Search Results (27,718)

This study addresses the brittleness, poor bonding, and low crack resistance of ordinary Portland cement (OPC) grouting materials by incorporating an ethylene-vinyl acetate (EVA) copolymer. The enhancement mechanisms and engineering applicability of EVA-modified cement grouts were systematically investigated. Using EVA contents from 0% to 20%, macro-scale tests covering fluidity, rheology, bleeding rate, and compressive strength were conducted, along with microstructural analyses (SEM, XRD, FT-IR). Results indicate that with 12% EVA, the 28-day compressive strength reached 21.03 MPa, reflecting a 68% increase over the unmodified grout. Most favorable amount of EVA promoted the formation of C–S–H gel, filled microcracks, and enhanced structural densification, whereas excessive EVA content led to the formation of a polymer film that hindered hydration and reduced strength. Furthermore, EVA effectively improved the rheological behavior of the grout, with the Vipulanandan model demonstrating superior accuracy over the Bingham model in characterizing its non-Newtonian flow. This study systematically established a quantitative–qualitative correlation between EVA content, nonlinear rheological behavior (characterized by advanced models), microstructure evolution (porosity, C–S–H, polymer film) and final macromechanics and durability. Full article

As debates intensify over establishing culture as a standalone Sustainable Development Goal (SDG) beyond 2030, this paper studies the policy implications of such a shift and its consequences for the future of global development frameworks. While acknowledging growing calls for a standalone cultural SDG—often framed as SDG18—this study cautions that isolating culture as a separate goal risks reinforcing sectoral silos and undermining its crosscutting relevance in sustainable development. Instead, the paper argues that cultural sustainability is more effectively advanced through systematic mainstreaming across the existing SDGs, ensuring balanced integration alongside economic, environmental, and social dimensions. Using qualitative and quantitative content analysis supported by NVivo, the research examines how culture is represented in SDG implementation reports, policy briefs, and Voluntary National Reviews (VNRs). The findings reveal persistent patterns of marginalization, thematic narrowness, and regional inconsistency in the treatment of culture, indicating structural limitations in SDG implementation rather than a lack of cultural relevance. This reinforces the fact that culture needs to be more visible within the SDG framework; however, the question remains: how? By comparing the two dominant policy trajectories—advocacy for a standalone cultural SDG and the mainstreaming of culture across the existing SDGs—this paper identifies pathways and a set of policy-oriented recommendations to strengthen cultural integration without further fragmenting the sustainability agenda. Full article

Living in the shadow of ‘Iddu’, the Stromboli volcano, requires a unique cultural adaptation. This study explores the risk perception of the permanent residents of Stromboli Island (Italy), a complex multi-hazard environment where persistent volcanic activity coexists with tsunami threats. Adopting a qualitative design based on 17 semi-structured interviews and focus groups (May 2024), we analysed residents’ narratives through the Cultural Theory of Risk. The findings reveal a hybrid risk culture: a dominant individualistic orientation (37%), driven by self-reliance, is balanced by a strong egalitarian ethos (33%) rooted in community solidarity. The analysis highlights three critical dynamics: (1) the normalization of volcanic risk versus the fear of rare tsunami events; (2) a ‘Trust Gap’ between the community’s horizontal preparedness strategies and the institutions’ vertical communication protocols; and (3) an ‘Economic Filter’ imposed by tourism, which creates a cognitive dissonance where risk is privately acknowledged but publicly downplayed. The study concludes that effective Disaster Risk Reduction (DRR) cannot rely solely on top-down technology but must integrate local knowledge and participatory approaches to bridge the distance between scientific monitoring and community experience. Full article

