Engineering Proceedings

14 pages, 225 KB

Open AccessProceeding Paper

Design and Validation of a Technology-Aided Interactive Mathematics Learning System for Primary Education

by Dana Kaye Fabiala, Jhun August Mendez II, Maria Belinda Galivo, Shairane Potoy, Liza Melchor, Hannah Grace Fampulme and Mary Diane M. Mortel

Eng. Proc. 2026, 143(1), 62; https://doi.org/10.3390/engproc2026143062 - 12 Aug 2026

Abstract

The increasing demand for technology-supported instructional solutions has created opportunities to develop learning systems that integrate curriculum alignment, instructional design, and learner-centered interaction within a unified educational framework. This study presents the design and validation of a Technology-Aided Interactive Mathematics Learning Framework that [...] Read more.

The increasing demand for technology-supported instructional solutions has created opportunities to develop learning systems that integrate curriculum alignment, instructional design, and learner-centered interaction within a unified educational framework. This study presents the design and validation of a Technology-Aided Interactive Mathematics Learning Framework that functions as an instructional support system for Grade 3 subtraction involving three- to four-digit numbers with and without regrouping. The proposed framework adopts the Four-D (4D) instructional development model as its system development methodology and incorporates structured content organization, interactive learning components, and curriculum-driven instructional configuration. A mixed-method developmental research approach was employed involving fourteen participants, including twelve Grade 3 teachers and two master teachers from public elementary schools in Alcantara, Romblon, Philippines. A needs assessment identified subtraction with regrouping as the highest-priority instructional challenge. The resulting instructional system was evaluated using the Department of Education Evaluation Rating Sheet for Print Resources and achieved an overall Very Satisfactory rating across content integrity, interface organization, presentation, instructional architecture, and information accuracy. Qualitative analysis further demonstrated that the framework supports curriculum alignment, contextual adaptability, learner engagement, and reusable instructional deployment while identifying opportunities for improving inclusivity and interface presentation. The findings demonstrate that the proposed technology-aided instructional framework provides a practical, scalable, and sustainable learning support configuration that can serve as a foundation for future intelligent educational systems, adaptive learning platforms, and digital instructional environments. Full article

6 pages, 513 KB

Open AccessProceeding Paper

Tail-Risk Profiling of Construction Accidents Using Text Data

by Hao Wang, Miaoling Wang, Liang Kong, Mushuang Liu and Xinxin Zhu

Eng. Proc. 2026, 146(1), 17; https://doi.org/10.3390/engproc2026146017 (registering DOI) - 12 Aug 2026

Abstract

Construction accident investigation reports provide rich narrative evidence for understanding why incidents occur, yet conventional text-mining studies in safety analytics often prioritize frequent patterns and may overlook low-frequency but high-consequence scenarios. This paper proposes a tail-risk profiling approach for construction accidents using text [...] Read more.

Construction accident investigation reports provide rich narrative evidence for understanding why incidents occur, yet conventional text-mining studies in safety analytics often prioritize frequent patterns and may overlook low-frequency but high-consequence scenarios. This paper proposes a tail-risk profiling approach for construction accidents using text data. We transform accident narratives into semantic scene representations and organize reports into ten stable scene clusters (S1–S10) using spherical K-means with HDBSCAN-based robustness validation. Tail behavior is quantified at the scene level via quantile-based indicators, where P50 represents typical consequences and P90 represents extreme consequences; we further derive the Heavy-Tail Index (HTI = P90/P50) and the P90 exceedance rate to measure extreme-outcome tendency. A case study on 409 official accident reports shows that the proposed profiling can distinguish “tail-heavy” scenarios and support severity-sensitive scenario prioritization beyond frequency statistics. The results indicate that tail-risk profiling offers an interpretable and scalable basis for targeted safety interventions focusing on extreme-risk scenarios. Full article

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12 pages, 3217 KB

Open AccessProceeding Paper

Ancient Projectile Identification Through Inverse Analysis Effects of Masonry Homogenization and Material Homogeneity

by Vincenzo Minutolo, Eugenio Ruocco, Simone Palladino and Renato Zona

Eng. Proc. 2026, 149(1), 7; https://doi.org/10.3390/engproc2026149007 - 12 Aug 2026

Abstract

The mechanical interpretation of impact traces on historical masonry structures offers a promising pathway for identifying the typology of ancient projectiles used in past conflicts. In recent years, inverse analysis approaches have been increasingly employed to infer projectile characteristics from residual damage patterns [...] Read more.

