International Multidisciplinary Journal of Emerging Technologies and Applications
International Multidisciplinary Journal of Emerging Technologies and Applications (IMJETA) is a peer-reviewed, open-access scholarly publication dedicated to the dissemination of high-quality research across a broad range of disciplines related to emerging technologies and their practical applications. The journal serves as a global platform for academics, researchers, and practitioners to share innovative ideas, theoretical advancements, experimental findings, and case studies that address current challenges and anticipate future trends in technology-driven fields. IMJETA welcomes original research articles, review papers, and technical reports from diverse areas including (detailed in the about/focus and scope session). - Digital Engineering & Design Innovation - Advanced Manufacturing & Industry 4.0 - Emerging Technologies & Computing - Sustainable Innovation & Green Technologies - Aerospace, Automotive & Transportation Systems - Healthcare & Life Sciences Engineering - Business Applications & Innovation Management - Cross-Disciplinary & Educational Innovations The IMJETA´s mission is to foster interdisciplinary collaboration, promote scientific excellence, and bridge the gap between technological innovation and real-world implementation. By connecting researchers worldwide, IMJETA contributes to the advancement of knowledge and the responsible development of emerging technologies for societal benefit. ISSN: 3135-6214
Theory of Objectification and Engineering Education
This article identifies the contributions of the forums held in 2026 on the Theory of Objectification (TO) to consider options in engineering education in the face of the Fourth Industrial Revolution or Industry 4.0. The study evaluates the applicability of this historical-cultural framework in the training of engineers proposing an alternative to traditional, transmissive, and individualistic educational models. Through an ontological approach, the transition from data and information toward the construction of real knowledge is analyzed through the pedagogical concept of joint labour. The document integrates Aldert Kamp's perspectives on interdisciplinary curricular design toward the year 2030, Klaus Schwab's views regarding stakeholder capitalism, and Nagib Callaos' concepts surrounding cybernetic humanism. Likewise, it examines the impact of technological infrastructure and Generative Artificial Intelligence (GAI), conceived here as a semiotic mediator and thinking partner capable of activating metacognitive processes in the student. In the context of developing countries like Venezuela, the need to transform the teaching of basic sciences through real problem-situations linked to the local socio-political reality is raised. Potential findings evidence the demand to migrate from an individual and punitive evaluation toward a formative and communal model. Finally, it concludes that true digital transformation in higher education does not lie in the acquisition of hardware, but rather in adopting an epistemological stance that recovers the protagonism of human activity and promotes self-regulation, solidarity, and social responsibility.
Copyright (c) 2026 Dr. Alfredo Avella Guevara (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Governing the Ungoverned: Institutional Leadership and Organizational Ambidexterity in the AI Adoption of Universities
The rapid integration of artificial intelligence into higher education has produced a significant imbalance: a growing body of research addresses AI tools and their effects on student learning, while the institutional and leadership dimensions of AI governance remain systematically underexplored. This systematic review with thematic synthesis (PRISMA 2020) examines how indexed literature conceptualizes AI governance and leadership in higher education institutions (HEIs), what institutional and managerial factors operate as facilitators or barriers, what structural tensions characterize the field, and what the Latin American context reveals about the limits of prevailing frameworks. Drawing on a corpus of 30 verified sources — 20 peer-reviewed studies (2021–2026) plus 10 foundational theoretical works included by exception — the analysis identifies five analytical themes: fragmented governance architectures without systemic accountability; the underresearched role of academic leadership as the critical institutional link; the theoretical limits of individual acceptance models (TAM/UTAUT) when applied to organizational-level decisions; datafication as covert governance operating beneath formal policy layers; and Latin America’s near-absence from indexed production as a structural finding rather than a methodological limitation. The study proposes an integrative conceptual framework articulating organizational ambidexterity (O’Reilly & Tushman, 2004, 2013), dynamic capabilities (Teece, 2007), and the critique of datafication (Williamson, 2018; Selwyn & Gasevic, 2020) as a unified analytical device for AI governance in HEIs, anchored in the specific conditions of Latin American higher education.