This paper addresses the growing sustainability fatigue in advanced economies. By analyzing Amazon’s artificial intelligence (AI) agent strategy as a model for “Rational Sustainability”, the study identifies a self-propagating growth trajectory that reconciles economic rationality with value creation. It provides a theoretical and empirical framework to overcome technological saturation and strategic homogenization in the generative AI era. To ensure methodological transparency, the analysis was conducted through two distinct stages: (i) Techno-econometric analysis (macro-level): Using an empirical dataset of 160 countries (40 advanced, 70 emerging, and 50 developing) from 2014 to 2024, the study utilized regression models to quantify the correlations and elasticities between three key proxies: GDP per capita (Y); the Human Capital Index (HCI), representing Institutional Capacity Building (ICB); and the E-Government Development Index (EGI), representing Endogenous Institutional Evolution (EIE). (ii) Hybrid AI analysis (case study): Utilizing process-tracing research, the paper examines Amazon’s R&D structure and AI agent strategy. This qualitative and structural analysis identifies how Amazon co-evolves EIE and ICB to conceptualize tacit knowledge and operationalize it into a competitive advantage. The findings reveal a marked disruption of the co-evolutionary mechanism in advanced economies, where the elasticity of EGI to GDP has declined since 2019, leading to a withdrawal state. In contrast, Amazon’s model demonstrates that the co-evolution of EIE and ICB creates a self-propagating growth engine. This research concludes that “Rational Sustainability”—grounded in evidence, economic rationality, and clear trade-offs—offers a viable pathway for revitalizing sustainability strategies in mature digital economies. Full article

Incomplete or faulty MRI sequences are common in clinical practice and can impair AI-based analyses that rely on complete multi-contrast data. The relative effectiveness of classical generative adversarial networks (GANs) versus modern diffusion and transformer-based models for clinically usable MRI synthesis remains unclear. This study evaluates cross-modality MRI synthesis using the BraTS 2019 brain tumour dataset, focusing on T1-to-T2 translation. We assess paired and unpaired CycleGAN models and compare them with two stronger but computationally intensive baselines, a conditional denoising diffusion probabilistic model (DDPM) and a transformer-enhanced GAN, using identical data splits and preprocessing pipelines. Inter-modality correlation was evaluated to estimate the achievable similarity between modalities. Conceptually, modality synthesis may be viewed as a representation-learning approach that compensates for missing imaging information by reconstructing clinically relevant features from available contrasts. Paired CycleGAN achieved correlations of $r\approx 0.92–0.93$ and SSIM $\approx 0.90–0.92$, approaching natural T1–T2 correlation ($r\approx 0.95$) while maintaining very fast inference (<50 ms/slice). Unpaired CycleGAN achieved $r\approx 0.74–0.78$ and SSIM $\approx 0.82–0.85$, producing clinically interpretable reconstructions without voxel-level supervision. DDPM achieved the highest fidelity (SSIM $\approx 0.93–0.95$, $r\approx 0.94$) but required substantially greater computational resources, while transformer-enhanced GAN performance was intermediate. Qualitative analysis showed that CycleGAN and DDPM best preserved tumour and tissue boundaries, whereas unpaired CycleGAN occasionally over-smoothed subtle lesions. These findings highlight the trade-off between fidelity and efficiency in cross-modality MRI synthesis, suggesting paired CycleGAN for time-sensitive clinical workflows and diffusion models as a computationally expensive accuracy upper bound. Full article

The Italian National Prevention Plan (NPP) 2020–2025 calls for a joint action on environmental and urban determinants of health. The recent reforms of primary health care (DM 77/2022) highlight the role of communities and Local Health Authorities in the promotion of health in everyday settings. However, practical tools which link prevention planning to small-scale urban transformations still remain poorly described. This study explores how international approaches to children’s school-travel and urban participatory practice in street design can guide the next cycle of the NPP. An extensive review of the available international grey literature and technical guidelines identified ten operational documents (toolkits, guidelines and practice-oriented reports) addressing two categories of interventions: (1) school-travel and “school streets” schemes and (2) tactical urbanism and placemaking initiatives. Each document was then evaluated using an adapted Urban HEART framework, expanded with a sixth domain, “Applicability to the Italian National Health Service”. They all scored qualitatively (1–5) across the six domains. The analysis shows consistently high scores for Health, Physical Environment, Participation and Governance, particularly with regard to school street toolkits and child-friendly street design guides. Equity and formal links to health-system planning and evaluation remain less systematically developed. Overall, findings suggest that school-travel interventions and child-centred placemaking around the schools are closely aligned with the logic and tools outlined in the NPP. These could be considered as potential prevention actions in the future NPP cycles, provided that explicit health outcomes, minimum indicators and stable intersectoral governance arrangements are co-designed with the Local Health Authorities. Full article

