Search Results (241)

Atmospheric aerosols influence the terrestrial carbon cycle through diverse radiative and biogeochemical effects, yet their net impact on vegetation productivity remains contentious and region-specific. To address this, we analyzed the spatiotemporal coupling between aerosol optical depth (AOD) and net primary productivity (NPP) over three African biomes (2013–2023), using multi-source datasets (MODIS, CERES, ERA5, CRU TS). We explicitly distinguished statistically significant relationships (p < 0.05) from non-significant ones when interpreting correlation patterns. Because AOD is an optical measure and does not provide aerosol composition, interpretations involving dust versus smoke are treated as qualitative and indirect. Through structural equation modeling (SEM), we identified two contrasting mechanistic pathways: in the humid Congo Basin rainforest, aerosols were associated with lower NPP via a cooling-mediated pathway (increased cloud albedo leading to reduced temperature and light availability), whereas in the arid savanna, they were associated with more substantial limitations on NPP via a warming-aggravated pathway (increased temperature and potentially coupled water stress). SEM fit was poor for the semi-arid South African plateau, underscoring the dominant role of water availability in strongly water-limited systems. This framework reconciles the paradox of dual aerosol effects by demonstrating that the net impact is dictated by regional climate context. Overall, our conclusions emphasize context-dependent associations rather than direct causal attribution from correlations alone. Our findings provide a process-based understanding that is critical for improving carbon cycle models and for formulating targeted climate adaptation strategies in Africa. Full article

As the largest urban agglomeration and a critical grain production base in northern China, the Beijing–Tianjin–Hebei (BTH) region faces a sharp conflict between rapid urbanization and cropland conservation. Urban expansion inevitably leads to the loss of high-quality agricultural land, posing dual threats to food security and the terrestrial carbon cycle. To accurately assess the ecological costs of this process, this study integrates the CASA model with a time-weighted cumulative model to quantify the spatiotemporal impacts of urban expansion on cropland NPP in the BTH region from 2001 to 2020. Furthermore, a Geographically Weighted Regression (GWR) model was employed to examine the spatially varying effects of key driving factors on cropland NPP loss. The results indicate that urban land in the BTH region expanded by 45.2% over the past two decades, with 91.04% originating from cropland. Despite an overall upward trend in regional cropland NPP driven by climate change and agricultural intensification, the time-weighted cumulative cropland NPP loss attributable to urban encroachment over 2001–2020 reached 29.24 Tg C, which is equivalent to 0.751× the annual total cropland NPP in 2020 (used as a reference benchmark). Crucially, this expansion exhibits distinct ecological selectivity toward high-quality cropland, meaning that urban development has disproportionately encroached upon highly productive land with productivity levels exceeding the regional average. This selective occupation has led to a structural decline in the region’s potential agricultural production capacity. Additionally, GWR results reveal significant spatial non-stationarity in the relationships between cropland NPP loss and its drivers, revealing differentiated response patterns between plains and mountainous areas in terms of socio-economic drivers and physical constraints. These findings expose the hidden threats of urban expansion to food security, providing a crucial scientific basis for formulating differentiated land management policies and coordinating regional urbanization with cropland protection. Full article

The Italian National Prevention Plan (NPP) 2020–2025 calls for a joint action on environmental and urban determinants of health. The recent reforms of primary health care (DM 77/2022) highlight the role of communities and Local Health Authorities in the promotion of health in everyday settings. However, practical tools which link prevention planning to small-scale urban transformations still remain poorly described. This study explores how international approaches to children’s school-travel and urban participatory practice in street design can guide the next cycle of the NPP. An extensive review of the available international grey literature and technical guidelines identified ten operational documents (toolkits, guidelines and practice-oriented reports) addressing two categories of interventions: (1) school-travel and “school streets” schemes and (2) tactical urbanism and placemaking initiatives. Each document was then evaluated using an adapted Urban HEART framework, expanded with a sixth domain, “Applicability to the Italian National Health Service”. They all scored qualitatively (1–5) across the six domains. The analysis shows consistently high scores for Health, Physical Environment, Participation and Governance, particularly with regard to school street toolkits and child-friendly street design guides. Equity and formal links to health-system planning and evaluation remain less systematically developed. Overall, findings suggest that school-travel interventions and child-centred placemaking around the schools are closely aligned with the logic and tools outlined in the NPP. These could be considered as potential prevention actions in the future NPP cycles, provided that explicit health outcomes, minimum indicators and stable intersectoral governance arrangements are co-designed with the Local Health Authorities. Full article