The mechanical interpretation of impact traces on historical masonry structures offers a promising pathway for identifying the typology of ancient projectiles used in past conflicts. In recent years, inverse analysis approaches have been increasingly employed to infer projectile characteristics from residual damage patterns observed on archaeological remains. However, the reliability of such reconstructions strongly depends on the mechanical representation adopted for the impacted masonry. Ancient masonry walls, particularly those composed of tuff blocks and mortar joints, exhibit a marked heterogeneity that cannot always be adequately captured through simplified homogeneous material models. In this study, a numerical framework is developed to investigate how different assumptions regarding masonry homogenization influence the identification of projectile parameters derived from impact evidence. The mechanical response of the masonry is modeled through a homogenization procedure based on representative volume elements (RVE), allowing the heterogeneous brick—mortar assemblage to be translated into an equivalent macroscopic constitutive description. The resulting elastic and limit mechanical properties are then employed within a Finite Element Limit Analysis (FELA) formulation grounded in Melan’s lower bound theorem to evaluate collapse mechanisms and energy dissipation during impact.The methodology is applied to a case study inspired by the masonry walls of Pompeii, where parametric variations in mortar thickness are considered to assess their influence on the homogenized stiffness and strength domain. The results highlight how even simplified yet mechanically consistent models are capable of capturing the anisotropic behavior of masonry and its implications for energy absorption. In particular, the study shows that adopting a homogenization-based representation leads to more reliable inverse estimates of projectile velocity and momentum compared to purely homogeneous approximations. Overall, the proposed approach provides a computationally efficient yet mechanically grounded framework for supporting archaeological interpretations of impact traces, contributing to a more quantitative understanding of ancient warfare technologies. Full article

(This article belongs to the Proceedings of Discovering Pompeii II: From Digitally Surveyed Data to Visualized Simulations (SCORPiò-NIDI 2026))

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14 pages, 2105 KB

Open AccessProceeding Paper

Mathematical Models of Systems for Measuring Pressure of Gas–Liquid Media and Their Comparative Analysis

by Petr Velmisov and Andrey Ankilov

Eng. Proc. 2026, 150(1), 129; https://doi.org/10.3390/engproc2026150129 (registering DOI) - 11 Aug 2026

Abstract

The paper considers a linear differential operator and a nonlinear integro-differential operator, on the basis of which the equations of vibration of a deformable plate are written down. The nonlinear operator takes into account the nonlinearity of the longitudinal force arising from the [...] Read more.

The paper considers a linear differential operator and a nonlinear integro-differential operator, on the basis of which the equations of vibration of a deformable plate are written down. The nonlinear operator takes into account the nonlinearity of the longitudinal force arising from the elongation of the plate due to its deformation. Based on the proposed equations, the mathematical models of the mechanical system “pipeline–pressure sensor” are developed. The system consists of a pipeline attached at one end to the combustion chamber of an aircraft engine and a sensor designed to measure the pressure in the combustion chamber at the other end. The sensing element of the sensor that transmits the pressure information is a deformable plate. The models take into account the transfer of heat flow through the pipeline with the working medium (gas or liquid) from the engine to the elastic element and the aerohydrodynamic effect of this medium on the plate. On the basis of the small parameter method, the asymptotic equations describing the joint dynamics of the working medium in the pipeline and the deformable element of the sensor are obtained. The dynamics study is based on the application of the Galerkin method and numerical experiment in Mathematica 12.0. The case of rigid fixation of the elastic element ends is considered. A comparative analysis of solutions for linear and nonlinear models is made. The insignificant influence of the nonlinearity of the longitudinal force on the value of the plate deflection is shown. Full article

(This article belongs to the Proceedings of The 15th International Scientific Conference TechSys 2025—Engineering, Technology and Systems)

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11 pages, 211 KB

Open AccessProceeding Paper

A Human-Centered Virtual Learning Environment Adoption Framework for Higher Education Systems

by Charmaine Shane S. Cuñada

Eng. Proc. 2026, 143(1), 61; https://doi.org/10.3390/engproc2026143061 (registering DOI) - 11 Aug 2026

Abstract

The increasing deployment of Virtual Learning Environments (VLEs) within higher education has highlighted the need for systematic frameworks that support technology adoption among digitally diverse users. This study presents a human-centered evaluation framework for analyzing the adoption of VLEs by digital immigrant teachers [...] Read more.

