Search Results (1,292)

This scoping review paper provides an overview of the evolution, the current stage, and the future prospects of fracture studies on composite laminates. A fundamental understanding of composite materials is presented by highlighting the roles of the fiber and matrix, outlining the applications of various synthetic fibers used in current structural sectors. Challenges posed by interlaminar delamination, one of the critical failure modes, are highlighted. This paper systematically discusses the fracture behavior of these laminates under mixed-mode and complex loading conditions. Standardized fracture toughness testing methods, including Mode I Double Cantilever Beam (DCB), Mode II End-Notched Flexure (ENF) and Mixed-Mode Bending (MMB), are initially discussed, which is followed by a decade-wide chronological analysis of fracture mechanics approaches. Key advancements, including toughening mechanisms, Cohesive Zone Modeling (CZM), Virtual Crack Closure Technique (VCCT), Extended Finite Element Method (XFEM) and Digital Image Correlation (DIC), are analyzed. The review also addresses recent trends in fracture studies, such as bio-inspired architecture, self-healing systems, and artificial intelligence in fracture predictions. By mapping the trajectory of past innovations and identifying unresolved challenges, such as scale integration, dataset standardization for AI, and manufacturability of advanced architectures, this review proposes a strategic research roadmap. The major goal is to enable unified multi-scale modeling frameworks that merge physical insights with data learning, paving the way for next-generation composite laminates optimized for resilience, adaptability, and environmental responsibility. Full article

This study introduces the Human-AI Symbiotic Theory (HAIST), designed to guide authentic collaboration between human researchers and artificial intelligence in academic contexts, while pioneering a novel AI-assisted approach to theory validation that transforms educational research methodology. Addressing critical gaps in educational theory and advancing validation practices, this research employed a sequential three-phase mixed-methods approach: (1) systematic theoretical synthesis integrating five paradigmatic perspectives across learning theory, cognition, information processing, ethics, and AI domains; (2) development of an innovative validation framework combining three established theory-building approaches with groundbreaking AI-assisted content assessment protocols; and (3) comprehensive theory validation through both traditional multi-framework evaluation and novel AI-based content analysis demonstrating unprecedented convergent validity. This research contributes both a theoretically grounded framework for human-AI research collaboration and a transformative methodological innovation demonstrating how AI tools can systematically augment traditional expert-driven theory validation. HAIST provides the first comprehensive theoretical foundation designed explicitly for human-AI partnerships in scholarly research with applicability across disciplines, while the AI-assisted validation methodology offers a scalable, reliable model for theory development. Future research directions include empirical testing of HAIST principles in live research settings and broader application of the AI-assisted validation methodology to accelerate theory development across educational research and related disciplines. Full article

This study investigates the nonlinear, regime-dependent, and frequency-specific interdependencies between rare earth element (REE) markets and key global critical sectors, including artificial intelligence, semiconductors, clean energy, defense, and advanced manufacturing, under varying levels of geopolitical and financial uncertainty. The main objective is to assess how REE markets transmit and absorb systemic risks across these critical domains. Using a mixed-methods approach combining Quantile-on-Quantile Regression (QQR), Continuous Wavelet Transform (CWT), and Wavelet Transform Coherence (WTC), we examine the dynamic connections between two REE proxies, SOLLIT (Solactive Rare Earth Elements Total Return) and MVREMXTR (MVIS Global Rare Earth Metals Total Return), and major sectoral indices based on a dataset of daily observations from 2018 to 2025. Our results reveal strong evidence of asymmetric, regime-specific risk transmission, with REE markets acting as systemic amplifiers during periods of extreme uncertainty and as sensitive receptors under moderate or localized geopolitical stress. High co-volatility and persistent low-frequency coherence with critical sectors, especially defense, technology, and clean energy, indicate deeply embedded structural linkages and a heightened potential for cross-sectoral contagion. These findings confirm the systemic relevance of REEs and underscore the importance of integrating critical resource exposure into global supply chain risk strategies, sector-specific stress testing, and national security frameworks. This study offers relevant insights for policymakers, risk managers, and institutional investors aiming to anticipate disruptions and strengthen resilience in critical industries. Full article

