Applied System Innovation

17 pages, 1252 KiB

Open AccessArticle

Exploring the Impact of Digital Platforms on Teaching Practices: Insights into Competence Development and Openness to Active Methodologies

by Víctor Díaz-Suárez, Miriam Martín-Paciente and Carlos M. Travieso-González

Appl. Syst. Innov. 2025, 8(3), 64; https://doi.org/10.3390/asi8030064 - 7 May 2025

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Abstract

This research examines the impact of digital transformation on teaching practices and evaluates educators’ training requirements within the European Framework for the Digital Competence of Educators (DigCompEdu), focusing specifically on its implementation in the Canary Islands’ educational system. Through a quantitative study involving [...] Read more.

This research examines the impact of digital transformation on teaching practices and evaluates educators’ training requirements within the European Framework for the Digital Competence of Educators (DigCompEdu), focusing specifically on its implementation in the Canary Islands’ educational system. Through a quantitative study involving 546 teachers from primary and secondary institutions during the 2023/2024 academic year (confidence level: 95%, margin of error: 4.15%), we analyzed the relationship between digital competence development and educational innovation. Results indicate significant gaps in four key areas: digital content creation, innovative teaching methodologies, assessment strategies, and feedback mechanisms. The findings reveal a direct correlation between insufficient educational funding and limited professional development opportunities in digital competencies. This study identifies critical areas requiring immediate attention, including increased budgetary allocation for technological infrastructure, systematic professional development programs aligned with DigCompEdu standards, and the restructuring of current innovation approaches in educational institutions. This research contributes to the understanding of how educational systems can effectively adapt to digital transformation while highlighting the crucial role of both financial investment and structured training programs in fostering successful educational innovation, ultimately emphasizing that adapting education systems to digital realities is essential for ensuring future success in an increasingly digitalized educational landscape. Full article

(This article belongs to the Section Applied Systems on Educational Innovations and Emerging Technologies)

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20 pages, 738 KiB

Open AccessArticle

Interdepartmental Optimization in Steel Manufacturing: An Artificial Intelligence Approach for Enhancing Decision-Making and Quality Control

by José M. Bernárdez, Jonathan Boo, José I. Díaz and Roberto Medina

Appl. Syst. Innov. 2025, 8(3), 63; https://doi.org/10.3390/asi8030063 - 4 May 2025

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Abstract

Recent advances in artificial intelligence have intensified efforts to improve quality management in steel manufacturing. In this paper, we present the development and results of a system that aims to learn from the decisions made by experts to anticipate the problems that affect [...] Read more.

Recent advances in artificial intelligence have intensified efforts to improve quality management in steel manufacturing. In this paper, we present the development and results of a system that aims to learn from the decisions made by experts to anticipate the problems that affect the final quality of the product in the steel rolling process. The system integrates a series of modules, including event filtering, automatic expert knowledge extraction, and decision-making neural networks, developed in a phased approach. The experimental results, using a three-year historical dataset, suggest that our system can anticipate quality issues with an accuracy of approximately 80%, enabling proactive defect prevention and a reduction in production losses. This approach demonstrates the potential for industrial AI applications for predictive quality assurance, highlighting the technical foundations and potential for industrial applications. Full article

(This article belongs to the Special Issue New Challenges of Innovation, Sustainability, Resilience in X.0 Era)

16 pages, 11641 KiB

Open AccessArticle

Using Drones to Estimate and Reduce the Risk of Wildfire Propagation in Wildland–Urban Interfaces

by Osvaldo Santos and Natércia Santos

Appl. Syst. Innov. 2025, 8(3), 62; https://doi.org/10.3390/asi8030062 - 30 Apr 2025

Viewed by 145

Abstract

Forest fires have become one of the most destructive natural disasters worldwide, causing catastrophic losses, sometimes with the loss of lives. Therefore, some countries have created legislation to enforce mandatory fuel management within buffer zones in the vicinity of buildings and roads. The [...] Read more.

