Search Results (1,606)

Generative artificial intelligence (GenAI) is increasingly used to produce scientific–visual material, but a polished result is not evidence that its content is correct or its sources are checkable. This study examined 41 infographics on scanning electron microscopy (SEM) and its geological applications, produced in one undergraduate Geology course in Ecuador in an assignment requiring GenAI use. The unit of analysis was the infographic, not the student. Two evaluators scored every artifact with a study-specific eight-criterion rubric applied after submission. The mean weighted score was 72.93 ± 13.01 on a 25–100 scale. Scientific accuracy (mean 3.52 of 4) and visual hierarchy (3.38) were the strongest criteria, whereas source quality and traceability were the weakest (1.63), with most artifacts at the minimum score. Agreement on the total score was moderate but imprecise, and one criterion, verifiability of images and claims, was scored too inconsistently to support firm conclusions. Artifacts with clearer source evidence tended to integrate text and images more coherently. Within this single course, the results indicate which aspects of scientific material produced under a GenAI-required assignment require human checking. Sustainability is an implication of that verification work, a quality-assurance practice aligned with Sustainable Development Goal (SDG) 4, rather than an outcome measured here. Full article

The implementation of the United Nations Sustainable Development Goals (SDGs) across educational contexts has generated substantial research on pedagogies aligned with technology-driven sustainability education. Recent scholarship increasingly recognises that sustainability transitions require not only technological innovation but also cultural transformations capable of reshaping social norms, environmental imaginaries, and collective values. However, the processes through which sustainability meanings are mediated across different cultural and media environments remain insufficiently explored. This article argues that intermediality provides a valuable analytical framework for understanding how sustainability knowledge is produced, transformed, and experienced through interactions among literary, visual, performative, audiovisual, and digital media. To examine the visibility of such approaches within sustainability education research, the study employs a bibliometric and thematic mapping of the Web of Science Core Collection, focusing on humanities and cognate social science perspectives. The findings reveal that sustainability education scholarship remains predominantly concentrated within scientific, technological, and policy-oriented domains, while intermedial approaches remain comparatively marginal within the indexed journal literature. However, complementary analysis of humanities scholarship beyond conventional bibliometric databases indicates a more substantial body of theoretical and creative engagement with sustainability, suggesting a mismatch between indexing structures and humanities modes of knowledge production. The study proposes that integrating intermedial literacy into sustainability education can expand existing approaches to environmental literacy by addressing how ecological meanings are culturally mediated across technologies, narratives, artistic practices, and material environments. By connecting bibliometric evidence with intermedial and new materialist perspectives, the article contributes to ongoing debates on how technology-driven sustainability education can move beyond instrumental models of digital innovation towards more culturally informed, relational, and transformative pedagogies for implementing the SDGs. Full article

Multimodal Knowledge Graphs (MMKGs) offer a promising paradigm for integrating heterogeneous sources into a unified, queryable, semantically structured representation. However, existing MMKG construction pipelines remain predominantly text-centric, extracting information from textual passages while leaving much of the visual and structural knowledge in scientific papers unrepresented. This produces fragmented graphs with disconnected components, isolated singleton nodes, and weak cross-modal connectivity, reducing the effectiveness of retrieval-augmented generation (RAG) systems that rely on interconnected graph traversal for multi-hop reasoning and evidence aggregation. We propose Multimodal Semantic-Oriented Alignment with Integrated Contrastive Learning (MOSAIC), an end-to-end framework for constructing semantically coherent MMKGs from heterogeneous scientific documents. MOSAIC combines four complementary contributions: (i) adaptive clustering via HDBSCAN, which infers data-driven entity boundaries without the brittle $\epsilon$ hyperparameters of conventional DBSCAN; (ii) a confidence-aware cross-modal alignment mechanism applying cosine-similarity gating to selectively invoke Large Language Models (LLMs), reducing spurious alignments and computational overhead; (iii) post-fusion semantic bridging, which links semantically related but structurally disconnected components through cosine-similarity-weighted bridge edges; and (iv) self-supervised contrastive embedding fine-tuning using an InfoNCE-style MultipleNegativesRankingLoss objective to specialise the embedding space for cross-modal entity representations. Empirical evaluation shows MOSAIC substantially improves graph topology and structural coherence, achieving a 110% increase in average clustering coefficient, reducing fragmentation, and strengthening intra-cluster semantic consistency. Evaluation across two challenging benchmark datasets, MMLongBench-Doc (134 documents, 1082 QA pairs) and DocBench (166 documents), shows that the MOSAIC-RAG engine significantly outperforms competing graph-based and dense retrieval systems, and that its advantage over lexical retrieval is concentrated in visually grounded questions, where it is statistically significant on both benchmarks. These results establish the effectiveness of our proposed MOSAIC framework for multimodal document understanding and its generalisability across diverse document categories. Full article