This study presents a comprehensive study integrating machine learning, life cycle assessment (LCA) and heuristic optimization to achieve a low-carbon medical waste (MW)-to fuel process. A detailed process simulation coupled with cradle to gate LCA is employed to generate a dataset covering diverse process operation conditions, embodied carbon of supplying H₂ and the associated carbon emission factor of MW treatment (CEF). Four machine learning techniques, including support vector machine, artificial neural network, Gaussian process regression, and XGBoost, are trained, each achieving test R² close to 0.90 and RMSE of ~0.26. These models are integrated with heuristic algorithms to optimize operating parameters under various green hydrogen mixes (20–80%). Our results show that machine learning models outperform the detailed process model (DPM), achieving a minimum CEF of ~1.3 to ~1.1 kg CO₂-eq/kg MW with higher computational stabilities. Importantly, the optimization times dropped from hours (DPM) to seconds (machine learning models) and the combination of Gaussian process regression and particle swarm optimization is highlighted, with an optimization time under one second. The optimized process holds promise in carbon reduction compared to traditional MW disposal methods. These findings show machine learning can achieve high predictive accuracy while dramatically enhancing optimization speed and stability, providing a scalable framework for extensive scenario analysis during waste-to-energy process design and further real-time optimization application. Full article

This study examines the intersection of artificial intelligence-driven personal branding strategies and female entrepreneurship within Indonesia’s unique hijabi startup ecosystem. Through a mixed-methods approach combining sentiment analysis of 2847 social media posts, in-depth interviews with 35 hijabi entrepreneurs, and machine learning analysis of branding patterns, this research reveals how AI technologies can be leveraged to create culturally sensitive personal branding frameworks for Muslim female entrepreneurs. The findings demonstrate that successful hijabi entrepreneurs employ distinct AI-enhanced communication strategies that balance religious identity, professional credibility, and market positioning. The study introduces the “Halal Personal Branding Framework,” a novel theoretical model that integrates Islamic values with contemporary digital marketing practices. Results indicate that AI-driven personal branding increases startup funding success rates by 34% and market reach by 58% among hijabi entrepreneurs when culturally appropriate algorithms are employed. This research contributes to entrepreneurship communication theory while providing practical guidelines for developing inclusive AI systems that respect religious and cultural diversity in the digital economy. Full article

The captive breeding of marine ornamental fish with specialized larval requirements—such as Chaetodontoplus mesoleucus—remains a major bottleneck in aquaculture, largely due to the lack of techniques tailored to their unique morphological and nutritional needs. The global marine ornamental aquaculture market is valued at approximately USD 2.15 billion annually; however, only around 10% of marine ornamental species are currently supplied through captive breeding, highlighting a substantial technological gap. The artificial propagation of C. mesoleucus is particularly challenging due to the species’ small mouth gape and high nutritional demands during early development. To address this issue, we evaluated the effects of three live-prey types—Euplotes sp., Brachionus sp., and Bestiolina coreana—as well as a mixed diet containing all three, on larval performance. From 3 days post-hatch, larvae were fed each prey type at equal densities (15–20 individuals/mL), and water quality was carefully maintained to minimize external influences. Survival and total length were assessed at 14 dph. At the end of the trial, the mixed-diet group showed the highest survival rate (36.2 ± 5.6%), whereas larvae fed only B. coreana exhibited the greatest total length (7.4 ± 1.2 mm) and a high metamorphosis rate (97.8%). These findings demonstrate that prey selection significantly influences the early survival and growth in C. mesoleucus larvae and highlight the critical role of copepods in promoting growth performance. However, as larval biomass was not quantified, the findings should be interpreted with caution, and future studies incorporating biomass assessments are needed to draw more conclusive inferences. The successful mass rearing of this species supports the feasibility of captive production to reduce wild harvesting, protect coral-reef biodiversity, and promote sustainable ornamental aquaculture. Full article

