Search Results (617)

Prototyping has evolved from a simple representational artifact into a central mechanism for learning, communication, risk reduction, and decision-making in engineering design. Despite its widespread adoption across engineering disciplines, existing research remains fragmented across domains, methodologies, and application contexts, making it difficult to distinguish broadly applicable principles from context-specific practices. This paper presents a critical review of prototyping research in engineering design, synthesizing evidence from peer-reviewed journal articles and conference papers from foundational studies of the 1980s to recent developments in rapid prototyping, additive manufacturing, digital engineering, and Industry 4.0 systems. A thematic literature review was conducted to identify recurring principles, domain-dependent variations, emerging trends, and persistent limitations in current prototyping practices. The review examines key factors influencing prototyping effectiveness, including purpose, fidelity, timing, stakeholder involvement, modeling and analysis, risk management, economic considerations, and learning-oriented iteration. Particular attention is given to how uncertainty influences prototyping decisions and the ways in which different uncertainty conditions influence the selection, scope, and implementation of prototyping activities. The findings indicate that prototyping is best understood as a context-dependent decision-support activity whose effectiveness depends on the uncertainties, constraints, stakeholders, and design objectives associated with a specific engineering problem. Although several common principles emerge across engineering domains, substantial differences exist in how prototypes are used to support design decisions and system validation. The review identifies research gaps related to uncertainty-driven fidelity selection, integration of modeling, experimentation, and verification activities, and the limited availability of systematic guidance for selecting prototyping strategies across diverse engineering contexts. Future research should focus on generalized prototyping frameworks, quantitative decision-support methods for uncertainty management, enhanced stakeholder integration, and the continued convergence of physical and virtual prototyping environments in next-generation engineering systems. Full article

Architecture design studios are complex learning environments in which knowledge develops through critique, collaboration, individual reflection, material exploration, and increasingly digital forms of practice. Although the physical organization of these spaces influences how such activities unfold, research on furniture arrangement remains fragmented across ergonomics, learning environments, educational research, technology-enhanced education, and participatory design. This structured integrative review examined the mechanisms reported between furniture arrangement, related physical spatial configurations, and learning processes in architecture design studios and relevant adjacent educational settings. It also investigated the pedagogical, ergonomic, technological, and cultural conditions under which these mechanisms were reported as supportive or restrictive. Scopus, Web of Science All Databases, and ScienceDirect were searched on 7 August 2026. The searches identified 64 source-level records: 26 from Scopus, 19 from Web of Science, and 19 from ScienceDirect. Eight duplicate records were identified within the ScienceDirect results. Following cross-source deduplication, title and abstract screening, and full-text eligibility assessment, 15 publications were included in the integrative synthesis. Study characteristics, methodological quality, contextual relevance, furniture-related conditions, pedagogical activities, reported outcomes, explanatory mechanisms, evidentiary directness, and transferability were examined through an integrative, mechanism-oriented synthesis. The resulting relationships were subsequently organized through an abductive framework-development process into the provisional Adaptive Studio Learning Ecosystem (ASLE) framework. ASLE comprises five interrelated dimensions: spatial flexibility, ergonomic responsiveness, pedagogical mediation, technological support, and cultural–participatory fit. The synthesis supports activity–layout alignment rather than a universally optimal furniture configuration and indicates that spatial adaptability becomes educationally meaningful only when supported by appropriate pedagogical practices, ergonomic conditions, technological integration, and patterns of user participation. Because the evidence base includes a limited number of direct architecture-studio studies and relies partly on mechanisms transferred from adjacent settings, ASLE should be treated as a provisional, review-derived framework requiring empirical validation. Full article

The Tianjin Container Freight Index (TCI) is a key benchmark for container shipping prices in northern China and is strongly driven by macroeconomic conditions and trade fluctuations. This study proposes a PCC–CNN–GRU hybrid deep learning framework for TCI forecasting using 25,870 daily observations from 13 April 2015, to 1 January 2024. The model combines Pearson Correlation Coefficient (PCC)-based feature selection, convolutional neural networks (CNN) for local temporal feature extraction, and gated recurrent units (GRU) for capturing long-term dependencies, thereby addressing the nonlinear and nonstationary characteristics of TCI data. Empirical results show that the proposed model achieves an R² of 91.24%, outperforming standalone CNN, GRU, and classical ARIMA and VAR models. The model demonstrates strong robustness to structural changes and noise, enhancing its suitability for complex market environments. The integrated framework provides reliable forecasting support for shipping companies, logistics planners, and policymakers in pricing, capacity planning, and sustainable maritime operations. This study contributes to the growing integration of intelligent forecasting methods with regional freight index analysis and supports the digital transformation of the container shipping industry. Full article