The increasing deployment of Virtual Learning Environments (VLEs) within higher education has highlighted the need for systematic frameworks that support technology adoption among digitally diverse users. This study presents a human-centered evaluation framework for analyzing the adoption of VLEs by digital immigrant teachers within higher education systems. The framework was examined using qualitative evidence obtained from thirteen educators aged 50 years and above at a Philippine state university through Husserlian descriptive phenomenology and Colaizzi’s seven-step analytical approach. The analysis identified three interconnected framework dimensions—System Transition Challenges, Adaptive Configuration Strategies, and Technology-Enabled Instructional Opportunities—representing the progression from initial system adoption to sustained digital integration. Findings indicate that technical complexity, increased workload, and interaction constraints initially hinder effective VLE utilization. However, structured professional development, instructional redesign, and collaborative support functioned as key framework components that improved system usability, digital competence, and instructional performance. The proposed framework conceptualizes VLE adoption as a socio-technical process integrating user capabilities, institutional support mechanisms, and digital platform functionality. These findings contribute to the design of more resilient, user-centered, and scalable VLE implementation strategies for higher education information systems. Full article

12 pages, 240 KB

Open AccessProceeding Paper

A Data-Driven ICT-Assisted Instruction Architecture for Pervasive Skills Development in Accounting Education

by Sherryll Fetalvero, Tomas Faminial, Emelyn Montoya, Errol Foja, Eddie Fetalvero and Garry Vanz Blancia

Eng. Proc. 2026, 143(1), 60; https://doi.org/10.3390/engproc2026143060 - 11 Aug 2026

Abstract

The increasing digitalization of higher education has created the need for ICT-assisted instructional frameworks capable of supporting both technical competency development and pervasive skills acquisition. This study presents a data-driven framework for informing ICT-assisted instruction based on the assessment of accountancy students’ perceived [...] Read more.

The increasing digitalization of higher education has created the need for ICT-assisted instructional frameworks capable of supporting both technical competency development and pervasive skills acquisition. This study presents a data-driven framework for informing ICT-assisted instruction based on the assessment of accountancy students’ perceived importance and readiness regarding pervasive skills. An online survey was conducted among students enrolled in the Accountancy program at Romblon State University using a researcher-developed instrument covering personal attributes, intellectual and professional skills, interpersonal and communication skills, and professional ethics and moral values. Descriptive statistics and paired-samples t-tests were employed to identify readiness gaps across the four competency domains. Results indicate statistically significant differences between perceived importance and readiness, with communication-related competencies exhibiting the largest readiness gaps. These findings provide empirical requirements for designing human-centered ICT-assisted instructional systems that integrate digital collaboration platforms, simulations, adaptive learning technologies, and analytics-driven learning activities. The proposed framework supports evidence-based instructional configuration by aligning technology-enhanced learning environments with learner competency needs, thereby contributing to the development of more responsive educational information systems for accounting education. Full article

13 pages, 1901 KB

Open AccessProceeding Paper

Modeling, Kinematic Analysis, and PID Control of a Two-Degree-of-Freedom Robotic Manipulator

by George Kirkopoulos, Stavros Gkanatsios and George F. Fragulis

Eng. Proc. 2026, 143(1), 59; https://doi.org/10.3390/engproc2026143059 - 11 Aug 2026

Abstract

The subject of this study is the control of a two-degree-of-freedom robotic arm. Initially, the theoretical foundation employed in this study is presented. Subsequently, homogeneous transformation matrices are computed utilizing the Denavit–Hartenberg (D-H) method. Subsequently, the problem of forward kinematics and inverse kinematics [...] Read more.

The subject of this study is the control of a two-degree-of-freedom robotic arm. Initially, the theoretical foundation employed in this study is presented. Subsequently, homogeneous transformation matrices are computed utilizing the Denavit–Hartenberg (D-H) method. Subsequently, the problem of forward kinematics and inverse kinematics is resolved. Subsequently, state space matrices are calculated, and finally, the parameters of the PID (Proportional Integral Derivative) controller are determined to ensure that specific specifications (e.g., overshoot, settling time, steady-state error) are met, even in the presence of disturbances. Full article

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9 pages, 591 KB

Open AccessProceeding Paper

Techno-Economic Comparison of Carbon Capture Technologies with Exhaust Gas Recirculation in NGCC Power Plants

by Hulkar Abdusalomova, Azizbek Kamolov, Zafar Turakulov, Botir Usmonov, Qilichbek Safarov, Jaloliddin Eshbobaev, Sarvar Rejabov, Komil Usmanov, Yoldoshkhon Akramkhodjayev, Adham Norkobilov, Miroslav Variny and Marcos Fallanza

Eng. Proc. 2026, 147(1), 13; https://doi.org/10.3390/engproc2026147013 - 11 Aug 2026

Abstract

Post-combustion amine absorption is the most mature CO₂ capture technology, while membrane separation is a promising alternative due to its modularity and operational simplicity. Process intensification through exhaust gas recirculation (EGR) has also gained attention for increasing flue gas CO₂ concentration [...] Read more.

