Search Results (3,120)

The increasing complexity of algorithmically mediated and AI-mediated digital environments is reshaping how health information is interpreted. Existing digital health literacy frameworks predominantly conceptualize vulnerability as a deficit in individual skills, thereby insufficiently capturing the structural and cognitive conditions that shape interpretation. This study develops an integrative conceptual model that reconfigures health information interpretation as an emergent outcome of interacting cognitive, social, and algorithmic mechanisms. The analysis identifies three broad categories of interpretative mechanisms—cognitive, social, and algorithmically mediated—which are subsequently integrated into the five-dimensional Informational Resilience Ecosystem Model (IREM). First, cognitive heuristics and biases shape information processing. Second, social credibility signals influence evaluative judgments and trust formation. Third, algorithmic systems structure the visibility, prioritization, and distribution of information. Interpretation is conceptualized as an emergent outcome of interactions among these mechanisms. Building on these findings, informational resilience is theorized as an emergent system-level property contingent upon the configuration of interacting conditions rather than reducible to individual capacities. The Informational Resilience Ecosystem Model (IREM) captures this structure through five interdependent dimensions: cognitive, informational, social, algorithmic, and credibility infrastructure. The study shifts the analytical focus from competencies to conditions of interpretation and provides a structured framework for empirical operationalization by specifying dimensions, mechanisms, and their interactions. The model provides a theoretically grounded and operationally structured framework for advancing empirical research, public health communication, and platform-level interventions. Full article

The coast redwood forest, populated by the ancient relict species Sequoia sempervirens, provides unique and essential ecological services along the Pacific coast of California. It is a haven for endemism and ecological diversity, offers habitat for threatened species, and is an important global terrestrial carbon sink. However, a long history of resource extraction has significantly impacted this ecosystem. Complex old-growth forests have largely been replaced with managed timber stands, and biological diversity has been reduced through the loss of habitat and basic ecological functions. Under natural conditions, coast redwood is highly resilient to disturbance, due to its propensity for basal and epicormic sprouting. The primarily clonal reproductive strategy of S. sempervirens allows for natural thinning as a stand matures, generally leading to the development of late-seral characteristics without the need for active restoration. The increasingly pervasive use of active silvicultural tools for restoration, such as forest thinning and commercial timber harvest, can create a density-driven cycle that requires periodic re-application of the treatment and hinders natural successional processes. In order to restore forest health and resiliency, natural successional processes inherent to coast redwood can be supported as a restoration alternative. Full article

Seasonal snowpack and snowmelt are critical regulators of forest ecosystem functioning in temperate, boreal, montane, and alpine regions. Snowpack acts as a temporary water and energy reservoir, while snowmelt determines the seasonal availability of water and influences ecosystem processes during the growing season. Climate change is altering snowfall patterns, snow accumulation, and melt timing, with consequences for forest productivity, resilience, and disturbance dynamics. This review synthesizes current knowledge on snow–forest interactions and identifies major research trends, methodological approaches, and remaining knowledge gaps. The study combines a bibliometric analysis and a qualitative literature review based on publications indexed in the Scopus and Web of Science databases. A total of 695 publications were included in the bibliometric dataset and analyzed to assess temporal trends, geographical patterns, research themes, and the ecological consequences of changing snow dynamics in forests. Representative studies from this dataset were subsequently synthesized to evaluate the influence of snowpack and snowmelt on forest ecosystem functioning, resilience, and sustainability. The reviewed literature shows that snowpack and snowmelt strongly regulate forest water availability, soil thermal conditions, nutrient cycling, vegetation responses, and carbon dynamics. Changes in snow regimes, particularly reduced snow accumulation and earlier melt, can increase the risk of soil freezing, modify moisture conditions, intensify water stress, and affect ecosystem carbon balance. However, the magnitude and direction of these effects depend on forest type, species composition, climate, and landscape characteristics. Forest structure also plays an important role in controlling snow interception, accumulation, persistence, and melt processes. The bibliometric analysis indicates a rapid increase in research interest in snow–forest interactions over the last two decades, with major contributions from the United States, Canada, China, and Northern Europe. Environmental sciences, hydrology, and ecology were the dominant research areas. Despite substantial progress, uncertainties remain regarding long-term ecosystem responses, species-specific vulnerabilities, and the interactions between declining snow cover and other climate-driven disturbances. This review emphasizes that understanding snowpack and snowmelt dynamics is essential for predicting forest ecosystem responses to climate change and for improving sustainable forest management and watershed conservation strategies in snow-dependent regions. Full article

