Search Results (1,316)

Mechanical, electrical, and plumbing (MEP) pipe installation discrepancies can increase rework, complicate inspection, and affect subsequent operation and maintenance. This study presents a 3D laser scanning and Building Information Modeling (BIM)-based workflow for localizing and preliminarily classifying MEP pipe installation discrepancies in a building project. Preprocessed scanned pipe point clouds are registered with BIM-derived pipe point clouds through a coarse-to-fine Scan-BIM registration process. Individual pipe instances are extracted using distance-threshold-based growing, and scan-to-BIM pipe correspondence is established using nearest-neighbor root mean square error (RMSE). Pipes with relatively large overall RMSE values are further divided into slices to identify local high-discrepancy intervals. A slice-level discrepancy distribution function R(s), together with derivative-magnitude and derivative-fluctuation thresholds, is used to support preliminary Type 1/Type 2 interpretation of representative discrepancy patterns. In a student dormitory case, the workflow screened local pipes with relatively large discrepancies, localized maximum-RMSE regions, and distinguished representative connection-related discrepancies from overall offset or inclination cases. A threshold perturbation check showed consistent Type 1/Type 2 labels for the four representative cases within the tested range. The workflow provides case-study evidence for localized MEP pipe inspection, while broader validation across projects and pipe systems remains necessary. Full article

Background: Tongue diagnosis is a fundamental diagnostic method in traditional medicine. Studies restricted to single ethnic groups may introduce bias and limit the clinical applicability of digital tongue diagnosis across diverse populations. Objectives: This study examined differences in tongue image features between Japanese and Brazilian (Caucasian ancestry) participants using digital tongue diagnosis technology and explored potential influencing factors. Methods: Tongue images were collected from 143 Japanese and 116 Brazilian participants attending traditional medicine clinics in Japan and Brazil. An independently developed tongue image analysis system (TIAS) was employed to extract shape, texture (gray level co-occurrence matrix), color (L*a*b color space), and deep-learning derived features (crack, prickle, tooth-mark, peel, greasy coating, stasis). Statistical analyses and machine learning models with SHAP explainability were used to compare features and identify key classification parameters. Results: Significant inter-group differences were observed in tongue shape, texture parameters (particularly at the root and tip), color parameters (especially middle-a-mean, middle-b-mean, tip-a-mean, and tip-b-mean), and deep features. The Japanese group showed a markedly higher prevalence of greasy coating (72.03% vs. 41.38%, p < 0.001) and stasis. Machine learning analysis revealed that the b value in the middle region of the tongue (middle-b-mean) contributed most strongly to the classification of greasy coating. Conclusions: The digital tongue image analysis system enables accurate and objective quantification of tongue features. Pronounced ethnic differences exist, particularly in the distribution of greasy coating. The middle-b-mean has the greatest impact on greasy coating classification. These findings underscore the importance of considering ethnic background when developing digital tongue diagnosis systems. Full article

P. massoniana is an important native economic and ecological tree species in southern China, where seasonal drought has emerged as a critical factor limiting its productivity. The SAUR gene family, recognized as core early auxin-responsive genes, plays a crucial role in balancing plant growth, development, and stress adaptation; however, research related to this family in conifers remains limited. Utilizing the chromosome-level genome of P. massoniana, this study identified 73 SAUR genes (PmSAUR1~73) through bioinformatics methods, systematically analyzing the physicochemical properties of the encoded proteins, chromosomal localization, phylogenetic relationships, gene structures, and cis-acting elements. Combined with transcriptome sequencing and molecular experiments, the drought stress response patterns of these genes were further elucidated. The results indicated that PmSAUR genes predominantly encode alkaline proteins, primarily localized in mitochondria and nuclei, with an uneven distribution across nine chromosomes, where tandem duplication serves as the primary mechanism driving family expansion. Phylogenetic analysis classified these genes into seven subfamilies, which include both conserved clades homologous to angiosperms and branches specific to P. massoniana. All members contain the Auxin_inducible conserved domain, with motif1 identified as the core essential motif. Promoter regions were enriched with MeJA (methyl jasmonate)-responsive (56%), ABA-responsive, and drought stress-related cis-elements. Under drought stress, 38 PmSAUR genes exhibited diverse temporal expression patterns. Four key genes (PmSAUR14, PmSAUR28, PmSAUR54, and PmSAUR73), which are localized in the nucleus and exhibit high expression specifically in male cones or roots, were identified. These genes exhibit an expression pattern consistent with an auxin-negative response (i.e., repressed by IAA and induced by drought) and display a distinctive response pattern characterized by drought-induced upregulation coupled with IAA-mediated downregulation. This mechanism may contribute to the drought adaptation strategies of P. massoniana, involving regulatory processes for aboveground reproduction and adaptation of the underground root system. This study represents the first effort to elucidate the evolutionary characteristics and drought response patterns of the SAUR gene family in P. massoniana, thereby addressing the existing research gap regarding the functions of SAUR genes in coniferous trees. Furthermore, it offers candidate gene resources and theoretical support for the molecular breeding of stress resistance in P. massoniana. In addition, two auxin-induced SAUR genes (PmSAUR22 and PmSAUR37) were identified as contrasting examples, but the main focus of this study is on the four auxin-repressed genes. Full article