Post-combustion amine absorption is the most mature CO₂ capture technology, while membrane separation is a promising alternative due to its modularity and operational simplicity. Process intensification through exhaust gas recirculation (EGR) has also gained attention for increasing flue gas CO₂ concentration and reducing capture energy demand. However, comprehensive techno-economic comparisons of absorption, membrane, and hybrid systems under EGR-integrated NGCC conditions remain limited. This study presents a techno-economic assessment of multiple CO₂ capture configurations for a 450 MW NGCC power plant. Process simulations were performed using Aspen Plus and Aspen Custom Modeler to evaluate absorption, membrane, and hybrid systems integrated with conventional and selective EGR. The analysis considers full steam cycle integration and compares energy consumption, net plant efficiency, levelized cost of electricity (LCOE), and CO₂ avoidance cost. The results show that EGR significantly improves carbon capture performance. Among all cases, selective EGR combined with amine absorption delivers the best performance, reducing the energy penalty by more than 30% compared with standalone absorption and by over 70% relative to membrane separation. This configuration achieves an LCOE of approximately 72 USD/MWh and a CO₂ avoidance cost of about 39 USD/tCO₂, outperforming the selective EGR–membrane system (77 USD/MWh and 51 USD/tCO₂). These findings demonstrate that integrating selective EGR with amine absorption is a highly promising strategy for improving the technical and economic feasibility of CCSU in NGCC power plants. Full article

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10 pages, 1412 KB

Open AccessProceeding Paper

Fault Detection and Diagnosis of DC Motor Using Long Short-Term Memory (LSTM)

by Ibrahim Abdulwahab, Badamasi Umar, Umar Musa, Sulaiman Haruna Sulaiman, Ibrahim Abdullahi Shehu, Ismaila Mahmud and Aminu Jibrin Aliyu

Eng. Proc. 2026, 145(1), 11; https://doi.org/10.3390/engproc2026145011 - 11 Aug 2026

Abstract

The reliability and continuous operation of DC motors are essential in industrial and engineering applications. However, unexpected faults such as brush wear and commutator faults can lead to efficiency reduction and unplanned downtime. This paper presents a machine learning approach for fault detection [...] Read more.

The reliability and continuous operation of DC motors are essential in industrial and engineering applications. However, unexpected faults such as brush wear and commutator faults can lead to efficiency reduction and unplanned downtime. This paper presents a machine learning approach for fault detection and diagnosis (FDD) of DC motors using Long Short-Term Memory (LSTM). A publicly available dataset was employed to train the models. To compare the performance, two other models, K-nearest neighbor (KNN) and artificial neural network (ANN), were used. The performance of the models was assessed through accuracy, precision, recall, and F1-score, and presented using confusion matrices. The results obtained from the developed scheme were compared with those obtained when KNN and ANN were used. The results revealed that KNN, while simple and computationally efficient with an accuracy of 93.3%, was prone to misclassifications in overlapping feature spaces. ANN demonstrated improved accuracy of 94.7% by capturing non-linear relationships among features but lacked the ability to effectively exploit time-dependent characteristics of the data. In contrast, LSTM achieved the highest performance, with a validation accuracy of 97.3% and strong precision and recall across all the classes, owing to its ability to capture temporal dependencies in sequential motor data. The study concludes that LSTM significantly outperforms KNN and ANN, making it the most suitable model for DC motor fault detection and diagnosis. Full article

(This article belongs to the Proceedings of The 3rd International Electronic Conference on Machines and Applications)

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26 pages, 3064 KB

Open AccessProceeding Paper

Analytical Evaluation of Elastic Rope Materials and Aerodynamics of Roman Scorpio Catapults Based on Archeological Evidence

by Monil Mihirbhai Thakkar, Amir Ardeshiri Lordejani and Mario Guagliano

Eng. Proc. 2026, 149(1), 6; https://doi.org/10.3390/engproc2026149006 - 11 Aug 2026

Abstract

Roman artillery represents a key element of ancient military technology, reflecting the advanced level of technical knowledge achieved in the late Republican and early Imperial periods. However, the operational capabilities and the design methodology of these weapons are not adequately described in historical [...] Read more.