Forest fires have become one of the most destructive natural disasters worldwide, causing catastrophic losses, sometimes with the loss of lives. Therefore, some countries have created legislation to enforce mandatory fuel management within buffer zones in the vicinity of buildings and roads. The purpose of this study is to investigate whether inexpensive off-the-shelf drones equipped with standard RGB cameras could be used to detect the excess of trees and vegetation within those buffer zones. The methodology used in this study was the development and evaluation of a complete system, which uses AI to detect the contours of buildings and the services provided by the CHAMELEON bundles to detect trees and vegetation within buffer zones. The developed AI model is effective at detecting the building contours, with a mAP50 of 0.888. The article analyses the results obtained from two use cases: a road surrounded by dense forest and an isolated building with dense vegetation nearby. The main conclusion of this study is that off-the-shelf drones equipped with standard RGB cameras can be effective at detecting non-compliant vegetation and trees within buffer zones. This can be used to manage biomass within buffer zones, thus helping to reduce the risk of wildfire propagation in wildland–urban interfaces. Full article

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14 pages, 1656 KiB

Open AccessArticle

A Hybrid Learning Framework for Enhancing Bridge Damage Prediction

by Amal Abdulbaqi Maryoosh, Saeid Pashazadeh and Pedram Salehpour

Appl. Syst. Innov. 2025, 8(3), 61; https://doi.org/10.3390/asi8030061 - 30 Apr 2025

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Abstract

Bridges are crucial structures for transportation networks, and their structural integrity is paramount. Deterioration and damage to bridges can lead to significant economic losses, traffic disruptions, and, in severe cases, loss of life. Traditional methods of bridge damage detection, often relying on visual [...] Read more.

Bridges are crucial structures for transportation networks, and their structural integrity is paramount. Deterioration and damage to bridges can lead to significant economic losses, traffic disruptions, and, in severe cases, loss of life. Traditional methods of bridge damage detection, often relying on visual inspections, can be challenging or impossible in critical areas such as roofing, corners, and heights. Therefore, there is a pressing need for automated and accurate techniques for bridge damage detection. This study aims to propose a novel method for bridge crack detection that leverages a hybrid supervised and unsupervised learning strategy. The proposed approach combines pixel-based feature method local binary pattern (LBP) with the mid-level feature bag of visual words (BoVW) for feature extraction, followed by the Apriori algorithm for dimensionality reduction and optimal feature selection. The selected features are then trained using the MobileNet model. The proposed model demonstrates exceptional performance, achieving accuracy rates ranging from 98.27% to 100%, with error rates between 1.73% and 0% across multiple bridge damage datasets. This study contributes a reliable hybrid learning framework for minimizing error rates in bridge damage detection, showcasing the potential of combining LBP–BoVW features with MobileNet for image-based classification tasks. Full article

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20 pages, 610 KiB

Open AccessArticle

TC-Verifier: Trans-Compiler-Based Code Translator Verifier with Model-Checking

by Amira T. Mahmoud, Walaa Medhat, Sahar Selim, Hala Zayed, Ahmed H. Yousef and Nahla Elaraby

Appl. Syst. Innov. 2025, 8(3), 60; https://doi.org/10.3390/asi8030060 - 29 Apr 2025

Viewed by 227

Abstract

Code-to-code translation, a critical domain in software engineering, increasingly utilizes trans-compilers to translate between high-level languages. Traditionally, the fidelity of such translations has been evaluated using the BLEU score, which predominantly measures token similarity between the generated output and the ground truth. However, [...] Read more.

Code-to-code translation, a critical domain in software engineering, increasingly utilizes trans-compilers to translate between high-level languages. Traditionally, the fidelity of such translations has been evaluated using the BLEU score, which predominantly measures token similarity between the generated output and the ground truth. However, this metric falls short of assessing the methodologies underlying the translation processes and only evaluates the translations that are tested. To bridge this gap, this paper introduces an innovative architecture, “TC-Verifier”, to formally employ the Uppaal Model-checker to verify trans-compiler-based code translators. We applied the proposed architecture to a trans-compiler translating between Swift and Java, providing insights into the verified and unverified aspects of the translation process. Our findings illuminate the strengths and limitations of using Model-checking for formal verification in code translation. Notably, the examined trans-compiler reached a verification success rate of 50.74% for the grammar rules and productions modeled. This study underscores the gaps in trans-compiler-based translations and suggests that these gaps could potentially be addressed by integrating Large Language Models (LLMs) in future work. Full article

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21 pages, 2504 KiB

Open AccessArticle

Constant Luminous Flux Approach for Portable Light-Emitting Diode Lamps Based on the Zero-Average Dynamic Controller

by Carlos A. Ramos-Paja, Fredy E. Hoyos and John E. Candelo-Becerra

Appl. Syst. Innov. 2025, 8(3), 59; https://doi.org/10.3390/asi8030059 - 29 Apr 2025

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Abstract

Constant luminous flux lamps are required for ensuring reliable and consistent illumination in various applications, including emergency lighting, outdoor activities, and general use. However, some activities may require maintaining a constant luminous flux, where the design must control the current during the use. [...] Read more.

