Search Results (2,831)

Tree pollen allergies are influenced by regional atmospheric pollen concentrations and flora distribution. Climate change and urban landscaping have altered airborne pollen profiles in Ukraine, potentially affecting sensitization patterns. We examined 7518 patients (57.63% children) sensitized to at least one of 26 molecular components from 19 tree species using ALEX testing (2020–2022). Atmospheric pollen data from Ukrainian aerobiology stations were integrated with clinical data. Regional sensitization was mapped using the Geographic Information System, and Bayesian network modeling determined hierarchical relationships. Sensitization to Cry j 1 (46.01%), Bet v 1 (41.67%), and Fag s 1 (34.38%) dominated across age groups. High Fagales sensitization correlated with elevated atmospheric Betula, Alnus, and Corylus pollen concentrations, confirming environmental exposure-sensitization relationships. Bayesian modeling identified Bet v 1 as the root allergen (89.43% accuracy) driving cascading sensitization to other Fagales and non-Fagales allergens. Unexpectedly high Cry j 1 sensitization despite minimal atmospheric Cryptomeria presence suggests Thuja and Ambrosia cross-reactivity. Fagales sensitization dominated 10 of 17 regions, correlating with forest geography and urban landscaping. This study validates aerobiological monitoring’s clinical relevance. Diagnostic protocols should prioritize Bet v 1 while interpreting Cry j 1 positivity as potential cross-reactivity. Climate-driven shifts in atmospheric pollen patterns require ongoing coordinated aerobiological and clinical surveillance. Full article

Climate change-induced extreme weather events increasingly threaten public health, with a particularly acute impact on the mental well-being of urban populations. This study evaluates regional disparities in mental health outcomes associated with climate-induced extreme weather in urban environments, where social and infrastructural vulnerabilities exacerbate environmental stressors. We synthesized data from cohort and cross-sectional studies using both traditional frequentist and Bayesian meta-analytic frameworks to assess the mental health sequelae of extreme weather events (e.g., heatwaves, floods, droughts, and storms). The traditional meta-analysis indicated a significant increase in the odds of adverse mental health outcomes (OR = 1.32, 95% CI: 1.07–1.57). However, this global estimate was characterized by extreme heterogeneity (I² = 95.8%), indicating that the risk is not uniform but highly context-dependent. Subgroup analyses revealed that this risk is concentrated in specific regions; the strongest associations were observed in Africa (OR = 2.23) and Europe (OR = 2.26). Conversely, the Bayesian analysis yielded a conservative estimate, suggesting a slight reduction in odds (mean OR = 0.92, 95% CrI: 0.87–0.98). This divergence is driven by the Bayesian model’s shrinkage of high-magnitude outliers toward the high-precision data observed in resilient, high-income settings (e.g., USA). Given the extreme heterogeneity observed (I² = 95.8%), we caution against interpreting either pooled estimate as a universal effect size. Instead, the regional subgroup findings—particularly the consistently elevated risks in Africa and Europe—offer more stable and policy-relevant conclusions. These findings emphasize urgent, context-specific interventions in urban areas facing compounded climate social risks. Full article

(1) Background: Urban traffic micro-environments show strong spatial and temporal variability. Short and intensive campaigns remain a practical approach for understanding exposure patterns in complex environments, but they need clear and interpretable summaries that are not limited to simple site or time segmentation. (2) Methods: We carried out a multi-site campaign across five traffic-affected micro-environments, where measurements covered several pollutants, gases, and meteorological variables. A machine learning framework was introduced to learn interpretable operational regimes as recurring multivariate states using clustering with stability checks, and then we evaluated their added explanatory value and cross-site transfer using a strict site hold-out design to avoid information leakage. (3) Results: Five regimes were identified, representing combinations of emission intensity and ventilation strength. Incorporating regime information increased the explanatory power of simple NO₂ models and allowed the imputation of missing H₂S day using regime-aware random forest with an $R^2$ near 0.97. Regime labels remained identifiable using reduced sensor sets, while cross-site forecasting transferred well for NO₂ but was limited for PM, indicating stronger local effects for particles. (4) Conclusions: Operational-regime learning can transform short multivariate campaigns into practical and interpretable summaries of urban air pollution, while supporting data recovery and cautious model transfer. Full article

