The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
Construction of a hierarchical classification and hierarchical security control system for multi-source, affordable data in smart grids
Introduction: To create an integrated hierarchical classification and hierarchical security control system to enhance real-time decision-making and protection in smart-grid contexts, Smart grids produce high-volume, heterogeneous multi-source data (smart meters, sensors, control systems) and are becoming more vulnerable to cyber threats. Method: The paper has provided hierarchical classification and security control system to manage multi-source data in smart grids. The system combines smart meter, sensor and control system data and allows the efficient real time decision making. The framework categorizes multi-source data into priority levels based on Decision Tree and Random Forest models and implements a multi-layer security control mechanism with real-time monitoring, anomaly detection, and response measures based on the classification result. A multi-layered security control framework is established to counter the cyber threats, and real-time monitoring mechanisms, anomaly detection mechanisms and response mechanisms are established. Results: The experimental findings demonstrate a high level of performance with an accuracy of 96.25, a precision of 97.49, a recall of 94.87 and an F1-score of 95.30. The system also guarantees a steady response time of 1.6 seconds on critical and non-critical threats, but there are scalability concerns with the system responding more slowly to both critical and non-critical threats (detected threats are lost when scaling to more than 5,000 devices). Conclusions: The results identify the high efficiency of the framework in offering secure, efficient, and scalable smart grid management solutions..
Copyright (c) 2026 Wu Zhichao, Gu Xueping, Duan Chaokun, Liang Haiping (Author) https://creativecommons.org/licenses/by/4.0
Bamboo cellulose composite flame-retardant filament for 3D printing
To find sustainable alternatives to traditional polymers, this study evaluated the potential of bamboo cellulose for the manufacture of flame-retardant filaments intended for 3D printing. Blending of five bamboo cellulose (BC)-based filament formulations was performed: S0 (C 5%, AR 95%), S1 (C 5%, AR 90%, U 0.4%, B 4%), S2 (C 4.8%, AR 86.45%, U 0.76%, B 7. 68%), S3 (C 5%, AR 82%, U 1%, B 12%) and S4 (C 5%, AR 69%, U 1%, B 25%). at 31.90C is left to stand for 30 min, 20 mL is placed inside the PET extruder and extruded through a 1 mm hole bamboo cellulose filament (BC). The results obtained using PAST 4 software revealed a normality of 95% in the data. The extruded filament titer (T) showed an average of 1.88 Ktex with no significant differences between formulations (P>0.05). Regarding combustion (C), samples S0 and S4 reached the lowest times, both 32 seconds. Regarding incandescence (I), a significant decrease in time was observed, from a maximum of 24.333 seconds in sample S0 to a minimum of 1.333 seconds in sample S4, representing a reduction of 94.521%. This behavior is attributed to the increase of urea (U) and borax (B) in the formulations. It is concluded that bamboo cellulose exhibits excellent flame-retardant properties and is suitable for processing in 3D printers.
Copyright (c) 2025 Willam Ricardo Esparza Encalada, Wilson Adrian Herrera Villarreal, Betty Alexandra Jaramillo Tituaña, Pamela Estefanía Naranjo Chávez (Author) https://creativecommons.org/licenses/by/4.0
Numerical simulation and process parameter simulation of dam vibration compaction construction
Introduction: vibration compaction is also essential in building earth-rock dams because it ensures the density of soil, mechanical stability, and avoids deformation with time. Nevertheless, the definition of internal soil behaviour under dynamic stress cannot be done properly by field data alone, and it is impossible to optimise the vibration frequency, lift thickness, and roller speed. Methods: the rationale behind this research is to unite the numerical simulation with the machine-learning-based prediction to evaluate the performance of vibration compaction and identify the most appropriate construction parameters to use in dam embankment projects. The Dam Vibration Compaction Dataset captures realistic field, lab, machine, and simulation-based parameters for earth-rock dam compaction, comprising 25 features. Data Preprocessing entailed normalization of data and filtering of noisy sensor readings. Results: internal stress distribution and densification behavior in soil were simulated using a coupled soil-vibration dynamic model based on the Finite Element Method. Simultaneously, a Scalable Random Vector Machine algorithm, combining the Scalable Support Vector Machine and the Random Forest algorithms, was created to forecast compaction quality under varying parameters. Conclusions: the FEM+SRVM model, implemented using Python, demonstrated a high level of prediction, with the most effective parameters being the vibration frequency of 30 Hz and Root Mean Square Error of 0,0083. The combined numerical and ML method enables a potent means of dam vibration compaction analysis and optimisation to remove the trial-and-error method and increase the reliability and quality of construction.