Roman artillery represents a key element of ancient military technology, reflecting the advanced level of technical knowledge achieved in the late Republican and early Imperial periods. However, the operational capabilities and the design methodology of these weapons are not adequately described in historical references. This study investigates the launch performance of Roman Scorpio catapult by integrating archeological evidence from impact craters on Pompeii’s northern walls with analytical modeling of torsion-spring behavior. The present study determines the required release velocity of an arrow capable of creating impact craters reported by applying ballistic and aerodynamic analytical models. Historically cited rope materials for torsion springs are evaluated using experimentally reported mechanical properties and geometric constraints. The integrated analysis highlights how material selection and drag-related energy losses influence projectile velocity, supporting the plausibility of the proposed catapult configuration and offering highlights into ancient artillery design. Full article

(This article belongs to the Proceedings of Discovering Pompeii II: From Digitally Surveyed Data to Visualized Simulations (SCORPiò-NIDI 2026))

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12 pages, 827 KB

Open AccessProceeding Paper

Design and Establishment of Ontology for Sustainable Bioenergy

by Adelina Ivanova, Boryana Deliyska and Anna Rozeva

Eng. Proc. 2026, 150(1), 128; https://doi.org/10.3390/engproc2026150128 - 10 Aug 2026

Abstract

Bioenergy (including biofuel) production and use are prerequisites for reducing greenhouse emissions and achieving sustainable development. In this work, on the basis of review and analysis of research achievements and elaborated ontologies in the field, an ontology of sustainable bioenergy is proposed. A [...] Read more.

Bioenergy (including biofuel) production and use are prerequisites for reducing greenhouse emissions and achieving sustainable development. In this work, on the basis of review and analysis of research achievements and elaborated ontologies in the field, an ontology of sustainable bioenergy is proposed. A methodology for its development includes: goals and scope definition, text corpus composition and extraction of the main concepts, controlled vocabulary and thesaurus building, ontology coding, reasoning, verification and querying. The established ontology has links to other related ontologies and is published in GitHub/Borydel/OSBE repository. Further extension of the sustainable bioenergy ontology is planned as well as its embedding in a dedicated repository. Full article

(This article belongs to the Proceedings of The 15th International Scientific Conference TechSys 2025—Engineering, Technology and Systems)

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11 pages, 1043 KB

Open AccessProceeding Paper

Assessment of Accuracy in Measuring Distance with Ultrasound

by Misho Matsankov and Nikolay Paunkov

Eng. Proc. 2026, 150(1), 127; https://doi.org/10.3390/engproc2026150127 - 10 Aug 2026

Abstract

Many measurements are performed using ultrasound in technology. The main advantage of this measurement method is the lack of contact between the measuring element and the object being measured. An application based on the Arduino platform is presented, created for measuring distances with [...] Read more.

Many measurements are performed using ultrasound in technology. The main advantage of this measurement method is the lack of contact between the measuring element and the object being measured. An application based on the Arduino platform is presented, created for measuring distances with ultrasound. The assessment of the measurement accuracy and the sensitivity of the measuring elements to different types of surfaces will be presented. The compilation of measurement systems and the mathematical processing of the results obtained from repeated measurements will assist students in the learning process in various engineering disciplines related to the measurement of non-electrical quantities and the creation of various systems for automatic control of measurement systems. Full article

(This article belongs to the Proceedings of The 15th International Scientific Conference TechSys 2025—Engineering, Technology and Systems)

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10 pages, 376 KB

Open AccessProceeding Paper

Assessing MQTT, CoAP, and HTTP Performance in Real-Life Scenarios on ESP32-Based IoT Nodes

by Aleksandar Kirilov, Denis Chikurtev, Galia Nedeltcheva and Peter So

Eng. Proc. 2026, 150(1), 126; https://doi.org/10.3390/engproc2026150126 - 10 Aug 2026

Abstract

The goal of the study is to compare the most popular protocols available in IoT (Internet of Things) nodes and how they perform across multiple samples in controlled conditions. The focus is on local telemetry transmission, in which the ESP32-C6 and ESP32-S3 boards [...] Read more.

