Search Results (224)

Social networks such as Twitter have grown rapidly and are now flooded with sarcastic comments, both in text and in images. Detecting sarcasm in multimodal data has significant social value and is attracting increasing research attention. However, most studies overlook the role of sentiment, even though sentiment information in text is closely linked to clues of sarcasm. Additionally, few consider how text and images align semantically. To address these issues, we propose a sentiment-guided mutual learning network (SGMLN) for multimodal sarcasm detection. SGMLN utilizes sentiment information to inform the combination of text and image features, and employs mutual learning to facilitate knowledge sharing among classifiers. We design a sentiment-guided attention layer that injects sentiment into both modalities, producing features that capture sarcasm more effectively. Sentic-BERT extracts sentiment-aware vectors from text, using word-level sentiment as a mask. In mutual learning, a logistic distribution function measures differences between classifiers, improving knowledge transfer between modalities. This step boosts multimodal understanding and model performance. By introducing sentiment-aware representations and semantic alignment, SGMLN bridges the gap between text and images, making them more consistent. Experiments on public datasets demonstrate that our model is effective and outperforms alternatives. Full article

The growing demand for Critical Raw Materials (CRMs) requires mining practices that align with sustainability and environmental, social, and governance (ESG) principles, while mining training increasingly benefits from advanced digital tools. Virtual Reality (VR) can provide high-resolution site representations that support both interactive learning and data-oriented analysis without operational risk. This study presents a VR-based framework for the quantitative assessment of pit lake rehabilitation using Virtual Excursions (VEs) developed from panoramic imagery and supported by machine-learning correction. High-resolution 360° panoramic images were used to extract geometric characteristics of a rehabilitated pit lake at the LARCO GMMSA Euboea mine site, Greece, including surface area, shoreline length, mean diameter, and maximum diameter. These image-derived estimates were validated against ground-truth data from field surveys and mine-closure documentation. To reduce systematic deviations associated with panoramic image measurements, a supervised multiple linear regression model was applied as a correction step. Validation based on Root Mean Square Error (RMSE) and the coefficient of determination ($R^2$) showed substantial improvement of the corrected estimates relative to the uncorrected image-based measurements. The results demonstrate that panoramic VR imagery can support site-specific quantitative environmental assessment in addition to its educational value. Although the present findings are limited to a single pit lake case study, the proposed workflow provides a structured basis for integrating immersive visualization, image-based measurement, and regression-based correction in post-mining rehabilitation assessment. Full article

Historic streets, as living heritage environments, preserve everyday cultural practices while facing increasing digital mediation in tourism and daily life. This study examines how a digital sense of place is constructed online in the Ming–Qing Old Street of Songyang, China. User-generated text and image data were collected primarily from Weibo, supplemented by user reviews from major travel platforms, including Dianping, Fliggy, Mafengwo, and Ctrip, and analysed through a multimodal framework. BERTopic was applied to identify thematic narratives in textual content, and ResNet-50 was used to classify visual scene elements in shared images, enabling an integrated interpretation of textual and visual representations. The results reveal four dominant dimensions of digital place perception: local food culture, living handicrafts, historic spatial fabric, and everyday atmosphere. Textual narratives emphasise emotional attachment and experiential interpretation, while visual representations highlight photogenic, performative, and shareable street scenes. The integration of these modalities forms a layered digital sense of place grounded in cultural continuity and daily life. The study demonstrates the value of multimodal social media analysis in understanding how living heritage streets are digitally represented and perceived, offering implications for sustainable heritage conservation, community-centred revitalisation, and data-informed cultural tourism management. Full article

