Search Results (1,655)

Bipolar plates are critical components of proton exchange membrane fuel cells (PEMFCs), strongly influencing thermal management, mechanical stability, and overall system efficiency. In this study, an integrated framework combining multiphysics simulation, artificial intelligence (AI), and data augmentation techniques was developed for PEMFC bipolar plate optimization. A coupled thermal–structural finite element model was established in COMSOL Multiphysics to evaluate temperature distribution, thermal stress, and structural deformation under varying operating conditions. A total of 80 parametric design cases were generated by varying six key parameters: hole radius, plate thickness, heating power, manifold pressure, plate number, and heat transfer coefficient. The dataset was expanded using SMOTE, GAN, and LLM-based augmentation techniques and used to train ANN, LR, RF, XGBoost, and SVR models. Model performance was evaluated using 5-fold cross-validation with MAE, RMSE, and LogCosh metrics. The results showed that ensemble tree-based methods, particularly RF and XGBoost, achieved the highest prediction accuracy and computational efficiency. XGBoost produced the best temperature prediction performance for the SMOTE-based dataset (RMSE = 3.668), while RF achieved the lowest stress prediction error (RMSE = 0.0490). GAN-augmented datasets provided stable and reliable predictions, whereas LLM-generated datasets resulted in higher prediction errors and lower physical consistency. Feature importance analysis revealed that plate thickness dominates displacement prediction (≈0.72 importance), manifold pressure governs stress behavior (≈0.999), and heating power is the primary factor affecting temperature prediction. The proposed AI-assisted surrogate modeling framework enables rapid and accurate thermo-mechanical prediction while significantly reducing computational cost compared to conventional multiphysics simulations. The findings demonstrate that integrating physics-based simulations with data-driven approaches provides an efficient strategy for the optimization of next-generation PEM fuel cell bipolar plates. Full article

Communities across the Southwestern United States (US) and Northern Mexico are making critical decisions regarding how they create long-term water resilience, including by reducing water demand and diversifying water supplies in the face of scarcity. There are several emerging frameworks encouraging collaborative governance approaches to water scarcity, such as Collaborative Water Governance and Adaptive Water Governance; however, examples of ongoing implementation of these frameworks by local governments in academic literature are less prevalent. This paper addresses this gap in the literature by sharing case studies and practitioner recommendations resulting from Growing Water Smart (GWS)—a training and assistance program for local communities to conduct collaborative water resilience action planning across jurisdictional borders, as well as between the historically separated disciplines of water resources management and land use planning. This paper presents and assesses the GWS curriculum as a model for local, cooperative responses to water scarcity, grounded in Collaborative Water Governance, Adaptive Governance, and related frameworks. This paper utilizes primary GWS program documents, firsthand participant perspectives, and direct practitioner experiences to present three case studies of GWS communities working across disciplinary and jurisdictional borders: a regionally collaborative facilitation process and intergovernmental agreement regarding water exports in the San Luis Valley of Colorado; a regional GWS workshop and emerging county-wide convening of jurisdictions within the Verde Watershed of central Arizona; and binational collaboration across the US-Mexico border through a workshop between the cities of Douglas, Arizona and Agua Prieta, Sonora, resulting in a deepened understanding of shared effluent flows. Finally, this paper posits that the GWS model initiates more collaborative and informed decision-making, builds capacity for localities through the support of third-party conveners and facilitators, and maximizes the limited financial and human resources available to local jurisdictions—resulting in a valuable and replicable process to advance water resilience across disciplinary and jurisdictional borders. Full article

This narrative review examines the historical evolution, current applications, and major challenges of machine learning (ML) in education, positioning ML as a transformative yet deeply contested force in contemporary teaching and learning. Tracing developments from early computer-assisted instruction and intelligent tutoring systems to contemporary deep learning, natural language processing, and generative AI, the review shows how these technologies have expanded education’s capacity for personalization, prediction, automation, content generation, and large-scale data-driven decision-making. It synthesizes evidence across key domains, including student performance prediction, early warning systems, adaptive learning, intelligent tutoring, automated assessment, learning analytics, curriculum design, and inclusive education. In addition, the review critically highlights persistent limitations and risks, particularly algorithmic bias, data privacy concerns, limited interpretability, uneven pedagogical value, infrastructure constraints, and the disruption of conventional assessment by generative AI. Rather than treating ML as a purely technical innovation, the paper argues that its educational significance depends on how responsibly it is designed, implemented, and governed. The review concludes that the future of ML in education will be shaped not only by advances in computational methods but also by ethical judgment, pedagogical alignment, and institutional commitment to equity, transparency, and human-centered educational practice across diverse learning contexts worldwide. Full article

