In this paper, we present a novel perception-driven hybrid rendering method leveraging the limitation of the human visual system (HVS). Features accounted in our model include: foveation from the visual acuity eccentricity (VAE), depth of field (DOF) from vergence & accommodation, and longitudinal chromatic aberration (LCA) from color vision. To allocate computational workload efficiently, first we apply a gaze-contingent geometry simplification. Then we convert the coordinates from screen space to polar space with a scaling strategy coherent with VAE. Upon that, we apply a stochastic sampling based on DOF. Finally, we post-process the Bokeh for DOF, which can at the same time achieve LCA and anti-aliasing. A virtual reality (VR) experiment on 6 Unity scenes with a head-mounted display (HMD) HTC VIVE Pro Eye yields frame rates range from 25.2 to 48.7 fps. Objective evaluation with FovVideoVDP - a perceptual based visible difference metric - suggests that the proposed method gives satisfactory just-objectionable-difference (JOD) scores across 6 scenes from 7.61 to 8.69 (in a 10 unit scheme). Our method achieves better performance compared with the existing methods while having the same or better level of quality scores.

Understanding heavy metal behavior in arid saline soils is critical for phytoremediation in degraded lands. This study investigated metal distribution and plant enrichment in the Tarim Basin using 323 soil and 55 plant samples (Populus euphratica, Tamarix ramosissima, cotton, jujube). Analyses included redundancy analysis (RDA) and bioconcentration factor (BCF) assessments. Key findings reveal that elevated salinity (total salts, TS > 200 g/kg) and alkalinity (pH > 8.5) immobilized As, Cd, Cu, and Zn. Precipitation and competitive leaching reduced metal mobility by 42–68%. Plant enrichment strategies diverged significantly: P. euphratica hyperaccumulated Cd (BCF = 1.59) and Zn (BCF = 2.41), while T. ramosissima accumulated As and Pb (BCF > 0.05). Conversely, cotton posed Hg transfer risks (BCF = 2.15), and jujube approached Cd safety thresholds in phosphorus-rich soils. RDA indicated that pH and total salinity (TS) jointly suppressed metal bioavailability, explaining 57.6% of variance. Total phosphorus (TP) and soil organic carbon (SOC) enhanced metal availability (36.8% variance), with notable TP-Cd synergy (Pearson’s r = 0.42). We propose a dual-threshold management framework: (1) leveraging salinity–alkalinity suppression (TS > 200 g/kg + pH > 8.5) for natural immobilization; and (2) implementing TP control (TP > 0.8 g/kg) to mitigate crop Cd risks. P. euphratica demonstrates targeted phytoremediation potential for degraded saline agricultural systems. This framework guides practical management by spatially delineating zones for natural immobilization versus targeted remediation (e.g., P. euphratica planting in Cd/Zn hotspots) and implementing phosphorus control in high-risk croplands.

Understanding spatial drivers of soil organic carbon in arid oasis ecosystems is essential for guiding precision soil management and enhancing land sustainability. This study integrates 644 surface samples and 9 soil profiles with multisource environmental data in the hyperarid eastern Tarim...

Soil salinization disrupts ecosystem material and energy cycling, leading to agricultural resource wastage, ecological degradation, and economic losses. This study developed soil salinity prediction models using 1247 samples from China’s Kashgar Oasis through five machine learning methods—Ordinary Least Squares (OLS), Backpropagation Neural Network (BP), Random Forest (RF), Support Vector Machine (SVM), and Extreme Learning Machine (ELM)—integrated with multi-source environmental variables. Dominant ions (Cl^–, Na⁺, \({\text{SO}}_{4}^{{2 - }}\)) classified regional salinity as chloride-sulfate type, exhibiting significant spatial heterogeneity (variation coefficients: 0.37–4.04). The SVM model achieved optimal performance (R² = 0.6885, lowest RMSE/MAE/MBE), demonstrating robust generalization and anti-overfitting capabilities via its Gaussian kernel. Spatial predictions from RF, BP, and OLS exhibited scattering, whereas SVM and ELM generated coherent patchy-zonal distributions aligned with field measurements. Variable optimization (EVI, NDVI, EC, TDS, DEM, TL, MAP) enhanced SVM’s RMSE by 23%, highlighting its superiority for soil salinity mapping in arid oasis ecosystems. The study provides critical insights into nonlinear drivers of salinization and informs targeted land management strategies.