Search Results (2,932)

Coastal and island tourism represents a key and environmentally sensitive component of the global tourism system, integrating ecological, cultural, and economic dimensions within marine and insular environments. This study presents a comprehensive bibliometric analysis of 1226 Scopus-indexed journal articles in accordance with the PRISMA protocol. By combining performance analysis and science mapping, it examines publication dynamics, thematic structures, intellectual foundations, and global collaboration patterns. The results show steady growth that accelerates after 2010, reflecting the development of descriptive case-based studies to multidisciplinary research. The research landscape reveals four major thematic clusters focusing on tourism development and management, governance and sustainability, climate change adaptation, and technological innovation. The intellectual structure is characterized by seminal works and conceptual foundations that have shaped the development of the field. However, global productivity and collaboration show significant geographic imbalances. This study provides a consolidated understanding of how coastal and island tourism scholarship has evolved and highlights the need for greater theoretical integration, inclusivity, and cooperation to promote sustainable and resilient tourism futures. Full article

This study establishes a synergistic Texture–Quota–Salinity (T–Q–S) model to optimize soil water–salt dynamics in arid agricultural systems. Key findings reveal a sand content threshold (S₀ = 45.2%) governing salt transport efficiency: (1) Sandy soils (S > 50%) exhibit high leaching capacity, enabling the use of saline water (4 g·L⁻¹) with a 270 mm quota to achieve >75% desalination. (2) Threshold soils (S ≈ 45.2%) balance leaching and retention, maximizing nutrient conservation under brackish water (2 g·L⁻¹) and 260 mm irrigation. (3) Clayey soils (S < 30%) require freshwater (≤2 g·L⁻¹) and reduced quotas (≤230 mm) to mitigate surface salinization. The S₀ threshold enables precise irrigation strategies: deep leaching in sandy soils, balanced management in threshold soils, and salt-suppression in clayey soils, enhancing water efficiency by 25% while controlling root zone salinity. Full article

With the rapid expansion of the civil aviation industry, the surge in flight numbers has led to increasingly pronounced issues of air route congestion and flight conflicts. 4D trajectory prediction, by dynamically adjusting aircraft paths in real time, can prevent air route collisions, alleviate air traffic pressure, and ensure flight safety. Therefore, this paper proposes a combined model—GAT-BiGRU-TPA—based on the Spatio-Temporal Graph Neural Network (STGNN) framework to achieve refined 4D trajectory prediction. This model integrates Graph Attention Networks (GAT) to extract multidimensional spatial features, Bidirectional Gated Recurrent Units (BiGRU) to capture temporal dependencies, and incorporates a Temporal Pattern Attention (TPA) mechanism to emphasize learning critical temporal patterns. This enables the extraction of key information and the deep fusion of spatio-temporal features. Experiments were conducted using real trajectory data, employing a grid search to optimize the observation window size and label length. Results demonstrate that under optimal model parameters (observation window: 30, labels: 4), the proposed model achieves a 45.72% reduction in mean Root Mean Square Error (RMSE) and a 43.40% decrease in Mean Absolute Error (MAE) across longitude, latitude, and altitude compared to the optimal baseline BiLSTM model. Prediction accuracy significantly outperforms multiple mainstream benchmark models. In summary, the proposed GAT-BiGRU-TPA model demonstrates superior accuracy in 4D trajectory prediction, providing an effective approach for refined trajectory management in complex airspace environments. Full article

This study compares certification systems for green healthcare facilities implemented worldwide. Healthcare facilities are complex structures designed to provide uninterrupted service while involving substantial resources, high energy consumption, and heavy human and material traffic. The COVID-19 pandemic emphasized the importance of designs that ensure hygiene, reduce environmental impact, and improve energy efficiency, making green certification systems for healthcare facilities increasingly critical. Eight certification systems currently in use across eight countries were examined, four from advanced economies (LEED in the U.S., BREEAM in the U.K., Green Star in Australia, and CASBEE in Japan) and four from developing economies (YeS-TR in Türkiye, IGBC in India, GBI in Malaysia, and GREENSHIP in Indonesia). Country selection considered regional diversity, similarities in environmental policies, and the potential for healthcare infrastructure development. A literature-based comparative analysis was conducted, and seven key categories were identified for evaluating sustainability: sustainable land and transport, water and waste management, energy efficiency, material and life cycle impact, indoor environmental quality, project management process, and innovation. The comparison revealed considerable overlap among the systems but also highlighted shortcomings in addressing healthcare-specific needs. This paper contributes to the advancement of sustainability assessment in the healthcare sector by highlighting the need for certification schemes specifically designed for medical facilities. The findings emphasize the necessity of developing healthcare-tailored frameworks that not only address environmental performance but also capture the unique operational, functional, and clinical dynamics of this sector. Full article

