Space exploration is significant for scientific innovation, resource utilization, and planetary security. Space exploration involves several systems including satellites, space suits, communication systems, and robotics, which have to function under harsh space conditions such as extreme temperatures (− 270 to 1650 °C), microgravity (10⁻⁶ g), unhealthy humidity (< 20
In modern agricultural practices, soils are increasingly exposed to multiple anthropogenic and natural pollutants, with heavy metals playing a significant role. Despite their critical environment and health impact, substantial gaps remain in understanding the levels of these contaminants and their outcomes on soil and water systems. To address the lack of pertinent data, this study assessed the speciation and contamination levels of nickel (Ni) and zinc (Zn) in soil samples collected from Varanasi (n = 9) and Mirzapur (n = 6) districts of eastern Uttar Pradesh, India. The modified Tessier method was used for the sequential extraction procedure to understand heavy metals mobility and availability. Among the geochemical fractions (water soluble, exchangeable, organic matter bound, carbonate bound/specifically sorbed, bound to Fe-oxides (Fe-MnOB)), the residual fraction was predominant. For Ni, principal component analysis revealed the highest positive loading factor for soil pH (0.941), and the residual fraction also demonstrated a significant positive loading value (0.779), indicating their stabilizing influence. In the case of Zn, the highest positive loading factor was observed for diethylenetriaminepentaacetic acid-Cu (0.956), while the highest negative loading factor was associated with soil pH (−0.935). For Zn, negative loadings of pH and CEC contrasted with positive associations of DTPA-Cu and Ni, suggesting differential mobility and source behavior. For Zn speciation, the residual fraction showed the highest positive loading (0.944). The study concluded that the majority of contamination indices for Ni and Zn fell within the slightly to moderately contaminated zone, with geogenic sources playing a dominant role compared to anthropogenic inputs. These results offer a valuable reference point for supervising heavy metal contamination and understanding the potential migration of pollutants within soil, water, and human systems. This study may be the first detailed speciation and index-based assessment of Ni and Zn in eastern UP agricultural soils. Periodic assessment and management of these contaminants are recommended to mitigate their environmental and health impacts.
Abstract Improving the accuracy of reference evapotranspiration (RET) estimation is essential for effective water resource management, irrigation planning, and climate change assessments in agricultural systems. The FAO-56 Penman-Monteith (PM-FAO56) model, a widely endorsed approach for RET estimation, often encounters limitations due to the lack of complete meteorological data. This study evaluates the performance of eight empirical models and four machine learning (ML) models, along with their hybrid counterparts, in estimating daily RET within the Gharb and Loukkos irrigated perimeters in Morocco. The ML models examined include Random Forest (RF), M5 Pruned (M5P), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), with hybrid combinations of RF-M5P, RF-XGBoost, RF-LightGBM, and XGBoost-LightGBM. Six input combinations were created, utilizing Tmax, Tmin, RHmean, Rs, and U2, with the PM-FAO56 model serving as the benchmark. Model performance was assessed using four statistical indicators: Kling-Gupta efficiency index (KGE), coefficient of determination (R2), mean squared error (RMSE), and relative root squared error (RRSE). Results indicate that the Valiantzas 2013 (VAL2013b) model outperformed other empirical models across all stations, achieving high KGE and R2 values (0.95–0.97) and low RMSE (0.32–0.35 mm/day) and RRSE (8.14–10.30%). The XGBoost-LightGBM and RF-LightGBM hybrid models exhibited the highest accuracy (average RMSE of 0.015–0.097 mm/day), underscoring the potential of hybrid ML models for RET estimation in subhumid and semi-arid regions, thereby enhancing water resource management and irrigation scheduling.
Accurate reservoir storage estimation is fundamental to sustainable water resources management; however, small dam projects are often hindered by the prohibitive costs and time required for high-precision topographic surveys. In this study, a rigorous validation of freely available one-arc-second SRTM DEMs was conducted as an alternative approach for estimating volume-elevation relationships at ten small dams in Iraq. High-precision surveys served as benchmarks, enabling statistical validation of DEM-derived estimates using absolute relative error (ARE), root mean square error (RMSE), mean absolute error, and the coefficient of determination (R²). Reservoir basin morphology was further characterised through planimetric indices, including area-to-volume ratio (AVR), shape factor, and solidity. In parallel, terrain complexity within a 5 km buffer zone was quantified using slope variability, curvature, vector ruggedness measure (VRM), and terrain ruggedness index (TRI). A strong structural agreement was demonstrated (R² > 0.98), although substantial variation in volumetric precision was observed. A global sensitivity analysis using the Morris Method identified the standard deviation of the Terrain Ruggedness Index (TRI) as the dominant predictor of accuracy, with µ* values of 83–86, while all other metrics showed minimal influence (µ* ≈ 0–24). These results establish a clear accuracy threshold for one-arc-second SRTM DEMs: they are sufficiently reliable for preliminary planning (< 20% error) in low-ruggedness terrain (TRI SD < 0.1) but become highly unreliable in rugged landscapes, where errors exceed 150% (TRI SD > 0.1). These findings provide a predictive framework for assessing DEM suitability, supporting the integration of satellite topography into small-scale reservoir planning.
Tribocorrosion studies have primarily focused on hard-on-hard articulations, with limited research on metal-on-polymer (MoP) configurations despite their clinical relevance. This study investigates the tribocorrosion behaviour of cobalt-chromium (CoCr) alloy against 3D-printed polyetheretherketone (PEEK), conventionally manufactured PEEK, and ultrahigh molecular weight polyethylene (UHMWPE), with UHMWPE as the reference material. Tests were conducted using a reciprocating tribometer under open circuit and potentiostatic conditions in phosphate buffered saline (PBS) and bovine calf serum, with in-situ electrochemical monitoring. The results demonstrated that the presence of serum significantly decreased the charge transfer of the CoCr surface, hence decreasing electrochemical degradation when compared to PBS lubrication. The manufacturing methods for PEEK resulted in different surface characteristics, leading to variations in tribocorrosion behaviour; however, polishing to achieve homogeneous roughness minimized these differences. Furthermore, CoCr exhibited significantly higher charge transfer when slid against all tested PEEKs compared with UHMWPE (at least two-fold higher), suggesting that a PEEK-CoCr tribocouple results in an increase of tribocorrosion and a greater potential for metal ion release than a UHMWPE-CoCr tribocouple.
