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Simple Summary: Fish passage facilities are crucial for restoring river connectivity and protecting ecosystems. This study quantitatively assessed the upstream migration of fish under various environmental conditions. In the Heishuihe River downstream of the Songxin Dam, 19 fish species were monitored, with 15 species reaching the fishway entrance and 12 successfully passing through. The entrance attraction and passage rates of the vertical-slot fishway at Songxin were 15.7% and 40.42%, respectively. The results indicate that May is the optimal period for fish migration, with better performance observed during nighttime than daytime. Optimal entrance attraction occurred at a flow rate of 6–7 m3/s and a temperature of 19–20 °C, while the best passage efficiency was achieved at a flow rate of 0–0.5 m3/s and a temperature of 17–20 °C. A multifactorial Cox regression model identified diurnal rhythms, release location, temperature, and flow rate as primary hindering factors, while body length and water level were found to be promoting factors. A nomogram was developed to predict the success rates of fishway attraction and passage based on these key factors. This study provides theoretical and data support for optimizing fishway operations and offers scientific insights into ecological restoration. Fish passage facilities are essential for restoring river connectivity and protecting ecosystems, effectively balancing economic and ecological benefits. Systematic and comprehensive monitoring, assessment, and optimized management are therefore crucial. This study quantitatively evaluated the entire upstream migration process of fish from the downstream river to the entrance and exit of the fishway and investigated the upstream movement patterns of fish under various environmental factors. A total of 19 fish species were monitored in the Heishuihe River downstream of the dam, with 15 species reaching the fishway entrance and 12 species successfully passing through it. The entrance attraction and passage rates of the vertical-slot fishway at the Songxin hydropower station were 15.7% and 40.42%, respectively. The best upstream performance was observed in May, with fish demonstrating better upstream timing and speed during nighttime compared to daytime. Specifically, the highest entrance attraction efficiency was recorded at a flow rate of 6–7 m3/s and a temperature of 19–20 °C, while the optimal passage efficiency was observed at a flow rate of 0–0.5 m3/s and a temperature of 17–20 °C. Additionally, a multifactorial Cox proportional hazards regression model was constructed to identify key factors influencing the probability of fishway entrance attraction and successful passage. The model elucidated the impact patterns of these key factors on fish upstream migration, ultimately generating an alignment diagram for prediction and control. This study provides a theoretical foundation and data support for developing optimized operational schedules for fishways. The findings offer a more comprehensive and systematic approach for monitoring and evaluating fish passage facilities, serving as a scientific basis for ecological restoration and fish conservation in this region and similar areas. [ABSTRACT FROM AUTHOR]

A landslide is a common natural disaster that causes environmental damage, casualties and economic losses, which seriously affects the sustainable development of society. In geomechanics, it is one of the largest deformation problems. Herein, the GPU-accelerated explicit smoothed particle finite element method (eSPFEM) for large deformation analysis in geomechanics was developed on the CUDA platform based on high-performance computing using a self-designed eSPFEM program code. The eSPFEM combines the strain smoothing nodal integration techniques found in the particle finite element method (PFEM) framework, which allows for the use of low-order triangular elements without volume locking and avoids frequent information transfer and mapping errors between Gaussian points and particles in PFEM. A numerical simulation of slope instability using the eSPFEM and based on a strength reduction technique was conducted using various examples, including a cohesive homogeneous slope, a non-cohesive homogeneous slope, a non-homogeneous slope and a slope with a thin soft band. The calculation results show that the eSPFEM can be applied to slope stability analysis under different working conditions, simulating the entire process of slope instability initiation, sliding and reaccumulation, and obtaining reliable FOS values. A numerical simulation was conducted to analyse a landslide that occurred in the Zhangjiazhuang tunnel on the Lanzhou–Xinjiang high-speed railway line on 18 January 2016. A natural unsaturated soil slope, a soil slope with a high moisture content and a soil slope with a high moisture content subjected to an earthquake were analysed. The findings of this study are in good agreement with the actual slope failure conditions. The primary triggers identified for the landslide were heavy rainfall and earthquakes. The verification results indicate that the eSPFEM can effectively simulate an actual landslide case, showcasing high accuracy and applicability in simulating the large deformation behaviour of landslides. [ABSTRACT FROM AUTHOR]

This study details microwave-absorbing materials made of natural rubber/nitrile butadiene rubber (NR/NBR) blends with multi-walled carbon nanotubes (MWCNTs) and molybdenum disulfide (MoS2). The mechanical blending method and the influences of fabrication on the morphology and microwave-absorbing performance of resulting compounds were logically investigated. It was found that interfacial differences between the fillers and matrix promote the formation of MWCNTs and MoS2 networks in NR/NBR blends, thus improving microwave-absorbing performance. Compared with direct compounding, masterbatch-based two-step blending is more conducive to forming interpenetrating networks of MWCNTs/MoS2, endowing the resulting composite with better microwave attenuation capacity. Composites with MWCNTs in NR and MoS2 in NBR demonstrate the best microwave-absorbing performance, with a minimum reflection loss of −44.54 dB and an effective absorption bandwidth of 3.60 GHz. Exploring the relationship between morphology and electromagnetic loss behavior denotes that such improvement results from the selective distribution of dual fillers, inducing networking and multi-component-derived interfacial polarization enhancement. [ABSTRACT FROM AUTHOR]