Round scad (Decapterus maruadsi) and jack mackerel (Trachurus japonicus) are economically significant pelagic species widely distributed in the northern South China Sea (SCS), with overlapping habitats and life history stages. To examine the distribution patterns of round scad and jack mackerel and their responses to environmental variables, we conducted a preliminary analysis using catch and environmental data from four seasonal surveys around Hainan Island. Three species distribution models—generalized linear models (GLM), generalized additive models (GAM), and random forests (RF)—were applied to quantify species–environment relationships. Explanatory variables included both biotic and abiotic factors: temperature, salinity, water depth, sea surface chlorophyll a concentration (SSC), phytoplankton abundance, and zooplankton abundance. The results revealed pronounced spatial heterogeneity in the high-density areas of both species. Among the models, GAM consistently explained a higher proportion of deviance in the observed distributions. Further analysis showed that round scad and jack mackerel responded differently to environmental gradients such as water depth and temperature, although their responses to varying plankton concentrations were largely consistent. Specifically, round scad are typically found in waters at depths ranging from 0 to 50 m, whereas jack mackerel tend to inhabit depths exceeding 100 m. In response to high plankton abundance, both species exhibit a notable increase in resource availability when plankton levels surpass 3. These findings indicate distinct spatial niches and suggest potential competition in feeding ecology between the two species. Overall, the study enhances understanding of the spatial dynamics of key commercial species in the northern SCS and provides valuable insights for sustainable fisheries management and conservation planning. Full article

The increasing prevalence of lithium-ion batteries in energy storage and electric transportation has led to a rise in overcharge-induced thermal runaway (TR) incidents. Particularly, the TR of Lithium Iron Phosphate (LFP) batteries demonstrates distinct evolutionary stages and multimodal hazard signals. This study investigated the TR process of LFP batteries under various charging rates through five sets of gradient C-rate experiments, collecting multimodal data (temperature, voltage, gas, sound, and deformation). Drawing on the collected data, this study proposes a three-stage evolution model that systematically identifies key characteristic signals and tracks their progression pattern through each stage of TR. Subsequently, fusion-based models (for both single- and multi-rate scenarios) and a time-series-based LSTM model were developed to evaluate their classification accuracy and feature importance in the classification of TR stages. Results indicate that the fusion-based models offer greater generalization, while the LSTM model excels at modeling time-dependent dynamics. These models demonstrate complementary strengths, providing a comprehensive toolkit for risk assessment. Furthermore, for the severe TR stage, this study proposes an innovative three-dimensional dynamic emergency decision matrix comprising a toxicity index (TI), flammability index (FI), and visibility (V) to provide quantitative guidance for rescue operations in the post-accident phase. Ultimately, this study establishes a comprehensive, closed-loop framework for LFP battery safety, extending from multimodal signal acquisition and intelligent early warning to quantified emergency response. This framework provides both a robust theoretical basis and practical tools for managing TR risk throughout the entire battery lifecycle. Full article

