Search Results (438)

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

Cracks pose a significant threat to the structural integrity, durability, and service life of concrete; therefore, sustainable, autonomous repair solutions are paramount. In the last 25 years, bio-based self-healing, particularly microbially induced calcium carbonate precipitation (MICP), has become an attractive technology. Self-healing by bacteria has been studied extensively, but the use of other biomineralization agents, such as fungi and algae, has unique benefits, namely, hyphal crack-bridging and photosynthetic mineralization. In this paper, a thorough state-of-the-art review is presented that compares bacteria, fungi, and algae as biomineralization agents. The comparative methodology involves a structured review of the peer-reviewed literature on these agents (2000–2025), and compares them on a set of common performance criteria: (i) biochemical precipitation mechanisms (ureolytic, non-ureolytic, photosynthetic and hyphal bridging); (ii) quantitative crack-healing efficiency (maximum width of crack closed); (iii) mechanical performance recovery (restoration of compressive and tensile strength); (iv) long-term durability enhancement. Moreover, it critically evaluates implementation challenges, including biological viability in extreme cementitious media, encapsulation methods, and the levels of technological maturity for practical engineering applications. The findings of this synthesis outline key research gaps and offer a roadmap for creating hybrid, consortium-based self-healing systems to help engineers and researchers select the best bio-based concrete for a given structure and environment. Full article

This study presents a systematic taxonomic review of risk modelling and assessment methods in construction projects over the past 35 years (1990–2025). Through a structured four-stage process, 91 peer-reviewed articles from 15 leading journals were analysed. The taxonomic approach enabled the classification and mapping of methods according to chronological evolution, study type, authorship patterns, and focus areas, while thematic analysis was employed to synthesise key themes, trends, and research gaps. The review examines publication trends, geographical distribution of research contributions, and methodological developments. The findings reveal that the probability-impact (P-I) model remains the dominant approach, despite its well-documented limitations in capturing risk interdependencies and their cascading effects on project quality and overall performance. Fuzzy Set Theory (FST), Analytic Hierarchy Process (AHP), and Monte Carlo Simulation (MCS) emerged as the most frequently adopted techniques. The analysis demonstrates a clear evolution in the field: from predominantly basic probabilistic methods in the 1990s to increasingly sophisticated hybrid, fuzzy logic-based, and AI-enhanced approaches after 2010. Notwithstanding these advancements, significant gaps persist, particularly the lack of integrated frameworks capable of simultaneously addressing risks across multiple project objectives—cost, time, quality, and performance. This review synthesises the state of knowledge in the field, identifies persistent theoretical and practical shortcomings, and offers a comprehensive roadmap for future research. Key directions include the development of machine learning applications, dynamic modelling techniques, and holistic multi-objective risk assessment frameworks to better align risk management theory with the complex realities of modern construction projects. Full article

Power transformers are critical components of electric power infrastructure, and their liquid insulation systems are decisive for operational safety, reliability, and longevity. Conventional insulating fluids, particularly mineral oil, face increasing scrutiny due to low biodegradability, poor thermal performance, and non-renewable origin. Insulating oil-based nanofluids, engineered by dispersing nanoparticles (1–100 nm) into base oils, have emerged as transformative candidates for next-generation transformer liquid insulation. This review provides a comprehensive and critically integrated analysis of insulating oil-based nanofluids, systematically covering historical development, synthesis methodologies, colloidal stabilization strategies, and multi-technique characterization approaches. Dielectric performance metrics, including AC, DC, lightning-impulse breakdown voltages, partial-discharge inception voltage, and dielectric loss, are critically reviewed alongside thermal-conductivity enhancements and the thermo-viscous trade-off. Experimental evidence demonstrates that optimally formulated nanofluids enhance AC breakdown voltage by 20–60%, improve thermal conductivity by 10–40%, and significantly elevate partial discharge inception voltage depending upon various factors such as doping concentration, dispersion quality, moisture content, particle size/morphology, nanoparticle-oil system compatibility, etc. However, long-term colloidal instability, nanoparticle migration, compatibility with ageing products, and absence of standardized testing protocols continue to impede industrial deployment. This review identifies key research gaps and outlines a roadmap toward reliable, sustainable, and commercially viable insulating nanofluids for power transformer applications. Full article