Destructive earthquakes are natural hazards highlighting the importance of introducing disaster preparedness from early childhood. Preschool children require developmentally appropriate educational approaches that connect scientific understanding with experiential, play-based, and participatory learning. This study presents the design and implementation of the “Earthy and Quaky” (EQ) earthquake education program, developed for children aged 3–6 years and implemented in two preschool educational settings in Zografou City (Attica, Greece). The program was structured as an interdisciplinary and progressive learning pathway comprising eight thematic units integrating natural and social sciences, language, mathematics, music and movement, visual arts, puppet theater, simulation, and earthquake drills. Follow-up activities and family involvement extended learning beyond the classroom. The implementation involved 28 children, whose participation, verbal responses, interactions, behavioral responses, and drawings were qualitatively observed. The observations indicated active engagement with concepts, including the Earth’s structure, earthquake occurrence and impact, and self-protection practices. Within the context of this small-scale implementation, the observations indicate that an interdisciplinary, experiential, and activity-based framework can provide a supportive learning context for earthquake preparedness during preschool years. The adaptable structure, low-cost materials, repeated reinforcement, and school–family connection may offer potential for adaptation to other preschool contexts after studying applicability and effectiveness with larger and more diverse samples. Full article

Cannabinoid signaling has attracted increasing interest in diabetes because the endocannabinoid system regulates energy balance, glucose and lipid metabolism, inflammation, and tissue homeostasis. However, its therapeutic relevance remains uncertain. This study combined scientometric mapping with a critical synthesis of mechanistic, pharmacokinetic, and clinical evidence on cannabinoids and diabetes published between 2004 and 2024. A structured search of the Web of Science Core Collection identified 459 original research articles, which were analyzed using Bibliometrix and complementary visualization tools. Scientific production increased markedly after 2016 and became progressively more diversified, with growing prominence of inflammation, oxidative stress, cannabis exposure-related themes, and broader mechanistic and population-level research questions. Mechanistic evidence strongly implicates excessive peripheral CB1 signaling in hepatic lipogenesis, adipose dysfunction, impaired insulin responsiveness, and related metabolic abnormalities, whereas CB2-mediated effects remain context-dependent. Cannabidiol is supported mainly by preclinical evidence of anti-inflammatory, antioxidant, and tissue-protective activity rather than consistent glucose-lowering effects. Clinical translation is further constrained by formulation-dependent oral exposure, extensive first-pass metabolism, food effects, broad tissue distribution, drug-interaction potential, and interindividual variability. Clinical evidence remains limited: brain-penetrant CB1 blockade improved selected metabolic outcomes but was restricted by psychiatric toxicity, while cannabidiol has not demonstrated consistent glycemic efficacy. Overall, cannabinoid research in diabetes shows substantial mechanistic development but limited clinical convergence. Future progress will require compound- and target-specific strategies, peripheral or tissue-selective modulation, standardized formulations, exposure–response characterization, appropriate patient stratification, and clinically meaningful outcome assessment. Full article

The continuous operation of the geophysical monitoring networks managed by the Istituto Nazionale di Geofisica e Vulcanologia (INGV)–Osservatorio Vesuviano relies on the reliability of a highly distributed infrastructure deployed in active volcanic areas. In these contexts, failures affecting power supply, environmental control, or communication systems may lead to interruptions in data transmission and consequent loss of scientific observations. This work presents PROMETEO, an integrated remote-control and infrastructure monitoring system designed to supervise heterogeneous monitoring stations through a multiparametric sensing approach. The system combines distributed sensors and intelligent edge devices for the acquisition of electrical, environmental, and connectivity-related parameters, including battery voltage, load current, cabinet temperature, signal quality, and network reachability. Data are collected and integrated in real time through standard Internet of Things (IoT) and industrial communication protocols, namely Message Queuing Telemetry Transport (MQTT), Simple Network Management Protocol (SNMP), and MODBUS, and centralized within the open-source Home Assistant platform. This architecture enables the fusion of heterogeneous sensor measurements into a unified supervisory framework for real-time visualization, alarm generation, historical storage, and trend analysis. The results show that the multiparametric correlation of sensor data significantly improves diagnostic capability, allowing rapid discrimination between power-related anomalies and communication failures, particularly in remote mobile stations. By reducing diagnostic uncertainty and limiting unnecessary field interventions, PROMETEO enhances the operational resilience of geophysical monitoring infrastructures and supports preventive and predictive maintenance strategies. The proposed system demonstrates how a scalable multiparametric sensing architecture can strengthen the reliability and continuity of monitoring networks operating in complex environmental conditions. Full article