Drought stress is the most pervasive abiotic constraint on global crop productivity, with projected intensification under climate change threatening the yields of staple crops including wheat, rice, maize, and legumes. Conventional breeding approaches have delivered limited gains against drought tolerance, constrained by the polygenic and multifactorial nature of stress adaptation, the complexity of genotype-by-environment interactions, and the inadequacy of field-based phenotyping under variable stress conditions. Omics technologies, including genomics, transcriptomics, proteomics, metabolomics, epigenomics, and phenomics, have substantially advanced the molecular dissection of drought tolerance by enabling high-resolution characterization of stress-responsive genes, regulatory networks, adaptive proteins, and metabolic reprogramming pathways. Specific traits targeted include root system architecture and depth, osmotic adjustment capacity through proline and glycine betaine accumulation, antioxidant defense mechanisms, ABA-mediated stomatal regulation, LEA protein accumulation, epigenetic stress memory, and yield stability under water deficit. This review systematically examines omics-based strategies for drought stress mitigation across major crops, highlighting individual omics contributions, multi-omics integration frameworks, computational tools including machine learning and AI-driven predictive modelling, and translational breeding applications. Case studies in wheat, rice, maize, and legumes illustrate how omics-driven approaches accelerate precision breeding for drought resilience through marker-assisted selection, genomic selection, and CRISPR-based gene editing. Challenges including data integration complexity, high implementation costs, limited cross-species transferability, and the need for field-scale validation of microbiome-based strategies are critically addressed. Future perspectives encompassing single-cell and spatial omics, AI-driven predictive breeding, digital agriculture integration, and international data governance frameworks are discussed. By aligning with climate-smart agriculture principles, multi-omics approaches provide a robust and transformative foundation for developing drought-resilient crop cultivars suitable for water-limited production systems worldwide. Full article

Phytochemical-assisted green synthesis of silver nanoparticles offers a sustainable alternative to conventional fabrication routes by utilising plant-derived metabolites as multifunctional reducing, capping, and stabilising agents. Polyphenols, flavonoids, tannins, alkaloids, and related biomolecules mediate the reduction of Ag⁺ to Ag⁰ under mild conditions while controlling nucleation, growth, and surface stabilisation, thereby dictating nanoparticle size, morphology, and colloidal stability. This review establishes clear links between phytochemical composition and the mechanistic pathways governing nanoparticle formation and biofunctional performance. Variations in extract chemistry influence electron transfer dynamics, surface functionalisation, and physicochemical properties, ultimately modulating biological activity. Enhanced antimicrobial and antioxidant effects arise from synergistic interactions between the silver core and phytochemical capping layers, promoting membrane disruption, reactive oxygen species generation, and biomolecular interference. Despite promising applications in antimicrobial coatings, food preservation, agriculture, and anticancer systems, key challenges remain, including compositional variability, limited mechanistic standardisation, and insufficient toxicological evaluation. Nonetheless, phytochemical-assisted synthesis provides a tunable and sustainable platform for AgNP production, aligning nanomaterial design with green chemistry principles while enabling multifunctional bioactivity. By integrating phytochemical composition, mechanistic synthesis pathways, and structure–activity relationships across diverse applications, this review provides a critical framework for the rational design, standardisation, and scalable development of next-generation phytochemical-mediated AgNP systems. Full article

Critical infrastructures are increasingly Internet-connected cyber–physical systems whose recovery after cyber incidents must satisfy safety, timing, regulatory, and interdependency constraints. Yet, the use of large language models (LLMs) for generating recovery plans remains fragmented across cybersecurity, industrial control, digital twins, and AI assurance research. This review synthesizes that emerging field through a structured critical survey of studies on LLMs in incident response, OT/ICS resilience, and cyber–physical recovery, with a focused perspective on grounding, trust, and assurance mechanisms relevant to recovery-plan generation. It develops an architecture-centric taxonomy spanning prompt-only assistants, retrieval-augmented copilots, graph-aware planners, multi-agent systems, and hybrid verification/simulation pipelines; maps realistic applications across energy, water, manufacturing, transportation, healthcare, and telecommunications; and organizes limitations into technical, security, governance, and human-factor categories. Based on this synthesis, the paper proposes the Grounded Recovery Planning Stack as a reference architecture and outlines a staged roadmap from human-in-the-loop copilots to bounded orchestration. The main conclusion is that near-term value lies in grounded, auditable, compliance-aware copilots, whereas autonomous recovery execution remains premature without stronger validation, state-aware grounding, sector-specific benchmarks, and formal safeguards. Full article