Copyright (c) 2026 Pablo Rivarola (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Automating Data Envelopment Analysis in Python: Functional Comparison with XlDEA/XIDEA and Methodological Assessment of Second-Stage Inference
This study evaluated whether a reproducible Python workflow can strengthen Data Envelopment Analysis in industrial efficiency studies when compared with spreadsheet-based tools such as XlDEA/XIDEA. A methodological, documentary, and computational comparative design was applied. The study examined two implementation environments: a reproducible Python workflow and spreadsheet-based analysis tools. Data were collected through a structured comparison matrix that assessed methodological coverage, automation and scalability, reproducibility and auditability, and second-stage inferential robustness. The analytical procedure reviewed input-oriented CCR estimation, bootstrap inference, Tobit modeling on inefficiency, truncated regression with double bootstrap, and automated report generation. The main result indicates that Python provides a more scalable and auditable architecture for repeated analysis, especially when monthly data, multiple decision-making units, and standardized outputs are required. However, spreadsheet tools remain useful for exploratory applications because they offer greater initial accessibility for non-programming users. The study concludes that Python is preferable for production-grade efficiency analysis, while truncated regression with double bootstrap should guide future second-stage inference when contextual determinants of efficiency are analyzed.
Copyright (c) 2026 Marlon Stalin Taco Arias (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Architectural Proposal for a Hybrid Recommender Engine for Accessible Educational Content
This paper presents an architectural proposal for a modular and adaptive hybrid recommender system designed to improve equitable access to PDF-based educational content for students with visual impairments. The system overcomes the limitations of traditional recommenders, which optimize rankings solely by thematic relevance while ignoring format-related barriers, by implementing a closed-loop approach that prioritizes technical accessibility prior to ranking the educational material. The architecture is structured into four independent layers: automated ingestion via multimodal artificial intelligence, dynamic student profiling, a sequential hybrid recommendation engine, and a semantic presentation interface. The technical viability of the proposal was successfully validated through a functional Python prototype integrated with the Gemini 2.5 Flash model from Google AI Studio, evaluating a repository of real-world documents split between the fields of algebra and programming. Experimental results demonstrated that the system accurately resolves the cold-start problem through an initial technical questionnaire and effectively mitigates the occurrence of false positives in practice, thanks to a reverse feedback loop that automatically updates functional security profiles and restricts resources reported with access flaws.
Copyright (c) 2026 Luis David Jiménez Martínez, Francisco J. Álvarez Rodríguez (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Optimization Models for Smart Rail Transport in Sustainable Logistics Systems: A Case Study of Semi-Arid Smart Cities in Southeastern Chihuahua (Parral Region)
This paper proposes a comprehensive and integrated smart rail transport optimization framework specifically designed for semi-arid logistics environments, with a focused application in the Parral region of southeastern Chihuahua, Mexico. The proposed approach addresses the structural inefficiencies and environmental constraints characteristic of resource-extraction and low-density territories by combining advanced optimization theory with emerging smart city technologies. At the core of the framework lies a multi-objective mathematical model that simultaneously minimizes transit time (T), carbon emissions (E), and operational costs (C). These objectives are balanced through adaptive, region-specific weighting parameters that reflect the economic priorities, environmental regulations, and infrastructural limitations of semi-arid regions. The model incorporates network constraints, stochastic demand behavior, rail capacity limitations, and intermodal transfer conditions, ensuring a realistic and scalable representation of freight transport dynamics. To enhance real-time decision-making and system responsiveness, the framework is augmented with an Internet of Things (IoT) architecture composed of distributed sensors deployed across rail infrastructure, cargo units, and transfer nodes. These sensors enable continuous monitoring of variables such as temperature, load conditions, vibration, and transit status. In parallel, machine learning modules are integrated to perform predictive analytics, including demand forecasting, anomaly detection, and dynamic route optimization under uncertain conditions. The system is further embedded within a smart city control layer, allowing centralized coordination, data fusion, and adaptive policy enforcement. A simulation-based evaluation was conducted using regionally calibrated datasets reflecting the logistical patterns of southeastern Chihuahua. The results indicate that the proposed framework achieves a reduction of approximately 30–35% in CO₂ emissions, driven by modal shift and improved energy efficiency of rail systems. Additionally, logistic flow efficiency improves by nearly 25%, as measured by reduced congestion, enhanced scheduling, and optimized routing. Operational costs exhibit a decrease ranging from 20% to 28% when compared to traditional highway-based freight transport, highlighting the economic viability of the approach. These findings underscore the strategic potential of intelligent rail transport systems as a sustainable and scalable alternative for freight mobility in semi-arid regions. Beyond immediate performance improvements, the proposed framework contributes to the broader transition toward smart, resilient, and low-carbon logistics ecosystems, particularly in regions where economic activity depends heavily on mining and agriculture.