Coastal lakes are highly vulnerable transitional systems in which sedimentological processes and benthic ecological conditions jointly control contaminant accumulation and preservation, particularly in densely urbanized settings. A robust understanding of the physical and ecological characteristics of bottom sediments is therefore essential for the correct interpretation of contaminant distributions, including those of potentially toxic metals. In this study, an integrated sedimentological–ecological approach was applied to Lake Ganzirri, a Mediterranean shallow coastal lake located in northeastern Sicily (Italy), where recent investigations have identified localized heavy metal anomalies in surface sediments. Sediment texture, petrographic and mineralogical composition, malacofaunal assemblages, and lake-floor morpho-bathymetry were systematically analysed using grain-size statistics, faunistic determinations, GIS-based spatial mapping, and bivariate and multivariate statistical methods. The modern lake bottom is dominated by bioclastic quartzo-lithic sands with low fine-grained fractions and variable but locally high contents of calcareous skeletal remains, mainly derived from molluscs. Sediments are texturally heterogeneous, consisting predominantly of coarse-grained sands with lenses of very coarse sand, along with gravel and subordinate medium-grained sands. Both sedimentological features and malacofaunal death assemblages indicate deposition under open-lagoon conditions characterized by brackish waters and relatively high hydrodynamic energy. Spatial comparison between sedimentological–ecological parameters and previously published heavy metal distributions reveals no significant correlations with metal hotspots. The generally low metal concentrations, mostly below regulatory threshold values, are interpreted as being favoured by the high permeability and mobility of coarse sediments and by energetic hydrodynamic conditions limiting fine-particle accumulation. Overall, the integration of sedimentological and ecological data provides a robust framework for interpreting contaminant patterns and offers valuable insights for the environmental assessment and management of vulnerable coastal lake systems, as well as for the understanding of modern lagoonal sedimentary processes. Full article

Cross-regional mega infrastructure (CrMI) is vital for sustaining economic vitality and social connectivity but is increasingly threatened by climate extremes and fragmented management. This study develops a targeted and interpretable evaluation system integrating 5 dimensions and 37 indicators. And social network analysis (SNA) with clustering methods is applied to the Hong Kong–Zhuhai–Macao Bridge as a representative case. Key indicators are classified into “Management Focuses,” “Management Challenges,” and “Management Sensitives,” reflecting varying levels of influence, feedback efficiency, and control capacity. The results reveal that the sustainable operation and maintenance management of CrMI should prioritize economic development while simultaneously strengthening resilience and intelligence. However, environmental protection remains a major challenge, and public attention and inter-regional cooperation are critical for management sensitivity. By embedding resilience intelligence into sustainable evaluation, this study advances sustainability theory and offers a more feasible and forward-looking pathway to sustaining CrMI under conditions of accelerating uncertainty. Full article

Carbon sequestration and oxygen release (CSOR) are core regulating functions of terrestrial ecosystems. However, regional assessments often fail to (i) separate scale-driven high supply from per-area efficiency, (ii) detect structural instability and degradation risk from long-term trajectories, and (iii) provide evidence that is comparable across units for management prioritization. Using Minnesota, USA, we integrated satellite-derived net primary productivity (NPP; 1998–2021) with a Quantity–Intensity–Structure (Q–I–S) framework to quantify CSOR, detect trends and change points (Mann–Kendall and Pettitt tests), map spatial clustering and degradation risk (Exploratory Spatial Data Analysis, ESDA), and attribute natural and human drivers (principal component regression and GeoDetector). CSOR increased overall from 1998 to 2021, with a marked shift around 2013 from a slight, variable decline to sustained recovery. Spatially, CSOR showed a persistent north–south gradient, with higher and improving services in northern Minnesota and lower, more degraded services in the south; persistent degradation was concentrated in a central high-risk belt. The Q–I–S framework also revealed inconsistencies between total supply and condition, identifying high-supply yet degrading areas and low-supply areas with recovery potential that are not evident from the totals alone. Climate variables primarily controlled CSOR quantity and structure, whereas human factors more strongly influenced intensity; the interactions of the two further shaped observed patterns. These results provide an interpretable and transferable basis for diagnosing degradation and prioritizing restoration under long-term environmental change. Full article

