: keywords
subject: IEEE 33-bus
  topic: keywords
subject: Power losses
  topic: keywords
subject: Voltage regulation
  topic: keywords
creator: Francisco Roberto Naranjo Cobo
creator: Julio Esteban Guerra Masson
creator: Daisy Imbaquingo Esparza
rights: Copyright (c) 2025 Francisco Roberto Naranjo Cobo, Julio Esteban Guerra Masson, Daisy Imbaquingo Esparza (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-27
publication date: 2025-11-27
starting page: 821
ending page: 821
doi: 10.56294/dm2025821

Development and Mechanical Evaluation of a Biocomposite Based on Abaca Fiber and Acrylic Resin
type: item
description: The biocomposite made from a nonwoven abaca fiber reinforced with acrylic resin is proposed as an ecological alternative to synthetic leather. The objective of this research was to develop and characterize the material, evaluating the effect of two variables: the length of the abaca fiber (2 cm and 5 cm) and the concentration of acrylic resin (70% and 80%). The manufacturing process consisted of immersing the nonwoven fabric in the resin solution, followed by drying at 120°C. A completely randomized 2² factorial design was implemented, with a total of 20 experimental runs. The response variables were tensile strength and elongation, evaluated using a Titan 5 James Heal dynamometer under ISO 1421. Statistical analysis was performed using Statgraphics Centurion software and revealed that fiber length has a significant positive effect on tensile strength, while elongation is less relevant. The results obtained with 5 cm fibers and 70% acrylic resin achieved a tensile strength of 118,3 N; in terms of elongation, the value obtained was 50,2 mm with 2 cm fibers and 70% resin. Taken together, these findings position vegetable leather as a functional and sustainable material with high potential for application in the textile and composite materials industries.
subject: Original
  topic: section
subject: Abaca
  topic: keywords
subject: Biocomposite
  topic: keywords
subject: Nonwoven
  topic: keywords
subject: Elongation
  topic: keywords
subject: Tensile strength
  topic: keywords
creator: Angelo Zambrano
creator: Elsa Mora Muñoz
creator: Marco Naranjo Toro
rights: Copyright (c) 2025 Angelo Zambrano, Elsa Mora Muñoz , Marco Naranjo Toro (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-26
publication date: 2025-11-26
starting page: 823
ending page: 823
doi: 10.56294/dm2025823

Bibliometric Analysis of Sports Running Socks Made from Bamboo, Cotton, and Acrylic
type: item
description: This bibliometric analysis examines sports socks for running made from bamboo, cotton, and acrylic, with a focus on comfort, durability, and sustainability in the sports field. This study analyzed the evolution of publications using the Scopus database, reviewing 153 documents since 1990. Using bibliometric tools such as Bibliometrix and VOSviewer, trends, patterns, and gaps in the scientific literature were identified, offering a comprehensive view of the development of knowledge in this area. The results show that the main focus of the research is on the relationship between the friction and thermoregulation properties of socks, aimed at preventing blisters and improving performance, especially in high-friction sports such as running. The driving themes identified include the impact on foot health, biomechanics, and athletic performance, while the basic themes analyze friction and the interaction between materials. Likewise, niche topics such as product design and antimicrobial properties emerge, reflecting a growing interest in the development of functional and sustainable materials. The citation trend highlights the relevance of early studies addressing the structure and properties of socks to prevent injuries and improve user comfort. The conclusions emphasize the need for more comprehensive research on the use of alternative materials and their impact on health and athletic performance, contributing to the advancement of sustainable, high-performance sports textiles.
subject: Review
  topic: section
subject: Bibliometrics
  topic: keywords
subject: Sports socks
  topic: keywords
subject: Innovation in sports textiles
  topic: keywords
subject: Sustainable materials
  topic: keywords
creator: Elsa Sulay Mora-Muñoz
creator: José Rafael Posso-Pasquel
creator: Elvis Ramírez Encalada
rights: Copyright (c) 2025 Elsa Sulay Mora-Muñoz , José Rafael Posso-Pasquel , Elvis Ramírez Encalada (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-25
publication date: 2025-11-25
starting page: 831
ending page: 831
doi: 10.56294/dm2025831