Copyright (c) 2026 Dr. Alberto Ochoa, José de la Torre, Isaac Colchero (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
IEEE 802.16 MAC Protocol for the Management of Connections and Dynamic Service Flows
The IEEE 802.16 standard specifies and describes the air interface of the Broadband Wireless Access Systems point to multipoint fixed and mobile networks, and it is limited to the description of the MAC (Medium Access Control) layer and physical (PHY) layer. The standard defines informally an unmanageable set of procedures associated with the service flows (SF) at the MAC layer, also related to a connection element called dynamic service flows (DSF). This research defines the management of these connections as "IEEE 802.16 MAC Protocol for Connection Management and DSF", and studies the protocol to facilitate the reuse of some the concepts in the specification of emerging wireless networks. This research modelled and analysed the protocol using Coloured Petri Nets and the protocol verification methodology proposed by J. Billington. The results are a detailed and precise description of the protocol, and the service specification; the functional validation of such models through the determination of regular expressions of the language; the generation of the protocol language and the service specification; different drawbacks in the specification; and the PROSEGA/CPN Software to simplify the analysis of the models.
Copyright (c) 2026 Dra. Ana Morales Bezeira (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Engineering Education in the Context of Industry 4.0
An approach is made to engineering studies in the face of the fusion of physical, digital, and biological technologies, which are transforming the society of the Fourth Industrial Revolution, or Industry 4.0. Methodologically, an analytical-prospective approach was applied, sustained by a conceptual review and the theoretical integration of Aldert Kamp, Klaus Schwab, and Nagib Callaos. The results demonstrate that contemporary digitalization does not constitute a technological phenomenon solely, but rather a cultural and structural transformation of production, knowledge management, and organizational dynamics. The cases of Ireland, Colombia, and Peru show different levels of digital readiness, connectivity, and development of innovative ecosystems. Likewise, the expansion of Generative AI (GAI) and other converging technologies, such as IoT, cloud computing, Blockchain, Big Data, and intelligent automation, requires redefining the engineer's profile toward systemic analysis competencies, ethical validation, and the interdisciplinary resolution of complex problems. It is suggested that universities and organizations move toward flexible, integrated educational models oriented toward technological adaptation. Weaknesses were identified in enabling infrastructure, both for computing and for quality connectivity. To build bridges toward technology-supported entrepreneurship, business models using "Startups" are suggested, through the Lean Canvas template, their "Business Model," and validation with GAI. It is concluded that the relevance of the contemporary professional depends on their capacity to adapt, overcome, and emerge strengthened within a context of legal certainty and technical infrastructure; both of which are pillars for Industry 4.0.