Artificial Intelligence (AI) and Machine Learning (ML) are increasingly driving the digital transformation of manufacturing systems, enabling the transition from conventional process operation toward intelligent, adaptive, and data-centric production environments. This work presents the development of AI-enabled advisory systems for casting processes, integrating singular value decomposition (SVD)-based reduced-order models with a Variational Autoencoder with Arbitrary Conditioning (AC-VAE) and hybrid simulation frameworks to support real-time process prediction and optimization. The proposed approach leverages manufacturing data to establish predictive models capable of rapidly evaluating process conditions, optimizing operating parameters, and enhancing product quality while reducing material waste, energy consumption, and production costs. By combining physics-based understanding with AI-driven analytics, the framework facilitates real-time decision support, adaptive process control, and continuous performance improvement within modern manufacturing ecosystems. These capabilities contribute to the broader objectives of Industry 4.0 and emerging Industry 5.0 paradigms, including automation, connectivity, operational resilience, sustainability, and human-centered manufacturing. A representative Horizontal Direct Chill (HDC) continuous casting case study is presented to demonstrate the practical implementation of the framework, encompassing database generation, model training, validation, and deployment of predictive advisory tools for real-time manufacturing applications. Full article

The growing integration of gamification and digital technologies in higher education has led to increasing interest in students’ perceptions of gamified learning environments. While existing research predominantly focuses on the effectiveness of gamification in enhancing motivation, engagement, and learning outcomes, less attention has been paid to differences between students’ expectations and actual experiences. This study addresses this gap by comparing the perceptions of students with and without prior experience with gamified learning. A quantitative survey was conducted among 189 engineering students from a technical university using a structured questionnaire based on a five-point Likert scale. Two groups were compared: students with prior experience in gamified learning and those without such experience. Data were analyzed using descriptive statistics, reliability analysis, the Mann–Whitney U test and Cliff’s Delta. The findings suggest that prior experience was associated primarily with differences in perceived learning interest. A statistically significant difference was identified only for this dimension, with students who had prior experience reporting significantly higher levels of agreement regarding the ability of gamification to make learning more interesting. For the remaining dimensions, including motivation, engagement, involvement, enjoyment of competitions, and distraction, differences between the groups were observed but were not statistically significant. Reliability analysis indicated higher internal consistency among students with prior experience. However, no formal statistical comparison of the Cronbach’s alpha coefficients was performed, and this finding should therefore be interpreted with caution. The study compares the perceptions of students with and without prior experience of gamified learning and discusses the findings from a theoretical perspective. Rather than directly measuring expectations, the findings provide a conceptual interpretation of how prior experience may be associated with students’ perceptions of gamified learning and offer implications for the design and implementation of gamification in higher education. Full article

The building sector is one of the largest contributors to global energy demand and carbon emissions, making the transition to net-zero buildings (NZBs) a critical component of climate change mitigation strategies. However, the persistent building energy performance gap (BEPG), defined as the discrepancy between predicted and actual energy consumption, continues to hinder the achievement of net-zero operational performance. Accordingly, this review examines the role of artificial intelligence (AI) in enabling NZBs through the integration of energy-efficient building systems, renewable energy technologies, and intelligent operational control. A comprehensive review of the literature published between 2018 and 2025 was conducted, focusing on three complementary domains: heating, ventilation, and air conditioning (HVAC) system efficiency as the demand-side pillar, renewable energy integration as the supply-side pillar, and AI as the enabling layer connecting both domains. Synthesis of the reviewed literature reveals that demand-side HVAC technologies achieve energy savings ranging from 20% to 67%, while supply-side renewable energy integration increases photovoltaic (PV) self-consumption by 11–13%. Furthermore, AI-driven optimization, particularly through reinforcement learning (22.3% ± 8.4% energy savings) and digital twins (up to 70% renewable energy utilization), substantially enhances building performance within integrated energy management frameworks. The reviewed studies further demonstrate that AI techniques, including machine learning, deep learning, reinforcement learning, and digital twins, enable accurate energy forecasting (R² > 0.90), intelligent operational control, and effective coordination of integrated PV–battery energy storage system–electric vehicle systems, improving building energy flexibility and reducing grid fluctuations by up to 12.78%. Despite these advances, challenges related to data quality, interoperability, model explainability, cybersecurity, and limited large-scale real-world validation remain significant barriers to widespread adoption. Overall, the evidence indicates that AI serves as a key enabler for reducing the BEPG and improving the reliability, resilience, and operational efficiency of NZBs, thereby supporting the transition toward intelligent, low-carbon built environments. Full article