River–lake systems in the middle and lower reaches of the Yangtze River Basin function as critical ecological interfaces for maintaining regional water security and biodiversity. However, the complex interplay between environmental factors and biological communities in these systems remains poorly understood, limiting evidence-based management strategies essential for achieving sustainable development goals. This study investigated the spatiotemporal heterogeneity of environmental variables and their relationships with biological communities across 36 sampling sites (4 rivers, 5 lakes) from January to November 2022. Significant spatial differences were observed between river and lake systems, with lakes exhibiting higher concentrations of biochemical oxygen demand (BOD₅), chlorophyll-a, permanganate index (CODMn), loss on ignition (LOI), sediment total nitrogen (STN), total organic carbon (TOC), and turbidity, while rivers showed elevated total nitrogen levels. These patterns reflected fundamental differences in hydrodynamic mechanisms, particularly water retention time and sedimentation processes. Environmental parameters displayed distinct seasonal variations, with BOD₅ increasing markedly in autumn, and chlorophyll-a showing system-specific peaks in lakes (May) and rivers (September). Multivariate analyses revealed that water temperature, organic matter, and nutrients (particularly phosphorus) were key drivers shaping both phytoplankton and benthic communities. The structural equation model identified a strong cascade pathway from turbidity through phosphorus to phytoplankton richness, and uncovered a “phosphorus paradox” wherein total phosphorus exhibited contrasting effects on different biological components—positive for phytoplankton richness but negative for benthic richness. The positive relationship between phytoplankton and benthic macroinvertebrate richness provided evidence for benthic–pelagic coupling in this river–lake system. These findings advance our understanding of the complex mechanisms linking physical factors, nutrient dynamics, and biological communities across river–lake continuums, providing a quantitative framework for ecosystem-based management that supports sustainable development in the Yangtze River Basin and similar freshwater systems globally. Full article

The implementation of the NIS2 Directive expands the scope of cybersecurity regulation across the European Union, placing new demands on both essential and important entities. Despite its importance, organizations face multiple barriers that undermine compliance, including lack of awareness, technical complexity, financial constraints, and regulatory uncertainty. This study identifies and models these barriers to provide a clearer view of the systemic challenges of NIS2 implementation. Building on a structured literature review, fourteen barriers were defined and validated through expert input. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method was then applied to examine their interdependencies and to map causal relationships. The analysis highlights lack of awareness and the evolving threat landscape as key drivers (i.e., causal factors) that reinforce each other. Technical complexity and financial constraints act as mediators transmitting the influence of these causal factors toward operational and governance failures. Operational disruptions, high reporting costs, and inadequate risk assessment emerge as the most dependent outcomes (i.e., effect factors), absorbing the impact of the driving and mediating factors. The findings suggest that interventions targeted at awareness-building, resource allocation, and risk management capacity have the greatest leverage for improving compliance and resilience. By clarifying the cause-and-effect dynamics among barriers, this study supports policymakers and managers in designing more effective strategies for NIS2 implementation and contributes to current debates on cybersecurity governance in critical infrastructures. Full article

Extreme climate events like droughts and floods are creating urgent challenges for sectors such as Agriculture or water management. Effective adaptation requires stakeholder collaboration, supported by stakeholder analysis (SA) methods, which are still evolving in environmental management. We briefly reviewed examples of recent existing systematic evidence syntheses on SA across different domains. This highlighted several SA challenges, including the lack of transparent, common methods—particularly for climate-induced extreme events—and weak links between SA results and policy or practice. We then present a case study that illustrates these challenges and suggests ways to address them. Cooperating with a local network organisation, the Living Lab Schouwen-Duiveland (LAB), we conducted a case study on the island of Schouwen-Duiveland (NL), which is trying to adapt to drought. Applying a novel stakeholder analysis method, the “Rings of involvement”, which enables the visualisation of stakeholders’ levels of affectedness regarding the issue, we were able to identify and categorise the stakeholder network in a systematic manner. We identified stakeholder groups, such as “Implementers”, who are not yet in the network but likely hold key practical knowledge to address local-regional climate adaptation. This calls for a better institutionalisation of and a more dynamic approach to SA in the local climate change adaptation practices. Based on our case study, we suggest that future studies could explore under which conditions a network organisation (such as the LAB) acts as a dynamic platform for facilitating stakeholder knowledge co-production. Full article

The development of Digital Twin (DT) technology in Battery Management Systems (BMSs) presents a transformative approach for maintenance, monitoring, and predictive diagnostics, especially in the demanding field of space applications. DTs, through their three-layer structure, provide an accurate and dynamic virtual representation of the physical entity, continuously updated via bidirectional data exchange provided by the communication link. Given the promising capabilities of the DT approach in real-time applications, its integration into BMSs is straightforward, as it can enhance monitoring and prediction of nonlinear electrochemical systems, such as space-grade lithium-ion batteries, supporting the mitigation of ageing effects under the unique constraints of the space environment. Despite notable progress in BMS technologies, the choice of estimation techniques consistent with the DT paradigm remains insufficiently defined. This survey examines the state of the art with the aim of bridging the conceptual framework of DTs and existing battery management algorithms, identifying the methodologies most suitable in accordance with DT architectures and principles. The scope of this paper is to provide researchers and engineers with a comprehensive overview of the advancements, key enabling technologies, and implementation strategies for Digital Twins in space BMSs, ultimately contributing to more reliable and efficient space missions. Full article