Forest carbon sinks are central to climate change mitigation, and prior work has established a solid basis for assessing carbon sinks at regional scales. At the basin scale, however, forest carbon density (vegetation biomass carbon density, i.e., aboveground + belowground biomass carbon; t C ha⁻¹) often shows pronounced spatial clustering and inequality, while its temporal evolution and underlying mechanisms remain poorly quantified and interpreted for management-relevant units such as townships. Using the Xiuhe River Basin as a case study and townships as the basic analytical units, this study identifies the clustered spatial structure and inequality characteristics of forest carbon density and clarifies the joint effects of natural constraints and human disturbances, including potential threshold responses. We first assessed global spatial autocorrelation within a spatial weights framework using Global Moran’s I with permutation tests, and delineated local clustering by classifying local indicators of spatial association (LISA) types based on Local Moran’s I. We then measured the magnitude and stage-wise evolution of inter-township disparities using the Gini coefficient and the Theil T index. Finally, we applied GeoDetector factor, interaction, and risk detection to identify dominant drivers, interaction enhancement, and class-based contrasts. The results show significant and persistent positive spatial autocorrelation in forest carbon density from 2002 to 2024, with Moran’s I ranging from 0.68786 to 0.73849 (p < 0.01). Significant LISA units account for 40.74%–45.37% of townships, and the pattern is dominated by high–high (HH) and low–low (LL) clusters. Inequality follows a stage-wise trajectory: it expanded slightly during 2002–2019, converged markedly during 2019–2021, and rebounded modestly by 2024, while remaining below the levels observed in 2002 and 2019. Strong type-based differentiation is evident in 2024: mean carbon density is 46.06 t C ha⁻¹ in HH areas versus 17.64 t C ha⁻¹ in LL areas; HH areas contribute 38.44% of total carbon stock, whereas LL areas contribute only 5.08%. In terms of drivers, natural and human factors jointly shape the spatial pattern and commonly exhibit interaction enhancement. Elevation (q = 0.7832), slope (q = 0.7133), and NPP (q = 0.6373) are the leading natural constraints, while population density (q = 0.6054) and the built-up land ratio (q = 0.5374) are key indicators of human disturbance. Risk detection further indicates a stable negative gradient for the built-up land ratio and nonlinear class differences for population density, implying that once disturbance intensity reaches higher levels, low-value clustering is more likely to persist. By linking clustered spatial structure, stage-wise inequality, and disturbance-related threshold signals, our results support basin-scale zoning and differentiated management at the township level. Specifically, HH clusters should be prioritized for conservation and connectivity maintenance, whereas LL clusters warrant stricter control of built-up expansion and fragmentation to reduce the risk of persistent low-carbon locking under high disturbance. By linking spatial structure, inequality dynamics, and threshold responses, this study provides a quantitative basis for basin-scale zoning to enhance carbon sinks and for implementing differentiated spatial controls. Full article

Unidirectional multilevel back-to-back (BTB) converters are widely employed in renewable energy generation systems and in motor drives for coal mining operations. However, the current zero-crossing distortion (CZCD) on the grid side and the neutral-point potential (NPP) imbalance on the common DC bus all restrict its applicability, such as in grids with stringent low harmonic requirements and in medium to high power situations. This paper proposes a coordinated control strategy to simultaneously address these issues theoretically. The study focuses on topology comprising a Vienna rectifier structure on the grid side and a three-level NPC inverter structure on the load side. In the proposed strategy, the current distortion angle, the manifestation of CZCD, is first eliminated by reactive current compensation on the Vienna rectifier side. Furthermore, the coupling between CZCD and NPP imbalance is resolved by reconstructing the neutral-point current target function. Ultimately, the optimal zero-sequence voltage (ZSV) is obtained using an interpolation function and then injected into the three-phase reference voltages of the inverter side to balance the NPP on the DC bus. The strategy transforms the influence of the rectifier on the NPP from an unknown coupling factor into a known disturbance and enables the inverter to actively compensate for variations in the overall converter system. An experimental platform was independently developed to verify the effectiveness of the proposed control strategy. Full article