Alternative ITIL in the Integration and Optimization of IT Technology Management Process
The paper explores the scope of ITIL (Information Technology Infrastructure Library) best practices as an efficient alternative for integrating and optimizing information technology (IT) management processes in organisations. A comprehensive examination of the ITIL service lifecycle illustrates how this framework optimizes service quality, operational efficiency, and the strategic alignment of IT capabilities with overarching corporate objectives. The ITIL service's core life cycle architecture consists of five interconnected phases: strategy, design, transition, operation, and continual service improvement, each with distinct functions to deliver value and secure service sustainability over time. The article emphasizes a systemic approach and feedback as key elements in monitoring, evaluating, and implementing continuous process improvement. It also indicates that ITIL is not a prescriptive model but a flexible set of best practices that enables service providers to develop and offer technologies that meet customers' needs, thereby improving satisfaction while reducing risk and cutting costs. It was concluded that adopting ITIL facilitates IT governance and promotes standardization.
Copyright (c) 2026 Dr. Dougglas Hurtado Carmona (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Conceptualizing Cognitive and Agentic Digital Twins
Digital Twins (DTs) have extended beyond the original concept of a static digital model, to a dynamic, data-driven, and increasingly intelligent cyber-physical structure, which underlies modern Industry 4.0 systems. The paper gives a comprehensive and integrative conceptualization of DTs, Cognitive Digital Twins (CDTs), and agentic AI-enhanced DTs through an organized narrative literature review which synthesizes the foundational definitions, the theoretical differences between DTs and Cyber-Physical Systems (CPS), and the evolution of Digital Models and Digital Shadows to fully synchronous, two-way DTs. The research synthesizes the major architectural models, such as layered, multi-dimensional, cognitive and agentic models, and discusses the facilitating technologies that enable real-time synchronization, semantic reasoning, and autonomous decision-making. The state-of-the-art analysis indicates that the key research trends include ecosystem-based DTs, AI-driven analytics, semantic enrichment, and the development of LLM-enabled agentic DTs that are able to plan and coordinate actions in a goal-oriented manner. The paper also compares industrial and open-source DT platforms, outlines the present-day limitations in semantic interoperability, cognitive integration, and autonomy and demonstrates real-world applications in manufacturing, healthcare, smart cities, and energy systems. The discussion proposes future research directions such as standardized cognitive layers, safe LLM grounding, multi-DT coordination, and governance frameworks of autonomous DT ecosystems. Overall, the paper contributes a unified conceptual model and a holistic synthesis that connects theory, architecture, technology, and application, providing a foundation for advancing the next generation of intelligent Digital Twin systems.
Copyright (c) 2026 Dr. Antonio Silva Sprock, Dr. Oscar Silva Sprock (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Bridging Model-Based Systems Engineering, Digital Twins, and Cyber-Physical Manufacturing Systems: A Foundational Framework for Operational Excellence
The accelerating complexity of modern manufacturing demands a unified approach to align system design, operational execution, and continuous improvement. Model-Based Systems Engineering (MBSE), Digital Twins (DTs), and Manufacturing Operations Management (MOM) each contribute critical but fragmented capabilities across this continuum, while Cyber-Physical Systems (CPS) enable real-time coupling between digital and physical layers. Despite their conceptual complementarity, the literature lacks an integrative framework that connects these domains into a continuous information and decision flow supporting Operational Excellence. This paper conducts a systematic bibliographic synthesis of 97 peer-reviewed studies and standards (INCOSE, ISO 23247, ISA-95) to develop the Dynamic Digital Thread Framework (DDTF)—a foundational architecture for digitally continuous, cyber-physical manufacturing ecosystems. The DDTF aligns MBSE’s model-centric discipline with DT’s analytical intelligence, MOM’s executional control, and CPS’s real-time adaptability. Through this integration, the framework operationalizes Lean and Six Sigma principles within a digital architecture capable of reducing conversion cost, synchronizing the 4Ms (Man, Machine, Material, Method), and eliminating non-value-added activities. The study contributes a theoretically grounded yet industry-relevant foundation for future empirical research on digital continuity and cyber-physical integration. By reframing digital transformation as a systemic redefinition of knowledge flow and decision logic—rather than a technology deployment—the DDTF establishes a conceptual bridge between engineering intent and operational reality, enabling sustainable Operational Excellence in the era of Industry 5.0.