Background: Uridine diphosphate glucose (UDP-Glc) is one of the key substrates for the biosynthesis of gallotannins in plants. UDP-glucose dehydrogenase (UGD) catalyzes the irreversible oxidation of UDP-Glc to UDP-glucuronic acid (UDP-GlcA), thus affecting the biosynthesis and accumulation of gallotannins in the Chinese gallnut. Methods and Results: In this study, we identified three members of the RcUGD family from the Rhus chinensis genome. Protein sequence alignment revealed that all three RcUGDs possess the conserved NAD⁺ coenzyme binding motif GAGYVGG and the catalytic motif GFGGSCFQKDIL. qRT-PCR analysis revealed that the expression levels of RcUGD3 in stem and root tissues were respectively 10-fold and 13-fold greater than that in the leaves, in which gallotannin accumulation was higher. RcUGD3 expression level declined by 63% during early (24 d) gallnut development, suggesting an inverse relationship between RcUGD3 expression level and gallotannin biosynthesis. In addition, subcellular localization analysis using the tobacco transient transformation system showed that RcUGD proteins are broadly distributed throughout the cell. Moreover, an in vitro enzyme activity assay indicated that the recombinant RcUGD3 protein catalyzed UDP-Glc to produce UDP-GlcA as shown by HPLC. Taken together, our results suggested that RcUGD3 protein is responsible for UDP-Glc degradation and probably plays a regulatory role in gallotannin biosynthesis in the Chinese gallnut. Conclusions: This study lays a foundation for further elucidating the function and expression regulation mechanism of the RcUGD gene family and provides new insights for the super-accumulation mechanisms of gallotannins in Chinese gallnuts. Full article

This study investigates the relationship between energy consumption and ecological sustainability in South Africa over the period 1990–2023, with a particular focus on the roles of renewable energy consumption, non-renewable energy consumption, and economic growth. Ecological sustainability is proxied by the Load Capacity Factor (LCF), a comprehensive measure that captures the balance between biocapacity and environmental pressure. The study employs the Nonlinear Autoregressive Distributed Lag (NARDL) model to capture both short-run and long-run asymmetric effects, decomposing renewable energy consumption into positive and negative shocks to identify nonlinear dynamics. Descriptive statistics reveal moderate stability in the LCF, increasing adoption of renewable energy, sustained economic growth, and persistent dependence on fossil fuels. Unit root tests confirm mixed integration orders, justifying the use of the NARDL framework. Empirical results indicate that positive shocks in renewable energy consumption significantly enhance ecological sustainability, while negative shocks reduce the LCF, highlighting the asymmetric impact of renewable energy. Non-renewable energy consumption exhibits a statistically significant long-run association with ecological sustainability, reflecting South Africa’s continued structural dependence on fossil-fuel-based energy systems during the study period. Granger causality tests show that renewable energy and non-renewable energy consumption are key drivers of ecological sustainability, whereas economic growth and environmental conditions exhibit bidirectional feedback. The findings provide evidence for the strategic importance of promoting renewable energy adoption, reducing fossil fuel reliance, and integrating sustainability considerations into economic planning. Policy recommendations emphasize investment in renewable energy infrastructure, incentives for green energy adoption, and the integration of environmental objectives into economic development strategies to enhance South Africa’s ecological resilience. Full article