Bone regeneration focuses on the creation of functional tissue to repair bone defects. Creating a biodegradable scaffold hydrogel that combines a hemostatic agent with bioactive ceramics can afford the biological and mechanical benefits of both components. In the present study, we developed an injectable gelatin–alginate dual-composite hydrogel, loaded with two functional fillers: hydroxyapatite (HA) and the hemostatic agent montmorillonite (MMT). HA (microparticles and nanoparticles) was incorporated at concentrations of 10–30 mg/mL, with and without MMT at 20 mg/mL. The effects of functional fillers and their concentration on the microstructure and resulting physical and mechanical properties were studied, and a qualitative model summarising these effects was developed. All formulations exhibited clinically appropriate gelation times (5–29 s). n-HA significantly prolonged gelation time, reaching 29 ± 3 s at 30 mg/mL, while MMT reduced gelation time at all concentrations. The tensile strength of the unloaded hydrogel reached 20 kPa and increased to 57 kPa with 30 mg/mL of n-HA. The tensile strength even increased further with the addition of MMT (77 kPa). The results indicate that the combination of HA and MMT produced dual micro-composite hydrogels with moderate reinforcement, whereas the combination of n-HA and MMT generated dual nano–micro composites with combined reinforcing effects. The latter exhibited the highest strength and sealing ability while maintaining clinically relevant gelation times and controlled swelling behaviour. In conclusion, the combination of MMT with n-HA or HA enables the creation of functional hydrogels with controlled properties, tailored to specific applications in bone regeneration. Full article

This study evaluates the quality and dynamics of the renewable energy transition in Poland’s electricity sector during the years 2010–2023 through an integrated Fuzzy Time Series (FTS) and Fuzzy Multi-Criteria Evaluation (FMCE) methodology. The evaluation is based on five production-related criteria: the production of renewable electricity, the capacity of installed renewable energy sources, investment costs, innovation costs, and total electricity production. Contrary to trend projection and elasticity ratio methods, the new approach determines qualitative transition states (Low, Medium, High) and their transitions over time in the presence of non-linearities and partial progress. The outcome shows a protracted pre-transformational period from 2010 to 2014, with features of perpetual Low → Low transitions and high system inertia. The first qualitatively detectable transition takes place in 2015, where the renewable electricity output regime shifts from Low to Medium, symbolizing the beginning of the moderate transition phase. The Medium regime continues until 2021, with little innovation expenditure, signifying a consolidation rather than acceleration phase. The most significant transition regime shift takes place in 2022, where the system advances from Medium to High, fueled by the cumulative growth of renewable electricity output, capacity, and total electricity production. The High regime is maintained in 2023, indicating a systemic rather than a temporary transition. The results show that the transition of Poland towards renewable energy sources has been following a non-linear and regime-dependent path, with turning points marking observable qualitative state transitions rather than the beginning of trends. The FTS-FMCE approach is a powerful method for separating growth from transformation, and it has been shown to be useful for coal-dependent economies that experience a delayed but accelerating energy transition. Full article

As deep learning models become increasingly integrated into critical decision-making systems, the need for xAI has grown paramount to ensure transparency, accountability, and trust.Post hocexplainability methods, which analyse trained models to interpret their predictions without modifying the underlying architecture, have become increasingly important, especially in fields such as healthcare and finance. Modern xAI techniques often produce feature importance rankings that fail to capture the true causal influence of features, particularly in transformer-based models. Recent quantitative metrics, such as Symmetric Relevance Gain (SRG), which measures the area between the feature corruption performance curves of the Most Important Feature (MIF) and the Least Important Feature (LIF), provide a more rigorous basis for evaluating explanation fidelity. In this study, we first show that existing xAI methods exhibit consistently poor performance under the SRG criterion when explaining transformer-based text classifiers. To address these limitations, we introduceEvoDropX, a novel framework that formulates explanation as an optimisation problem. EvoDropX leverages Grammatical Evolution (GE) to evolve sequences of feature corruption with the explicit objective of maximising SRG, thereby identifying features that most strongly influence model predictions. EvoDropX provides interventional, input–output (behavioural) explanations and does not attempt to infer or interpret internal model mechanisms. Through comprehensive experiments across multiple datasets (IMDB, Stanford Sentiment Treebank (SST-2), Amazon Polarity (AP)), multiple transformer models (BERT, roberta, distilbert), and multiple metrics (SRG, MIF, LIF, Counterfactual Conciseness (CFC)), we demonstrate that EvoDropX significantly outperforms all state-of-the-art (SOTA) xAI baselines including Attention-Aware Layer-Wise Relevance Propagation for Transformers (AttnLRP), SHapley Additive exPlanations (SHAP), and Local Interpretable Model-agnostic Explanations (LIME), when evaluated using intervention-based faithfulness criteria. Notably, EvoDropX achieves $74.77\%$ improvement in SRG than the best-performing baseline on the IMDB dataset with the BERT model, with consistent improvements observed across all dataset-model pairs. Finally, qualitative and linguistic analyses reveal that EvoDropX captures both sentiment-bearing terms and their structural relationships within sentences, yielding explanations that are both faithful and interpretable. Full article