This study examines the intercorrelations that exist between brand equity, expectation confirmation, and sustainable repurchase intentions within Saudi Arabia’s burgeoning e-commerce sector, emphasizing its cultural and digital transformation context aligned with Vision 2030. The main objectives are to identify how brand perceptions influence customer satisfaction, and to explore the applicability of integrated theoretical frameworks, namely Brand Equity Theory and Expectation-Confirmation Theory in explaining sustainable consumer behavior in an emerging market. Utilizing a quantitative research approach, data was collected through an online self-reported questionnaire distributed via social media platforms targeted at active e-commerce consumers in the Hail region. Convenience sampling combined with snowballing yielded a sample size of 361 respondents, ensuring broader demographic representation. Data analysis was conducted using structural equation modeling with partial least squares (SEM-PLS), a technique suited for theory exploration and handling complex variable relationships. The findings demonstrate that brand awareness and brand image significantly positively influence customer satisfaction, which in turn positively impacts repurchase intentions in e-commerce platforms. Similarly, expectations and perceived performance also have significant positive effects on satisfaction, which in turn positively impacts repurchase intentions in e-commerce platforms. All hypotheses were supported, with significant relationships observed between the variables, with the model demonstrating robust validity and fit, evidenced by acceptable SRMR, d_ULS, and d_G values. The study’s originality lies in its culturally contextualized application of these theories to a less studied yet vital emerging market, providing novel insights into how cultural nuances influence digital consumer loyalty. These outcomes contribute to both academic theory and practical strategies for e-commerce firms aiming to build sustainable, trust-based relationships within culturally diverse digital environments, offering a valuable blueprint for similar markets undergoing digital transformation. Full article

Autonomous vehicles (AVs) must make predictable and socially compliant behavioral decisions to ensure safe and efficient interactions with other road users. To address this challenge, this paper proposes a game-theoretic behavioral decision-making model integrated with spatial motion planning to capture the interactive intentions between the ego vehicle (EV) and target vehicle (TV) in pairwise scenarios. First, the study defines an intention representation method that characterizes intentions using spatial area boundaries, feasible speed ranges, and a set of goal points (speed goal points, position-orientation goal points). Second, a spatial motion planning approach is adopted to evaluate the intention, which optimizes the driving scheme using a multi-objective cost function (incorporating pursuit precision, comfort, energy efficiency, and travel efficiency). Finally, the game-theoretic decision-making model is constructed. The Social Value Orientation (SVO) is introduced to quantify drivers’ social preferences, and the payoff function, which integrates safety rewards (based on inter-vehicle distance) and performance rewards (based on motion planning indices), is established. Simulation results verify that the proposed model can effectively address the interactive intention decision-making problem between the AV and other road users and handle different scenarios. Full article

This paper develops a bilevel multi-market coupling optimization framework to analyze the strategic participation of nuclear power plants in modern electricity systems where energy, reserve, and capacity markets are simultaneously cleared. The upper-level problem represents the Independent System Operator’s objective of maximizing system-wide social welfare under network, reserve, and carbon-cap constraints, while the lower-level problem captures the nuclear operator’s profit maximization subject to ramping limits, minimum uptime requirements, fuel-cycle depletion, and deliverability restrictions. By embedding these technical constraints into a bilevel structure reformulated through tractable complementarity conditions, the model captures the interdependence of nuclear scheduling, reserve deployment, capacity commitments, and carbon compliance in a single optimization environment. The proposed framework is applied to a stylized but realistic case study with 96-h time resolution, 12-bus network topology, and detailed representations of wind variability, demand elasticity, and emission caps. The model quantifies how nuclear participation displaces 40,000 tCO₂ over the horizon, raises producer surplus by 12 percent, and increases total social welfare by nearly 18 percent when all three markets are coupled, relative to an energy-only benchmark. Nuclear profitability is shown to be highly sensitive to renewable volatility, with ±20 percent swings in wind uncertainty altering profits by 0.24 million USD. Reserve deliverability emerges as the second most influential driver, while policy variables such as carbon price shifts play a smaller role. Reliability analysis based on the complementary cumulative distribution of unserved energy demonstrates that joint market clearing reduces the probability of load shedding at the 0.5 percent threshold from 8 percent in energy-only markets to 2 percent under full coupling. Overall, the study provides the first integrated modeling treatment of nuclear bidding across energy, reserve, and capacity markets within a bilevel optimization framework. By jointly considering operational constraints and policy targets, the framework reveals how nuclear power can simultaneously improve economic efficiency, enhance system reliability, and support carbon mitigation. The results highlight that nuclear’s value extends well beyond baseload energy provision, with multi-market strategies offering measurable gains for both individual operators and social welfare under conditions of uncertainty and constraint. Full article