Background: The EU Health Technology Assessment Regulation (EU-HTA R), effective January 2025, mandates Joint Clinical Assessments (JCAs) to harmonize HTA across Member States. However, its implementation raises fundamental questions about methodological coherence, institutional capacity, and epistemological alignment. Objectives: This manuscript (1) systematically assesses whether the stated strategic and operational objectives of the EU-HTA R are achievable under current implementation conditions; (2) examines the implications for EU institutional legitimacy if these objectives are not met; and (3) proposes an epistemological framework as a prerequisite for developing a coherent joint HTA methodology. Methods: We conducted a critical policy analysis of the EU-HTA R, its implementing guidance documents, and published templates, supplemented by a comparative review of Member State HTA methodologies and their underlying philosophical foundations. Results: The analysis reveals that the EU-HTA R is unlikely to achieve its strategic goals under current conditions. Key findings include: guidance documents of substandard methodological quality; a restricted assessment scope that excludes scientific judgement and contextualization; insufficient resources and additional workload for national HTA bodies without reducing existing obligations; unresolved epistemological divergences among Member States spanning Bayesian vs. frequentist approaches, Fisher vs. Neyman–Pearson frameworks, and utilitarian vs. deontological ethical foundations; and procedural shortcomings in stakeholder consultation and expert involvement. These shortcomings risk undermining the epistemic authority and legitimacy of EU institutions. Conclusions: Prior epistemological and normative alignment across Member States is a prerequisite for any robust shared HTA methodology. Revisions to the EU-HTA R and comprehensive updates of guidance documents are necessary, with concrete safeguards—including independent peer review, identified authorship, and adequate resourcing—to ensure substantive rather than merely nominal implementation. A phased roadmap is proposed: establishing clear objectives, aligning epistemological foundations, developing institutional structures, and creating operationally consistent guidance. Full article

Reliable preconstruction cost estimation (PCE) is a fundamental stage to project planning and investment decision-making. However, the early-stage uncertainty and limited information make early-stage cost estimation challenging. Artificial intelligence has attracted growing interest as a way out of this impasse, producing a substantial empirical literature worth systematic examination. This study synthesizes the findings of 30 empirical studies published up to December 2025 using PRISMA protocols and examines the AI techniques, project types, dataset characteristics, validation practices, and model interpretability. The results show that artificial neural networks (ANNs) and hybrid approaches dominate the literature, with applications concentrated in building and transportation projects. Reported model performance is generally strong across commonly used evaluation metrics. However, several structural limitations persist: poor generalizability across project contexts, inconsistent validation procedures, limited adoption of explainable AI, and minimal integration of domain expertise. These factors, together, limit the transferability of existing models to real-world practice. This review contributes a structured methodological synthesis, maps the gaps that most limit progress, and proposes a conceptual AI–Expert Integration Framework to support estimation approaches that are more robust, interpretable, and decision-oriented. The findings offer both a current assessment of the field and a practical roadmap for advancing AI-driven PCE research. Full article

In the current era, the tourism sector faces interconnected challenges that require a rapid transition toward sustainable and smart management models. Within the framework of the project ‘Tourism as a Service’ (TAAS), this paper examines the transferability potential of good practices across nine diverse European areas. The study adopts a dual methodological approach that integrates technical feasibility with local relevance. First, the identified practices are clustered into seven thematic pillars. Second, a Multi-Criteria Decision Analysis (MCDA), employing the PROMETHEE method, is applied to assess transferability based on five key dimensions: institutional complexity, financial requirements, technical infrastructure, human resources capacity and regulatory constraints. This quantitative evaluation is complemented by qualitative insights from local stakeholders, ensuring that the proposed solutions align with regional priorities. The findings indicate that communication-oriented practices, such as online marketing campaigns, demonstrate high transferability across most contexts, whereas infrastructure-dependent tools encounter significant implementation requirements. By reconciling constraints rankings with stakeholder perspectives, this paper provides a roadmap for policy improvements aimed at fostering digital transformation in European tourism. Full article

This study presents a systematic literature-based discovery of the energy management process within household microgrids, combining the methodologies of Systematic Literature Review (SLR) and Literature-Based Discovery (LBD). The objective is to identify and structure key activities that ensure the efficient, scalable, and resilient operation of household microgrids. Drawing on an extensive analysis of the literature, the study proposes a conceptual, process-oriented framework that integrates technological and organizational perspectives into an eight-step roadmap for household energy management. These steps include data acquisition, local weather forecasting, energy production and consumption prediction, demand and supply management, energy generation and storage, power distribution, control of technological and organizational infrastructure, and compliance with safety and regulatory standards. The model supports the integration of predictive, self-learning control systems and highlights the importance of user competence development alongside automation. By mapping out a structured and replicable approach to household microgrid energy management, the study provides a foundation for improved energy independence, operational reliability, and effective integration into decentralized energy markets. The roadmap offers practical insights for both researchers and practitioners aiming to support the sustainable development and governance of household microgrids. Full article

Pseudo-Spark Switches (PSSs) are characterised by high-voltage withstand, high-current conduction capabilities, and an extended lifetime compared to alternative gas discharge switches. Despite numerous studies investigating PSS characteristics across different geometries, construction materials, and triggering methods, a generalised design methodology is still lacking. This work addresses this gap by establishing foundational design guidelines synthesised from the available literature data. By analysing diverse electrode geometries, gas pressure configurations, and trigger mechanisms, the core operational boundaries of the electrical parameters, as well as the lifetime, are mapped. Furthermore, the practical significance of these guidelines is demonstrated through a complete design example of a PSS tailored for a Marx generator used in Pulsed-Power Geo-Drilling (PPGD). The resulting framework provides a systematic roadmap that reduces trial-and-error in PSS development for high-power applications. Full article