Climate change is increasingly altering the ecological dynamics of apple orchard ecosystems, creating new challenges for sustainable management of phytophagous mites. Rising temperatures, prolonged drought periods, and increasing climatic variability influence mite population dynamics, destabilize predator–prey interactions, and reduce the effectiveness of traditional pest management approaches. This review examines sustainable mite management in apple orchards through the interconnected perspectives of ecological stability, climatic stress, and resilience-oriented agroecosystem management. Particular attention is given to the ecological mechanisms underlying mite outbreaks, including climate-driven acceleration of reproduction, trophic destabilization, biodiversity loss, and disruption of biological regulation processes. The ecological limitations of both conventional chemical control and biological control strategies are critically analyzed, highlighting issues related to pesticide-induced ecological disturbance, resistance development, climatic sensitivity of natural enemies, and operational constraints. The review further explores resilience-oriented management frameworks based on ecological intensification, habitat diversification, conservation biological control, adaptive management, and system-oriented regulation. Current research gaps are identified, including the lack of long-term ecological studies, insufficient integration of climatic and ecological datasets, limited development of resilience indicators, and underrepresentation of continental and semi-arid orchard systems. The findings suggest that future sustainable mite management should move beyond reactive pest suppression toward ecosystem-based approaches that strengthen ecological resilience and adaptive capacity under increasing climatic uncertainty. Full article

This paper examines the vulnerability and adaptive capacity of mountain communities in South and Central Asia, with specific reference to the Himalayas and the Central Asian highlands. Using a comparative framework, the study combines the Livelihood Vulnerability Index (LVI), LVI-IPCC, and the Livelihood Equity/Endowment Index (LEI) to measure multidimensional vulnerability. A mixed-methods approach combining household surveys and qualitative field evidence is used to analyze primary data from 600 households across four mountain regions: Leh (India), Sindhupalchok (Nepal), Batken (Kyrgyzstan), and Urgut (Uzbekistan). The results show that vulnerability is not explained only by climatic exposure; it is also associated with socio-economic conditions, institutional access, and livelihood assets. Leh and Sindhupalchok show higher vulnerability associated with water insecurity, food dependence, weak infrastructure, and climate variability, whereas Batken’s vulnerability is mainly linked to limited adaptive capacity. Urgut shows greater resilience associated with stronger adaptive capacity, despite persistent structural inequalities. The paper identifies financial access, social networks, and knowledge systems as important factors in strengthening resilience. It concludes that context-specific, inclusive, and asset-based policy interventions may help strengthen adaptive capacity and reduce vulnerability in fragile mountain ecosystems. Full article

The digital transformation has revolutionized document management, making Digital Transaction Management (DTM) systems essential for enhancing efficiency, security, and regulatory compliance in a wide range of organizations. This paper investigates the challenges, innovations, and state-of-the-art solutions in DTM platforms, with a focus on their integration with emerging technologies such as Artificial Intelligence (AI) and blockchain. In particular, the work introduces a reference architecture for next-generation DTM platforms, emphasizing blockchain-based security, smart-contract-based automation, and semantics-enhanced document retrieval and analysis. A case study in the utilities sector illustrates the benefits of the envisioned proposal, showcasing its suitability for managing complex, high-volume workflows. This research provides a foundation for future developments in resilient and interconnected DTM solutions, addressing the evolving demands of modern organizations within the broader digital ecosystem. Full article