Accurate forest height reference points are essential for large-scale forest canopy mapping and carbon stock estimation. Currently, spaceborne Light Detection and Ranging (LiDAR) systems, primarily GEDI and ICESat-2, serve as the main data sources for acquiring global forest height reference points. To ensure data quality, conventional processing often relies on strict physical parameter filtering, such as retaining only nighttime and strong (full power) beam observations, which considerably reduces the available data density. Moreover, gross errors caused by signal attenuation or solar background noise often remain, limiting the accuracy of subsequent spatial modeling. To address the trade-off between measurement accuracy and data density, this study proposes a physically constrained outlier filtering strategy for spaceborne LiDAR retrievals, assisted by a priori continuous canopy height model (CHM) products. Aiming to maximize data retention, this method introduces a morphologically consistent global continuous CHM (such as the 10 m Pauls CHM) as a prior spatial envelope. By calculating the local height difference distribution and applying a 1$\sigma$ adaptive truncation, outliers are effectively removed. Comparative validations in the Genhe (coniferous forest, China) and HARV (mixed broadleaf forest, USA) study areas indicate that: (1) traditional filtering results in a data loss of over 80% while yielding limited accuracy; (2) after relaxing the initial filtering conditions, the proposed strategy reduces the overall root mean square error (RMSE) of GEDI and ICESat-2 retrievals by 12.6% to 36.0%; (3) owing to the effective removal of gross errors, the conventionally discarded daytime and weak (or coverage) beam data achieve substantially reduced error levels, sometimes even lower than those of traditional nighttime strong beam observations. Consequently, the spatial density of high-quality reference points is increased by 1.5 to 4.4 times. This study demonstrates the application value of low signal-to-noise ratio (SNR) spaceborne observations and provides a practical approach for obtaining high-quality, high-density control points for large-scale forest structure mapping. Full article

This study examines the determinants of carbon dioxide (CO₂) emissions across 25 European Union countries over the period 2000–2021, with particular emphasis on the roles of environmental taxation and green innovation in shaping environmental sustainability. The analysis is grounded in ecological modernization theory, endogenous growth theory, and the Environmental Kuznets Curve hypothesis, which collectively explain the long-run and dynamic interactions between environmental policy, economic activity, structural transformation, and environmental outcomes. To ensure robust empirical inference, this study applies a comprehensive econometric framework that accounts for cross-sectional dependence, heterogeneity, non-stationarity, cointegration, and endogeneity. The empirical strategy begins with Pesaran cross-sectional dependence tests and slope heterogeneity diagnostics, followed by second-generation panel unit root tests (Pesaran CADF/CIPS) and Westerlund cointegration tests to establish the existence of long-run equilibrium relationships among the variables. Long-run coefficients are estimated using Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), Canonical Cointegrating Regression (CCR), and Common Correlated Effects Mean Group (CCEMG) estimators. In addition, the Panel Autoregressive Distributed Lag (ARDL) model is employed to capture both short-run dynamics and long-run adjustment processes, while the System Generalized Method of Moments (System GMM) estimator addresses potential endogeneity, reverse causality, omitted variable bias, and dynamic persistence in CO₂ emissions. The empirical results indicate that environmental taxation has a positive and statistically significant association with CO₂ emissions, suggesting that current fiscal environmental policies in EU-25 countries may not yet be sufficiently effective in discouraging pollution-intensive activities. In contrast, green innovation is found to significantly reduce CO₂ emissions, underscoring the critical role of innovation-driven environmental investment and technological progress in improving environmental quality. Economic growth, exports, and urbanization are associated with higher emissions, while imports contribute to emission reductions, reflecting differences between domestic production-based effects and trade-related structural adjustments. The System GMM results further confirm the persistence of CO₂ emissions over time and validate the robustness of the long-run relationships identified by alternative estimators. Likewise, the CCEMG and Panel ARDL results support the stability and consistency of the findings under conditions of cross-sectional dependence and heterogeneous country dynamics. Taken together, the results highlight the importance of integrating environmental taxation with green innovation policies, innovation-driven investment, and sustainable trade policies to achieve long-term emission reductions in the European Union. This study contributes to the environmental economics literature by providing robust empirical evidence using second-generation panel econometric techniques that explicitly address cross-sectional dependence, heterogeneity, and endogeneity in the analysis of environmental sustainability. Full article