The heat island effect and air stagnation issues caused by high-density built-up areas are becoming increasingly severe. Optimising urban ventilation structures and establishing ventilation corridors have become key approaches to improving the urban thermal environment and enhancing liveability. However, traditional methods for constructing ventilation corridors often rely on empirical weighting or linear models, which struggle to accurately reveal the resistance coefficients of resistance indicators and fail to reflect the threshold at which indicators transition between positive and negative impacts. Consequently, this study employs Shanghai, China, as a case study, integrating machine learning models with the minimum cost path (MCR) model. Key variables were screened through multiple linear regression and variance inflation factor (VIF) analysis. Subsequently, machine learning models were compared to select the optimal model, with parameter optimisation conducted using Optuna, followed by computational implementation. The results indicate that built environment factors (such as building height, shape complexity, and road density) exert a significantly greater influence on ventilation potential than natural green space factors. By introducing the SHAP method, the positive and negative effects of each indicator on the ventilation environment and their threshold relationships were revealed. Negative indicators were converted into ventilation resistance factors to construct a resistance surface. Building upon this, cold and heat sources were identified using LST, NPP, and population density data. The MCR model was then employed to calculate the minimum resistance paths from cold to heat sources, forming an urban ventilation corridor network. The results indicate that primary corridors align with prevailing wind directions, following urban rivers and low-density green spaces. This study reveals the nonlinear effects of building and green space elements on ventilation systems, proposing machine learning-based optimisation strategies for ventilation corridors. It provides quantitative decision support for mitigating the urban heat island effect and enhancing city livability. 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

Excessive application of chemical fertilisers has led to soil phosphorus immobilisation and aquatic eutrophication, making the development of highly efficient acid/neutral phosphatases crucial for sustainable phosphorus utilisation. In this study, we systematically investigated strain J2, which was isolated from phosphate-contaminated soil in Laoshan, Nanjing, China. 16S rRNA gene sequence analysis identified this strain as Acinetobacter nosocomialis J2, with 99.78% sequence similarity. Whole-genome sequencing generated a 3.83 Mb genome with a GC content of 38.59%, revealing multiple phospho-metabolism-related enzyme genes, including phospholipase C and α/β-hydrolases. A large language model–based protein representation learning strategy was employed to mine acid/neutral phosphatase genes from the genome, in which the model learned contextual and functional features from known phosphatase sequences and was used to identify semantically similar genes within the J2 genome. This approach predicted nine phosphatase candidate sequences, including AnACPase, a putative acid/neutral phosphatase. Biochemical characterisation showed that AnACPase exhibits optimal activity at pH 6.0 and 50 °C, with a K_m value of 0.2454 mmol/L for the p-NPP substrate, indicating high substrate affinity. Mn²⁺ and Ni²⁺ significantly enhanced enzyme activity, whereas Cu²⁺ and Zn²⁺ strongly inhibited it. Soil remediation experiments further validated the application potential of AnACPase, which solubilised 171.56 mg/kg of phosphate within seven days. Overall, this study highlights the advantages of deep learning-assisted genome mining for functional enzyme discovery and provides a novel technological pathway for the bioremediation of phosphorus-polluted soils. Full article

Nuclear Power Plant (NPP) site preparation in tropical regions faces significant schedule and cost risks due to rainfall, which are often addressed with inadequate and unspecified contingencies. This study develops an integrated framework to address these issues by converting multi-year daily rainfall data into auditable seasonal risk inputs for project simulations. The methodology involves synthesizing rainfall data from multiple stations with quality weighting, mapping rainfall to Lost Time Hours (LTH) using a double logistic function, and applying time–cost co-sampling analysis in Primavera Risk Analysis. Applied to the Indonesian case study, the framework predicts an increase in P80 duration of 36 days, or 10.17%, and an increase in cost of USD 64,809, or 8.41%. This analysis reveals that the raw rainfall index is only weakly correlated with delays and cost overruns at the project level, because the network structure and monthly usage levels filter out the weather signal; this weak correlation and the systematic time–cost decoupling encourage comprehensive network simulations rather than simply accounting for uniform weather allowances. This methodology has potential applications for site preparation activities and other types of infrastructure. However, validation on external datasets and calibration to local climate and operational contexts remain critical future steps. This framework provides a transparent and replicable approach to converting local climate data into project-specific contingency data, improving schedule reliability and cost control for construction projects in tropical regions. Full article