Rumour propagation in online social networks poses significant risks to public trust, economic stability, and crisis management. Existing models often struggle with heterogeneous feature spaces, adversarial dynamics between rumours and debunking information, and data sparsity in early outbreak stages. This study introduces a cross-domain framework for group behaviour prediction that integrates unified representation learning, game-theoretic adversarial modelling, and transfer adaptation. A hybrid BERT–Node2Vec encoder captures both semantic richness and structural influence, while evolutionary game theory quantifies competitive interactions between rumour-spreaders and refuters. To alleviate data scarcity, Joint Distribution Adaptation (JDA) aligns heterogeneous feature spaces across domains, enabling robust transfer learning. Evaluated on simulated and real-world social media datasets, the proposed model demonstrates improved accuracy and interpretability in predicting rumour diffusion trends under adversarial conditions. These findings highlight the value of integrating semantic, structural, and behavioural signals into a scalable architecture, offering a practical solution for safeguarding digital ecosystems against misinformation. Full article

Introduction: End-of-life (EOL) HIV cure-related research offers a unique opportunity to advance scientific discovery while honoring the values, dignity, and legacy of people with HIV. However, participation remains demographically skewed, mirroring long-standing inequities in who is informed, invited, and supported to take part. Synthesizing eight years of experience, published literature reviews, and community engagement from the University of California San Diego’s Last Gift program, we propose strategies to embed justice, equity, diversity, inclusion, and accessibility (JEDIA) throughout the design and implementation of EOL HIV cure-related studies. Discussion: Using intersectionality as a structural analytic framework, we examine how interlocking systems and social determinants shape access, consent, and participant experience, and we translate ethics into action across three themes and eight domains. As examples, we facilitate equitable access by implementing solutions that address gaps limiting awareness and feasibility of participation. We establish ongoing consent through multi-session consent processes with teach-back methods, clear healthcare proxy pathways, and explicit separation of research activities from clinical care. We center lived experiences by partnering with people with HIV and community groups, customizing participation, and honoring cultural and spiritual needs. We enable real-time course correction by using a dashboard that monitors enrollment patterns and representation. Conclusions: An intersectionality-informed, participant-centered approach is both feasible and essential to ensure HIV cure-related research advances with fairness, trust, and global relevance. Programs such as the Last Gift show that scientific rigor, integrity, and participant dignity can coexist, establishing a model for equitable HIV cure discovery. Full article

Background: The humanization of care is increasingly recognized as a core component of healthcare quality; however, its meaning remains complex and strongly shaped by organizational, professional, and educational contexts. Nursing students, as future healthcare professionals, play a crucial role in the development and transmission of humanized care values, making their representations of the humanized hospital particularly relevant for understanding how these values are constructed during professional education. Aim: To explore how undergraduate nursing students conceptualize the humanized hospital. Methods: A qualitative exploratory study was conducted involving 742 undergraduate nursing students enrolled in a Bachelor of Science in Nursing program in Italy. Data were collected through a single open-ended written question inviting students to describe how they imagine a humanized hospital. Textual data were analyzed using Automatic Analysis of Textual Data within an Exploratory Multidimensional Data Analysis framework, enabling the identification of shared lexical patterns, discursive clusters, and latent semantic dimensions within a large textual corpus. Findings: Students articulated the humanized hospital as an integrated and system-oriented care environment in which relational, organizational, professional, and holistic dimensions are deeply interconnected. Humanization was associated not only with empathy, respect, and emotional engagement, but also with organizational functioning, teamwork, adequate resources, and professional competence. Two latent dimensions structured these representations: the first highlighted organizational systems as enabling conditions for person-centered care, while the second framed professional operability and technical competence as foundations for a holistic understanding of patients’ physical, psychological, and social well-being. Conclusions: Undergraduate nursing students’ discourse revealed an articulated and multidimensional representation of hospital humanization, conceptualizing it as an emergent property of healthcare environments rather than as a function of individual attitudes alone. These findings underscore the importance of addressing hospital humanization simultaneously at relational, educational, and organizational levels and highlight the need for nursing education programs and healthcare institutions to foster structural and professional conditions that sustainably support humanized care in clinical practice. Full article