This study investigated the root cause of severe corrosion damage observed on G-X4CrNi13-4 martensitic stainless steel turbine blades at the Brežice hydroelectric power plant on the Sava River in Slovenia. The investigation employed a comprehensive analytical approach, including on-site visual inspections, non-destructive testing with a portable microscope (Struers, Ballerup, Denmark), X-ray fluorescence spectroscopy (Thermo Fisher Scientific, Waltham, MA, USA), and 3D topographical mapping (Bruker Alicona, Graz, Austria). Laboratory analyses utilized scanning electron microscopy (ZEISS, Oberkochen, Germany) and energy-dispersive X-ray spectroscopy (EDAX, Pleasanton, CA, USA) to examine surface deposits, biofilms, and corrosion products, complemented by physical and chemical water quality assessments. A visual inspection revealed a dense layer of biological deposits, approximately 0.3 mm thick, with stochastic pitting damage located beneath the biofilm. The analytical results confirmed that the blade material met the specifications; however, EDS analysis (EDAX, Pleasanton, CA, USA) revealed significant localized manganese enrichment within the pits (~1.4 wt%) and in the biofilm (>10 wt%). Although water analysis showed relatively low manganese concentrations (7–18 µg/L), these values may be sufficient to support the metabolic activity of manganese-oxidizing microorganisms. Historical hydrological data suggest that extreme drought conditions in 2022, stagnant water, and elevated temperatures facilitated the attachment of microorganisms and the formation of a corrosive biofilm. These findings highlight the impact of changing environmental conditions and low-flow periods on the integrity of stainless steel components in hydroelectric facilities. Full article

Due to the cross-section characteristics of rectangular pipe jacking, the “soil-carrying effect” of overlying soil migration with the pipeline is prone to occur during jacking in shallow strata, resulting in a sharp increase in jacking resistance and large deformation of the strata. In this paper, a visual similarity model test of the soil-carrying effect is carried out for shallow buried large-section rectangular pipe jacking. The experiment innovatively combines VIC-3D digital image correlation technology, a 3D laser scanner and a thin-film pressure sensor to monitor the displacement of deep soil, surface heave and pipe resistance in an all-round and high-precision way. The influence of the overburden ratio and pipe–soil friction coefficient on the evolution of back soil was systematically studied. The results show that the evolution of the soil-carrying effect presents the three-stage characteristics of ‘elasticity-slip-strengthening’, and the smaller the overburden ratio, the larger the friction coefficient. And the smaller the critical displacement of the back soil, the more severe the formation disturbance. Based on the principle of mechanical balance, this paper puts forward the theoretical prediction model of the whole soil-carrying effect, deduces the critical friction coefficient and the critical jacking mileage, and compares it with the experimental results, which provides a scientific basis for the optimization of construction parameters and safety control of shallow buried rectangular pipe jacking. Full article

Ancestral halls represent vital vernacular cultural heritage, attracting growing academic interest in their spatial order, social functions, conservation and adaptive reuse. This paper takes Ninghai ancestral hall architectural heritage as the research object and applies Space Syntax theory. It selects four nationally protected historical and cultural sites to carry out convex space, axial and visual field analyses, exploring their spatial form, patriarchal ritual order and spatial logic. The findings indicate that Ninghai ancestral halls serve dual functions of opera performance and ancestor worship. Adopting a central axis layout, they separate the ritual center from the public activity center. In the selected cases, Temple-type ancestral halls indicate a tendency towards higher spatial integration and openness than clan-type ones, reflecting evident functional and spatial differentiation. This study combines Space Syntax with traditional ancestral hall research, shifting from qualitative description to quantitative analysis. It supplies empirical support for ancestral hall spatial structure correlation, and provides new technical methods and perspectives for the scientific conservation and sustainable utilization of such architectural heritage. Full article