Evaluating and identifying paths for cultural tourism development (CTD) in the Yellow River Basin (YRB) is crucial for establishing the Yellow River Cultural Tourism Belt in China. This study utilised Fuzzy-set Qualitative Comparative Analysis (fsQCA) to address the complex factors affecting CTD, unlike econometric approaches. An evaluation framework based on sustainable development and inclusive growth, consisting of 24 factors across three rule layers, was created to assess 78 cities in the YRB, China, using the entropy weight–TOPSIS method. Analysis with ArcGIS 10.5 revealed that from 2004 to 2019, CTD increased overall, with notable regional disparities: downstream and central regions thrived, while northern and southern regions lagged. High development followed three paths: policy-assisted consumer market-driven, economic investment-driven, and government-guided economic investment and innovation-driven development. Conversely, low development followed three paths: insufficient economic policies and innovation, deficient economy and consumer market, and insufficient economic and social investment. These findings could help develop sustainable cultural tourism in the birthplaces of global civilisations, specifically within the Chinese context. Full article

Pediatric neurodevelopmental disorders (NDDs) encompass a highly heterogeneous group of conditions characterized by complex interactions among genetic, molecular, developmental, and environmental factors. Growing evidence increasingly supports an important role for neuroglial dysfunction, including disturbances in astrocytic, microglial, and oligodendroglial biology, in the pathophysiology of disorders such as autism spectrum disorder, global developmental delay, intellectual disability, and rare neurogenetic syndromes. At the same time, artificial intelligence (AI)-assisted analytical approaches are becoming increasingly relevant in pediatric diagnostics through integration of multidimensional datasets, including clinical phenotypes, neuroimaging, genomic sequencing, and molecular biomarkers. This review examines the evolving intersection of neuroglial biology and AI-based analytical methods in pediatric NDDs. Current understanding of neuroglial mechanisms underlying disease vulnerability and developmental heterogeneity is discussed alongside emerging applications of machine learning, deep phenotyping platforms, radiogenomics, and large language models in diagnostic interpretation and clinical decision support. Important translational and ethical challenges, including algorithmic bias, interpretability limitations, data governance, and disparities in data accessibility, are also considered. Overall, integration of neuroglial research with AI-assisted analytical frameworks may contribute to more biologically informed interpretation of pediatric neurodevelopmental disorders and support ongoing development of increasingly individualized diagnostic approaches. Full article

Spent lithium-ion battery electrolytes contain fluorine-, sulfur-, and phosphorus-bearing toxins, necessitating deep detoxification and directional conversion into C₁–C₆ light hydrocarbons. To elucidate the specific catalytic roles and sequential activation of cathode metals (Li, Ni, Co, Mn), this work systematically deconvolutes their mono- and multi-metallic migration mechanisms over a CaO-ZSM-5* catalyst during vacuum catalytic pyrolysis (530 °C, 100 Pa). Results reveal that Li⁺ and Ni²⁺ dominate C–O bond cleavage in carbonates and CaO-ZSM-5*-assisted decarboxylation and oxygen fixation, significantly increasing the relative hydrocarbon content. Conversely, Co^2/3+ and Mn⁴⁺ release reactive oxygen species, causing deep oxidation of hydrocarbons into CO₂ and antagonizing the targeted conversion. In multi-metallic systems, forming composite metal oxides (M_xN_yO_z) increases the energy barrier for releasing active catalytic ions, hindering carbonate cleavage and leaving unreacted carbonate feedstocks. For detoxification, F and P are effectively immobilized as CaF₂ and Ca₂P₂O₇. The relative content of detected gas-phase nitriles is minimized to <2% due to the strong antagonistic effect of Ni²⁺ on Li⁺-promoted hexanedinitrile cleavage, while sulfur species derived from 1,3-propane sultone are converted to SO₂ and ultimately mineralized as calcium and metal-sulfur salts. Mechanistically, product distributions and crystallographic properties suggest a hypothesized sequential activation model—Li⁺ → Ni²⁺ → Mn⁴⁺—governing reactivity, whereas Co^2/3+ does not participate in the synergistic detoxification and selective upgrading process. This migration–reaction coupling framework provides critical insights for cathode-assisted in situ catalytic pyrolysis and closed-loop electrolyte recycling. Full article