Despite local and national road authorities striving to provide motorists with a durable and safe infrastructure environment, one in six UK roads are currently classed as being in poor condition. In terms of safety, Department for Transport statistics report high numbers of road incidents; 29,711 killed or seriously injured in 2023, representing little change compared to 2022. As such, reported in this paper is research aimed at developing artificial geopolymer cement mortar-based aggregate as a cost/environmentally attractive alternative to calcined bauxite for high friction surfacing applications. Work was undertaken in two distinct phases. In the first, the performance of alkali silicate-based geopolymers comprising a range of industrial wastes as binder materials was assessed using modified versions of standardized polished stone value and micro-Deval tests. In phase two, selected mixes were assessed for resistance to simulated wear by exposing test specimens to 20,000-wheel passes on an accelerated road test machine. Performance was further investigated using a dynamic friction test method developed by the Indiana Department of Transportation. Despite commercially sourced calcined bauxite aggregates exhibiting the highest performance levels, the findings from this preliminary research were generally positive, with acceptable levels of performance noted for manufactured geopolymer-based aggregates. For instance, in accordance with recommended levels of performance prescribed in BBA/HAPPAS standards, this included attainment of polished stone values higher than 65 and, following accelerated road testing, average texture depths greater than 1.1 mm. It is recognized that further research is needed to investigate geopolymer binder systems and blends of aggregate types, as well as artificial aggregate manufacturing procedures. Full article

The development of innovative and environmentally sustainable construction materials is a strategic priority in the context of the ecological transition and circular economy. Geopolymers and alkali-activated materials, derived from industrial and construction waste rich in aluminosilicates, are gaining increasing attention as low-carbon alternatives to ordinary Portland cement (OPC), which remains one of the main contributors to anthropogenic CO₂ emissions and landfill-bound construction waste. This review provides a comprehensive analysis of geopolymer-based solutions for building and architectural applications, with a particular focus on modular multilayer panels. Key aspects, such as chemical formulation, mechanical and thermal performance, durability, technological compatibility, and architectural flexibility, are critically examined. The discussion integrates considerations of disassemblability, reusability, and end-of-life scenarios, adopting a life cycle perspective to assess the circular potential of geopolymer building systems. Advanced fabrication strategies, including 3D printing and fibre reinforcement, are evaluated for their contribution to performance enhancement and material customisation. In parallel, the use of parametric modelling and digital tools such as building information modelling (BIM) coupled with life cycle assessment (LCA) enables holistic performance monitoring and optimisation throughout the design and construction process. The review also explores the emerging application of artificial intelligence (AI) and machine learning for predictive mix design and material property forecasting, identifying key trends and limitations in current research. Representative quantitative indicators demonstrate the performance and environmental potential of geopolymer systems: compressive strengths typically range from 30 to 80 MPa, with thermal conductivity values as low as 0.08–0.18 W/m·K for insulating panels. Life cycle assessments report 40–60% reductions in CO₂ emissions compared with OPC-based systems, underscoring their contribution to climate-neutral construction. Although significant progress has been made, challenges remain in terms of long-term durability, standardisation, data availability, and regulatory acceptance. Future perspectives are outlined, emphasising the need for interdisciplinary collaboration, digital integration, and performance-based codes to support the full deployment of geopolymer technologies in sustainable building and architecture. Full article

This research aims to examine the effect of artificial intelligence (AI)-supported sustainable geography education on the preparation process for the International Geography Olympiad (IGEO). Research was designed according to the simultaneous triangulation design, which is one of the mixed-methods designs. The research is a quasi-experimental model in terms of revealing the effects of independent variables (IGEO) on dependent variables (artificial). In this study, a quasi-experimental design with a pre-test–post-test control group was used. In this mixed-method study, quantitative data were obtained from questionnaires and achievement tests, while qualitative data were obtained from semi-structured interviews with students and teachers. The quantitative data collection tools used in the study were a mapping literacy achievement test and a problem-solving skills perception scale. The data were obtained from students across various class sections of the same school. Qualitative data were collected through semi-structured individual interview forms, observation forms, participant diaries, and focus group interview forms. Hierarchical regression analysis and ANOVA were used to analyze the statistical data, and the inductive analysis technique was used to analyze the qualitative data. The findings show that AI-supported sustainable geography education improves spatial thinking skills, individualized learning, and learning motivation. In the IGEO exam, students answered the field questions. Full article