The goal of the study is to compare the most popular protocols available in IoT (Internet of Things) nodes and how they perform across multiple samples in controlled conditions. The focus is on local telemetry transmission, in which the ESP32-C6 and ESP32-S3 boards communicate with two other devices—a router and a laptop—over the 2.4 GHz band. For the experiment, three standard nominal transmission sizes of 16, 64, and 256 bytes were pre-set. Each size was trialed for 30 samples for each protocol. All transmissions achieved a 100% success rate and were received by the end device. Both latency and success rate were used as the main performance indicators. The end results were that CoAP had the lowest mean latency of 47.0 ms, followed by MQTT with 63.0 ms and HTTP with a mean overall latency of 1419.0 ms. A comparative test with the ESP32-S3 revealed that while the overall protocol ranking remained identical, the gap eventually narrowed. The more powerful ESP32-S3 processed HTTP significantly faster (mean latency of 449.0 ms) but yielded slower latencies for the lightweight CoAP and MQTT protocols compared to the C6. The results indicate an order of magnitude faster performance of CoAP and MQTT than HTTP in the test setup. The study offers a simple and reproducible setup and benchmarking, allowing it to serve as a practical reference point when selecting the right protocol for a given use case. Full article

(This article belongs to the Proceedings of The 15th International Scientific Conference TechSys 2025—Engineering, Technology and Systems)

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9 pages, 2960 KB

Open AccessProceeding Paper

Integrating AI-Generated 3D Models into Education—A Methodological and Practical Approach

by Plamen Petrov and Tatiana Atanasova

Eng. Proc. 2026, 150(1), 125; https://doi.org/10.3390/engproc2026150125 - 10 Aug 2026

Abstract

As education embraces digital transformation, integrating AI into pedagogy is increasingly important. One promising innovation is AI-generated 3D models, which help visualize complex concepts, enhance spatial reasoning, and foster creativity. Unlike traditional 3D modeling, which requires advanced skills and time, AI tools make [...] Read more.

As education embraces digital transformation, integrating AI into pedagogy is increasingly important. One promising innovation is AI-generated 3D models, which help visualize complex concepts, enhance spatial reasoning, and foster creativity. Unlike traditional 3D modeling, which requires advanced skills and time, AI tools make content creation more accessible to educators and students. This study develops and validates a structured methodology for using AI-generated 3D models in education. It supports personalized, rapid, and intuitive content development, particularly in STEM. The framework includes guidelines for effective text-to-3D prompts, validation of educational impact, and a roadmap toward immersive technologies like VR, AR, and digital twins, enabling scalable, student-centered learning experiences. Full article

(This article belongs to the Proceedings of The 15th International Scientific Conference TechSys 2025—Engineering, Technology and Systems)

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5 pages, 2558 KB

Open AccessProceeding Paper

Influence of Chemical Composition on Microstructure and Hardness of High-Chromium Cast Irons

by Gergana Buchkova, Boryana Ivanova and George Lutov

Eng. Proc. 2026, 150(1), 124; https://doi.org/10.3390/engproc2026150124 - 10 Aug 2026

Abstract

High-chromium white cast irons represent an important group of wear-resistant engineering materials widely used in mining, mineral processing and cement industries due to their excellent abrasion resistance and high hardness. The present study investigates the influence of chemical composition and magnesium modification on [...] Read more.

High-chromium white cast irons represent an important group of wear-resistant engineering materials widely used in mining, mineral processing and cement industries due to their excellent abrasion resistance and high hardness. The present study investigates the influence of chemical composition and magnesium modification on the microstructure and hardness of two high-chromium cast irons. Two alloys were examined: a 28 mass% Cr cast iron without magnesium addition and a modified alloy containing 14 mass% Cr and 0.88 mass% Mg. Optical metallographic analysis revealed significant differences in carbide morphology between the investigated alloys. The alloy without magnesium exhibited coarse primary M7C3 chromium carbides embedded in the metallic matrix, whereas the Mg-modified alloy showed a significantly refined eutectic structure with fine carbide distribution. Hardness measurements revealed values of approximately 475 HV for the non-modified alloy and 750 HV for the Mg-modified alloy. The obtained results demonstrate the strong relationship between chemical composition, microstructure and hardness of high-chromium cast irons. Full article

(This article belongs to the Proceedings of The 15th International Scientific Conference TechSys 2025—Engineering, Technology and Systems)

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15 pages, 3803 KB

Open AccessProceeding Paper

A Modular Framework for Cloud-Based Educational Content Delivery Systems: Design, Implementation, and Quality Evaluation

by Ritchfildjay L. Mariscal, Reymark R. Boniza, Diosdado T. Erandio, Jr. and Angelou S. Tupaz

Eng. Proc. 2026, 143(1), 58; https://doi.org/10.3390/engproc2026143058 - 10 Aug 2026

Abstract

The increasing demand for scalable digital learning environments has created a need for cloud-based educational content delivery systems that support efficient resource management, platform accessibility, and quality-assured learning experiences. While low-code web development platforms have enabled rapid deployment of educational websites, many implementations [...] Read more.