Generative AI (GenAI) adoption in higher education (HE) raises significant ethical concerns. The focus is shifting from rules- or outcomes-based learning environments towards the development of moral character, personality traits, integrity, and practical wisdom (phronesis). However, most existing AI ethics validation instruments are predominantly shaped by Global North paradigms. In Global South HE contexts, in particular, open distance e-learning (ODEL) HE institutions (HEIs) characterised by limited direct supervision and a digital divide, validation remains scant. Ethical risks are intensified by the adoption and integration of GenAI tools, such as large language models (LLMs), to enhance teaching, learning, research, and student support, thus recognising the need to develop and validate virtue ethics scales. The current paper attempts to address this gap by validating the Virtue Ethics Measurement Scale (VEMS) within South Africa’s largest comprehensive ODEL institution. Guided by the positivist paradigm, a 36-item cross-sectional survey of 503 undergraduate and postgraduate students measured six virtue dimensions (justice, honesty, responsibility, care, prudence, and fortitude). Confirmatory factor analysis (CFA) compared four competing models. The single-factor model showed poor fit, rejecting unidimensionality. A second-order hierarchical model demonstrated an acceptable fit (χ²/df = 2.992, CFI = 0.933, RMSEA (Root Mean Square Error of Approximation) = 0.063, SRMR (Standardized Root Mean Squared Residual) = 0.043) with subscale reliabilities ranging from Cronbach’s α = 0.84 to 0.90, supporting a multidimensional yet hierarchical virtue structure. The VEMS offers a psychometrically sound instrument for evaluating ethical AI use in ODEL institutions. This aligns with virtue ethics theory, which emphasises that moral character is a constellation of dispositions (e.g., honesty, care, prudence) rather than a single trait. The VEMS thus enables HEIs to assess students’ virtues, design targeted ethics capacity-development programmes, and inform policy reform for responsible GenAI adoption in under-researched Global South HE settings. Full article

Modern manufacturing faces increasing demands for flexibility, customization, and productivity under dynamic conditions. Multi-robot systems offer a promising solution by enabling cooperative execution of complex tasks, such as assembly and cooperative manipulation. In this context, Multi-Agent Reinforcement Learning (MARL) has emerged as a promising paradigm to enhance coordination and adaptability in industrial settings. MARL enables multiple agents to learn and interact in shared environments to achieve common goals within complex and dynamic industrial processes. In this paper, a deep analysis of MARL applied to industrial multi-robot systems based on a systematic review is presented, with particular focus on cooperative manipulation tasks. Following PRISMA guidelines, we analyze a total of 30 articles published between 2016 and 2026, selected independently by two of the authors from an initial pool of 102 records retrieved from Scopus and Web of Science. These articles were used to address five key questions regarding MARL algorithms, control architectures, industrial applications and validation practices. These research questions seek to examine gaps and trends at the research level which are important for the development of multi-agent control technologies. This review shows a clear prevalence of model-free algorithms under Centralized Training with Decentralized Execution (CTDE) architectures, with validation mainly performed in simulation. Despite promising results and high potential for impact, critical gaps remain in scalability, reproducibility, and sim-to-real transfer, limiting real deployment in manufacturing environments. To address these challenges and fill current gaps, we outline actionable research directions, such as hybrid MARL approaches, standardized industrial benchmarks, digital twin pipelines, and safety-aware deployment strategies, to accelerate MARL adoption in industrial environments. Full article

Credit risk assessment forms a cornerstone of banking risk management and the stability of the wider financial system. Over the past decade, the rapid development of machine learning (ML) techniques has substantially enhanced traditional credit risk assessment methodologies. ML has now emerged as a core technological pillar for the banking sector, strengthening risk identification capabilities, optimising credit decision-making, and advancing financial inclusion. Conventional credit scoring models, dominated by logistic regression (LR) and scorecard approaches, offer inherent strengths in interpretability and regulatory compliance. However, constrained by their linear assumptions, these methods struggle to capture complex non-linear relationships within credit data and deliver insufficient predictive accuracy for the “credit-invisible” population lacking formal credit histories. This paper presents a systematic literature review (SLR) of ML applications in credit risk assessment (CRA), covering publications from January 2016 to May 2026. A total of 894 papers were retrieved from five digital libraries, and following a rigorous multi-stage screening process, 129 studies were selected for final inclusion. Our analysis reveals that tree-based ensemble models and deep learning (DL) architectures predominate in contemporary research in this field. Meanwhile, post hoc explanation methods and machine learning operations (MLOps) are gaining significant traction as solutions to address fairness, transparency, and system maintenance challenges in real-world production environments. We synthesise prevailing methodologies into a unified end-to-end credit risk modelling framework spanning data preprocessing, feature engineering, model training, evaluation, and operational deployment. Through a critical assessment of the advantages, limitations, and inherent trade-offs of existing approaches, this SLR not only identifies current research gaps and future directions for the academic community, but also provides practical guidance for the banking sector to build compliant, fair, and efficient intelligent risk assessment systems. Full article