How is climate-related content produced on fact-checking platforms? This study addresses this question by analysing the climate-related content of two Iberian platforms, Polígrafo (Portugal, n = 26) and Maldita (Spain, n = 277). It combines topic modelling (LDA) and named entity recognition (NER) applied to 303 articles published between 2019 and 2025 with manual coding of the discursive functions of entity mentions. The material includes fact-checks and other editorial genres. The results reveal two contrasting editorial profiles in the analysed corpora that nonetheless share a common epistemic foundation. Both platforms anchor their verdicts and explanations in scientific and meteorological authority, but they diverge in genre and in scale. The Maldita corpus displays an adversarial register devoted to the refutation of climate denialism and to visual fact-checking, with a dense repertoire of named targets, whereas Polígrafo concentrates on a pedagogical mediation of everyday environmental practices, produced largely within an externally funded project. The functional coding also shows that the polarising public figures examined enter verification discourse predominantly as objects of verification, never as sources of authority. The mapping of these differences, which suggest different forms of organising climate-related content on these platforms, opens an avenue for future research on how the impact of verification on disinformation could vary according to the production model that sustains it. Full article

The global plastic crisis, driven by 367 million tons of annual production and persistent environmental contamination, has intensified research into biobased compounds as sustainable alternatives. However, significant gaps remain in understanding the scientific landscape, research priorities, and industrial scalability of these materials. This study conducts a comprehensive bibliometric and systematic review to map the knowledge structure of biobased compounds and circular economy research. A refined search strategy was executed in Scopus (June 2026), retrieving 1350 documents, which were screened following PRISMA guidelines, resulting in a final dataset of 1306 peer-reviewed documents. The search strategy was designed to be comprehensive, capturing both the core biobased compounds literature and adjacent sustainability science (e.g., life cycle assessment, circular economy frameworks) that provides essential methodological foundations for the field. Bibliometric analyses were performed using RStudio/bibliometrix for descriptive statistics, VOSviewer for co-authorship and keyword co-occurrence networks, and Plotly Studio for interactive visualizations. The results reveal exponential growth in scientific production (R² = 0.92046), with publications increasing from 15 in 2010 to 95 in 2026 (partial year, data through June). India leads in publication volume (293 documents), while the United States exhibits the highest citation impact (114.74 citations per paper). “Sustainable development” (489 occurrences) and “circular economy” (419 occurrences) are the dominant keywords, with the latter showing the highest annual growth rate (2333.33%). The Journal of Cleaner Production (31 publications, 2040 citations) and Bioresource Technology (95.53 average citations) are the most productive and impactful journals, respectively. Ten transitional research gaps were identified, with “synthetic biology” and “bioaccumulation” showing the highest priority indices (122.16). The most common barriers are weak theoretical foundations and limited research duration, while mechanical properties and reprocessability face additional interdisciplinary complexity. These findings indicate that biobased compounds research is rapidly expanding, suggesting a need for strengthened theoretical frameworks and increased academic attention to interdisciplinary collaboration to achieve circular economy transitions aligned with Sustainable Development Goals 12 and 13. Full article

This paper proposes a novel ontology, called SciOnt, for representing scientometric indicators. Scientometrics is the field that studies the quantitative aspects of science and scientific phenomena. Scientometric indicators are used to evaluate scientific publications and research. However, many indicators and their diversity make them challenging to understand and use. SciOnt addresses this challenge by providing a formal and structured representation of scientometric indicator knowledge. It defines classes, relationships, and instances of these indicators. The ontology can be used for various purposes, including improving the analysis and comparison of scientometric indicators, facilitating the development of new and more efficient indicators, and enhancing the understanding and criticism of existing indicators. The paper details the methodology used to design SciOnt, including identifying and classifying scientometric indicators, determining competency questions, and constructing the ontology with Protégé 5.5 software. Finally, the paper evaluates the ontology and visualizes the SciOnt ontology structure using various plugins. Full article

Urban vitality (UV) is the core index to measure the quality and sustainability of urban development. Accurately analyzing the complex association mechanism between UV and built environment (BE) is critical to urban planning practice. Focusing on the main urban area of Chengdu, this study integrates eight categories of multi-source data, including nighttime light data, WorldPop population distribution data, street view images, and POI data, to construct a four-dimensional UV evaluation system and identify 26 BE factors. Firstly, the UV level is quantified by objective weighting methods. Secondly, an XGBoost model combined with a SHAP framework is adopted to investigate the nonlinear association between UV and BE factors. Finally, a spatial autocorrelation model, SHAP spatial visualization and clustering methods are employed to reveal the spatial pattern of UV and the spatial heterogeneity of the association between UV and BE. The results indicate: (1) Various elements of the BE show a significant nonlinear association and threshold effect for UV. Catering services and public transit services are the core factors for UV prediction, with their combined contribution accounting for 37.47%. (2) UV shows obvious spatial differentiation and agglomeration characteristics. It presents a spatial pattern with a gradual decline from the core to the periphery. (3) The association between UV and BE presents spatial heterogeneity, and the predictive contribution logic differs distinctly across different concentric rings. The study conclusions provide a scientific basis for UV improvement and BE optimization in Chengdu. Full article