Cirsium arvense (L.) Scop is a perennial plant of the family Asteraceae that is mainly distributed in the temperate regions of the Northern Hemisphere. Despite being widely recognized as an invasive weed in agriculture, most of the scientific evidence shows its significant phytochemical and pharmacological importance. In the present review article, a comprehensive summary of the available literature on C. arvense’s botanical properties, phytochemical composition, biological activities, standardization potential, and future therapeutic prospects has been carefully provided. This plant has been used traditionally for the treatment of inflammation, infections, bleeding disorders, and liver-related disorders. Phytochemical investigations showed the presence of many bioactive compounds such as flavonoids, phenolic acids, triterpenes, sterols, tannins, glycosides, and volatile compounds. Among the reported biological activities, antioxidants and antimicrobial properties are the most studied activities. In addition, anticancer, antidiabetic, neuroprotective, anti-inflammatory, and antiproliferative activities have also been investigated. The environmental adaptability, rapid growth, and extensive root system of C. arvense highlight its potential for development as a sustainable medicinal and industrial crop. However, there are critical research gaps present in phytochemical standardization, toxicity assessment, pharmacokinetics, and clinical validation, warranting further comprehensive studies. Full article

Low-voltage electrical contacts are core components of power distribution systems, renewable energy installations, and industrial automation equipment. The electric arc generated during contact switching is the primary cause of contact erosion, material transfer, and equipment failure, posing significant threats to system reliability and operational safety. The accurate prediction of arc parameters is hindered by two challenges: the high scatter in available data undermines empirical models, and purely data-driven approaches risk physically implausible results. To address this, a Gaussian Mixture-enhanced Bayesian-optimized Physics-Informed Neural Network (GB-PINN) is proposed. Three core contributions are made: (1) High-fidelity MHD simulation foundation: A magnetohydrodynamic (MHD) multi-physics coupling model of the contact arc was constructed and validated against experiments, showing high fidelity with only 1.63% error in arc duration and 1.82% in arc energy. A multivariate simulation dataset was generated by varying key contact parameters based on this validated model. (2) GMM-based data augmentation: The measured and simulated data were modeled and sampled via Gaussian Mixture Model (GMM) to enrich the dataset while preserving physical consistency. (3) BOHB-optimized PINN prediction: The Bayesian Optimization and Hyperband (BOHB) algorithm was employed to optimize the PINN hyperparameters, enhancing training efficiency and predictive accuracy. Experimental results demonstrated that the proposed GB-PINN achieved superior performance in predicting arc duration and energy, with mean absolute errors (MAE) of 0.079 ms and 0.624 mJ, root mean square errors (RMSE) of 0.099 ms and 0.774 mJ, and coefficients of determination (R²) of 0.980 and 0.979, significantly outperforming grey model (GM (1, N)), long short-term memory (LSTM), and Transformer models. As a physics-informed data-driven tool, GB-PINN enables high-precision arc prediction, providing reliable support for electrical contact design. Full article

Remote-sensing land surface deformation (LSD) is a powerful and effective approach for investigating regional alpine permafrost variations. However, alpine permafrost is often distributed in areas characterized by earthquakes, and the LSD of alpine permafrost is potentially contaminated or diminished by earthquake-related LSD. Therefore, this study aimed to derive the effective LSD in the alpine permafrost of the Source Area Yellow River (SAYR) by removing LSD originating from the M_w 7.4 Maduo earthquake in 2021-05-22 and analyzing the spatiotemporal variations in LSD during 2017–2024. Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) was used to obtain the initial LSD time series from Sentinel-1 images acquired during 2017–2024. The LSD of the M_w 7.4 Maduo earthquake, its aftershocks and the post-seismic relaxation in SAYR was simulated separately by considering its temporal process and removed from the LSD time series in SAYR. The final LSD was validated against in situ Global Navigation Satellite System (GNSS) measurements, and the spatiotemporal variations in LSD in SAYAR were subsequently analyzed. The study found the following: (1) the removal of the earthquake-related LSD was successful both spatially and temporally and the final LSD has mean absolute error (MAE) of 3.22 mm and root mean squared error (RMSE) of 3.92 mm; (2) during 2017–2024, the vertical LSD in SAYR was mostly −8–8 mm/y; (3) soil moisture determined the spatial distribution of the LSD direction in SAYR as a result of local drainage conditions, air temperature, precipitation and snow melt. This study demonstrated the necessity of removing the earthquake-related LSD when investigating the alpine permafrost LSD in tectonically active areas. The strategy adopted in this study serves as a technical reference for future investigations of this kind. The findings in this study provide insight for a thorough understanding of permafrost evolution on the Tibetan Plateau in the context of climate change. Full article