Camellia oleifera, a woody oil crop unique to China, plays a crucial role in alleviating the global pressure of edible oil supply and maintaining ecological security. However, it remains challenging to accurately forecast Camellia oleifera yield in complex landscapes using only remote sensing data. The aim of this study is to develop an interpretable deep learning model, namely Shapley Additive Explanations–guided Attention–long short-term memory (SALSTM), for estimating Camellia oleifera yield by integrating an improved spectral bloom index and environmental parameters. The study area is located in Hengyang City in Hunan Province. Sentinel-2 imagery, meteorological observation from 2019 to 2023, and topographic data were collected. First, an improved spectral bloom index (ISBI) was constructed as a proxy for flowering density, while average temperature, precipitation, accumulated temperature, and wind speed were selected to represent environmental regulation variables. Second, a SALSTM model was designed to capture temporal dynamics from multi-source inputs, in which the LSTM module extracts time-dependent information and an attention mechanism assigns time-step-wise weights. Feature-level importance derived from SHAP analysis was incorporated as a guiding prior to inform attention distribution across variable dimensions, thereby enhancing model transparency. Third, model performance was evaluated using root mean square error (RMSE) and coefficient of determination (R²). The result show that the constructed SALSTM model achieved strong predictive performance in predicting Camellia oleifera yield in Hengyang City (RMSE = 0.5738 t/ha, R² = 0.7943). Feature importance analysis results reveal that ISBI weight > 0.26, followed by average temperature and precipitation from flowering to fruit stages, these features are closely associated with C. oleifera yield. Spatially, high-yield zones were mainly concentrated in the central–southern hilly regions throughout 2019–2023, In contrast, low-yield zones were predominantly distributed in the northern and western mountainous areas. Temporally, yield hotspots exhibited a gradual increasing while low-yield zones showed mild fluctuations. This framework provides an effective and transferable approach for remote sensing-based yield estimation of flowering and fruit-bearing crops in complex landscapes. Full article

The main objective of this research was to evaluate the seasonal variability of PM₁₀-bound polycyclic aromatic hydrocarbons (PAHs), their sources, and analyse their health impacts We confirmduring the COVID-19 pandemic period. The chemical composition of PM₁₀ in terms of PAH content was carried out using the gas chromatography-mass spectrometry (GC-MS) technique. PM₁₀ samples were collected in Krakow from 2020 to 2021. A total of 92 samples of particulate matter (PM₁₀ fraction) were analysed. The analyses contained 16 basic PAHs identified by the United States Environmental Protection Agency (U.S. EPA) as the most harmful. The information obtained on the concentrations of PAHs was used to determine the profiles of pollution sources, exposure profiles, and the values of toxic equivalency factors recommended by the EPA: mutagenic equivalent to B[a]P (ang. mutagenic equivalent, MEQ), toxic equivalent to B[a]P (ang. toxic equivalent, TEQ), and carcinogenic equivalent to 2,3,7,8-tetrachlorodibenzo-p-dioxin (ang. carcinogenic equivalent, CEQ). In Kraków, heavy PAHs accounted for over 90% of the total PAHs detected in the PM₁₀ samples. In addition, air trajectory frequency analysis was performed to obtain information on the possibility of transporting pollutants from selected areas in the vicinity of the studied site. Interpreting the trajectory results provided information on the nature of air pollution sources. Analysis of Kraków’s air mass trajectory showed that the highest daily concentration of PM₁₀ in the air flow was from the southwest and east for days. Full article

Automatic Milking Systems (AMSs) enable the continuous recording of production, milkability, behavioral, and physiological traits, offering new opportunities for genetic evaluation in dairy cattle. This study aimed to estimate variance components and genetic parameters for milk yield-related traits, milking efficiency traits, rumination time (RT), and daily average milk temperature (MTEMP) using AMS-derived data from 1252 Holstein cows. 65,475 weekly records from a single commercial herd were analyzed using repeatability animal models fitted by restricted maximum likelihood. Heritability estimates were moderate to high for milking time (MT) (0.31), milking speed (MS) (0.38), RT (0.30), and MTEMP (0.28), whereas behavioral traits such as number of milking (NoM) (0.26) and number of refused (NoREF) (0.11) showed lower but meaningful heritabilities. Repeatability was highest for MT and MS (0.77 and 0.79), indicating consistent milking performance across repeated records. MTEMP demonstrated clear seasonal variation, increasing in warmer periods and decreasing during colder months, indicating sensitivity to environmental conditions. Genetic correlations among traits revealed both favorable and unfavorable associations; however, several estimates were associated with relatively large standard errors and should therefore be interpreted with caution. The inclusion of MTEMP as a proxy physiological trait derived from AMS data showed measurable genetic variation, although its biological interpretation requires careful consideration. Overall, the results suggest that AMS-derived phenotypes may contribute useful information for genetic studies of functional traits, but the single-herd structure, limited pedigree depth, and data aggregation procedures restrict the generalizability of the findings. Further multi-herd and genomics-based studies are required to validate these results and assess their applicability in breeding programs. Full article