Sustainable agricultural development requires regional agricultural systems to balance output growth, resource efficiency, ecological protection, and long-term resilience. In mountainous and ecologically sensitive regions, identifying the development constraints and statistically derived marginal contribution thresholds of agriculture is essential for promoting green transformation and sustainable land use. Taking Yunnan Province, China, as a representative plateau mountainous agricultural region, this study uses provincial annual data from 1990 to 2023 to quantitatively identify the key drivers and threshold characteristics of agricultural development under multidimensional constraints. A multidimensional indicator system was constructed covering fiscal and investment support, agricultural production inputs, rural infrastructure, and labor and population conditions. Ridge regression was employed to address multicollinearity among explanatory variables, Bootstrap approximate inference was used to improve the robustness of coefficient estimation, and the SHAP interpretation framework was introduced to rank key driving factors and identify marginal contribution thresholds. By integrating ridge regression, Bootstrap approximate inference, SHAP-based contribution ranking, and threshold identification, the proposed framework advances prior agricultural sustainability studies by linking coefficient-based factor analysis with interpretable marginal contribution thresholds under conditions of high multicollinearity and multidimensional resource constraints. The results show that agricultural development in Yunnan is characterized by multidimensional resource and infrastructure constraints. Rural electricity consumption, total reservoir storage capacity, fixed asset investment in agriculture, forestry, animal husbandry and fisheries, local public fiscal budget expenditure, and agricultural population generally act as positive supporting factors. Rural electricity consumption is the most stable and core driver across the aggregate and three sectoral models. In contrast, pesticide and fertilizer inputs show significant negative associations in most models, suggesting that future agricultural development in Yunnan is unlikely to be sustainably supported by continued expansion of high-intensity chemical inputs. Sectoral heterogeneity is also evident: agriculture and animal husbandry are more dependent on energy, water resources, and mechanization, whereas forestry shows a more distinct operational structure. The SHAP dependence analysis identifies several statistically derived marginal contribution thresholds, including rural electricity consumption of approximately 6.055 billion kWh, total reservoir storage capacity of approximately 10.395 billion m³, total agricultural machinery power of approximately 19.8324 million kW, pesticide use of approximately 37,500 tons, and fertilizer application of approximately 1.5238 million tons. These values should be interpreted as empirical transition points in the modeled marginal contributions rather than definitive biophysical ecological limits. They indicate that the sustainability-related constraint structure of agricultural development in Yunnan is not a single output ceiling but a composite interval shaped by infrastructure support capacity, factor allocation conditions, and the declining marginal contribution of high-intensity chemical inputs. The findings provide directional quantitative evidence for sustainable agricultural governance, agricultural green transformation, and differentiated policy discussion in mountainous agricultural regions and offer reference implications for advancing SDG 2 and SDG 15 through the coordination of food-related production, resource use efficiency, and ecosystem conservation. The identified thresholds should be interpreted as model-derived marginal contribution transition points rather than operational policy cutoffs or directly enforceable ecological standards. Full article

For a networked swarm system with a directed path graph, this paper investigates a group evasive attack strategy against actuators from the attacker’s perspective. The core objective of the proposed strategy is to force the global network state to converge to a pre-specified synchronization trajectory. First, the evasive attack signal with an ecosystem-based generation mechanism is modeled, from which the pre-specified synchronization trajectory can be derived. Subsequently, an evasive attack protocol is designed by superimposing the developed evasive attack signals onto the nominal synchronization protocol of the networked swarm system. By exploring the projection of evasive attack signals onto the synchronization subspace, an explicit expression of the pre-specified synchronization trajectory is determined, which depicts the global network state of the system under group evasive attacks. Then, to mitigate the impacts of evasive attacks on the synchronization performance of the networked swarm system, a resilient framework integrated with robust H_∞ regulation mechanisms is constructed to derive the design criteria for the group evasive attack strategy. Finally, a numerical simulation example is conducted to demonstrate the validity of theoretical results. Full article

Vegetation recovery after forest fires is a vital indicator of ecosystem resilience. However, the specific differences between structural and functional recovery after fire have remained unclear. In this study, we quantified and compared post-fire recovery using two distinct vegetation indicators: the Enhanced Vegetation Index (EVI) for structural recovery and Solar-Induced Chlorophyll Fluorescence (SIF) for functional recovery. We analyzed the spatiotemporal dynamics and drivers of post-fire recovery. A Transformer model was used to simulate pre- and post-fire variations in EVI and SIF, while a Random Forest model was employed to identify the key drivers of recovery. We analyze the spatiotemporal dynamics and drivers of post-fire recovery. A Transformer model simulates pre- and post-fire variations in EVI and SIF, while a Random Forest model identifies key drivers of recovery. Our results show a steep decline in both indicators after fires, with SIF recovering more slowly than EVI. Three years after the fire, about 78% of burned areas regain at least 80% of their pre-fire EVI levels, but SIF recovery reaches only 70%. Bivariate dependency analysis indicates that precipitation and temperature promote recovery, whereas topography and the Differenced Normalized Burn Ratio (dNBR) have the opposite effect. This study advances a phased, analytical approach to post-fire forest vegetation recovery, offering a dual-perspective framework for understanding forest resilience and providing actionable insights for sustainable restoration and management. Full article