Along with the development of remote sensing technology, the spatial–temporal variability of vegetation productivity has been well observed. However, the drivers controlling the variation in vegetation under various climate gradients remain poorly understood. Identifying and quantifying the independent effects of driving factors on a natural process is challenging. In this study, we adopted a potent machine learning (ML) model and an ML interpretation technique with high fidelity to disentangle the effects of climatic variables on the long-term averaged net primary productivity (NPP) across the Amazon rainforests. Specifically, the eXtreme Gradient Boosting (XGBoost) model was employed to model the Moderate-resolution Imaging Spectroradiometer (MODIS) NPP data, and the Shapley addictive explanation (SHAP) method was introduced to account for nonlinear relationships between variables identified by the model. Results showed that the dominant driver of NPP across the Amazon forests varied in different regions, with temperature dominating the most considerable portion of the ecoregion with a high importance score. In addition, light augmentation, increased CO2 concentration, and decreased precipitation positively contributed to Amazonia NPP. The wind speed for most vegetated areas was under the optimum, which benefits NPP, while sustained high wind speed would bring substantial NPP loss. We also found a non-monotonic response of Amazonia NPP to VPD and attributed this relationship to the moisture load in Amazon forests. Our application of the explainable machine learning framework to identify the underlying physical mechanism behind NPP could be a reference for identifying relationships between components in natural processes. [ABSTRACT FROM AUTHOR]

This study investigated the strength and limitations of two widely used multi-model averaging frameworks—Bayesian model averaging (BMA) and reliability ensemble averaging (REA), in post-processing runoff projections derived from coupled hydrological models and climate downscaling models. The performance and weight distributions of five model ensembles were thoroughly compared, including simple equal-weight averaging, BMA, and REAs optimizing mean (REA-mean), maximum (REA-max), and minimum (REA-min) monthly runoff. The results suggest that REA and BMA both can synthesize individual models' diverse skills with comparable reliability, despite of their different averaging strategies and assumptions. While BMA weighs candidate models by their predictive skills in the baseline period, REA also forces the model ensembles to approximate a convergent projection towards the long-term future. The type of incorporation of the uncertain future climate in REA weighting criteria, as well as the differences in parameter estimation (i.e., the expectation maximization (EM) algorithm in BMA and the Markov Chain Monte Carlo sampling method in REA), tend to cause larger uncertainty ranges in the weight distributions of REA ensembles. Moreover, our results show that different averaging objectives could cause much larger discrepancy than that induced by different weighting criteria or parameter estimation algorithms. Among the three REA ensembles, REA-max most resembled BMA because the EM algorithm of BMA converges to the minimum aggregated error, and thus emphasize the simulation of high flows. REA-min achieved better performance in terms of inter-annual temporal pattern, yet at the cost of compromising accuracy in capturing mean behaviors. Caution should be taken to strike a balance among runoff features of interest. [ABSTRACT FROM AUTHOR]

Analyzing the spatiotemporal characteristics and causes of landscape pattern changes in watersheds around big cities is essential for understanding the ecological consequence of urbanization and provides a basic reference for the watershed management. This study used a land-use transition matrix and landscape indices to explore the spatiotemporal change of land use and landscape pattern over Liuxihe River basin of Guangzhou in the southeast of China from 1980 to 2015 with multitemporal Landsat satellite data in response to the rapid urbanization process. Primary temporal and spatial influencing factors were first quantitatively identified through grey relation analysis (calculating correlation degree between land use changes and influencing factors) and Geodetector (detecting landscape spatial heterogeneity and its driving factors), respectively. Considerable spatial and temporal differences in land use and landscape pattern changes were observed herein, thus determining the influencing factors of these differences in the Liuxihe River basin. These changes were characterized by a large increase in construction land converted from cropland, particularly in the middle and lower reaches of the basin from 2000 to 2010, causing dramatic fragmentation and homogenization of the landscape pattern there. Meanwhile, the landscape pattern gradually transitioned from an agricultural land use dominant landscape to a construction land use dominant landscape in these regions. Furthermore, the rapid growth of a nonagricultural population and the transformation of industry primarily caused the temporal changes of landscape pattern, and the landscape spatial heterogeneity was mainly caused by the interaction of complicated geomorphology and anthropogenic activities in different spatial locations, particularly after 2000. This study not only provides an improved approach to quantifying the main spatiotemporal influencing factors of landscape pattern changes during different time periods, but also offers a reference for decision-makers to formulate optimal strategies on ecological protection and urban sustainable development of different regions in this study area. [ABSTRACT FROM AUTHOR]