Copyright (c) 2026 Sheng Su, He-Cheng Huang, Zhi-Yu Zhao, Jing-Wei Sun, Zhen-Bin Du, Xuan Zhang, Man-Lin Liang, Zhi-Zhi Zheng (Author) https://creativecommons.org/licenses/by/4.0
Analyzing the Impact of Self-Regulated Learning on Academic Performance and Student Engagement
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.
Copyright (c) 2026 Fany Rifqoh, Muhammad Nur Wangid, Suwarjo, Yulia Azriya (Author) https://creativecommons.org/licenses/by/4.0
Quantum Optimization for Intelligent User Data Allocation in Distributed Cloud Infrastructure
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.
Copyright (c) 2025 Viji C, Rajkumar N, Stephen R, Gobinath R, Balusamy Nachiappan, Prabhu Shankar B (Author) https://creativecommons.org/licenses/by/4.0
Presence of Ecuador in the Web of Science from open access in post-pandemic period 2019- 2021: A multivariate analysis
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.
Copyright (c) 2026 Patricio Álvarez Muñoz, Fernando Erasmo Pacheco Olea, Dennis Alfredo Peralta-Gamboa (Author) https://creativecommons.org/licenses/by/4.0
Mathematical model based on Machine Learning Enabled IoT Sensing and Decision-Making in Farming
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.
Copyright (c) 2026 Mosleh Hmoud Al-Adhaileh (Author) https://creativecommons.org/licenses/by/4.0
Transformer guided and GAN augmented deep learning for medical image diagnostics
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.
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
Financial predictors of sme failure: variable selection with lasso versus random forest
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.
Copyright (c) 2026 Amina Guennoun, Siham Ammari , Saad Saadouni , Souad Habbani (Author) https://creativecommons.org/licenses/by/4.0
Methodology for Vulnerability Assessment in WSNs Using CVSS and NIST SP 800-30
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.
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
Validation of learning styles in Higher Education: A case study at the Faculty of Applied Sciences (FICA)
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.
Copyright (c) 2025 Silvia Arciniega Hidrobo, José Jácome León, Stefany Flores (Author) https://creativecommons.org/licenses/by/4.0
Improving Autism Detection Accuracy with an Optimized Local-Asymmetric Adaptive Hybrid GCN for EEG Data
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 mechanics to the GTO method, it can better find its way through complicated search spaces and stay away from local optima. This makes hyperparameter selection more successful. Finally, the test results show that the DEBCWGAN- OLA2HGCN on the EEG Dataset for ASD and the ABC-CT dataset are 95.04% and 92.27% accurate, respectively, when compared to other algorithms.
Copyright (c) 2026 K. Lalli, Senbagavalli. M (Author) https://creativecommons.org/licenses/by/4.0
IoT and AI for Smart Rural Fishing: System Architecture, TEK Integration, and Economic Viability Analysis
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.
Copyright (c) 2026 Manoj Krishnan , Dr. R. Karthik (Author) https://creativecommons.org/licenses/by/4.0
Development of IT Project Management Learning Media Using YOLO and Reinforcement Learning
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.
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
AI-Driven Hybrid Defense Mechanisms for Enhancing Cybersecurity in Cyber-Physical Systems Through Packet Sniffing and Cyber Ranges
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.
Copyright (c) 2026 Deepa Singh Sisodiya, Ritu Tiwari, Priyank Jain (Author) https://creativecommons.org/licenses/by/4.0
Artificial intelligence: prototype of an automated irrigation system for the cultivation of roses in Cotopaxi
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
Copyright (c) 2024 Manuel William Villa Quisphe, José Augusto Cadena Moreano, Juan Carlos Chancusig Chisag (Author) https://creativecommons.org/licenses/by/4.0
Predicting saturation for a new fabric using artificial intelligence (fuzzy logic): experimental part
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
Copyright (c) 2024 Mhammed El Bakkali, Redouane Messnaoui, Mustapha Elkhaoudi, Omar Cherkaoui, Aziz Soulhi (Author) https://creativecommons.org/licenses/by/4.0
A proposed method for detecting network intrusion using an ensemble learning (stacking -voting) approach with unbalanced data
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 %
Copyright (c) 2024 Anouar Bachar, Omar EL Bannay (Author) https://creativecommons.org/licenses/by/4.0
Management of operational activities at the Regional Human Milk Collection Center: an analysis from the perspective of local intervention strategies and information systems
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
Copyright (c) 2022 Matias Orsetti, Yamila Bertolini, Andrea Fabiana Villaalta, Fernanda Creo, Patricia Santillan (Author) https://creativecommons.org/licenses/by/4.0
Bridging the Gap to Success: A 50-Year Bibliometric Analysis of Preparatory Education and Academic Performance in Engineering and STEM Fields
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.