Quantitative preclinical imaging enables non-invasive characterization of physiological, molecular, and functional processes providing measurable biomarkers for longitudinal and translational studies. This review systematically analyzes 60 studies published between 2015 and 2025, covering major imaging modalities including Positron emission tomography (PET), Single-Photon Emission Computed Tomography (SPECT), Magnetic resonance imaging (MRI), Computed Tomography (CT), optical imaging, and hybrid systems across murine and zebrafish models. We examine methodological frameworks for parameter extraction, reproducibility, and validation against biological reference standards, evaluating each modality through a cross-cutting analytical framework that distinguishes technical, biological, and computational sources of quantitative variance and identifies the current metrological maturity of harmonization infrastructure across platforms. Key comparative findings indicate that variability sources can be broadly categorized into technical (instrumentation, reconstruction, calibration) and biological (physiological heterogeneity, model-specific factors), with their interaction governing overall measurement uncertainty. Emerging computational approaches, including parametric modeling and artificial intelligence–assisted pipelines, show potential in reducing variance and improving parameter stability, although they introduce additional dependencies requiring validation. Collectively, this review frames quantitative preclinical imaging as a metrological discipline, emphasizing that reproducibility, bias control, and cross-modality harmonization are critical for generating robust and translationally relevant imaging biomarkers. Full article

This study investigates the structure–performance relationship of Fe³⁺- and Zr⁴⁺-modified layered double hydroxides (LDHs) for fluoride removal from water. Mg–Al LDHs with different metal loadings (Zr0.05, Zr0.1, Fe0.8, and Fe1) were synthesized via ultrasound-assisted coprecipitation and characterized using XRD, SEM–EDS, FTIR, XPS, and N₂ physisorption. Among the synthesized materials, Zr0.05-LDH exhibited the highest adsorption performance. Response surface methodology identified adsorbent dosage as the most influential parameter, achieving a maximum fluoride removal efficiency of 98.17% under optimal conditions (pH ≈ 5, adsorbent dose of 0.88 g/L, and initial fluoride concentration of 12.6 mg/L). Zr0.05-LDH showed the largest specific surface area (261 m²/g) and a maximum adsorption capacity of 137 mg/g, as described by the Langmuir isotherm model. Kinetic studies indicated rapid adsorption, with equilibrium reached at approximately 180 min. Fluoride removal was governed primarily by inner-sphere complexation at Zr⁴⁺ and Fe³⁺ sites, accompanied by anion exchange and electrostatic interactions. The adsorbent retained 89% of its capacity after five regeneration cycles. Groundwater tests from Durango, Mexico, demonstrated effective fluoride reduction below Mexican and WHO guideline limits despite competing anions. These results demonstrate the potential of modified LDHs for fluoride-contaminated groundwater treatment. Full article

Smart cities increasingly depend on electrical grid infrastructures capable of operating under high levels of digitalization, decentralization, and intelligence while maintaining reliability, security, and governance at the city scale. However, conventional power systems, historically designed for centralized generation and passive operation, are poorly aligned with the operational complexity, multi-actor coordination, and cross-sector integration characteristic of urban energy systems. This review develops an architecture-first perspective on smart-city electrical grids, tracing their evolution from digitalized power networks to decentralized and AI-enabled urban energy ecosystems. Rather than focusing on individual technologies, the study evaluates grid architectures using a multi-layer framework that integrates physical grid infrastructure, distributed energy resources and microgrids, communication and data platforms, intelligence placement, cybersecurity exposure, and governance accountability. Smart-city grid architectures are assessed using deployability beyond pilot projects, auditability, and regulatory alignment as primary evaluation criteria alongside conventional technical considerations. Through this perspective, the review explains a recurring pattern observed in the literature: many technically mature smart-grid solutions fail to scale in real urban deployments due to architectural fragmentation and governance constraints. By synthesizing insights from power systems engineering, information and communication technologies, and smart-city research, the paper highlights architectural trade-offs related to decentralization, interoperability, resilience under compound threats, and assisted autonomy. The resulting framework supports researchers, system designers, and policymakers in the coordinated development of resilient, secure, and governable electrical grids for future smart-city energy systems. Full article