The Qinling–Bashan mountainous region and its surrounding areas in Shaanxi Province constitute a critical ecological security barrier and significant socio-economic zone within China, currently experiencing mounting ecological stress from both natural processes and anthropogenic activities. This study proposes an ecological restoration zoning framework built upon assessments of ecological vulnerability (EV) and ecosystem service value (ESV). The InVEST model was used to quantify major ecosystem services, while the Vulnerability Scoping Diagram (VSD) model evaluated ecological vulnerability. Both the ESV and EV layers were classified using the natural breaks method and aggregated at the township level to delineate restoration zones. Unlike previous studies relying on subjective judgment, this study constructs a standardized ‘vulnerability–service value’ decision matrix for the Qinling–Bashan region, providing a clear technical pathway for spatial restoration. Key findings include the following: (1) Spatial Vulnerability Pattern: The Qinling and Bashan mountain cores exhibit predominantly low vulnerability (potential and slight), while severe vulnerability is concentrated in the urbanizing Guanzhong Plain, emphasizing the need for urban ecological restoration. (2) Dominant Ecosystem Services: Carbon storage and net primary productivity (NPP) together account for 93% of the total ESV, highlighting the importance of forest conservation for national climate regulation. (3) Zoning Strategy: Four functional zones were defined, with the largest being the ecological conservation zone (44.8%), while a smaller ecological restoration zone (2.8%) in urban peripheries requires targeted intervention. Full article

The data collection and analytical capabilities of the Instrumentation and Control (I&C) system in nuclear power plants (NPPs) continue to advance, thereby enhancing operational state awareness and enabling more precise control. However, the data acquisition, transmission, and storage devices in nuclear power plant (NPP) I&C systems typically operate in harsh environments. This exposure can lead to device failures and susceptibility to external interference, potentially resulting in data anomalies such as missing samples, signal skipping, and measurement drift. This paper presents a Gated Recurrent Unit and Multilayer Perceptron (GRU-MLP)-based method for anomaly detection and correction in NPP I&C system data. The goal is to improve operational data quality, thereby supplying more reliable input for system analysis and automatic controllers. Firstly, the short-term prediction algorithm of operation data based on the GRU model is studied to provide a reference for operation data anomaly detection. Secondly, the MLP model is connected to the GRU model to recognize the difference between the collected value and the prediction value so as to distinguish and correct the anomalies. Finally, a series of experiments were conducted using operational data from a pressurized water reactor (PWR) to evaluate the proposed method. The experiments were designed as follows: (1) These experiments assessed the model’s prediction performance across varying time horizons. Prediction steps of 1, 3, 5, 10, and 20 were configured to verify the accuracy and robustness of the data prediction capability over short and long terms. (2) The model’s effectiveness in identifying anomalies was validated using three typical patterns: random jump, fixed-value drift, and growth drift. The growth drift category was further subdivided into linear, polynomial, and logarithmic growth to comprehensively test detection performance. (3) A comparative analysis was performed to demonstrate the superiority of the proposed GRU-MLP algorithm. It was compared against the interactive window center value method and the ARIMA algorithm. The results confirm the advantages of the proposed method for anomaly detection, and the underlying reasons are analyzed. (4) Additional experiments were carried out to discuss and verify the mobility (or transferability) of the prediction algorithm, ensuring its applicability under different operational conditions. Full article

The marine environment in the South Pacific Island Countries (SPICs) is sensitive and vulnerable to climate change. While large-scale changes in this region are well-documented, national-scale analyses that address management needs remain limited. This study evaluated the performance of satellite-derived datasets—including sea surface temperature (SST), sea surface salinity (SSS), Secchi disk depth (SDD), chlorophyll-a (Chl-a), net primary production (NPP), and sea level anomaly (SLA)—against in situ observations, and analyzed their spatial and temporal variability across 12 national Exclusive Economic Zones (EEZs) during 1998–2023. Validation results presented that current satellite datasets could provide applicable information for EEZ-scale analyses. In the past decades, the SPICs experienced a general increase in SST and SLA, accompanied by marked within-EEZ heterogeneity in Chl-a and NPP variations, with Papua New Guinea exhibiting the largest within-EEZ inter-annual variability. In addition to monitoring, satellite data would help to constrain the uncertainty of CMIP6 results in the SPICs, subject to the accuracy of specific products. By 2100, Nauru might experience the most vulnerable EEZ, while the marine environment in the French Polynesian EEZ can keep relatively stable among all 12 EEZs. Meanwhile, CMIP6 projections in the Southeastern EEZs are more sensitive to satellite-based constraints, showing pronounced adjustments. Our results demonstrate the potential of combining validated satellite data with CMIP6 models to provide national-scale decision support for climate adaptation and marine resource management in the SPICs. Full article