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
Non-Invasive Blood Density Measurement using Photoplethysmography Technique
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.
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
Development and Mechanical Evaluation of a Biocomposite Based on Abaca Fiber and Acrylic Resin
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.
Copyright (c) 2025 Angelo Zambrano, Elsa Mora Muñoz , Marco Naranjo Toro (Author) https://creativecommons.org/licenses/by/4.0
Simulated Annealing-Based Optimization of IEEE-33 Radial Distribution Networks with Integrated Auxiliary PV Sources Using PyPower
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.
Copyright (c) 2025 Francisco Roberto Naranjo Cobo, Julio Esteban Guerra Masson, Daisy Imbaquingo Esparza (Author) https://creativecommons.org/licenses/by/4.0
Intelligent Monitoring and Early Warning System Against Frosts for Sustainable Agriculture
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.
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
Heterogeneous Computing for High-Complexity Problems: A Performance Study with DPC++ on HPC Platforms
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.
Copyright (c) 2025 Katari Tituaña, MacArthur Ortega-Bustamante , Pedro Granda , Marco Pusdá-Chulde (Author) https://creativecommons.org/licenses/by/4.0
Bibliometric Analysis of Sports Running Socks Made from Bamboo, Cotton, and Acrylic
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.
Copyright (c) 2025 Elsa Sulay Mora-Muñoz , José Rafael Posso-Pasquel , Elvis Ramírez Encalada (Author) https://creativecommons.org/licenses/by/4.0
Spectrum-Based Security Assessment of Electric Gates Using Offensive Security Methodology
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.
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
Route Optimization for Urban Waste Collection via LoRa Communication and Deep Q-Network in a Controlled Simulation
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.
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
Implantation of Information Systems in the Popular Financial Sector: Two Case Studies Based on the SImplE Framework
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.
Copyright (c) 2025 Irving Reascos, Fausto Salazar, Carpio Pineda, Luz Córdova, Jonathan Segovia (Author) https://creativecommons.org/licenses/by/4.0
Effects of Vehicular Congestion on Air Quality in High-Altitude Cities: Case Study of Ibarra, Ecuador
Air pollution, primarily driven by vehicular emissions, is a growing challenge in Latin American cities. In Ibarra, Ecuador, the lack of air quality records prompted the implementation of a low-cost monitoring system. In this study, one AirGradient O-1PST sensor were installed near the Amazonas central market, a zone characterized by heavy traffic, where PM₂.₅, PM₁₀, and CO₂ concentrations were measured over seven consecutive days. Results showed that PM₁₀ daily concentrations remained below the limits established by national regulations, whereas PM₂.₅ levels exceeded the World Health Organization (WHO) recommendations most of the days, reaching an average of approximately 0.019 mg/m³. The reliability of the AirGradient O-1PST sensor was validated through comparison with a reference-grade instrument (Thermo Andersen FH62C14), revealing a strong correlation, with a coefficient of determination close to 95% for daily PM₂.₅ averages. Additionally, a direct association was found between vehicular traffic density and the concentrations of both air pollutants and greenhouse gases, particularly during peak hours. The findings demonstrate that, when properly validated, low-cost sensors are effective tools for air quality assessment in urban areas, and they can serve as a valuable complement to official monitoring networks. This study contributes to promoting the adoption of accessible environmental surveillance technologies in intermediate Latin American cities, thereby supporting informed decision-making in public health, sustainable urban planning, and transport management.
Copyright (c) 2025 Ahmed A.F Osman, Rami Taha Shehab, Sami A. Morsi, Hikmat A. M. Abdeljaber, Ramgopal Kashyap, Mosleh Hmoud Al-Adhaileh, Asma Abdulmana Alhamadi, Sultan Ahmad, Sultan Ahmad, Theyazn H.H Aldhyani (Author) https://creativecommons.org/licenses/by/4.0