Digital transformation demands schools to act as learning organizations in order to rethink and reform their structures and practices. Using a mixed-methods design (quantitative analysis of code co-occurrences within 60 semi-structured group interviews and qualitative structural content analysis), the study examines how teachers who act as promoters for digital transformation facilitate organizational learning (OL) processes and how these processes can be described. While five OL processes emerge (collective sense making, knowledge creation and transfer, evaluation and feedback, experimentation and piloting, and external cooperation and knowledge import), each process is mainly shaped by a distinct promoter activity. Findings reveal that school-wide systematic structural conditions for OL processes, for instance formal evaluation and scheduled collaboration time for the whole teaching staff, are scarce, leaving many learning processes informal and project-based. The study concludes that sustainable digital transformation requires schools to institutionalize adequate structural conditions for OL activities and to empower promoters through both top-down mandates and bottom-up support, ensuring all OL processes become habituated routines. Full article

The rapid integration of artificial intelligence (AI) into higher education is reshaping how learners develop academic, linguistic, and research competencies. This mixed-methods study examines how second-year EFL computer science students employ prompt engineering techniques across four task domains—research summarization, academic video note-taking, style transformation, and concept mapping—within a smart learning environment. Sixty-nine students completed a structured survey requiring AI-assisted draft generation followed by student-led revision. Quantitative analyses included descriptive statistics, chi-square tests, Cramer’s V, t-tests, ANOVA, Kruskal–Wallis tests, and three text-similarity measures (cosine, Jaccard, and Levenshtein). Qualitative evidence was drawn from students’ revised outputs and reflective responses. Results indicate that students consistently preserved semantic meaning while significantly rephrasing AI-generated text, demonstrating moderate conceptual alignment but substantial lexical and structural transformation. Frequent AI users said they were better at searching and revising, but the type of prompt didn’t have much of an effect on how deep the revision was or how well they learned. Iterative prompting and revision emerged as central drivers of metacognitive growth, academic language development, and sustainable learning behaviors. Across tasks, students viewed AI prompts as effective scaffolds for organizing information and synthesizing multimodal input, though reliance varied by learner. The findings underscore that sustainable AI use in EFL technical education depends not on AI output alone, but on structured prompting, iterative human revision, and critical engagement—practices that cultivate autonomy, digital literacy, and long-term academic resilience. Full article

To improve diagnostics and prediction of changes caused by increased impact of anthropogenic activity, it is necessary to increase the comparative analysis of measurements and modeling of ozone—one of the climatically important atmospheric gases due to the decisive influence of stratospheric ozone on the radiation balance of the Earth-atmosphere system and the role of tropospheric ozone, the third most significant anthropogenic factor contributing to the greenhouse effect. This task is particularly relevant for Russia, as its geographical location makes it more vulnerable to climate change than other countries, whereas its regional tendencies in ozone variability have not yet been studied in sufficient detail. An analysis of IKFS-2 tropospheric ozone content (TrOC) measurements for 2015–2022 revealed that in Siberian, Far Eastern, North Caucasian, and Southern federal districts of Russia TrOC maximum, caused by photochemical formation of ground-level ozone, is observed in July (up to 30–35 DU for monthly means in surface-400 hPa layer). In Northwestern federal district, TrOC maximum (up to 25–30 DU), determined by meridional transport, is observed in late spring. No statistically significant linear trends in TrOC are detected. The WRF-Chem model qualitatively describes the seasonal variations of TrOC as well as the anomalous increase in TrOC caused by forest fires. The variability of total ozone content (TOC) is analyzed by OMI (2005–2023) and IKFS-2 (2015–2022) measurements as well as by SOCOLv3 simulations. Ozone negative anomalies in spring (up to 15% for monthly means) are generally observed with positive Arctic oscillation index values and a westerly phase of Quasi-biennial oscillations. For the 2008–2022 period, a statistically significant increase in TOC (+1.6–1.7% per year) is obtained for European Russia and Western and Central Siberia in November. Full article