Intelligent Monitoring and Early Warning System Against Frosts for Sustainable Agriculture
type: item
description: Frosts are an adverse climatic phenomenon that severely affects agriculture, causing irreversible damage to plants. This process leads to dehydration, deterioration of plant tissue, and, in extreme cases, total crop loss. The lack of effective prevention and response strategies exacerbates its consequences, resulting in significant economic losses, particularly among small-scale farmers. In Ecuador, the Andean region is especially vulnerable, as its agricultural production depends on stable climatic conditions to achieve adequate harvests. This study presents the development of an early warning system against frosts, based on an Internet of Things (IoT) architecture, which employs long-range Wireless Sensor Networks (WSNs) and supervised learning algorithms specifically decision trees to anticipate frost-prone conditions and issue timely alerts to farmers. This enables the adoption of preventive measures that minimize agricultural losses and strengthen the sector’s resilience to extreme weather events. The research was conducted using the Action Research methodology, which involved an analysis of the phenomenon and the requirements of the stakeholders, followed by the design of a solution that included the selection of appropriate hardware and technologies, as well as the validation of technical requirements. The system was designed and implemented in a real agricultural environment, and the results demonstrated that the proposed solution is reliable, accurate, and adaptable, enabling timely frost detection and real-time alert generation. Furthermore, its effectiveness in crop protection was evident, contributing to the development of a more efficient, sustainable, and technologically advanced agriculture.
subject: Original
  topic: section
subject: Frost control
  topic: keywords
subject: Internet of Things (IoT)
  topic: keywords
subject: Wireless Sensor Networks (WSN)
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Early warning system
  topic: keywords
subject: Agricultural resilience
  topic: keywords
subject: Climate change
  topic: keywords
creator: Katherine Guatemal-Neppas
creator: Luis Suárez Zambrano
creator: Carlos Vásquez-Ayala
creator: Jaime Michilena-Calderón
creator: Fabián Cuzme-Rodríguez
creator: Alejandra Pinto-Erazo
rights: Copyright (c) 2025 Katherine Guatemal-Neppas , Luis Suárez-Zambrano , Carlos Vásquez-Ayala , Jaime Michilena-Calderón , Fabián Cuzme-Rodríguez , Alejandra Pinto-Erazo (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-25
publication date: 2025-11-25
starting page: 830
ending page: 830
doi: 10.56294/dm2025830

Heterogeneous Computing for High-Complexity Problems: A Performance Study with DPC++ on HPC Platforms
type: item
description: The All-Pairs Shortest Paths (APSP) algorithm is crucial for a wide range of computing applications that necessitate the efficient calculation of minimum distances between all pairs of nodes in a graph. High-performance computing (HPC) has made significant progress in response to the demand to solve complex computing problems that process large amounts of data. The cubic complexity of the Floyd-Warshall algorithm makes its execution a computational challenge with large graphs, limiting its applicability on conventional platforms. This research addresses this problem by evaluating optimized and parallelized implementations in HPC architectures. The study aims to evaluate the performance of the APSP algorithm by parallel implementation using Intel DPC++ on heterogeneous high-performance architectures, including CPU, GPU, and FPGA. The Intel OneAPI platform was used for the implementation and execution of the algorithm on Intel Xeon Gold 6128 CPU-3.40 GHz processors, Intel Data Center GPU Max 1100, and FPGA Emulation Device processors. The solution was evaluated considering quantitative and statistical metrics, obtaining a significant improvement in the performance of the algorithm for large graphs, with 700x speedup and 70% efficiency in GPU architectures with 65536 nodes and 1024 parallelism subblocks. This study confirms the feasibility of implementing efficient and portable HPC solutions using DPC++ for massive data processing in heterogeneous architectures, offering a scalable and effective solution for modern computational challenges.
subject: Original
  topic: section
subject: Heterogeneous Programming
  topic: keywords
subject: All Pairs Shortest Paths
  topic: keywords
subject: HPC
  topic: keywords
subject: Intel oneAPI
  topic: keywords
subject: DPC++
  topic: keywords
subject: Parallelism
  topic: keywords
creator: Katari Tituaña
creator: MacArthur Ortega-Bustamante
creator: Pedro Granda
creator: Marco Pusdá-Chulde
rights: Copyright (c) 2025 Katari Tituaña, MacArthur Ortega-Bustamante , Pedro Granda , Marco Pusdá-Chulde (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-25
publication date: 2025-11-25
starting page: 835
ending page: 835
doi: 10.56294/dm2025835