In the context of global climate change, frequent summer heavy rainfall events act as significant disturbances to the ecosystem functions of shallow lakes. This study examined the response of phytoplankton community structure and dynamics to heavy rainfall in Lake Changhu, a shallow eutrophic lake, through monthly monitoring during the summer months (June–August) of 2020–2022. The results revealed that heavy rainfall induced substantial water level fluctuations and shifts in key environmental parameters. Marked interannual variations were observed in the phytoplankton community, with the highest species richness in summer 2021 and lowest in 2022. While Chlorophyta dominated in species composition, Cyanobacteria overwhelmingly dominated in abundance, with key taxa including Dolichospermum flos-aquae L., Pseudanabaena limnetica L., Oscillatoria princeps V., Microcystis wesenbergii K., and Merismopedia minima B. Both phytoplankton abundance and biomass peaked in summer 2021. Community diversity indices were consistently lower in June compared to July–August, indicating higher environmental stress and a more simplified community structure during the initial rainfall period. A comprehensive water quality evaluation suggested that Lake Changhu was in a lightly to moderately polluted state. Correlation and redundancy analyses (RDA) identified rainfall, water temperature, and nutrient concentrations as the primary environmental drivers shaping phytoplankton community succession. These findings systematically elucidate the mechanistic responses of phytoplankton to heavy rainfall disturbances, offering a scientific foundation for ecological resilience assessment and adaptive management of shallow lakes under climate change. Full article

This review provides a comprehensive analysis of the technological, environmental, economic, regulatory, and social dimensions shaping the development and operation of agricultural biogas plants. The paper adopts a primarily European perspective, reflecting the comparatively high share of agricultural inputs in anaerobic digestion (AD) across EU Member States, while drawing selective comparisons with global contexts to indicate where socio-geographical conditions may lead to different outcomes. It outlines core principles of the AD process and recent innovations—such as enzyme supplementation, microbial carriers, and multistage digestion systems—that enhance process efficiency and cost-effectiveness. The study emphasises substrate optimisation involving both crop- and livestock-derived materials, together with the critical management of water resources and digestate within a circular-economy framework to promote sustainability and minimise environmental risks. Economic viability, regulatory frameworks, and social dynamics are examined as key factors underpinning successful biogas implementation. The paper synthesises evidence on cost–benefit performance, investment drivers, regulatory challenges, and support mechanisms, alongside the importance of community engagement and participatory governance to mitigate land-use conflicts and ensure equitable rural development. Finally, it addresses persistent technical, institutional, environmental, and social barriers that constrain biogas deployment, underscoring the need for integrated solutions that combine technological advances with policy support and stakeholder cooperation. This analysis offers practical insights for advancing sustainable biogas use in agriculture, balancing energy production with environmental stewardship, food security, and rural equity. The review is based on literature identified in Scopus and Web of Science for 2007 to 2025 using predefined keyword sets and supplemented by EU policy and guidance documents and backward- and forward-citation searches. Full article