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

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

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

There is limited research examining how technology-enhanced assessment functions in nontraditional schools. The following research question guided our study: How are technology-enhanced formative assessment strategies perceived to support student learning in a flexible-paced high school environment? The study employed an educational design approach to co-develop and implement formative assessment strategies with teacher-participants during six professional learning sessions throughout the school year. Drawing on data from pre- and post-surveys administered to teachers (n = 36), researcher field notes with reflections during the professional learning sessions, and open-ended survey responses from students at the end of the school year (n = 43), we found: (a) a greater reported use of digital methods for formative assessment and a significant increase in the use of authentic, real world assessment criteria; (b) teacher and student perceptions that timely, constructive, and accessible digital feedback supported learning experiences; and (c) recognition that students could be more actively involved in determining assessment criteria. Students also reported benefits from receiving digital feedback, opportunities for self-assessment, and greater clarity in their learning progression. While technology can enrich feedback and efficiency, careful attention must be paid to sustaining equity and learner voice in assessment design. Full article

The rapid digitalisation of the construction and built-environment sectors has increased demand for innovative teaching methods in higher education. Among emerging digital tools, metaverse applications offer immersive and interactive learning environments with the potential to transform traditional pedagogical practices. This study evaluates the adoption of the metaverse application (EON-XR) in built-environment education by examining educator training outcomes using the Kirkpatrick model. The evaluation focuses on a structured training workshop designed to equip built-environment educators with the skills to integrate metaverse-based applications into first-year teaching modules. A post-training evaluation employed a Google Forms survey instrument explicitly aligned with Kirkpatrick’s four evaluation levels: reaction, learning, behaviour, and results, with Levels 3 (Behaviour) and 4 (Results) operationalised as behavioural-intention and perceived-results proxies collected immediately post-training, ahead of any classroom implementation. The instrument captured educators’ perceptions of usability, relevance, pedagogical value, behavioural intention to apply acquired skills, and the broader institutional and disciplinary implications of metaverse adoption. Both qualitative and quantitative data were analyzed to assess the training programme’s effectiveness across these four dimensions. Findings indicate a generally positive reaction to metaverse training, with educators recognizing the relevance and user-friendliness of the EON-XR application. Evidence of learning was demonstrated by increased confidence and perceived competence in applying metaverse tools for teaching. At the behavioural level, participants expressed strong intentions to integrate metaverse applications into their teaching practice. At the results level, the training was perceived as enhancing teaching engagement, stimulating interest in construction digitalisation, and contributing to the institutional standing of higher education providers. The study concludes that metaverse-based educator training can play a critical role in advancing digital pedagogy within built-environment education. By applying the Kirkpatrick model, this research provides a structured, replicable framework for evaluating the adoption of immersive technology in higher education contexts. Full article

Image steganography is an information-hiding technique, aiming to achieve confidentiality and data privacy while transmitting the data in a digital environment. Over the last two decades, steganography has evolved from classical spatial domain embedding to intelligent and adaptive steganographic systems capable of balancing imperceptibility, embedding capacity, robustness and security. This paper presents a review by categorizing the existing techniques into spatial domain-based, transform domain-based, hybrid and adaptive intelligent techniques. The review follows the PRISMA approach to make the selection process transparent for the inclusion and exclusion of papers in the study. Initially, the reviews include the spatial domain-based methods focusing on higher embedding capacity and simple embedding strategy, followed by transform-domain based techniques, including discrete cosine transform, discrete wavelet transform, and other multi-resolution wavelet transforms aiming to enhance robustness and imperceptibility by embedding the data into frequency coefficients. This paper further explores the methods that combine these techniques with other recent trends to develop adaptive and hybrid techniques. These techniques mainly integrate chaotic theory to enhance the security of secret data before embedding and optimization algorithms such as genetic algorithm, particle swarm optimization, Firefly, etc., for adaptive embedding to achieve an improved tradeoff. Finally, intelligent and adaptive techniques based on deep learning models such as convolutional neural networks, autoencoders, and generative adversarial networks are examined, highlighting their ability to learn intelligent embedding strategies and resist modern steganalysis. A comparative analysis is presented, including the technique, strengths, and limitations, together with the discussion of performance evaluation metrics and vulnerability analysis under image processing attacks. The review highlights the current trends and outlines the future direction to develop next-generation secure image steganographic systems. Full article