Neuropathic pain (NPP) remains therapeutically challenging, with oxidative/nitrosative stress and neuroinflammation—amplified by nitric oxide (NO)—as key drivers. This study investigated geraniin (GRN), a naturally occurring hydrolyzable ellagitannin widely distributed in various plant species, including Phyllanthus spp. and Nephelium lappaceum (rambutan), in a rat model of sciatic nerve chronic constriction injury (CCI), focusing on NO-pathway involvement. Male Wistar rats (n = 8/group) received intraperitoneal GRN (3, 10, 30, or 100 mg/kg) or vehicle (1% DMSO in saline) daily for 21 days. Behavioral (thermal hyperalgesia, mechanical allodynia, sciatic functional index), electrophysiological (nerve conduction velocity), and biochemical markers—oxidative/nitrosative stress (nitrite, MDA), antioxidant defenses (GSH, SOD, CAT), inflammation (TNF-α, IL-1β, IL-6, MPO), and apoptosis (caspase-3)—were quantified. L-arginine or L-NAME was co-administered to probe NO signaling. GRN at 30 and 100 mg/kg produced significant antinociceptive and neuroprotective effects; 30 mg/kg was selected for detailed analysis. By day 21, GRN improved pain thresholds and nerve conduction, enhanced antioxidant capacity, suppressed inflammatory mediators, and reduced caspase-3 activity. L-arginine reversed, whereas L-NAME potentiated these effects, confirming NO-dependent modulation. Collectively, GRN mitigates CCI-induced NPP via coordinated antioxidant, anti-inflammatory, and anti-apoptotic actions, supporting its potential as a multi-target candidate for pharmacokinetic and translational development. Full article

This study presents a graph-based framework for improving predictive maintenance in nuclear power plants (NPPs), integrating data balancing techniques with a proposed Graph Attention Network (GAT) with a Mutual k-Nearest Neighbor (Mk-NN) strategy, named GAT-Mk-NN. To enhance the system’s ability to discriminate between genuine faults and sensor anomalies, we introduce a novel procedure for generating synthetic false positives that simulate realistic sensor failures. To mitigate class imbalance, we employ structured oversampling and multiple synthetic data generation strategies. Our results demonstrate that our GAT-Mk-NN model achieves the best trade-off between accuracy and computational efficiency, reaching an F1-score of 0.882 and an accuracy of 0.884. Performance analysis reveals that low to moderate graph connectivity enhances both robustness and model generalization. Our GAT-Mk-NN model structure outperformed other state-of-the-art graph architectures (enhanced GCN, GraphSAGE, GIN, graph transformer, ChebNet, TAG, ARMA graph, simple GCN, GATv2, and hybrid GNN). The findings highlight the potential of graph-based learning for fault detection in sensor-dense industrial environments, offering actionable insights for deploying fault-tolerant diagnostics in critical systems. Full article

While climate is known to regulate forest productivity, the mechanistic contribution of soil microbial communities—and whether it differs between natural and plantation forests—remains poorly quantified at broad scales. Here, we provide a synthesis-level, unified analysis that jointly evaluates climate, edaphic conditions, and soil microbes to compare mechanistic pathways underlying productivity divergence between forest types. We synthesized 237 observations across China and integrated productivity metrics—gross primary productivity (GPP) and net primary productivity (NPP)—with microbial diversity, dominant taxa, and soil drivers to compare natural and plantation forests within the current environmental coverage. Plantation productivity showed nonlinear responses to microbial diversity and appeared more sensitive than natural forests. Natural forests exhibited higher bacterial Shannon and Chao1 but lower fungal Chao1 and were characterized by taxa such as Nitrobacter, Bradyrhizobium, and Cortinarius. In contrast, plantations were characterized by taxa often associated with disturbance tolerance and opportunistic life-history strategies (e.g., Sphingomonas, Fusarium, Gemmatimonas), consistent with potential functional simplification. Structural equation models identified climate as the strongest correlate of productivity, while soil properties showed contrasting associations with microbial diversity across forest types. Random forest models further highlighted cation-exchange capacity and total nitrogen as key predictors of microbial diversity in plantations. Overall, our results indicate that soil microbial communities are differentially associated with forest productivity across forest types and environmental contexts and underscore the need for future climate-comparable designs and management-intensity information to more robustly isolate microbial contributions. Full article