Copyright (c) 2026 Oscar Silva Sprock (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
School Access and Educational Inclusion of Children with Functional Difficulties in Latin America: Empirical Evidence for Educational Policy based on MICS Data
This study applies data mining techniques and multivariate statistical analysis to the 2019 Multiple Indicator Cluster Surveys (MICS) conducted by UNICEF in six Latin American and Caribbean countries, focusing on children aged 5 to 17. The research examines the main determinants of school attendance among children with and without functional difficulties. The analysis was structured in three phases: (1) descriptive analysis through SQL queries in BigQuery, (2) confirmatory analysis using correlation coefficients between age, country, and functional condition with school attendance, and (3) predictive modelling with Boosted Trees in BigQuery ML. Results indicate that 40.1% of children present at least one functional difficulty, primarily associated with learning, concentration, and behavioural adaptation. Nevertheless, school attendance was high in both groups: 97.19% for children with functional difficulties and 98.04% for those without. Correlation coefficients were low, indicating weak linear relationships. However, predictive analysis showed that age and country are the most influential factors in predicting school attendance, while functional difficulties had a minor impact. These findings provide relevant empirical evidence for the design of data-driven educational policies, especially regarding the targeting of resources for inclusive education
Copyright (c) 2026 Beatriz Angélica Toscano de la Torre, Placido Salomón Álvarez López, Julio Cesar Ponce Gallegos, Juan Contreras-Castillo, Norma Baron-Ramirez (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
A Comparative Analysis of Convolutional Neural Networks for Automated Myopia Classification via Fundus Imaging
According to recent reports from the World Health Organization (WHO), myopia now affects 30% of the global population and continues to rise steadily. Given that early detection is critical for timely and effective treatment. This study evaluates and compares the performance of two prominent convolutional neural network (CNN) architectures—ResNet50 and InceptionV3—for the automated classification of myopia using blue-light fundus imagery. Employing a cross-sectional, non-experimental design, the research utilized a large-scale dataset of 124,794 images (63,294 Myopia; 61,500 Normal). The data were partitioned into training (70%), validation (20%), and testing (10%) sets. Models were implemented in Python using TensorFlow and Keras, leveraging the Google Colab Pro environment with A100 GPU acceleration. To mitigate overfitting and enhance generalization, rigorous preprocessing and data augmentation techniques were applied. Experimental results indicate that both architectures achieved exceptional performance; notably, InceptionV3 outperformed ResNet50, achieving a validation accuracy of 99.97% and a significantly lower loss of 0.0075. These results confirm the robustness of deep learning models for clinical myopia screening in response to the growing global prevalence.
Copyright (c) 2026 Manuel J. Ibarra-Carrera, Danny Noe Cuaquira Huachaca (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Enhancing Listening Comprehension Through the British Council Website Among Grade VII Students of Smp Swasta Teladan Pematangsiantar
This community service program was carried out to address students’ low confidence and difficulty in understanding spoken English texts. The purpose was to improve the listening comprehension of Grade VII-B students at SMP Swasta Teladan Pematangsiantar through the use of digital media provided by the British Council website (LearnEnglish Teens). The method used was a qualitative descriptive approach, involving socialization, material preparation, classroom implementation, and student reflection. The partners in this program were 27 Grade VII-B students, the English teacher, and the school principal. Evaluation was conducted through classroom observation and written student reflections. The results showed that although most students struggled to understand authentic English audio and lacked confidence, a small number of students demonstrated high interest and willingness to continue learning through the British Council platform. The activity successfully raised awareness of the importance of digital media in listening instruction and laid the groundwork for further development of similar programs in the future.