The increasing demand for scalable digital learning environments has created a need for cloud-based educational content delivery systems that support efficient resource management, platform accessibility, and quality-assured learning experiences. While low-code web development platforms have enabled rapid deployment of educational websites, many implementations remain content-centric and lack systematic architectural design, deployment frameworks, and software quality evaluation mechanisms. This study proposes a modular architecture for cloud-based educational content delivery systems that integrates content management, user access, resource delivery, platform administration, and quality monitoring components within a unified web-based environment. The proposed architecture adopts a structured development framework consisting of requirements analysis, system architecture design, prototype development, deployment configuration, performance testing, and quality evaluation. The framework is designed to support the rapid development of lightweight educational platforms using low-code technologies while maintaining software engineering principles related to reliability, usability, accessibility, compatibility, and performance efficiency. The architecture further incorporates cloud-hosted deployment strategies that facilitate scalable content distribution and cross-platform accessibility for technology-enhanced learning environments. To demonstrate the feasibility of the proposed architecture, a prototype implementation was developed using a low-code web platform and deployed as a cloud-based educational content delivery system. The prototype was evaluated by expert validators using selected software product quality characteristics derived from the ISO/IEC 25010 standard. The evaluation results indicated a high level of technical acceptability across multiple quality dimensions, including performance efficiency, reliability, usability, compatibility, accessibility, and capacity. The findings support the effectiveness of the proposed architecture as a practical framework for developing quality-assured educational delivery platforms. The study contributes a replicable systems architecture and implementation framework for educational content delivery applications. The proposed model provides guidance for the design, deployment, and evaluation of cloud-based learning platforms and offers a foundation for future integration with learning analytics, adaptive content delivery mechanisms, and intelligent educational support systems. Full article

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12 pages, 214 KB

Open AccessProceeding Paper

A Data-Driven Architecture for Digital Capability Analytics and Readiness Assessment in Technology-Enhanced Educational Systems

by Ritchfildjay L. Mariscal, Dave Francis F. Bonso, James M. Bulaga and Jericho I. Gudito

Eng. Proc. 2026, 143(1), 57; https://doi.org/10.3390/engproc2026143057 - 10 Aug 2026

Abstract

The rapid digital transformation of education has increased the demand for intelligent assessment systems and architecture capable of evaluating institutional readiness for technology-enhanced teaching, learning, and workforce development. As educational organizations adopt digital platforms, cloud-based learning environments, and globally connected instructional models, there [...] Read more.

The rapid digital transformation of education has increased the demand for intelligent assessment systems and architecture capable of evaluating institutional readiness for technology-enhanced teaching, learning, and workforce development. As educational organizations adopt digital platforms, cloud-based learning environments, and globally connected instructional models, there is a growing need for systematic frameworks that can assess human, technological, and organizational capabilities required for successful implementation. This study proposes a digital capability assessment framework for technology-enhanced educational systems that integrates instructional competency evaluation, technology readiness analysis, infrastructure assessment, and institutional support monitoring within a unified analytics-driven model. The proposed framework consists of multiple assessment components, including digital literacy measurement, technology integration capability analysis, instructional innovation indicators, collaborative learning readiness metrics, and institutional resource evaluation mechanisms. These components are designed to support continuous monitoring of digital transformation initiatives and provide evidence-based decision support for educational planning, resource allocation, and technology adoption strategies. The framework further incorporates analytics and reporting functions that enable stakeholders to identify capability gaps, evaluate implementation risks, and prioritize system improvement initiatives. To demonstrate the applicability of the framework, a pilot assessment was conducted using competency and readiness data collected from instructional personnel within a technology-enhanced educational environment. Analytical results revealed strong capability levels across digital instructional practices, technology-supported curriculum development, online learning delivery, and collaborative knowledge-sharing activities. The assessment also identified infrastructure and support-related constraints that may affect the scalability and sustainability of advanced digital learning initiatives. The proposed framework contributes a scalable architecture for institutional readiness assessment and digital capability analytics within technology-enhanced educational systems. By integrating human capability indicators, infrastructure readiness measures, and organizational support metrics into a unified evaluation model, the framework provides a foundation for intelligent decision-support systems, digital transformation monitoring platforms, and technology governance mechanisms in modern educational ecosystems. Full article

15 pages, 514 KB

Open AccessProceeding Paper

A Computational Architecture for Learning Behavior Analytics in AI-Enhanced Educational System

by Ritchfildjay L. Mariscal, Nemuel H. Awid, Kurt Andrew O. Jale and Stanley J. Sy

Eng. Proc. 2026, 143(1), 56; https://doi.org/10.3390/engproc2026143056 - 10 Aug 2026

Abstract

The rapid integration of Generative Artificial Intelligence (GenAI) technologies into educational environments has generated new opportunities for developing intelligent systems capable of monitoring learner interactions, modeling learning behaviors, and supporting adaptive educational decision-making. As learners increasingly engage with AI-powered tools for content generation, [...] Read more.