Cardiometabolic diseases, including cardiovascular disorders and type 2 diabetes mellitus, are the leading cause of morbidity and mortality worldwide, placing a significant burden on healthcare systems. Although advances in imaging and risk stratification have improved disease management, conventional diagnostic and prognostic tools often lack the requisite sensitivity and specificity for early and precise risk stratification. This limitation stems from their poor ability to capture the full molecular complexity of these conditions, underscoring an urgent need for innovative biomarkers to bridge these gaps. MicroRNAs, small non-coding RNAs that regulate gene expression post-transcriptionally, have emerged as promising candidates. Their characteristics offer several advantages over traditional methods, including exceptional stability in biological fluids, strong tissue and disease specificity, and the ability to reflect dynamic pathological changes. These unique features enable miRNAs to detect subtle molecular alterations that may precede clinical symptoms, thereby overcoming key limitations of current diagnostic approaches. Altered circulating miRNA profiles have been linked to pathological processes such as endothelial dysfunction, inflammation, oxidative stress, and maladaptive cardiac remodeling. This review provides a comprehensive overview of the current evidence supporting the diagnostic and prognostic role of circulating miRNAs in cardiometabolic disease. We highlight their potential as early detection biomarkers, tools for patient stratification, and indicators of therapeutic response. Furthermore, we discuss key limitations to clinical translation, including methodological variability, challenges in sample handling, differences in normalization strategies, and platform-dependent quantification inconsistencies. Overcoming these obstacles and achieving robust large-scale clinical validation will be essential to fully harness the potential of miRNAs as next-generation molecular signatures in precision medicine. Full article

Transgenic Bt maize commercialization has become a critical pest management strategy against lepidopteran insects in southwest China, but its ecological impact on arthropod biodiversity remains insufficiently characterized. This two-year field investigation (2023–2024) conducted in Bazhong City, Sichuan Province utilized systematic field monitoring to compare arthropod community dynamics between conventional maize and Bt-Cry1Ab maize (DBN9936) cultivation systems. This study documented 575,970 arthropod specimens representing 80 species/types across 45 families and 17 orders. Analysis of variance revealed significant differences (p < 0.05) between non-Bt and Bt maize in the abundance and species richness of target herbivorous pests, non-target herbivorous pests, and natural enemy insects. Field investigations revealed a notable absence of Macrocentrus cingulum, a key larval parasitoid of Ostrinia furnacalis, in Bt-maize plots compared to conventional counterparts. The populations of non-target herbivorous pests and natural enemies such as Aphididae, Chrysoperla sinica, Frankliniella tenuicornis, and Orius sauteri were higher in Bt maize fields than in non-Bt maize fields, while the populations of target herbivorous pests including O. furnacalis and Mythimna loreyi were lower than those in non-Bt maize fields. However, no significant differences (p > 0.05) were observed in arthropod abundance, species richness, or in a suite of ecological indices including the Simpson diversity index, Shannon–Wiener diversity index, Pielou evenness index, McIntosh diversity index, and community stability indices (N_n/N_p, N_d/N_p, and S_d/S_p). Redundancy analysis identified maize growth stages (6.75% variance explained) and interannual variations (2.44%) as principal drivers of arthropod community dynamics, with maize genotype contributing minimally (1.53%). These findings demonstrate that Bt-Cry1Ab maize (DBN9936) cultivation maintains functional arthropod community structure while effectively controlling target pests, providing substantial empirical evidence to support its sustainable deployment in southern China’s agricultural landscapes. Full article

Island building change detection is a critical technology for environmental monitoring, disaster early warning, and urban planning, playing a key role in dynamic resource management and sustainable development of islands. However, the imbalanced distribution of class pixels (changed vs. unchanged) undermines the detection capability of existing methods and severe boundary misdetection. To address issue, we propose the MSDT-Net model, which makes breakthroughs in architecture, modules, and loss functions; a dual-branch twin ConvNeXt architecture is adopted as the feature extraction backbone, and the designed Edge-Aware Smoothing Module (MSA) effectively enhances the continuity of the change region boundaries through a multi-scale feature fusion mechanism. The proposed Difference Feature Enhancement Module (DTEM) enables deep interaction and fusion between original semantic and change features, significantly improving the discriminative power of the features. Additionally, a Focal–Dice–IoU Boundary Joint Loss Function (FDUB-Loss) is constructed to suppress massive background interference using Focal Loss, enhance pixel-level segmentation accuracy with Dice Loss, and optimize object localization with IoU Loss. Experiments show that on a self-constructed island dataset, the model achieves an F1-score of 0.9248 and an IoU value of 0.8614. Compared to mainstream methods, MSDT-Net demonstrates significant improvements in key metrics across various aspects. Especially in scenarios with few changed pixels, the recall rate is 0.9178 and the precision is 0.9328, showing excellent detection performance and boundary integrity. The introduction of MSDT-Net provides a highly reliable technical pathway for island development monitoring. Full article