Multi-criteria methods are widely used in sustainability assessments because of their ability to handle large and complex datasets. The MIVES method (Integrated Value Model for Sustainability Assessment) has proven to be a versatile and adaptable tool that can be applied to both products and services across a variety of research fields. However, evidence of its integration with other analytical tools is still limited. This study combines the MIVES method with Geographic Information Systems (GIS) to evaluate the sustainability of tourism activities in seven destinations in southern Costa Rica, all located near national parks and nature reserves. First, a MIVES-based model was designed to compute sustainability indices across environmental, economic, and social dimensions, using thirteen normalized and weighted indicators. These calculations produced specific sustainability values for each destination analyzed. The results were then integrated into GIS using ArcGIS Pro 3.6, representing each requirement and indicator as a geographic layer with the corresponding sustainability value. This made it possible to create spatial maps that visually identify the destinations best positioned within the protected natural areas in terms of sustainability, as well as the indicators that most strongly influence each site’s performance—positively or negatively. The destinations that received the highest sustainability scores were Ojochal, La Palma, Puerto Jiménez, and Carate–Matapalo, with averages ranging from 60% to 61%, while Bahía Drake, Bahía Ballena, and Sierpe showed the lowest values, averaging between 58% and 59%. Of the three domains, the social dimension received the highest evaluation, followed by the environmental dimension and, finally, the economic dimension. Overall, all destinations achieved satisfactory sustainability levels, with an overall mean index of 0.60. The visual representation of results simplifies interpretation and serves as a valuable tool to support decision-making for sustainable tourism management. Full article

The rapid expansion of corporate sustainability reporting has increased transparency requirements while raising concerns about greenwashing driven by selective, narrative-based disclosure. This study assesses the credibility of Environmental, Social, and Governance (ESG) communication by comparing corporate sustainability reports with external media coverage for a sample of 204 large firms operating in Central and Eastern Europe in 2023. Using natural language processing techniques, the analysis constructs a Greenwashing Severity Index (GSI) that captures discrepancies between firms’ ESG self-representation and external public narratives. The index combines ESG-specific focus measures, sentiment analysis, TF–IDF-based term weighting, and topic modeling to quantify imbalances in ESG communication. Results indicate moderate but widespread greenwashing across countries, industries, and firm sizes, with substantial heterogeneity linked to differences in regulatory maturity and stakeholder scrutiny. Higher alignment between corporate disclosures and external narratives is observed among larger firms and in sectors subject to stronger public accountability, while finance, aviation, and online commerce exhibit higher greenwashing severity. A propensity score matching analysis further shows that firms with imbalanced emphasis across ESG dimensions display significantly higher GSI values, consistent with strategic disclosure behavior rather than substantive sustainability engagement. Overall, the findings demonstrate that transparency alone is insufficient to ensure credible ESG communication, highlighting the need for EU sustainability governance to move beyond disclosure-based compliance toward digitalized, data-driven monitoring frameworks that systematically integrate external information sources to curb strategic ESG misrepresentation and enhance corporate accountability under evolving regulatory regimes. Full article

Background: Intimate partner violence (IPV) represents a major public health problem in Europe, with significant physical, psychological, and social consequences. Nurses are often the first professionals capable of detecting early signs of IPV, yet they lack validated instruments to assess their clinical competency in detection, evaluation, documentation, and intervention. This study aimed to develop and validate the Intimate Partner Violence Nursing Competency Scale (IPVNCS), aligned with the Nursing Intervention Classification (NIC 6403). Methods: A cross-sectional psychometric study was conducted among registered nurses in the Community of Madrid. A 30-item Likert-type self-administered instrument (1–5 scale) was developed based on NANDA, NIC 6403, and NOC frameworks. A total of 202 nurses participated. Reliability was assessed through Cronbach’s alpha. Construct validity was examined using exploratory factor analysis (EFA) with Promax rotation and confirmatory factor analysis (CFA) using AMOS 26. Ethical approval was obtained (CEU San Pablo, code 843/24/104). Results: After item refinement, 26 items remained across four dimensions: (1) Intervention and Referral, (2) Detection and Assessment, (3) Documentation and Recording-keeping, (4) Psychosocial Support. The instrument showed excellent reliability ($\alpha$ = 0.97). KMO was 0.947 and Bartlett’s test was significant (p < 0.001). CFA demonstrated satisfactory fit: ${\chi }^2$/df = 2.066, RMSEA = 0.073, CFI = 0.92, TLI = 0.91, NFI = 0.86. The final model adequately represented the latent structure. After debugging, its psychometric properties were significantly improved. Four redundant items were eliminated, achieving internal consistency ($\alpha$ = 0.97), a KMO value of 0.947 and a significant Bartlett’s test of sphericity. It showed a better fit, according to ${\chi }^2$/df = (2.066); Parsimony = (720.736); RMR (0.0529; RMSEA (0.073); NFI (0.860); TLI (0.910) and CFI (0.920). The final model provides an adequate representation of the latent structure of the data. This study provides initial evidence of construct validity and internal consistency reliability of the IPVNCS. Conclusions: The IPVNCS is a valid and reliable tool to assess nursing competencies for clinical management of IPV. It supports structured evaluation across four core nursing domains, enabling improved educational planning, clinical decision-making, and quality of care for victims. The scale fills a gap in clinical nursing assessment tools and can support protocol development in emergency, primary care, and hospital settings. Full article

