Exposure to heavy metals during pregnancy poses significant health risks to both pregnant women and the developing fetus. This study aimed to conduct a comprehensive bibliometric analysis of global research on heavy metal exposure during pregnancy and its impact on fetal development over the past five decades (1974−2024). Data were retrieved from the Scopus database, yielding 173 English-language publications for analysis. Bibliometric mapping was performed using VOSviewer, while trend visualization and geographical analysis were conducted using Tableau to identify publication trends, research hotspots, and knowledge gaps. The results revealed a marked increase in research output beginning in 2010, with lead (Pb) and mercury (Hg) emerging as the most extensively investigated metals, followed by growing attention to arsenic (As), cadmium (Cd), and manganese (Mn). Prominent research themes focused on associations between prenatal heavy metal exposure and adverse birth outcomes, including low birth weight, preterm birth, and impaired neurodevelopment. Geographically, research output was dominated by the United States, China, and European countries, whereas contributions from low-income and high-exposure regions remained limited. Frequently occurring author keywords included heavy metals, pregnancy, and fetal development. These findings highlight the need for more targeted research in underrepresented regions and on emerging heavy metals, in alignment with global public health priorities and the Sustainable Development Goals (SDGs). Overall, this analysis provides strategic insights to inform future research directions and policy initiatives aimed at reducing prenatal heavy metal exposure and improving maternal and fetal health outcomes.

Unplanned urban expansion poses significant challenges to sustainable territorial development in intermediate cities. This study analyzes the dynamics of urban expansion and land use change in the city of Cajamarca (Peru) during the period 1986−2040, integrating Geographic Information Systems (GIS) techniques, Google Earth Engine (GEE) and CA-Markov prediction models. Landsat satellite images from 1986, 2004 and 2022, classified by Random Forest (RF), were used to generate thematic maps and evaluate their accuracy. Subsequently, a spatial simulation model was implemented to project urban expansion to 2040. The results indicate an increase in the urban area from 789.68 hectares to 5,768.19 hectares, while forests and crops also changed. The driving factors for this expansion include rural-urban migration, the availability of services, and real estate development. Projections highlight growth toward the east, southeast, and south of the city. This approach provides strategic inputs for sustainable urban planning and effective land management in transforming Andean cities.

Unmanned aerial vehicles (UAVs) have gained increasing importance due to their expanding application areas and operational flexibility. Selecting the most suitable UAV, however, represents a complex multi-criteria decision-making (MCDM) problem that involves numerous technical and performance-related factors. This study addresses the UAV selection problem by employing four distinct MCDM approaches: Evidential fuzzy MCDM based on Belief Entropy, Intuitionistic Fuzzy Dempster-Shafer Theory (DST), Spherical Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Type-2 Neutrosophic Fuzzy CRITIC-MABAC. Each method incorporates different fuzzy set theories, while a common seven-point linguistic scale is utilized to ensure consistency across models. The evaluation criteria were determined through a comprehensive literature review, and expert opinions were collected from experienced UAV pilots and technical personnel. The analysis identified the most suitable UAV alternative among the considered options. Sensitivity analyses were conducted to assess the robustness of the obtained results. The findings demonstrate that the proposed framework enables a simultaneous comparison of different fuzzy set environments on a unified linguistic scale. Overall, the results are consistent, reliable, and practically applicable, offering valuable insights and methodological contributions to the field of UAV selection and fuzzy MCDM applications.

The rapid growth of wealth-tech platforms has intensified the importance of digital trust, particularly among Generation Z investors who rely heavily on social media–driven information sources when making investment-related decisions. While prior studies have examined influencer marketing, electronic word-of-mouth (e-WOM), and social media engagement in fintech contexts, empirical research that integrates these persuasion mechanisms into a unified trust-based model of wealth-tech adoption intention remains limited. Drawing on Source Credibility Theory, Trust Transfer Theory, and digital engagement frameworks, this study proposes and tests an integrative model in which influencer credibility, e-WOM, and social media engagement simultaneously influence wealth-tech adoption Intention through the mediating role of digital trust. Using survey data collected from 255 Generation Z actual users of wealth-tech platforms in Indonesia, this study employs Structural Equation Modelling (SEM) to simultaneously test the measurement model and the proposed trust-based structural relationships, including the mediating role of digital trust. A purposive sampling approach was adopted to ensure respondents possessed direct experience with wealth-tech applications, thereby enhancing construct validity in this specialized digital investment context. The results indicate that influencer credibility, e-WOM, and social media engagement each exert a significant positive effect on digital trust, which in turn strongly influences wealth-tech adoption Intention. Digital trust is found to play a critical mediating role, reinforcing its central importance in investment-oriented digital platforms characterized by heightened perceived risk. This study contributes to the literature by extending digital trust and fintech adoption research in three ways: (1) by integrating multiple digital persuasion mechanisms into a single trust-centered framework, (2) by empirically validating digital trust as a key mediating mechanism in a wealth-tech investment context, and (3) by providing contextual insights from an emerging market characterized by rapid digital adoption and persistent trust challenges. Practically, the findings offer guidance for wealth-tech platforms and digital marketers in designing trust-enhancing strategies targeting Generation Z investors.

Endometriosis remains underdiagnosed due to reliance on invasive laparoscopy. Artificial Intelligence (AI) using either imaging or structured clinical data have shown promise, but single modality approaches face limitations in sensitivity, calibration, and clinical reliability. This work seeks to evaluate whether decision-level multimodal fusion of Magnetic Resonance Imaging (MRI)-based and clinical data-based AI systems improves diagnostic performance, calibration, and net clinical benefit, compared with single-modality models. Two previously validated models were combined with retrospective data from 1,208 patients with suspected endometriosis: a Dual U-Net trained on pelvic MRI with Gradient-weighted Class Activation Mapping (Grad-CAM) interpretability and a dense neural network trained on structured clinical features with SHapley Additive exPlanations (SHAP). This study tested weighted averaging, stacking via logistic regression, and confidence-gating. Performance was assessed using accuracy, precision, recall, F1-score, and area under the curve (AUC). Calibration was evaluated using the Brier score, expected calibration error (ECE), and reliability diagrams. Clinical utility was quantified with decision curve analysis (DCA). Statistical significance was tested with McNemar’s test for accuracy and DeLong’s test for AUC. Multimodal fusion outperformed both single modality models. Weighted averaging accuracy was 0.89, precision was 0.89, recall was 0.87, and F1-score was 0.86, thus improving on either modality alone. Stacking further enhanced calibration (ECE reduction from 0.8 to 0.04) and yielded higher net benefit across clinically relevant probability thresholds (20 to 60%). DCA indicated fusion would avoid 12 to 18 unnecessary surgical investigations per 100 patients, compared with single modality strategies. Confidence-gating maintained performance under simulated distribution shifts to support robustness. Decision-level multimodal fusion enhanced non-invasive diagnosis of endometriosis by improving accuracy, calibration, and clinical utility. These results demonstrated the value of integrative AI gynecological care and justify prospective validation in real-world clinical settings.