Human activities continuously alter the delivery of ecosystem services (ESs), which play a crucial role in human well-being. There is a pressing need for effective ecological management strategies that consider the spatial heterogeneity of ESs to support the transition from trade-offs to synergies. This study focuses on the Haba River Basin and examines characteristics of land-use change and the shift from trade-offs to synergies. The results indicate that from 1990 to 2000, the initial phase of land development, 10.65% of the land experienced change. Subsequently, during the intensive period of land development from 2000 to 2010, 30.29% of the land underwent significant transformation, with approximately 78% of grassland, sparse grassland, forested land, and desert converted into arable land. However, between 2010 and 2020, as the focus shifted towards the establishment of native vegetation. The intensity of land development decreased, and only a small percentage (3.65%) of the total area underwent changes. Based on an in-depth analysis of spatial heterogeneity from 1990 to 2020, it is believed there has been a shift from trade-offs to co-benefits between 2000–2010 and 2010–2020. The years 2010 and 2020 were pivotal time nodes for the transition from trade-offs to synergies and for reducing trade-offs, with NPP identified as a critical driving factor for comprehensive ES (CES) functions. By considering the trade-off–synergy relationship and hotspots of ecological service functions, combined with unified water resource management policies, comprehensive ecological management measures tailored to different regions are proposed. These measures have facilitated the implementation of robust ecological protection policies to shift ES development from trade-offs to synergies in arid areas, thereby enhancing overall ecosystem service functions in the Haba River Basin. The research findings offer crucial scientific support and guidance for ecosystem management in arid areas, particularly within Central Asia.

The Chinese Loess Plateau (CLP) is ecologically fragile, and water resources are extremely scarce. Soil thermal conductivity (λ) is a vital parameter for controlling surface heat transfer and is the key to studying the energy exchange and water balance of the soil surface. The objective of this study is to investigate the spatial distribution characteristics of soil thermal conductivity on the Loess Plateau. The research primarily employed soil heat transfer models and the Google Earth Engine (GEE) platform for remote sensing cloud computing, compares and analyzed the suitability of six models (Cambell model, Lu Yili model, Nikoosokhan model, LT model, LP1 model, and LP2 model), and utilized the selected improved model (LT model) to analyze the spatiotemporal characteristics of thermal conductivity on the CLP, examining the impacts of soil particle composition, bulk density, elevation, moisture content, and land use on thermal conductivity. The results show that the LT model is the best in the relevant evaluation indices, with a determination coefficient (R²) of 0.84, root mean square error (RMSE) of 0.18, and relative error (RE) of 0.16. Furthermore, the λ on the CLP shows an overall trend of increasing from northwest to southeast, with a lower λ between May and August and a higher one between September and October. The λ of different land use types is as follows: built-up land > cropland > grassland > forest land > barren. The bulk density (ρ_b) and altitude mainly influence λ in the CLP. The results of this study can provide a theoretical basis for studying hydrothermal variation in the CLP, model application, energy development, and land resource use.

Human activities continuously alter the delivery of ecosystem services (ESs), which play a crucial role in human well-being. There is a pressing need for effective ecological management strategies that consider the spatial heterogeneity of ESs to support the transition from trade-offs to synergies. This study focuses on the Haba River Basin and examines characteristics of land use change and the shift from trade-offs to synergies. The results indicate that from 1990 to 2000, the initial phase of land development, 10.65% of the land experienced change. Subsequently, during the intensive period of land development from 2000 to 2010, 30.29% of the land underwent significant transformation, with approximately 78% of grassland, sparse grassland, forested land, and desert converted into arable land. However, between 2010 and 2020, as the focus shifted towards the establishment of native vegetation and the intensity of land development decreased, only a small percentage (3.65%) of the total area underwent changes. Based on an in-depth analysis of spatial heterogeneity from 1990 to 2020, it is believed there has been a shift from trade-offs to co-benefits between 2000-2010 and 2010-2020. The years 2010 and 2020 were pivotal time nodes for the transition from trade-offs to synergies and for reducing trade-offs, with NPP identified as a critical driving factor for Comprehensive ES （CES） functions. By considering the trade-offs/synergies relationship and hotspots of ecological service functions, combined with unified water resource management policies, comprehensive ecological management measures tailored to different regions are proposed. These measures have facilitated the implementation of robust ecological protection policies to shift ES development from trade-offs to synergies in arid areas, thereby enhancing overall ecosystem service functions in the Haba River Basin. The research findings offer crucial scientific support and guidance for ecosystem management in arid areas, particularly within Central Asia.