Constant luminous flux lamps are required for ensuring reliable and consistent illumination in various applications, including emergency lighting, outdoor activities, and general use. However, some activities may require maintaining a constant luminous flux, where the design must control the current during the use. This paper presents the design of a portable light-emitting diode (LED) lighting system powered by batteries that maintains constant luminous flux using the zero-average dynamic control (ZAD) and a proportional-integral-derivative (PID) controllers. This system can adapt the current to maintain the luminous flux required for reliable portable lighting applications used in outdoor activities. The results show that the system can provide constant illumination with 12-volt, 18-volt, and 24-volt batteries, and a 12-volt battery with a state of charge of 10%, enhancing usability for outdoor activities, emergency situations, and professional applications. Full article

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32 pages, 4936 KiB

Open AccessArticle

Optimization and Performance Evaluation of PM Motor and Induction Motor for Marine Propulsion Systems

by Theoklitos S. Karakatsanis

Appl. Syst. Innov. 2025, 8(3), 58; https://doi.org/10.3390/asi8030058 - 29 Apr 2025

Viewed by 558

Abstract

The electrification of ships and the use of electric propulsion systems are projects which have attracted increased research and industrial interest in recent years. Efforts are particularly focused on reducing pollutants for better environmental conditions and increasing efficiency. The main source of propulsion [...] Read more.

The electrification of ships and the use of electric propulsion systems are projects which have attracted increased research and industrial interest in recent years. Efforts are particularly focused on reducing pollutants for better environmental conditions and increasing efficiency. The main source of propulsion for such a ship’s shafts is related to the operation of electrical machines. In this case, several advantages are offered, related to both reduced fuel consumption and system functionality. Nowadays, two types of electric motors are used in propulsion applications: traditional induction motors (IMs) and permanent magnet synchronous motors (PMSMs). The evolution of magnetic materials and increased interest in high efficiency and power density have established PMSMs as the dominant technology in various industrial and maritime applications. This paper presents a comprehensive comparative analysis of PMSMs and both Squirrel-Cage and Wound-Rotor IMs for ship propulsion applications, focusing on design optimization. The study shows that PMSMs can be up to 3.11% more efficient than IMs. Additionally, the paper discusses critical operational and economic aspects of adopting PMSMs in large-scale ship propulsion systems, such as various load conditions, torque ripple, thermal behavior, material constraints, control complexity, and lifetime costs, contributing to decision making in the marine industry. Full article

(This article belongs to the Special Issue Evolution of Electric Motors: Current Trends and Future Prospects for Industrial Applications)

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27 pages, 10754 KiB

Open AccessArticle

Efficient and Explainable Human Activity Recognition Using Deep Residual Network with Squeeze-and-Excitation Mechanism

by Sakorn Mekruksavanich and Anuchit Jitpattanakul

Appl. Syst. Innov. 2025, 8(3), 57; https://doi.org/10.3390/asi8030057 - 24 Apr 2025

Viewed by 283

Abstract

Wearable sensors for human activity recognition (HAR) have gained significant attention across multiple domains, such as personal health monitoring and intelligent home systems. Despite notable advancements in deep learning for HAR, understanding the decision-making process of complex models remains challenging. This study introduces [...] Read more.

Wearable sensors for human activity recognition (HAR) have gained significant attention across multiple domains, such as personal health monitoring and intelligent home systems. Despite notable advancements in deep learning for HAR, understanding the decision-making process of complex models remains challenging. This study introduces an advanced deep residual network integrated with a squeeze-and-excitation (SE) mechanism to improve recognition accuracy and model interpretability. The proposed model, ConvResBiGRU-SE, was tested using the UCI-HAR and WISDM datasets. It achieved remarkable accuracies of 99.18% and 98.78%, respectively, surpassing existing state-of-the-art methods. The SE mechanism enhanced the model’s ability to focus on essential features, while gradient-weighted class activation mapping (Grad-CAM) increased interpretability by highlighting essential sensory data influencing predictions. Additionally, ablation experiments validated the contribution of each component to the model’s overall performance. This research advances HAR technology by offering a more transparent and efficient recognition system. The enhanced transparency and predictive accuracy may increase user trust and facilitate smoother integration into real-world applications. Full article

(This article belongs to the Special Issue Smart Sensors and Devices: Recent Advances and Applications Volume II)

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Appl. Syst. Innov., Volume 8, Issue 3 (June 2025) – 8 articles

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