Achieving ambient temperature storage of ready-to-eat sea cucumber (RTE-SC) remains a critical scientific challenge that urgently needs to be addressed in the industry. Recently, bioactive packaging materials have been increasingly applied in the aquatic product preservation, demonstrating their effectiveness in enhancing storage stability and extending the shelf-life. In this study, Seaweed extract (SE) was employed as a functional and bioactive component to fabricate gelatin/chitosan composite films (G-C–SE), and the preservative efficacy of the prepared films was systematically evaluated on ready-to-eat sea cucumber (RTE-SC) under ambient temperature storage. The SE was prepared and characterized in terms of extraction yield, total phenolic content, and total flavonoid content, all of which confirmed its satisfactory in vitro antioxidant and antimicrobial activities. The experimental results demonstrated that extraction using 60% (v/v) ethanol solvent yielded the most favorable outcomes, exhibiting satisfactory antioxidant and antimicrobial activities. Subsequently, the characterization and other physicochemical properties revealed that the incorporation of SE effectively facilitated the hydrogen-bonding interactions and improved the mechanical strength, barrier performance, and thermal stability of composite film matrix. When applied to the preservation of RTE-SC, the G-C–SE films exhibited remarkable quality-regulating effects. After 5 days storage, compared with the control group (CON), the protection of G3.3:C0.2−0.5% film (containing 3.3 wt% gelatin, 0.2% wt% chitosan, and 0.5 wt% L. japonica extract) resulted in a 26.07% reduction in the TCA-soluble oligopeptides content, a 10.39-fold decrease in glycosaminoglycan release, and an 11.28-fold suppression of TBARS elevation. Additionally, this treatment effectively retarded the rise in TVB-N accumulation, delayed textural deterioration, and maintained superior visual appearance throughout the storage period. These findings demonstrate that the G-C–SE composite film, as an active packaging material, can significantly mitigate the quality degradation of RTE-SC during ambient temperature storage, thereby providing a promising technological strategy for the application of bioactive films in aquatic food preservation. Full article

Explainable artificial intelligence (XAI) is increasingly important in scientific and engineering applications where predictive performance alone is insufficient and model outputs must also be physically credible, transparent, and reliable under unseen conditions. This study proposes a domain-informed XAI framework for predicting total suction in xanthan gum-treated clays using 139 experimental observations covering different mineralogical, moisture, polymer-dosage, and curing conditions. Eleven linear, kernel-based, ensemble, boosting, and physics-guided algorithms were evaluated using leakage-resistant five-fold grouped cross-validation, including a matched constrained–unconstrained HGB comparison with identical model settings. The methodological contribution is an evidence-linked XAI validation protocol in which model explanations are not accepted from feature attribution alone, but are audited through their agreement with leakage-resistant grouped generalization, physically constrained response directions, matched experimental contrasts, residual behavior, predictive uncertainty, and applicability-domain support. Selective monotonic constraints, physics-guided residual learning, SHAP explanations, and nonlinear response visualization are integrated within this protocol as complementary sources of evidence rather than treated as independent indicators of interpretability. The unconstrained histogram–gradient-boosting model achieved the highest out-of-fold predictive performance (R² = 0.958, RMSE = 0.098, and MAE = 0.070 in log10(MPa)). The corresponding monotonic model produced R² = 0.935, RMSE = 0.121, and MAE = 0.094 but eliminated the directional violations detected in the unconstrained response, revealing a measurable trade-off between predictive accuracy and guaranteed physical consistency. Explanations identified moisture content as the dominant negative control and revealed that xanthan-gum effects were non-monotonic and dependent on curing, moisture, and mineralogy. The residual model remained interpretable but underperformed the leading ensembles. Overall, the framework validates explanations against experimental contrasts, physical directions, grouped generalization, residual behavior, uncertainty, and domain support, offering a transferable strategy for trustworthy XAI in structured scientific datasets. Full article