Biodiversity and landscape heterogeneity are key components of agroecosystem functioning because they support ecosystem services and strengthen the capacity of agricultural systems to undertake sustainable agroecological transitions. This study assesses the landscape structure of the municipality of Ruvo di Puglia, within the Bio-Distretto delle Lame, to evaluate its potential to support such a transition. Bio-districts are territories in which farmers, local authorities, citizens, and other stakeholders collaborate to manage natural and agricultural resources sustainably, often with a strong connection to organic farming. The research combines freely available Sentinel-2 imagery with UAV-based ground truthing to update land-use/land-cover information and to derive landscape indicators. A systematic sampling scheme was designed in QGIS, and UAV flights over 14 areas were used to generate training and validation vectors. Two classification strategies were tested on 2024 Sentinel-2 data: a supervised pixel-based approach and an unsupervised multi-temporal object-based approach (GEOBIA). The best-performing map was obtained from the supervised classification of July NDVI data, with an overall accuracy of 91.76%. In respect to the 2018 official land-cover dataset indicates a decrease in agricultural land (−490.91 ha), a reduction in arable crops (−1216.43 ha), and an increase in permanent crops (+725.52 ha), suggesting a shift toward specialization. At the same time, natural and semi-natural areas increased, improving the landscape potential for ecological functions. However, the high fragmentation detected by the landscape metrics (average patch size approximately 0.25 ha) may limit habitat continuity and species stability. The results should therefore be interpreted as an assessment of landscape structure and potential biodiversity support, rather than as a direct measurement of biological diversity. Strengthening ecotones, hedgerows and semi-natural linear elements with native species would further improve landscape resilience and support agroecological planning. Full article

The post-COVID-19 tourism recovery exposes a structural divergence between economic resilience and environmental sustainability. Traditional tourism planning metrics consistently fail to diagnose how macroeconomic growth dynamics decouple from environmental pressures, leaving policymakers without empirical tools to identify structural vulnerabilities or prevent carbon-intensive recoupling during post-crisis transitions. This study integrates macroeconomic, environmental, and digital data across a global panel to map actionable pathways for sustainable tourism transitions. Employing a multi-stage methodology, the analysis first utilizes K-Means clustering (n = 80) to isolate the structural fixed effects of baseline destination archetypes driving a K-shaped recovery. Second, using a synchronized environmental panel (n = 41), a Decoupling Index evaluates eco-efficiency elasticity to test the alignment between tourism value recovery and aviation-induced CO₂ emissions. Third, regression analysis of an elite digital cohort (n = 18) measures dynamic exogenous catalysts, revealing that digital attractiveness, proxied by the global digital nomad market share, is a significantly stronger accelerator of recovery (β = 55.59, p = 0.019) than traditional physical air connectivity (β = −46.48, p = 0.036). Synthesizing these insights, a 2 × 2 Strategic Decision Matrix (n = 41) classifies destinations into Sustainable Leaders, Mass-Market Traps, Value Pivoters, and Vulnerable Laggards. The empirical results demonstrate that pre-pandemic structures do not deterministically dictate recovery (p > 0.05, Partial η² ≤ 0.077), yet rapid financial recovery often masks deep atmospheric vulnerabilities, with specific absolute decoupling leaders achieving exceptional value expansion alongside strict carbon contraction (e.g., Saudi Arabia, DE = −0.35; El Salvador, DE = −0.26). This framework provides a data-driven roadmap for policymakers to utilize “soft” digital infrastructure to transition from carbon-intensive, volume-dependent models toward value-optimized, low-emission ecosystems. Full article

Rapid urbanization and climate change have intensified the interconnected challenges of surface heating, air pollution, and declining ecosystem functions, with important implications for regional sustainability. Taking Henan Province, China, as the study area, this study selected 2013, 2018, and 2023 as representative years and used land surface temperature (LST), fine particulate matter (PM2.5), ozone (O3), and net primary productivity (NPP) to characterize the thermal environment, air pollution, and carbon sequestration capacity. Pearson correlation analysis, multiple linear regression, and XGBoost-SHAP were integrated to examine bivariate associations, independent linear associations, factor importance, nonlinear responses, and potential threshold characteristics associated with natural, ecological, and anthropogenic factors. The results showed marked spatial differences in the four environmental variables. The multiple linear regression models explained 57.4–69.0% of the variation in LST, 23.8–72.0% in O3, 81.0–84.8% in PM2.5, and 57.4–62.5% in NPP. Natural factors generally showed relatively large and temporally stable standardized coefficients. Precipitation and potential evapotranspiration were positively associated with LST, whereas elevation and precipitation were negatively associated with PM2.5 and O3. NDVI showed an environmentally favorable pattern, being negatively associated with LST, PM2.5, and O3 but positively associated with NPP. Anthropogenic variables generally exhibited smaller and less temporally stable coefficients. The XGBoost models demonstrated good predictive performance, particularly for PM2.5, with R² values of 0.945, 0.920, and 0.905 in 2013, 2018, and 2023, respectively. SHAP analysis identified DEM, PRE, PET, and NDVI as the main contributors to model predictions and revealed nonlinear responses and potential threshold characteristics. These findings indicate that coordinated management of vegetation cover, hydrothermal conditions, and urban development can support heat mitigation, air pollution control, ecosystem productivity, and more sustainable, climate-resilient, and low-carbon development in rapidly urbanizing regions. Full article