The medical device manufacturing industry in Mexico faces a critical risk of losing competitiveness and sustainability due to its concentration on low-value-added manufacturing activities and limited integration into advanced stages of the value chain, such as R&D. This research addresses the lack of validated quantitative methodologies to identify the critical factors that promote sectoral sustainability in the national context. Through a literature review and the analysis of MCDM, a taxonomy of the problem was developed that integrates dimensions of governance, technological innovation, and human capital. The findings emphasize the need to transition toward circular economy and additive manufacturing models, supported by hybrid algorithms such as AHP, TOPSIS, and DEMATEL to mitigate uncertainty in strategic decision making. As a main result, an innovation management flow aligned with international standards and several maturity levels (TRLs, MRLs, CRLs, and PRLs) is proposed, providing a structured roadmap to scale the Mexican industry toward more-sophisticated global segments. Full article

Nearshoring is accelerating industrial expansion across Mexico, reshaping electricity demand profiles and placing increasing stress on a power system already challenged by reliability, affordability, and decarbonization pressures. This paper analyzes the impact of nearshoring on Mexico’s economy, with a particular focus on the electricity sector, examining challenges across generation, transmission, and distribution infrastructure. A critical dimension of this analysis is Mexico’s structural dependence on natural gas for electricity generation, which introduces systemic vulnerability to global supply disruptions and price volatility, particularly under geopolitical tensions in the Middle East. Using a systematic literature review, this study synthesizes current evidence on how nearshoring-driven demand growth affects Mexico’s electricity distribution system and evaluates solution pathways to meet rising energy needs while maintaining system reliability and equity. Special emphasis is placed on solar photovoltaic (PV) deployment as a grid-supporting resource rather than solely as generation capacity, highlighting its role in enhancing resilience against external fuel shocks. A PRISMA-based methodology was applied using Web of Science, complemented by official Mexican sources such as SENER, CENACE, CRE, and CFE to contextualize technical findings within national policy and operational frameworks. The paper concludes with a research agenda addressing key gaps, including corridor-level load evolution, empirical validation of PV-plus-flexibility solutions for distribution reliability, and strategies to incorporate energy justice into infrastructure planning. Its main contribution lies in Integrating previously separate evidence streams on nearshoring-driven industrial load growth, natural-gas dependence, and distribution-level constraints into a Mexico-specific synthesis of resilient industrial electrification pathways. Providing a distribution-centered perspective on nearshoring, integrating the role of natural gas dependency under global uncertainty, and proposing an actionable roadmap for resilient and sustainable industrial electrification in Mexico. Full article

Machine learning techniques for network-based intrusion detection systems (NIDS) have advanced considerably over the past decade. Still, improvements are inhibited by handcrafted feature pipelines, isolated public benchmark data, and evaluation procedures that do not reflect real-life deployment. AutoML, a branch of ML automating model selection, automated architecture search, and the creation of model pipelines, may help overcome these shortcomings. While numerous NIDS applications employing automated ML techniques have been proposed, and recent surveys have mapped the AutoML framework landscape for network intrusion detection, no existing review critically audits the evaluation practice of this literature: the quality of its benchmark datasets, the reproducibility of its reported results, and the realism of its deployment assumptions. This paper critically reviews 26 research works published between January 2023 and June 2026, collected via a two-phase structured search: a documented keyword search across five databases (Scopus, IEEE Xplore, Web of Science, ACM Digital Library, and Google Scholar), followed by full-text eligibility screening, citation chaining, and expert evaluation. Findings drawn from this collection capture trends observed among the selected studies, rather than reflecting the broader state of the field. Analysis of the corpus reveals that 88% of dataset-verified studies evaluate exclusively or partly on the legacy benchmark family (KDD-derived, CICIDS, UNSW-NB15, CIDDS), 21% evaluate on a single dataset only, and among attribute-verified studies only 32% release source code, 40% report statistical significance testing, and 36% include variance analysis, findings that collectively motivate the four contributions of this study. First, a recommended evaluation framework is proposed, addressing baseline parity, transparent search-space and budget reporting, nested cross-validation for selection-bias control, and stability reporting across multiple random seeds. Second, a dataset quality scoring framework is introduced, assessing five dimensions: overlap rate, duplication rate, label correctness, attack-type representativeness, and coverage of benign, IoT, and IIoT traffic. Third, a cross-domain justification is provided for neural architecture search (NAS) and meta-learning in NIDS, grounded in advances in federated NAS, out-of-distribution robustness, edge-constrained search cost reduction, and few-shot adaptation. Fourth, a structured research roadmap is outlined, targeting real-world validation, standardized benchmarks, curated datasets, resource-aware AutoML, and privacy-preserving federated NAS. In contrast to prior surveys of AutoML for network intrusion detection, which map frameworks and computational paradigms, this review contributes a formalized evaluation checklist, an explicit and partially empirically validated dataset quality scoring scheme, and evidence-based methodological guidance grounded in a transparent, fully enumerated study corpus. Full article