The concept of individual cellular intelligence reframes cells as dynamic entities endowed with sensory, reactive, adaptive, and memory-like capabilities, enabling them to navigate lifelong metabolic and extrinsic stressors. A likely vital component of this intelligence system is stress-responsive G protein-coupled receptor (GPCR) networks, interconnected by common signaling adaptors. These stress-regulating networks orchestrate the detection, processing, and experience retention of environmental cues, events, and stressors. These networks, along with other sensory mechanisms such as receptor-mediated signaling and DNA damage detection, allow cells to acknowledge and interpret stressors such as oxidative stress or nutrient scarcity. Reactive responses, including autophagy and apoptosis, mitigate immediate damage, while adaptive strategies, such as metabolic rewiring, receptor expression alteration and epigenetic modifications, enhance long-term survival. Cellular experiences that are effectively translated into ‘memories’, both transient and heritable, likely rely on GPCR-induced epigenetic and mitochondrial adaptations, enabling anticipation of future insults. Dysregulation of these processes and networks can drive pathological states, shaping resilience or susceptibility to chronic diseases like cancer, neurodegeneration, and metabolic disorders. Employing molecular evidence, here, we underscore the presence of an effective cellular intelligence, supported by multi-level sensory GPCR networks. The quality of this intelligence acts as a critical determinant of somatic health and a promising frontier for therapeutic innovation. Future research leveraging single-cell omics and systems biology may unravel the molecular underpinnings of these capabilities, offering new strategies to prevent or reverse stress-induced pathologies. Full article

Semantic segmentation of high-resolution remote sensing imagery is a critical technology for the intelligent interpretation of sensor data, supporting automated environmental monitoring and urban sensing systems. However, processing data from dense urban scenarios remains challenging due to sensor signal occlusions (e.g., shadows) and the complexity of parsing multi-scale targets from optical sensors. Existing approaches often exhibit a trade-off between the accuracy of global semantic modeling and the precision of complex boundary recognition. While the Segment Anything Model (SAM) offers powerful zero-shot structural priors, its direct application to remote sensing is hindered by domain gaps and the lack of inherent semantic categorization. To address these limitations, we propose a dual-branch cooperative network, PriorSAM-DBNet. The main branch employs a Densely Connected Swin (DC-Swin) Transformer to capture cross-scale global features via a hierarchical shifted window attention mechanism. The auxiliary branch leverages SAM’s zero-shot capability to exploit structural universality, generating object-boundary masks as robust signal priors while bypassing semantic domain shifts. Crucially, we introduce a parameter-efficient Scaled Subsampling Projection (SSP) module that employs a weight-sharing mechanism to align cross-modal features, freezing the massive SAM backbone to ensure computational viability for practical sensor applications. Furthermore, a novel Attentive Cross-Modal Fusion (ACMF) module is designed to dynamically resolve semantic ambiguities by calibrating the global context with local structural priors. Extensive experiments on the ISPRS Vaihingen, Potsdam, and LoveDA-Urban datasets demonstrate that PriorSAM-DBNet outperforms state-of-the-art approaches. By fine-tuning only 0.91 million parameters in the auxiliary branch, our method achieves mIoU scores of 82.50%, 85.59%, and 53.36%, respectively. The proposed framework offers a scalable, high-precision solution for remote sensing semantic segmentation, particularly effective for disaster emergency response where rapid feature recognition from sensor streams is paramount. Full article

As digital technologies increasingly permeate urban governance and economic systems, the digital economy (DE) is widely regarded as a key driver of green urban transformation. However, its environmental effects remain complex under the dual constraints of resource dependence (RD) and spatial structure. Drawing on panel data from 277 Chinese prefecture-level cities from 2011 to 2019, this study systematically evaluates the green impacts of the DE across varying resource conditions and urban lifecycle stages. The results reveal a dual-effect pattern: while digitalization significantly promotes local green sustainable development (GSD), it simultaneously suppresses the green performance of neighboring cities through siphoning effects, creating spatial divergence. Cities with lower levels of RD are more likely to benefit from digital dividends, whereas in high-dependence settings, the green effects of digitalization reverse beyond a critical threshold. Grouped regressions for resource-based (RBCs) and non-resource-based cities (NRBCs) further confirm this moderating mechanism. Moreover, lifecycle heterogeneity among RBCs leads to differentiated green outcomes. By introducing the dual mechanisms of “resource lock-in” and “digital siphoning” into the framework of GSD, this study expands the theoretical understanding of the interaction between digitalization and RD. The findings provide empirical support for interpreting the structural divergence in DE–GSD linkages and offer a quantitative basis for differentiated policy strategies in resource-intensive urban contexts. Full article