Distributed photovoltaic (PV) power stations are core enablers for dual-carbon goals in modern power systems, with IoT-based monitoring systems serving as their nerve center for real-time data collection and grid dispatch. However, PV monitoring nodes operate in harsh, unattended outdoor environments with severe computational resource constraints, exposing them to critical hardware security risks that can trigger cross-domain cascading hazards. Existing research focuses primarily on communication and software security, lacking systematic hardware security modeling and lightweight defense designs. Generic IoT hardware security solutions are also inapplicable due to excessive overhead. To address these gaps, this paper proposes LHSF, a lightweight hardware security framework tailored for resource-constrained PV edge nodes. It integrates an on-chip OTP-based lightweight hardware root of trust (L-HROT) with an SRAM-PUF-driven non-resident key management protocol, which implements full-lifecycle key management via a “power-on generation, on-demand usage, post-use destruction, zero-residue storage” paradigm. Experiments on ESP32 and Raspberry Pi 4B show that LHSF provides robust resistance to side-channel recovery, physical extraction, malicious firmware boot and rollback attacks, reducing fault injection bypass rate to 6.8%. Compared to standard TPM 2.0, it cuts boot delay by 60.7%, power consumption by 18.6% and memory footprint by 72.7% with negligible performance overhead. This work fills the hardware security gap for PV monitoring systems and provides a reusable technical pathway for distributed energy IoT terminals. Full article

Accurate short-term forecasting of residential electricity demand is increasingly important for smart distribution systems, particularly in the context of demand-side management and flexibility-oriented grid operation. In this study, a high-resolution forecasting framework is proposed in which household electricity demand is classified into fixed, shiftable, and adjustable load categories and forecasted together with total load. A one-minute-resolution synthetic residential load dataset is generated using the Centre for Renewable Energy Systems Technology (CREST) demand model for households with two to five occupants over a 31-day winter period in January. The appliance-level demand data are grouped according to operational characteristics and integrated into a representative four-bus distribution feeder. Minute-level power flow analysis is then performed to calculate technical losses, which are incorporated into the forecasting dataset together with meteorological variables (temperature, wind speed, and solar irradiance) and temporal descriptors. Using this multi-input structure, random forest (RF), support vector machine (SVM), feed-forward neural network (FFNN), and long short-term memory (LSTM) models are comparatively evaluated for the prediction of fixed, shiftable, adjustable, and total residential loads. Model performance is assessed using root mean square error (RMSE) and Pearson correlation coefficient (R), while mean absolute error (MAE) is additionally reported for the final test set. The results show that the LSTM model provided the most consistent overall forecasting performance, particularly for shiftable, adjustable, and total load estimation, while RF yielded competitive results for fixed-load correlation and short-window forecasting in Buses 1 and 2. In contrast, SVM and FFNN exhibited weaker generalization performance across several load categories. The proposed framework provides a practical foundation for the development of dynamic pricing mechanisms that consider load-type-based controllability levels. Overall, the findings demonstrate that integrating load categorization with meteorological, temporal, and technical loss information provides a robust and reproducible framework for smart grid applications such as demand-side management, peak load mitigation, and flexibility-aware residential load analysis. Full article

Early fault prediction of wind turbines is critical for ensuring wind farm safety and reducing operation and maintenance costs. However, the latent and progressive nature of incipient faults, together with concurrent failures across multiple subsystems, makes accurate root-cause identification challenging. In addition, severe class imbalance between normal and faulty samples further degrades prediction performance, particularly for minority fault types. To address these challenges, this paper proposes a novel fault prediction model, M²ANet, using SCADA data within a 30-min pre-fault window. The model combines a dual-memory module with progressive dilated convolutions to efficiently capture multi-scale temporal dependencies from high-dimensional operational variables. An entropy-bias penalty is further introduced into the loss function to adaptively regularize the predicted probability distribution, alleviating overconfidence under imbalanced data conditions and improving the recognition of minority faults. Experiments on a real-world wind farm dataset show that M²ANet achieves an overall accuracy of 90.73% and a weighted F1-score of 90.62% in multi-class fault prediction, outperforming 10 representative baseline models. In addition to these aggregate metrics, per-class evaluation confirms the model’s robustness under class imbalance. Notably, for yaw system faults, which account for only 1.9% of the samples, M²ANet achieves a recall of 95.92% with a 30-min-ahead warning. These results demonstrate its effectiveness and reliability for early fault prediction in practical wind turbine applications. Full article