Facial expression recognition (FER) in naturalistic settings is constrained by label ambiguity and variability in stimulus–response alignment. Adopting a data-centric perspective, this study examined whether emotional intelligence (EI)-stratified training data influence FER performance by treating EI as a qualitative factor associated with affective data consistency. Naturally elicited facial expressions were collected in a controlled emotion induction experiment with subjective arousal and valence ratings. Using response-driven labeling, neutral ratings were retained as indicators of ambiguity. Participants were grouped into High and Low EI based on the alignment between subjective evaluations and outputs from a pretrained affect estimator. Identical binary classifiers for arousal and valence recognition were trained while varying only the training data composition and evaluated across baseline, unambiguous, and ambiguous test sets using independent training repetitions with repetition-level statistical aggregation. EI-stratified training was associated with statistically detectable, context-dependent performance differences: group effects were observed primarily under baseline conditions and, to a lesser extent, under ambiguous conditions, whereas no reliable differences emerged under unambiguous conditions. Pooled discrimination differences were modest, but item-level analyses identified significant differences in classification correctness in specific task–condition combinations. Comparable patterns were observed across alternative backbone architectures. These findings indicate that FER performance in naturalistic contexts is influenced not only by model architecture but also by the statistical structure and internal coherence of the training data, supporting EI-informed data selection in ambiguity-prone scenarios. Full article

The disadvantaged position of racialised minorities in accessing the UK’s social housing sector has been extensively documented in previous research, with evidence dating back to the 1960s. However, the specific challenges encountered by migrants remain under-researched. Further, attention has focused on the agency of migrants rather than the reflexivity which underpins the actions they can take. These gaps limit understanding of the extent to which the impacts of migration on individuals seeking to access the social rented sector can be disentangled from the challenges associated with racial disadvantage. They also limit understanding of the nature of migrant reflexivity. To address these omissions, we employed a critical realist framework informed by a literature review to analyse qualitative data generated from a subset of fifteen individuals from a wider sample of a hundred racialised minorities living in the UK. Fieldwork was undertaken across four case study areas with high concentrations of racialised minorities: Tower Hamlets (London), Bradford, Manchester, and Glasgow. By exploring participants’ experiences of engaging with the digital allocation system of Choice-Based Lettings (CBL), we highlight the role of migration-related factors in determining access to social housing through online systems, including country of origin, age at migration, length of stay and education in the UK, employment trajectories, proficiency in English and familiarity with digital systems. We also reveal three components of migrant reflexivity: emotional, communal and strategic. We conclude that attention to reflexivity increases understanding of the distinctive engagement of migrants with CBL systems compared to other racialised minorities. Full article

Sustainability education increasingly calls for innovative learning environments that help learners recognize ecological interdependencies and challenge anthropocentric worldviews. Everyday multispecies relationships, such as with companion animals, often underexplored, offer opportunities for cultivating ecological literacy and care. This paper introduces Augmented Ecological Relating (AER), an approach that combines Augmented Reality (AR) with embodied inquiry to explore multispecies perspectives. Going beyond embodied inquiry, AER specifies how digital augmentation can systematically support learners’ iterative noticing, ethical reasoning, and action within everyday multispecies ecosystems. We draw on a virtual summer workshop for adolescents in which participants used AR filters simulating dog and cat vision to investigate their pets’ sensory worlds. We used qualitative case study methods to examine how AR tools mediated human youths’ noticing, inquiry, and reflection. We found that the AR filters used in the study’s context enabled participants to critically reconsider pet behaviors within home ecologies. Participants recognized companion animals as ecological beings with distinct sensory experiences, explored interconnections among humans, animals, and environments, and reflected on ethical responsibilities in multispecies relationships. Through iterative inquiry, youth moved beyond companionship to sustainability-oriented perspectives grounded in relational care, systems thinking, and practical action. By embedding digital augmentation into everyday contexts, AER enabled learners to engage with more-than-human perspectives, fostering ecological awareness, ethical reflection, and sustainability literacy in accessible, meaningful ways. Full article