The integration of sustainable materials such as fly ash, blast-furnace slag, recycled aggregates, and seawater into concrete mixes offers significant potential for reducing the environmental impact of construction. However, traditional experimental methods for determining the compressive strength of these concrete mixes are time-consuming and resource-intensive. This study leverages Artificial Neural Networks (ANNs) and Machine Learning (ML) to develop a predictive model for the compressive strength of sustainable concrete, using a dataset of 768 concrete mix samples. Input variables include the concrete age as well as concrete composition, including cement, water, fine and coarse aggregates, seawater, fly ash, blast-furnace slag, and superplasticizer contents, while the output is the compressive strength. The developed model captures the non-linear relationships among these variables to predict compressive strength efficiently. The best ANN model achieved a test loss of 0.051, demonstrating its ability to accurately predict compressive strength and reduce reliance on traditional testing methods. Moreover, the model’s results were compared with those of alternative algorithms to ensure its validity. These findings highlight the potential of machine learning in advancing sustainable construction practices. A relevant future research direction is to analyze feature importance in machine learning models to identify key variables and guide more effective optimization and decision-making, in addition to extending their application to other material properties and advanced concrete mixes. Full article

Service learning (SL) is at a pivotal moment as education systems worldwide confront the challenges and opportunities posed by artificial intelligence (AI) and digital technologies. This scoping review synthesizes regional perspectives on SL and examines the barriers to its implementation in higher education. This study adopts a methodological approach widely used in prior educational research, enriched with selected PRISMA processes, namely identification, screening, and eligibility, to enhance its transparency and rigor. A total of 101 peer-reviewed articles were analyzed, using a mixed methods approach. Results are presented for six regions, Africa, Asia, Latin America, Europe, North America, and Oceania, revealing context-specific constraints, such as technological infrastructure, policy frameworks, linguistic diversity, and socio-economic disparities. Common barriers across regions include limited faculty training, insufficient institutional support, and misalignment with community needs. AI is explored as a potential enabler of SL, not as an empirical outcome, but as part of a reasoned argument emerging from the documented complexity of SL implementation in the literature. Ethical considerations, including algorithmic bias, equitable access, and the preservation of human agency, are addressed, alongside mitigation strategies that are grounded in participatory design and community engagement. This review offers a comparative, context-sensitive understanding of SL implementation challenges, providing actionable insights for educators, policymakers, and researchers, aiming to integrate technology-enhanced solutions responsibly. Full article

As intelligent vessel technology moves from the proof-of-concept stage to engineering applications, the performance testing and evaluation of autonomous collision avoidance algorithms have become core issues for safeguarding maritime traffic safety. The International Maritime Organization (IMO)’s Maritime Safety Committee (MSC), at its 109th session, agreed to a revised road map for the development of the Maritime Autonomous Surface Ships (MASS) Code; the field has experienced the development stages of single-vessel collision avoidance validation based on COLREGs, multimodal algorithm collaborative testing, and the current construction of a progressive validation system for the integration of a mix of virtual reality and actual reality. In recent years, relevant studies have achieved research achievements, especially in the compatibility of COLREGs and in accurate collision avoidance in complex situations, and other algorithm tests and evaluations have made great breakthroughs. However, a systematic literature review is still lacking. In this paper, we systematically review the research progress of autonomous collision avoidance performance testing and the evaluation of intelligent vessels; summarize the advantages and disadvantages of virtual testing, model testing, and full-scale vessel testing; and analyze the applicability and limitations of mainstream algorithms such as the velocity obstacle algorithm, the artificial potential field algorithm, and reinforcement learning. It focuses on the key technologies such as diverse scene generation, local scene slicing, and the construction of an evaluation index system. Finally, this paper summarizes the challenges faced by autonomous collision avoidance performance testing and the assessment of intelligent vessels and proposes potential technical solutions and future development directions in terms of virtual–real fusion testing, dynamic evaluation index optimization, and multimodal algorithm co-validation, aiming to provide a reference for the further development of this field. Full article

Geopolymers are an environmentally sustainable class of low-calcium alkali-activated materials (AAMs), distinct from high-calcium C–A–S–H gel systems. Synthesized from aluminosilicate-rich precursors such as fly ash, metakaolin, slag, waste glass, and coal gasification fly ash (CGFA), geopolymers offer a significantly lower carbon footprint, valorize industrial by-products, and demonstrate superior durability in aggressive environments compared to Ordinary Portland Cement (OPC). Recent advances in thermodynamic modeling and phase chemistry, particularly in CaO–SiO₂–Al₂O₃ systems, are improving precursor selection and mix design optimization, while Artificial Neural Network (ANN) and hybrid ML-thermodynamic approaches show promise for predictive performance assessment. This review critically evaluates geopolymer chemistry and composition, emphasizing precursor reactivity, Si/Al and other molar ratios, activator chemistry, curing regimes, and reaction mechanisms in relation to microstructure and performance. Comparative insights into alkali aluminosilicate (AAS) and aluminosilicate phosphate (ASP) systems, supported by SEM and XRD evidence, are discussed alongside durability challenges, including alkali–silica reaction (ASR) and shrinkage. Emerging applications ranging from advanced pavements and offshore scour protection to slow-release fertilizers and biomedical implants are reviewed within the framework of the United Nations Sustainable Development Goals (SDGs). Identified knowledge gaps include standardization of mix design, LCA-based evaluation of novel precursors, and variability management. Aligning geopolymer technology with circular economy principles, this review consolidates recent progress to guide sustainable construction, waste valorization, and infrastructure resilience. Full article