The Arid Region of Northwest China (ARNC) functions as a critical ecological barrier for the Eurasian hinterland. To clarify the non-linear drivers of eco-environmental dynamics, a long-term (2000–2024) Remote Sensing Ecological Index (RSEI) time series was constructed and analyzed using an interpretable machine learning framework (XGBoost-SHAP). The analysis reveals pronounced spatial asymmetry in ecological evolution: improvements are concentrated in localized, human-managed areas, while degradation occurs as a diffuse process driven by geomorphological inertia. The ARNC exhibits low-level stability (mean RSEI 0.25–0.30) and marked unbalanced dynamics, with significant degradation (19.9%) affecting more than twice the area of improvement (6.5%). Attribution analysis identifies divergent driving mechanisms: ecological improvement (R² = 0.559) is primarily anthropogenic (58.3%), whereas degradation (R² = 0.692) is mainly governed by natural constraints (58.4%), particularly structural topographic factors, where intrinsic landscape vulnerability is exacerbated by human activities. SHAP analysis corroborates a “Greenness-Quality Paradox” in stable agroecosystems, where high vegetation cover coincides with reduced evaporative cooling and secondary salinization from irrigation, resulting in declining Eco-Environmental Quality (EEQ). A zero-threshold effect for grazing intensity is also identified, indicating that any increase beyond the baseline immediately initiates ecological decline. In response, a Resist-Accept-Direct (RAD) framework is proposed: direct salt-water balance regulation in oases, resist hydrological cutoff in ecotones, and accept natural dynamics in the desert matrix. These findings provide a scientific basis for reconciling artificial greening initiatives with hydrological sustainability in water-limited regions. Full article

Conventional acoustic time-of-arrival (TOA) estimation in complex indoor environments is highly susceptible to multipath reflections and occlusions, resulting in unstable measurements and limited physical interpretability. This paper presents a smartphone-based indoor localization method built on vision-assisted acoustic channel modeling, and develops a fusion anchor integrating a pan–tilt–zoom (PTZ) camera and a near-ultrasonic signal transmitter to explicitly perceive indoor geometry, surface materials, and occlusion patterns. First, vision-derived priors are constructed on the anchor side based on line-of-sight reachability, orientation consistency, and directional risk, and are converted into soft anchor weights to suppress the impact of occlusion and pointing mismatch. Second, planar geometry and material cues reconstructed from camera images are used to generate probabilistic room impulse response (RIR) priors that cover the direct path and first-order reflections, where environmental uncertainty is mapped into path-dependent arrival-time variances and prior probabilities. Finally, under the RIR prior constraints, a path-wise posterior distribution is built from matched-filter outputs, and an adaptive fusion strategy is applied to switch between maximum a posteriori (MAP) and minimum mean square error (MMSE) estimators, yielding debiased TOA measurements with calibratable variances for downstream localization filters. Experiments in representative complex indoor scenarios demonstrate mean localization errors of 0.096 m and 0.115 m in static and dynamic tests, respectively, indicating improved accuracy and robustness over conventional TOA estimation. Full article

To achieve the highest efficiency from the turbines used in wind power plants, the region where the plant will be located must meet the appropriate conditions. One of these conditions, and the most important, is that the wind potential be above the critical value for energy production and be continuous. Locations that meet these conditions contribute positively to energy production and produce high efficiency. Based on the interpreted data, temperature, wind direction, and wind speed data from three turbines located at altitudes of 432, 454, and 492 m in the Sebenoba area of Yayladağ, Hatay, where wind potential is high, were collected at 10 min intervals between 1 January 2017, and 19 September 2018, yielding a total of 50,986 data points. Wind speed was estimated for this region using temperature, wind direction, and time information. Daily, monthly, and seasonal analyses were used to generate forecasts for the three altitudes. Wind speed was estimated using Decision Tree Regression and 10-Fold Cross Validation methods, and Root Mean Square Error (RMSE) values were found to be 0.64917, 0.66629, and 0.59954 for the three altitudes, respectively; the overall RMSE value was found to be 0.60188. RMSE values decreased in daily, monthly, and seasonal analyses, and an inverse relationship existed between wind speed and RMSE. Analysis of these results indicated that the forecast model was suitable. This study supports sustainability by enabling accurate wind speed forecasting for optimal turbine placement, improving energy efficiency, and promoting long-term environmentally and economically sustainable wind energy planning. Full article