Copyright (c) 2026 Venyta Sinaga, Syalaisha Syafira Irwansyah, Viola Sasti Bizora L. Tobing, Herman Herman, Yanti Kristina Sinaga, Natalina Purba, Ridwin Purba (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Investigating Students’ Readiness on TOEFL Reading Comprehension: A Case on Senior High School
This research investigates the level of reading comprehension and TOEFL readiness among 12th-grade students at SMA Negeri 1 Pematangsiantar. A TOEFL style multiple choice test and a structured questionnaire were used to assess students’ reading abilities and their general awareness of TOEFL preparation. Findings revealed that the majority of students demonstrated low proficiency in reading, particularly in interpreting implied information and unfamiliar vocabulary. Despite acknowledging the importance of TOEFL, most students had limited exposure to test strategies and formal preparation. The study underscores the need for targeted instruction and institutional support to help students become more strategic, confident, and prepared for academic English assessments.
Copyright (c) 2026 Rimtahi Marbun, Trivena Gracia Sirait, Trinita Rumapea, Raysha Zahra Harahap, Elan Purba, Yanti Kristina Sinaga, Herman Herman (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Presence, Embodiment, and Emotional Response in Mixed Reality Product Prototyping: Toward a Psychophysiological Model for XR-Based Design Evaluation
The integration of extended reality technologies into industrial product design evaluation introduces a class of psychological variables, such as sense of presence, embodied cognition, and emotional arousal, that are absent from conventional flat-screen computer-aided design environments. Despite growing adoption of immersive head-mounted display systems for prototype assessment, no empirically validated model exists to explain how these psychophysiological states influence the quality and reliability of design judgments produced within immersive conditions. This study proposes and tests a four-layer psychophysiological framework linking extended reality modality type to design judgment reliability through three simultaneous mediators: subjective presence, galvanic skin response amplitude, and eye-tracking fixation duration. A within-subject experiment was conducted with 48 industrial designers and mechanical engineers who evaluated an identical computer-aided design prototype across four counterbalanced conditions: flat-screen display, augmented reality, virtual reality, and mixed reality. Heart rate variability, galvanic skin response, and fixation duration were recorded continuously alongside the ITC-Sense of Presence Inventory and a semantic differential judgment scale. Structural equation modelling confirmed full mediation of the display-condition effect on judgment reliability by the three psychophysiological pathways, with emotional arousal emerging as the strongest mediator. Extended reality experience level moderated arousal responses in expert users, preserving judgment reliability across immersive conditions. These findings offer the first empirically grounded framework linking immersive display technology to evaluative cognition in engineering design contexts.
Copyright (c) 2026 Septien Dwi Savandha, Adelia Azzahra, Elvira Fitriyanti, Nabilaa Faizatuz Zuhriyah, Pegi Sugiartini (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
The Effect of GEFT Based Cognitive Style on Students Mathematical Creative Thinking Ability
This study aims to analyze the effect of cognitive style on students' mathematical creative thinking skills. This study uses an ex post facto approach with a 2 × 2 factorial design (gender × cognitive style) and was conducted on 10th grade students at Senior High School 5 Tegal City in the 2025/2026 academic year. The research sample consisted of 49 students selected through proportional stratified random sampling. Cognitive ability was measured using the Group Embedded Figures Test (GEFT) to categorize students into field-dependent (FD) and field-independent (FI) cognitive styles, while mathematical creative thinking ability was measured through a mathematical creative thinking ability test. Data analysis used two-way ANOVA to test main effects and interactions. The results showed a significant difference in mathematical creative thinking ability between FD and FI students, with higher achievement in the FI group. Meanwhile, differences based on gender and gender × cognitive style interactions were not significant. These findings confirm that cognitive ability influences students' mathematical creative thinking ability, so learning needs to consider students' cognitive characteristics to optimize the development of mathematical creative thinking.
Copyright (c) 2026 M. Taufik Qurohman, Suwarto, Nuryadi (Author) https://creativecommons.org/licenses/by-nc-nd/4.0