Reprocessing historical lead–zinc (Pb–Zn) slag offers a circular-economy pathway for secondary metal recovery, yet it can remobilize legacy contaminants where containment is inadequate, transferring risk to the surrounding land. Sustainable management of such sites requires frameworks that link contamination assessment to actionable remediation. We integrated ICP-OES geochemistry, native-plant biomonitoring, and US EPA RAGS-based risk modeling at an active Pb–Zn slag reprocessing site in Shymkent, Southern Kazakhstan. Twenty-four soil samples along four cardinal transects, two reference samples, and four composite plant samples (Centaurea pseudosquarrosa + Plantago lanceolata) were analyzed for ten metals by ICP-OES. UCC-referenced indices classified six metals as geoaccumulation Class 6 at most points (enrichment factors up to 90,871, confirming an exclusively anthropogenic origin). Peak concentrations reached 9350 mg·kg⁻¹ Pb, 290 mg·kg⁻¹ Cd, and 10,900 mg·kg⁻¹ As—exceeding Kazakhstan MPC by 72×, 290×, and 5450×. Worst-case carcinogenic risk reached 4.3 × 10⁻³ (43× above the US EPA threshold), driven almost entirely by arsenic (93%); ecosystem risk (RCR_total = 223) was dominated by cadmium (43%), arsenic (27%), and mercury (16%)—a disconnect between mass-based and toxicity-based prioritization. On this basis we propose a three-tier remediation framework (engineered containment, phytostabilization, monitored attenuation) that couples resource recovery with contamination control, is transferable to analogous Pb–Zn legacy sites, and supports sustainable land use, urban resilience, and responsible secondary-resource use. Full article

Over the past fifty years, urban and rural spaces have been reshaped by global sustainability policies, digital innovation, and emerging socio-ecological needs. This article investigates the convergence of agro-urban planning strategies, Smart City infrastructures, and adaptive governance models, proposing an integrated agro-urban paradigm for sustainable territorial transformation. Drawing on a literature review and comparative analysis of international case studies—including Toronto, Milan, and Woven City—the research develops a triadic interpretative framework based on worldview, program, and faith. The study identifies AgroCities as systems centered on food sovereignty and ecological resilience, Smart Cities as efficiency-driven digital ecosystems, and Adaptive Cities as flexible, human-centered responses to complexity. Findings suggest that integrating food systems, technological innovation, and participatory governance enhances urban resilience and sustainability across scales. The article concludes by advocating for multi-scalar planning tools, cross-sectoral policies, and civic engagement to support the transition toward inclusive and regenerative cities. This framework offers a theoretical and operational contribution to reimagining urban planning in line with the principles of Smart Land and adaptive urbanism. Full article

Carex is among the most species-rich genera of angiosperms and plays important ecological roles in wetlands, alpine regions, and temperate ecosystems worldwide. However, research on this genus has long been challenged by pronounced phenotypic plasticity, reduced floral morphology, frequent hybridization, and complex chromosomal evolution. Although recent advances in molecular phylogenetics, comparative genomics, reproductive biology, and ecophysiology have substantially expanded the knowledge of Carex, these findings remain fragmented across disciplines. Here, we synthesize current evidence on Carex taxonomy and phylogeny, genomic and karyotypic evolution, reproductive and life history strategies, abiotic stress responses, ecosystem functions, and bioresource potential within a cross-scale framework. This review emphasizes how genomic architecture, life history variation, and ecophysiological adaptation jointly shape species diversification and ecosystem functioning, while clarifying their implications for habitat restoration and the sustainable use of Carex resources. Finally, we identify key priorities for future research, including improved phylogenomic resolution, comparative functional studies, climate-resilience assessment, and germplasm conservation and sustainable use. Full article