Inclusive ecotourism promotes equal access, community participation, and environmental conservation, thereby generating both social and economic benefits. Although scholarly interest in inclusive ecotourism has increased, empirical research examining how specific policy frameworks address the needs of people with disabilities remains limited. This study presents a systematic review of the existing literature to evaluate the extent to which ecotourism policies enhance accessibility, foster community awareness, and support environmental sustainability. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive literature search was conducted using the Scopus, Web of Science, and ProQuest databases. Of the 1,215 records identified, only seven studies met the inclusion criteria, indicating that research in this area is still at an early stage of development. The review highlights several key policy strategies, including the provision of accessible infrastructure, stakeholder engagement, and the integration of sustainability-oriented practices. However, the findings also reveal persistent challenges, such as weak policy enforcement, limited intersectoral collaboration, and gaps in physical infrastructure. By synthesizing insights related to accessibility, community awareness, and environmental policy, this study provides an integrated perspective to inform the development of more inclusive and sustainable ecotourism initiatives. It underscores the need for stronger cross-sector collaboration to address existing policy shortcomings and to promote tourism systems that equitably benefit all visitors, including individuals with disabilities.
Digital finance has increasingly influenced the functioning and stability of industrial systems by reshaping interregional economic linkages. Based on panel data from 31 Chinese provinces spanning the period 2012–2021, this study investigates how the development of digital finance is associated with the spatial structure of industrial chain resilience. A modified gravity model is used to construct interprovincial interaction networks, and social network analysis is applied to examine their structural characteristics and temporal evolution. The empirical results show that the spatial network related to digital finance and industrial chain resilience has become progressively more connected over time, as reflected by a gradual increase in network density. However, substantial regional heterogeneity persists in network position and influence. Provinces with relatively advanced digital finance tend to occupy more central positions and exert stronger structural influence, whereas peripheral provinces remain weakly connected and play limited roles within the network. This asymmetric network configuration constrains the overall stability of the industrial chain system and highlights the importance of coordinated development in digital finance for improving systemic resilience.
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.
Reliable and timely perception of road surface conditions is a fundamental requirement in intelligent transportation systems (ITS), as it directly affects traffic safety, infrastructure maintenance, and the operation of connected and autonomous vehicles. Vision-based pothole detection has emerged as a practical solution due to its low sensing cost and deployment flexibility; however, existing deep learning approaches often struggle to achieve a satisfactory balance between detection accuracy, robustness to scale variations, and real-time performance on resource-constrained platforms. This study presents Partial Group-You Only Look Once (PG-YOLO), a lightweight real-time vision framework designed for road infrastructure monitoring in ITS. Built upon a compact one-stage detector, the proposed framework introduces a Partial Multi-Scale Feature Aggregation (PMFA) module to enhance the representation of small and irregular potholes under complex road conditions, as well as a Grouped Semantic Enhancement Attention (GSEA) module to improve high-level semantic discrimination with limited computational overhead. The framework is specifically designed to meet the low-latency and low-complexity requirements of vehicle-mounted and roadside sensing devices. Experimental evaluations conducted on a mixed road damage dataset demonstrate that the proposed approach achieves consistent improvements in detection accuracy while reducing model parameters and maintaining real-time inference speed. Compared with the baseline model, PG-YOLO improves precision, recall, and detection stability under challenging illumination and scale variations, while remaining suitable for edge deployment. These results indicate that the proposed framework can serve as an effective perception component for ITS, supporting continuous road condition awareness and data-driven maintenance and safety management.
Studies on ChatGPT within the context of online consumer reviews (OCRs) have emerged as part of the broader exploration of generative AI across multiple disciplines. However, to date, no research has systematically examined the current research focus or other key aspects related to the application of ChatGPT in OCRs. To address this gap, this study conducts a systematic literature review to identify dominant research focus areas, highlight existing research gaps, and propose directions for future research. Guided by the PRISMA 2020 protocol and employing a thematic analysis approach, 22 relevant studies were analysed, revealing three overarching themes: (1) ChatGPT for review analytics, (2) ChatGPT for review modeling and evaluation, and (3) ChatGPT for review management. The findings indicate that current research primarily emphasizes ChatGPT’s potential as an analytical tool for OCR datasets, enabling the extraction of valuable and actionable insights for both marketers and researchers. In addition, the review identifies growing concern regarding fake reviews and highlights the emerging use of ChatGPT-generated synthetic reviews as datasets for developing fake review detection models, offering a practical alternative for studies facing challenges in obtaining high-quality training data. Finally, findings related to the third theme demonstrate ChatGPT’s utility in supporting managerial responses to customer reviews, providing insights into its role in enhancing customer relationship management. Overall, this review suggests that research on ChatGPT in OCRs remains at an early stage but offers significant insights and opportunities for future investigation in this emerging field.
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.
Coastal management is crucial for achieving the blue economy, which prioritizes the sustainable utilization of marine resources for economic growth, improved livelihoods, and the welfare of the ocean ecosystem. However, the current body of knowledge on the interface of coastal management and the blue economy is fragmented. The identified fragmentation leads to the demand for a thorough understanding of research trends, major issues, and prospects. Therefore, this research aims to provide a systematic overview of the global research landscape on coastal management in the context of the blue economy. Bibliometric analysis was applied to examine 85 articles indexed in the Web of Science (WoS) and Scopus databases, with a focus on the period covering 2013 to 2024. The analysis was conducted with different tools such as biblioshiny R package, VOSViewer and NVivo 12 Plus to map the co-occurrence of keywords and thematic evolution. The results demonstrated several emerging research trends, including sustainable development, marine spatial planning, conservation management, marine environment and policy, as well as environmental impact assessment. Despite these developments, gaps were identified in areas such as policy integration, technological innovation for coastal monitoring, and equitable benefit-sharing mechanisms in the blue economy framework.
Climate change, which has intensified to a global governance crisis, demands adaptation strategies that are faster, precise, and more inclusive than ever before. Artificial intelligence (AI), increasingly positioned at the core of this transformation, is offering powerful tools for climate risk forecasting, disaster preparedness, energy optimization, agricultural efficiency, and business resilience. Yet the growing adoption of AI exposes a fundamental paradox: while it promises unprecedented analytical capacity, its benefits remain unevenly distributed across communities. The current study addressed this tension by presenting a comprehensive and governance-oriented analysis of AI-driven climate adaptation. Drawing on an extensive review of academic research and major institutional reports, this paper identified three interlinked challenges including methodological limitations, ethical and equity risks, as well as governance gaps which continuously undermine the effectiveness of AI-enabled adaptation. Predictive models struggled to incorporate complex social vulnerabilities; algorithmic opacity limited trust and accountability whereas persistent data inequality prevented low-income regions from leveraging advanced digital tools. In response, the study introduced a multi-layered governance framework encompassing technical capacity, regulatory and ethical infrastructure, and socially inclusive outcomes. The findings revealed that the contributions of AI to climate adaptation were fundamentally shaped by institutional quality, transparent data governance, equitable digital access, and participation of vulnerable populations in decision making. The paper concluded that AI held extraordinary potential to strengthen resilience, only if deployed within governance systems that prioritize fairness, accountability, transparency, ethics, and social inclusion. By aligning technological innovation with just and sustainable governance, AI becomes not only a predictive instrument but a transformative catalyst for equitable climate adaptation worldwide.
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