The use of artificial intelligence (AI) in the radiological field has been extensively investigated from the radiologists’ perspective. Existing studies have primarily focused on AI’s contribution to diagnostic processes and on how its introduction has transformed—and continues to transform—radiologists’ professional practice. The perspectives of radiographers remain underrepresented in the literature, despite their central role in image acquisition and their position as the primary “on-the-ground” operators and managers of imaging technologies. The objective of this study was to analyze the perceptions, attitudes, and expectations of Italian radiographers regarding the introduction of AI, and to provide insights to inform professional training and organizational strategies within healthcare systems. A cross-sectional survey study with qualitative enhancement was adopted as the study design. A survey was administered to a convenience sample, comprising 222 respondents. The findings reveal a high level of familiarity with AI in everyday life, accompanied by an almost complete absence of cultural resistance, suggesting a workforce that is both receptive and ready to evolve. Nevertheless, this individual readiness is contrasted with a substantial institutional and operational gap, characterized by the lack of standardized protocols, regulatory uncertainty, and an uneven distribution of technological resources. The effective integration of AI therefore requires a comprehensive and coordinated approach. Educational reform is necessary to integrate AI and radiomics into university curricula and continuing professional development programs, encompassing not only technical competencies but also ethical, deontological and communication skills. Finally, national and European regulatory frameworks must evolve to clearly define radiographers’ responsibilities within AI-assisted workflows, to establish robust guidelines for data governance and the management of algorithmic outputs. Full article

The design of polygonal pretensioned concrete girders involves a practical conflict among load-carrying capacity, stiffness, ductility, damage control, and material cost. In conventional design, these characteristics are strongly coupled: increasing web thickness may improve stiffness but reduce ductility, while modifying tendon inclination or inflection-point position may improve prestress efficiency but may also induce local stress concentration near tendon-deviation regions. This coupling makes it difficult to identify rational design solutions through trial-and-error procedures alone. To address this problem, this study proposes a mechanism-informed and interpretable design framework for 30 m polygonal pretensioned concrete Bulb-T girders by integrating nonlinear finite-element analysis, surrogate-assisted modeling, multi-objective trade-off evaluation, and SHAP-based feature-attribution analysis. The scientific problem addressed in this study is the insufficient understanding of how tendon geometry and sectional parameters interact to govern structural response, while the applied problem is the lack of transparent design guidance for balancing performance and cost in polygonal pretensioned girders. The results show that girder behavior is controlled by coordinated parameter interactions rather than isolated parameter changes. Tendon inclination, inflection-point location, and web thickness are identified as the dominant variables affecting load-carrying capacity, damage evolution, stiffness–ductility balance, and cost-effectiveness. Compared with the conventional design, the representative optimized design increased the ultimate load-carrying capacity by approximately 26%, reduced the peak concrete damage index by approximately 24%, and increased the structural performance index by approximately 8.5%, lowered the normalized material–cost indicator by approximately 5%, and improved the performance–cost index by approximately 14–15%. These findings indicate that the proposed framework is not a fundamentally new girder form, but an improved interpretable design methodology that converts numerical optimization results into transferable engineering design principles for polygonal pretensioned concrete girders. Full article

This study analyzes the impact of socio-demographic factors on citizens’ perceptions of the functioning of local-level inclusion mechanisms, focusing on four dimensions: information, participation, transparency, and effectiveness. A mixed-methods approach is employed, combining: (1) a large-scale survey with 885 residents in three municipalities (Patos, Elbasan, and Mat) and (2) in-depth interviews with mayors, municipal councilors, and social service managers. The quantitative analysis was conducted through binary logistic regression models in SPSS version 27, as well as ordered logistic regression, examining the impact of socio-demographic factors such as age, education level, gender, employment status, and area of residence on the four dimensions of the study and the Inclusion Index. The qualitative component analyzes how local officials address citizen inclusion in key social policy areas such as employment, education, housing, social assistance, and social services. The results show that residence is the strongest predictor, with citizens in urban areas reporting higher levels of information, transparency, and effectiveness of participatory processes. Employment status is also associated with more positive perceptions, while gender and educational level show limited and inconsistent effects. Qualitative findings suggest that these differences are mediated by structural and institutional factors, such as infrastructure, administrative capacity and access to information. The study contributes to the literature on democratic innovation and participatory governance by showing that the impact of demographic factors on civic engagement is mediated by institutional and territorial conditions, particularly in developing countries. Full article