The crown conveys a rich tapestry of history and deep cultural resonances among the Yoruba people of South-western Nigeria, beyond its representation as an emblem of leadership, royalty, and nobility. This article explores layers of the meaning of crown in the Yoruba personal naming system. It relies on an ethnopragmatic theory to analyse the cultural significance and symbolic impact of crown-related names among the Yoruba. Drawing on a qualitative research approach using participant observation and semi-structured interviews with 25 participants who were purposively sampled in Ikeja, Lagos State, we argue that crown-related names are not mere identifiers or person reference labels, but they provide cultural insights and reflections on the foundation of authority and continuity, and carry the aspirational principles of the Yoruba traditional structure. The names symbolise personal journey; reinforce the hierarchical structure of the Yoruba society; and highlight the people’s deep connection to their ancestral lineage. This study concludes that crown-related names encapsulate the values, beliefs, and social structures of the Yoruba society, serving as enduring markers of dynastic identity and cultural values. In this way, crown-related names represent badges of honour that validate their bearers’ self-worth and dignity. Full article

Background: The acceptance of new vaccines under uncertainty—such as during the COVID-19 pandemic—poses a major public health challenge because efficacy and safety information is still evolving. Methods: We propose an integrative analytical framework that combines a theory-based model of vaccine acceptance—the cognitive–affective–normative (CAN) model—with machine-learning techniques (decision tree regression, random forest, and Extreme Gradient Boosting) and SHapley Additive exPlanations (SHAP) integrated into an importance–performance map (IPM) to prioritize determinants of vaccination intention. Using survey data collected in Spain in September 2020 (N = 600), when the AstraZeneca vaccine had not yet been approved, we examine the roles of perceived efficacy (EF), fear of COVID-19 (FC), fear of the vaccine (FV), and social influence (SI). Results: EF and SI consistently emerged as the most influential determinants across modelling approaches. Ensemble learners (random forest and Extreme Gradient Boosting) achieved stronger out-of-sample predictive performance than the single decision tree, while decision tree regression provided an interpretable, rule-based representation of the main decision pathways. Exploiting the local nature of SHAP values, we also constructed SHAP-based IPMs for the full sample and for the low-acceptance segment, enhancing the policy relevance of the prioritization exercise. Conclusions: By combining theory-driven structural modelling with predictive and explainable machine learning, the proposed framework offers a transparent and replicable tool to support the design of vaccination communication strategies and can be transferred to other settings involving emerging health technologies. Full article

Many nations have been investing in hydrogen energy in the most recent wave of development and numerous projects have been proposed, yet a substantial share of these projects remain at the conceptual or feasibility stage and have not progressed to final investment decision or operation. There is a need to identify initial potential sites for green hydrogen production from renewable energy on an objective basis with minimal upfront cost to the investor. This study develops a decision support system (DSS) for identifying optimal locations for green hydrogen production using solar and wind resources that integrate economic, environmental, technical, social, and risk and safety factors through advanced Multi-Criteria Decision Making (MCDM) techniques. The study evaluates alternative weighting scenarios using (a) occurrence-based, (b) PageRank-based, and (c) equal weighting approaches to minimize human bias and enhance decision transparency. In the occurrence-based approach (a), renewable resource potential receives the highest weighting (≈34% total weighting). By comparison, approach (b) redistributes importance toward infrastructure and social indicators, yielding a more balanced representation of technical and economic priorities and highlighting the practical value of capturing interdependencies among indicators for resource-efficient site selection. The research also contrasts the empirical and operational efficiencies of various weighting methods and processing stages, highlighting strengths and weaknesses in supporting sustainable and economically viable site selection. Ultimately, this research contributes significantly to both academic and practical implementations in the green hydrogen sector, providing a strategic, data-driven approach to support sustainable energy transitions. Full article