Spectrum-Based Security Assessment of Electric Gates Using Offensive Security Methodology
type: item
description: This study investigates the security vulnerabilities of electric gate systems when exposed to radio-frequency attacks. Three Software Defined Radio (SDR) platforms, Flipper Zero, RTL-SDR and ADALM-PLUTO, were used to evaluate seven representative real-world scenarios by applying an Offensive Security methodology. Spectrum analysis served exclusively to capture and decode RF signals; the identification of security weaknesses and the design of mitigation strategies arose from a structured risk assessment rather than from the spectrum analyzer itself. The findings demonstrate that fixed-code protocols are highly susceptible to replay attacks, whereas rolling-code implementations substantially reduce the attack surface. A quantitative risk analysis based on the CIA triad and the MAGERIT framework was performed to determine the probability and impact of successful intrusions. The results support the adoption of rolling-code protocols and regular firmware updates to strengthen RF-based access control. This work provides the first empirical assessment in the region that combines three SDR platforms with a formal CIA/MAGERIT risk model to guide manufacturers, installers and end users in improving the security of electric gate systems.
subject: Original
  topic: section
subject: Wireless vulnerabilities
  topic: keywords
subject: Spectrum analysis
  topic: keywords
subject: MAGERIT methodology
  topic: keywords
subject: Software Defined Radio (SDR)
  topic: keywords
subject: RF security
  topic: keywords
creator: Smith Francisco Tandayamo-Valencia
creator: Fabián Cuzme-Rodríguez
creator: Luis Suárez-Zambrano
creator: Edgar Jaramillo-Vinueza
creator: Jorge Benalcázar-Gómez
rights: Copyright (c) 2025 Smith Francisco Tandayamo-Valencia, Fabián Cuzme-Rodríguez, Luis Suárez-Zambrano, Edgar Jaramillo-Vinueza, Jorge Benalcázar-Gómez (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-25
publication date: 2025-11-25
starting page: 832
ending page: 832
doi: 10.56294/dm2025832

Route Optimization for Urban Waste Collection via LoRa Communication and Deep Q-Network in a Controlled Simulation
type: item
description: Traditional urban solid waste management in Latin American cities operates using fixed routes, without considering the dynamic state of waste containers. This rigid approach leads to operational inefficiencies, excessive fuel consumption, and a significant environmental impact. A distributed architecture was designed that integrates HC-SR04 sensors (fill level) and MQ-135 sensors (air quality), connected to Heltec WiFi LoRa 32 V3 nodes. The captured data is transmitted via LoRa technology to a central gateway and stored in real-time on Firebase. These data were used to train a Deep Q-Network (DQN) model, developed in PyTorch using OpenAI Gym, with an input of 30 parameters (15 containers × 2 variables) and 15 possible actions. Training was performed over 1000 epochs with a learning rate of 0.0005 and a discount factor γ = 0.99. The model achieved a stable decision policy to dynamically prioritize critical collection points. Compared to static routes, there was a 16.4% reduction in distance traveled, 16.3% in operational time, and 16.4% in fuel consumption. Route planning was complemented by the Dijkstra algorithm and visualized in a geospatial interface using the Google Maps API. The system was implemented as a Flask API integrated with a hybrid mobile application, allowing real-time visualization of optimized routes and container status. This intelligent and scalable solution reduces resource usage, improves urban sustainability, and is well-suited for deployment in smart cities.
subject: Original
  topic: section
subject: Routing Algorithms
  topic: keywords
subject: Deep Reinforcement Learning (DQN)
  topic: keywords
subject: LoRa Technology
  topic: keywords
subject: Smart Cities
  topic: keywords
subject: Internet of Things (IoT)
  topic: keywords
creator: Gabriela Caragulla-Briceño
creator: Francisco Ger-Ortega
creator: Fabián Cuzme-Rodríguez
creator: Luis Suárez-Zambrano
creator: Alejandra Pinto-Erazo
creator: Henry Farinango-Endara
rights: Copyright (c) 2025 Gabriela Caragulla-Briceño, Francisco Ger-Ortega, Fabián Cuzme-Rodríguez, Luis Suárez-Zambrano, Alejandra Pinto-Erazo, Henry Farinango-Endara (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-25
publication date: 2025-11-25
starting page: 829
ending page: 829
doi: 10.56294/dm2025829