This study aimed to investigate physical and biochemical strategies to optimize the preservation and fertilizing capacity of rooster semen during chilled storage and after artificial insemination (AI), respectively. Two semen extenders—0.9% sodium chloride (NaCl) and IGGKPh—were evaluated through two factorial experiments. In Experiment 1, a 2 × 2 factorial design was used to examine the effects of oxygen exposure (aerobic vs. reduced-oxygen conditions) and tube rotation (rotated vs. non-rotated) on semen quality during 24 h of storage at 5 °C. Sperm quality was evaluated based on progressive motility, viability, pH, and malondialdehyde (MDA) concentration. IGGKPh was significantly more effective than NaCl in preserving sperm function, maintaining motility above 70% and viability near 90%. Aerobic conditions and tube rotation improved motility but also increased MDA levels, indicating a tradeoff between enhanced metabolic activity and oxidative stress. Semen stored in NaCl lost its fertilizing capacity after 22 h, whereas IGGKPh under aerobic and rotated conditions resulted in significantly higher fertility rates (91.77%) compared with non-rotated samples. In Experiment 2, the effects of HEPES buffer supplementation (present vs. absent) and handling temperature (5 °C vs. 25 °C) were evaluated under simulated AI conditions. Semen extended in IGGKPh was stored at 5 °C for 22 h prior to handling, while NaCl samples were used immediately after dilution. Sperm quality was assessed at 0, 30, and 60 min of exposure. HEPES significantly reduced MDA levels and improved motility and viability in both extenders. Fertility rates were highest in the HEPES-supplemented groups, especially under chilled handling. In conclusion, optimal preservation of rooster semen requires a combination of metabolic support, adequate oxygen availability, gentle mixing, and pH stabilization. While IGGKPh was effective for storage up to 24 h, its performance was further enhanced by HEPES buffer. These findings offer practical recommendations for AI programs in poultry, particularly under field conditions where temperature fluctuations and delayed insemination are common. Full article

Background: The rapid shift from open to endovascular techniques in vascular surgery has significantly decreased trainee exposure to high-stakes open procedures. Simulation-based training, especially that incorporating virtual reality (VR) and artificial intelligence (AI), provides a promising way to bridge this skill gap. Objective: This narrative review aims to assess the current evidence on the integration of extended reality (XR) and AI into vascular surgeon training, focusing on technical skill development, performance evaluation, and educational results. Methods: We reviewed the literature on AI- and XR-enhanced surgical education across various specialties, focusing on validated cognitive learning theories, simulation methods, and procedure-specific training. This review covered studies on general, neurosurgical, orthopedic, and vascular procedures, along with recent systematic reviews and consensus statements. Results: VR-based training speeds up skill learning, reduces procedural mistakes, and enhances both technical and non-technical skills. AI-powered platforms provide real-time feedback, performance benchmarking, and objective skill evaluations. In vascular surgery, high-fidelity simulations have proven effective for training in carotid artery stenting, EVAR, rAAA management, and peripheral interventions. Patient-specific rehearsal, haptic feedback, and mixed-reality tools further improve realism and readiness. However, challenges like cost, data security, algorithmic bias, and the absence of long-term outcome data remain. Conclusions: XR and AI technologies are transforming vascular surgical education by providing scalable, evidence-based alternatives to traditional training methods. Future integration into curricula should focus on ethical use, thorough validation, and alignment with cognitive learning frameworks. A structured approach that combines VR, simulation, cadaver labs, and supervised practice may be the safest and most effective way to train the next generation of vascular surgeons. Full article