The rapid integration of Generative Artificial Intelligence (GenAI) technologies into educational environments has generated new opportunities for developing intelligent systems capable of monitoring learner interactions, modeling learning behaviors, and supporting adaptive educational decision-making. As learners increasingly engage with AI-powered tools for content generation, information retrieval, problem solving, and knowledge construction, educational platforms require robust analytics architectures that can transform human–AI interaction data into actionable insights for instructors, administrators, and learning support systems. This paper proposes a computational architecture for learning behavior analytics in AI-enhanced educational environments. The architecture integrates multiple analytical components, including learner interaction monitoring, behavioral data aggregation, AI utilization profiling, performance-related indicator analysis, and decision-support modules for adaptive intervention and learner support. The proposed framework is designed to capture measurable dimensions of AI-assisted learning behavior, enabling educational systems to identify usage patterns, model learner engagement, and generate analytics-driven recommendations for instructional improvement. The architecture adopts a data-driven approach in which behavioral indicators derived from learner interactions with GenAI tools are processed through learning analytics mechanisms to support predictive modeling, learner classification, and intelligent feedback generation. The framework further incorporates dashboards and reporting components that facilitate real-time monitoring of AI-assisted learning activities and provide evidence-based insights for educational stakeholders. To demonstrate the applicability of the proposed architecture, a pilot implementation was conducted using learner interaction and perception data collected from higher education students. Preliminary analytical results indicate that task-specific AI utilization behaviors provide meaningful behavioral signals that can be incorporated into learner modeling and adaptive learning analytics processes. These findings support the feasibility of integrating GenAI interaction data into intelligent educational systems for monitoring and decision-support purposes. The proposed architecture contributes to the development of next-generation educational technologies by providing a scalable framework for learning behavior analytics, human–AI interaction modeling, and intelligent educational decision support. The study offers practical implications for the design of adaptive learning platforms, educational data analytics systems, and AI-enabled learning environments that support effective and responsible human–AI collaboration. Full article

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11 pages, 3550 KB

Open AccessProceeding Paper

Digital Twins for Integrated Energy and Structural Performance Assessment of Buildings: A Systematic Review and Research Gap Analysis

by Haris Abdullah and Muhammad Khubaib

Eng. Proc. 2026, 147(1), 12; https://doi.org/10.3390/engproc2026147012 - 10 Aug 2026

Abstract

Digital twins (DTs) have become a promising approach for intelligent building management by enabling real-time monitoring, simulation, and decision support. However, the operational integration of building energy and structural performance remains limited. This systematic review evaluates the current state of integrated energy–structural DTs, [...] Read more.

Digital twins (DTs) have become a promising approach for intelligent building management by enabling real-time monitoring, simulation, and decision support. However, the operational integration of building energy and structural performance remains limited. This systematic review evaluates the current state of integrated energy–structural DTs, focusing on architectures, interoperability, semantic integration, validation, uncertainty, and scalability. Following PRISMA 2020 reporting guidance, the literature published between 2015 and 2025 was collected from Scopus, Web of Science, and IEEE Xplore, resulting in the synthesis of 82 studies. Among the reviewed studies, 68.3% addressed energy applications, 53.7% focused on structural applications, while only 17.1% demonstrated partial integration and 12.2% reported bidirectional information exchange. Most studies relied on scenario- or simulation-based validation (69.5%), and only 8.5% considered district-scale implementations. The review identifies BIM, IoT, BEM, FEM/SHM, semantic models, and APIs as key enabling technologies, while synchronized co-simulation, semantic interoperability, uncertainty propagation, and long-term field validation remain major challenges. The study contributes a five-level operational maturity framework, a conceptual integrated energy–structural DT architecture, a distinction between topical co-occurrence and operational integration, and a future research roadmap for developing interoperable, scalable, and lifecycle-oriented building digital twins. Full article

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Journal Description

Engineering Proceedings

Vol. 152

IOCIV 2026

Vol. 150

TechSys 2026

Vol. 149

SCORPiò-NIDI 2026

Vol. 145

IECMA 2026

Vol. 144

STR2E 2026

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