Implantation of Information Systems in the Popular Financial Sector: Two Case Studies Based on the SImplE Framework
type: item
description: The failure of information system implantations in small-scale organizations, such as savings and credit cooperatives, remains a recurring challenge in Latin America, primarily due to a lack of awareness and the underutilization of structured frameworks. This article presents two case studies conducted in Ecuadorian Segment 5 cooperatives to analyze their experiences in adopting financial management software using the SImplE framework (Simple Implantation of Enterprise IT Applications in SMEs) as an analytical reference. Although each cooperative carried out the implantation process empirically and under the guidance of its technology provider, the SImplE model enabled a structured analysis across three phases: pre-implantation, implantation, and post-implantation. The research employed a qualitative methodology, based on semi-structured interviews and document analysis. Findings reveal that the absence of formal planning, ad hoc definition of system requirements, empirical software selection, and the lack of written contracts all contributed to the system's long-term sustainability. Nonetheless, the implantation was made possible through institutional commitment and technical support. The study concludes that proper software selection, a well-managed stabilization phase, and the adoption of structured frameworks such as SImplE are critical factors for improving the effectiveness, continuity, and scalability of technology implantation processes in resource-constrained organizations.
subject: Original
  topic: section
subject: Case study
  topic: keywords
subject: digital transformation
  topic: keywords
subject: financial inclusion
  topic: keywords
subject: software implementation
  topic: keywords
subject: SImplE framework
  topic: keywords
creator: Irving Reascos
creator: Fausto Salazar
creator: Carpio Pineda
creator: Luz Córdova
creator: Jonathan Segovia
rights: Copyright (c) 2025 Irving Reascos, Fausto Salazar, Carpio Pineda, Luz Córdova, Jonathan Segovia (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-25
publication date: 2025-11-25
starting page: 833
ending page: 833
doi: 10.56294/dm2025833

Binary classification of defects in multiple coffee beans using lightweight convolutional neural networks for embedded systems
type: item
description: Coffee and its derivatives are part of the world's gastronomy and are also one of the main export products in Imbabura, Ecuador. Coffee producers and marketers must ensure the quality of their coffee beans, as a single seed can present several morphological defects. However, the selection of quality beans is a manual, tedious, and time-consuming process, prone to misclassification due to human limitations such as fatigue and varying subjective classification criteria. This work aims to detect and classify multiple coffee beans automatically present in an image (both good and bad) using modern convolutional neural networks, specifically YOLOv11 (Nano and Small variants), which are selected for their computational efficiency and good performance in real-time detection tasks. A custom dataset with a total of 4,090 RGB images with multiple coffee beans randomly located within each image was collected and manually labeled into two classes (good and bad) using the CVAT tool. Image preprocessing included background variation (white, gray, and black) to improve dataset variability and model robustness. Models were trained on the Kaggle platform with an NVIDIA Tesla P100 GPU (16 GB VRAM), using the PyTorch framework and the Ultralytics library. Results showed an accuracy of 0.880 for YOLO v11-Nano and 0.871 for YOLO v11-Small, along with inference times of 7.89 ms (126.7 fps) and 10.08 ms (99.20 fps), respectively. These results are comparable to those reported in the literature, but now considering the challenge of superimposing multiple coffee beans randomly located in each image with a variable background. These results suggest the potential for applying the model to embedded systems in practical settings for small and medium-sized producers and associations, thereby contributing to technological innovation in the Ecuadorian coffee sector.
subject: Original
  topic: section
subject: Coffee beans classification
  topic: keywords
subject: coffee defects
  topic: keywords
subject: CNN
  topic: keywords
subject: YOLO
  topic: keywords
subject: lightweight network
  topic: keywords
creator: Anderson Chamorro-Pinchao
creator: Marco Pusdá-Chulde
creator: Diego Trejo-España
creator: Victor Caranqui-Sánchez
creator: Iván García-Santillán
rights: Copyright (c) 2025 Anderson Chamorro-Pinchao , Marco Pusdá-Chulde , Diego Trejo-España , Victor Caranqui-Sánchez , Iván García-Santillán (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-25
publication date: 2025-11-25
starting page: 840
ending page: 840
doi: 10.56294/dm2025840

Effects of altitude on On-Board Fuel Consumption and CO₂ Emissions of a Vehicle: An analysis based on experimental data
type: item
description: Fuel efficiency and CO₂ emissions of vehicles are influenced by various factors, including altitude, whose effect is often ignored or underestimated. This study evaluates the effect of altitude on fuel consumption and CO₂ emissions in a light vehicle through an experimental campaign in four cities in Ecuador located between sea level and 3,000 meters above sea level. Fuel consumption and engine operation data were obtained through the OBD system. This information was used to generate engine maps and analyse vehicle performance at different altitude levels. The results showed that in cities at higher altitudes, the vehicle operated with engine loads above 70% due to slopes greater than 7%, while in coastal cities the loads were lower. Under similar engine load conditions, both fuel consumption and CO₂ rates were lower in high-altitude cities, showing a positive effect of altitude on combustion. However, energy-based emission factors were higher in high-altitude cities, probably due to lower air density. In terms of distance, fuel consumption and CO₂ emission factors were up to 7% higher in high-altitude cities. Overall, this study proposes a low-cost methodology and shows the importance of considering the effect of altitude when designing sustainable transport strategies for Latin American cities.
subject: Original
  topic: section
subject: Fuel Consumption
  topic: keywords
subject: CO2 emissions
  topic: keywords
subject: Altitude
  topic: keywords
subject: OBD data
  topic: keywords
subject: altitude
  topic: keywords
subject: vehicle emissions
  topic: keywords
creator: Fredy Rosero
creator: Carlos Xavier Rosero
creator: Fausto Tapia
creator: Daniel Arias
rights: Copyright (c) 2025 Fredy Rosero , Carlos Xavier Rosero , Fausto Tapia , Daniel Arias (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-25
publication date: 2025-11-25
starting page: 838
ending page: 838
doi: 10.56294/dm2025838