Deep neural networks (DNNs) have significantly advanced machine perception and reasoning; however, their lack of transparency in decision-making continues to pose a major challenge, particularly in high-stakes domains such as healthcare, finance, and law. This is especially concerning with the black-box nature of convolutional neural networks (CNNs), where the rationale for making a decision can be as important as the decision itself. This paper is driven by a question that is easier to ask than to answer: how can CNNs be made to explain themselves? To answer the question, we wrote a PRISMA-compliant systematic review of 154 studies published between 2017 and 2025. These studies were selected from 4421 studies retrieved through Web of Science, Scopus, IEEE Xplore, and ACM Digital Library. CNN-specific taxonomy was developed. This taxonomy organizes explainable artificial intelligence (XAI) methods on four axes: explanation timing, model dependency, output type, and target component. We found that there is a huge bias in the field regarding post hoc visual methods. Grad-CAM is the most widely cited visual explanation methodology, and within the model-agnostic framework, LIME and SHAP prevail. This research was also the first to analyze standard assessment methods. It was found that out of the 154 studies in the review, 98 used objective methods to evaluate fidelity, stability, or sensitivity. Conversely, fewer than ten of them used human-centered methods to evaluate how tasks were performed, how the users trusted the method, or how the users were prepared to interact with the system. We argue for a dual-reporting convention under which metrics should be reported together at least once, as per the family of metrics. The third contribution is an evidence-based challenge map, where we outline four issues: absence of standardized benchmarks, post hoc mechanism scalability limitations, vulnerability to adversarial perturbations, and the persistent gap between the technical descriptions and human understanding. For each challenge, we propose concrete directions: integrating causal reasoning, adopting participatory evaluation design, and building hybrid transparent architectures. We offer this review as a practical roadmap for researchers and practitioners working toward more explainable deep neural networks. Full article

Maritime transport contributes 2.9% of global greenhouse gas emissions, traditionally evaluated through operational fuel cycles while neglecting lifecycle impacts. This study redefines merchant ship recycling as a strategic front-end pillar for global decarbonization by assessing Embodied Carbon Trade-offs. Utilizing a mixed PRISMA systematic and semi-systematic methodology of literature from 2019–2025, we analyzed bulk carriers, container ships, and tankers across six thematic clusters. Our findings demonstrate that scenarios involving the potential decommissioning of up to 22.2% of the global merchant fleet exceeding 20 years of age could significantly mitigate lifecycle emissions by displacing primary iron-ore smelting with circular electric arc furnace marine-steel recovery. Crucially, this environmental dividend is non-linear and bound by regional energy matrices; under deeply decarbonized grids, this technological displacement can theoretically yield an upper-bound 72% emissions reduction, whereas fossil-heavy power supplies significantly diminish net mitigation margins. Practically, this research provides an operational roadmap for shipowners and regulators navigating the Hong Kong Convention and carbon border mechanisms. This work concludes that sustainable shipbreaking has the potential to function as an economically viable, strategic reservoir of low-carbon raw materials essential for achieving international net-zero targets throughout the entire shipping structural lifecycle. Full article

The growing demand for accurate and computationally efficient combustion mechanisms for simulations has driven significant advances in chemical kinetic mechanism reduction for multicomponent fuels. This review systematically examines the evolution of reduction methodologies, from early size- and stiffness-reduction techniques to modern construction-based and optimisation-driven strategies. Key approaches including directed relation graph (DRG) methods, sensitivity analysis, path flux analysis, computational singular perturbation (CSP), and genetic algorithms are critically assessed. The review further explores the foundational kinetic mechanisms of hydrocarbons, alcohols, ethers, and esters, highlighting the hierarchical role of C₀–C₄ core chemistry in multicomponent fuel oxidation. It provides a consolidated understanding of reduction strategies and workflows; highlights access to detailed kinetic mechanisms for practical fuels. A central finding is that co-oxidation interactions between fuel components significantly influence combustion behavior and must be explicitly retained in reduced mechanisms. Despite substantial progress, challenges remain in developing universal reduction frameworks, establishing standardised validation datasets, and achieving fully automated mechanism construction. This review provides a roadmap for researchers seeking to implement skeletal/reduced chemical kinetic models in multidimensional engine simulations while retaining chemical fidelity across wide operating conditions. Full article