Machine learning (ML) approaches, widely used in civil engineering, have the potential to reduce computing costs and enhance predictive capabilities. However, many ML methods have yet to be applied to develop models that accurately analyze the nonlinear dynamic response of cross-fault hydraulic tunnels (CFHTs). To predict CFHT models and fragility curves effectively, we identify the most effective ML techniques and improve prediction capacity and accuracy by initially creating an integrated multivariate earthquake intensity measure (IM) from nine univariate earthquake IMs using principal component analysis. Structural reactions are then performed using incremental dynamic analysis by a multimedium-coupled interaction system. Four techniques are used to test ML–principal component analysis (PCA) feasibility. Meanwhile, mathematical statistical parameters are compared to standard probabilistic seismic demand models of expected and computed values using ML-PCA. Eventually, multiple stripe analysis–maximum likelihood estimation (MSA-MLE) is applied to assess the seismic performance of CFHTs. This study highlights that the Gaussian process regression and integrated IM can improve reliable probability and reduce uncertainties in evaluating the structural response. Thorough numerical analysis, using the suggested methodology, one can efficiently assess the seismic fragilities of the tunnel by the predicted model. ML-PCA techniques can be viewed as an alternate strategy for seismic design and CFHT performance enhancement in real-world engineering.

Saline water irrigation is increasingly used in arid regions, but its influence on soil aggregate stability remains uncertain when combined with magnetization pretreatment. A two-year field-pit experiment was conducted during 2024–2025 with four magnetization intensities (0, 0.2, 0.4, and 0.6 T) and three irrigation water salinity levels (≤1, 3, and 6 g L⁻¹). Mechanically stable and water-stable soil aggregates were analyzed in the 0–60 cm soil profile. Magnetization treatments changed aggregate-size distribution, but the dominant response differed between aggregate types. Mechanically stable aggregates mainly showed an increase in the >2 mm fraction and a decrease in the 0.053–0.25 mm fraction, whereas water-stable aggregates mainly showed an increase in the 0.25–1 mm fraction and a decrease in the 0.25 mm (R_0.25), mean weight diameter (MWD), and geometric mean diameter (GMD) varied with soil depth, salinity level, and year. Under S6, C4S6 increased the R_0.25 of mechanically stable aggregates in both years, reaching 73.90% in 2025 compared with 61.60% under C0S6. Correlation and exploratory path analyses suggested that changes in aggregate stability were mainly associated with the redistribution of key aggregate-size fractions. Under the field-pit conditions of this study, some magnetization treatments, particularly 0.4 T, were often associated with higher stability indices than non-magnetized saline irrigation, but this pattern was not consistent across all depths, salinity levels, years, and indicators. Long-term open-field validation is still required.

Freshwater scarcity in arid regions is driving increased use of saline irrigation, yet salinity severely degrades soil structure and suppresses enzymatic function. To address this critical challenge for sustainable soil management, this study systematically evaluated magnetized saline water (MSW) across three salinity levels (1, 3, and 6 g L⁻¹) and four magnetic field strengths (0, 0.2, 0.4, and 0.6 T), confirming the magnetic field intensity (C) × salinity (S) interaction. The comprehensive analysis integrated data on aggregate stability, key ion concentrations (Ca²⁺, Mg²⁺, Cl⁻), and major enzyme activities. Structural Equation Modeling (SEM) was utilized to quantify the underlying mechanisms, demonstrating that structural improvement is primarily driven by strong indirect pathways, mediated by optimized ion dynamics and increased enzyme-mediated organic matter turnover. The moderate-salinity (3 g L⁻¹), moderate-magnetic-field (0.4 T) regime emerged as the optimal balanced strategy for overall soil health. These findings offer a scalable approach, guiding future field-scale research toward long-term agricultural sustainability.