Gamification and Formative Feedback with Kahoot! in Programming Fundamentals: A TAM–SDT–UCD Approach with Mechatronics Engineering Students
type: item
description: Immediate and meaningful feedback is a key element in improving academic performance in computational foundational disciplines. This study aimed to evaluate the impact of gamified strategies in the feedback process of the Programming Fundamentals course. The intervention was conducted with a population of 31 first-level students from the Mechatronics Engineering program at Universidad Técnica del Norte, of which 15 students voluntarily participated from the same course. Activities were designed using the Kahoot! platform, focused on reinforcing theoretical and practical content related to the course syllabus and the programming languages Java and Python. The adopted methodology was applied in nature, with a descriptive approach and exploratory scope. The results showed increased active participation, improved retention of key concepts, and a more positive attitude towards programming learning. In addition, recurring error patterns were identified, which served as input for tailored feedback. The use of playful dynamics fostered a collaborative learning environment, contributing to the development of cognitive and metacognitive skills. It was concluded that the integration of educational technologies with gamification elements enhances the effectiveness of feedback processes, increases students' intrinsic motivation, and enables timely identification of knowledge gaps. This approach is especially valuable in programming education, where active engagement and continuous practice are critical to academic success.
subject: Original
  topic: section
subject: gamification
  topic: keywords
subject: Formative feedback
  topic: keywords
subject: programming
  topic: keywords
subject: Kahoot!
  topic: keywords
subject: Mechatronics engineering education
  topic: keywords
creator: Ana C Umaquinga-Criollo
creator: Carlos A Dávila Montalvo
creator: David A Ojeda Peña
rights: Copyright (c) 2025 Ana C. Umaquinga-Criollo, Carlos A. Dávila Montalvo, David A. Ojeda Peña (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-25
publication date: 2025-11-25
starting page: 839
ending page: 839
doi: 10.56294/dm2025839

Exploring user perceptions at public transport stops in a SEM approach to Accessibility and Safety
type: item
description: Bus stops in consolidated urban areas of the Global South often feature minimal infrastructure, compromising accessibility and safety. This study explored user perceptions of accessibility and safety at public transport stops in Quito, Ecuador, addressing gaps in subjective assessment methodologies. Using immersive audiovisual stimuli (360° videos and spatial audio), 16 real-world bus stop scenarios were replicated. A sample of 529 bus users including university students/staff personal evaluated six perceptual indicators of accessibility and safety indicators via digital surveys on tablets with noise-cancelling headphones. Structural Equation Modelling analysed relationships between latent constructs and sociodemographic and residential location variables. The results revealed that accessibility negatively influenced safety perceptions. Strong loadings for internal security and theft protection. Easy access outweighed stop size for accessibility perception. Users in living in south of Quito reported higher safety, while northern residents perceived lower safety. Group travel increased safety perceptions, and higher user volumes improved accessibility. The inverse accessibility-safety relationship highlights design trade-offs in high-density areas. Location-based heterogeneity (e.g., south Quito’s higher safety) underscores contextual influences. Immersive methods effectively captured perceptual complexity, but future research should expand to representative samples and integrate additional latent variables. Policy interventions require modular infrastructure adaptable to urban density gradients.
subject: Original
  topic: section
subject: Perceived safety
  topic: keywords
subject: Accessibility
  topic: keywords
subject: Structural Equation Modelling
  topic: keywords
subject: Bus stops
  topic: keywords
subject: Immersive stimuli
  topic: keywords
subject: Urban transport
  topic: keywords
creator: Luis Garzon
creator: Zamir Mera
creator: Ramiro Rosero
creator: Christiam Garzon
rights: Copyright (c) 2025 Luis Garzon , Zamir Mera , Ramiro Rosero , Christiam Garzon (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-25
publication date: 2025-11-25
starting page: 841
ending page: 841
doi: 10.56294/dm2025841