Enhancing mariculture’s green total factor productivity (GTFP) is essential to balance industrial growth with ecology, safeguard global food security, and meet UN Sustainable Development Goal 14 amid mounting marine stress. As a global leading mariculture producer, China provides a typical research sample. This study constructs a mariculture GTFP measurement index system, estimates GTFP in China’s coastal provinces via the global Super-SBM model, identifies root causes of efficiency loss, and explores influencing factors and spatial spillover effects using a spatial econometric model. The results show that the overall mariculture GTFP of China’s coastal provinces exhibits a fluctuating upward trend with significant regional heterogeneity, specifically presenting a distribution pattern of “the highest in the South China Sea Region, followed by the East China Sea Region, and the lowest in the Yellow Sea and Bohai Sea Region”. Meanwhile, mariculture GTFP shows significant positive spatial autocorrelation, with distinct High-High and Low-Low agglomeration characteristics. Excessive resource consumption and undesirable output discharge are the core drivers of efficiency loss. For direct effects, industrial scale, industrial structure, fishermen’s income, transportation accessibility, internet development, technology adoption, and environmental regulation significantly boost local GTFP, while fishery disasters exert a significant negative impact. For spatial spillovers, industrial scale, industrial structure, and internet development show significant positive effects, while fishermen’s income and urbanization present negative effects. Based on these findings, this study proposes targeted multi-stakeholder optimization paths, providing decision support for China’s mariculture green development and replicable experience for global coastal countries. Full article

Drought, one of the most severe natural disasters globally, has inflicted notable impacts on animal husbandry production, yet the current research on drought impact assessment in pastoral systems is plagued by obvious gaps, such as the lack of comprehensive quantitative evaluations integrating grassland ecosystem and livestock production indicators, unclear quantitative relationships between drought severity gradients and multi-level pastoral impacts, and the absence of validated quantitative assessment frameworks linking drought indices with actual pastoral economic losses. To fill these gaps, this study takes Inner Mongolia grasslands as the research area, analyzes the spatiotemporal characteristics of drought and its impacts on grassland net primary productivity (NPP) over the 50-year period from 1961 to 2012, and quantifies the differential impacts of three representative gradient drought events (1974 moderate, 1986 severe, and 1965 extreme) on grassland NPP, standard hay yield, sheep units and livestock economic losses. The long-term analysis shows that drought frequency in the study area decreases with increasing severity, with the typical steppe having the highest drought frequency and a “nine droughts in ten years” pattern in the central and western regions; drought intensity increases westward, and duration extends with rising severity, and its spatial distribution is highly consistent with the east–west precipitation gradient. Drought is the dominant driver of NPP variation, explaining up to 84% of NPP anomalies, with meadow steppe being the most sensitive to drought and desert steppe showing stronger drought resilience due to adaptive traits such as deeper root systems. The assessment of the three representative drought events reveals that drought impacts exhibit a linear amplification effect with severity, with extreme drought causing an average NPP loss 2.8 times greater, hay yield loss 1.1 times greater, and economic loss 4.4 times greater than those caused by moderate drought, and different grassland types show distinct response characteristics to drought of varying severity. The NPP loss spatial distribution is highly consistent with severe drought areas, and sheep unit loss is directly correlated with drought severity. Most importantly, the study validates a robust quantitative assessment framework ($SPI\to NPP\to hay yield\to sheep units\to economic loss$) with relative errors of less than 9% compared with historical disaster records, which systematically links drought indices with practical pastoral economic losses. This research clarifies the quantitative relationships between drought and multi-dimensional pastoral impacts, and provides actionable scientific insights for drought risk governance in arid and semi-arid pastoral areas such as Inner Mongolia. Full article