Search Results (468)

Cement–Soil Mixing (CSM) Pile is an important technology for soft ground reinforcement, and its as-formed compressive strength directly affects engineering design and construction quality. To address the significant discrepancy between laboratory-tested strength and field as-formed strength arising from differing environmental conditions, this study conducted modified laboratory experiments simulating key field formation characteristics. A cement–soil preparation system considering actual immersion conditions was established, based on controlling the initial water content state of the foundation soil before pile formation and applying submerged conditions post-formation. Utilizing data mining on 84 sets of experimental data with various preparation parameter combinations, a prediction model for the as-formed strength of CSM Pile was developed based on the Particle Swarm Optimization-Extreme Gradient Boosting (PSO-XGBoost) algorithm. Engineering validation demonstrated that the model achieved an RMSE of 0.138, an MAE of 0.112, and an R² of 0.961. It effectively addresses the issue of large prediction deviations caused by insufficient environmental simulation in traditional mix proportion tests. The research findings establish a quantitative relationship between as-formed strength and preparation parameters, providing an effective experimental improvement and strength prediction method for the engineering design of CSM Pile. Full article

Concrete mix design plays a pivotal role in ensuring the mechanical performance, durability, and sustainability of construction projects. However, the nonlinear interactions among the mix components challenge traditional approaches in predicting compressive strength and optimizing proportions. This study presents a two-stage hybrid framework that integrates deep learning with reinforcement learning to overcome these limitations. First, a Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model was developed to capture spatial–temporal patterns from a dataset of 1030 historical concrete samples. The extracted features were enhanced using an eXtreme Gradient Boosting (XGBoost) meta-model to improve generalizability and noise resistance. Then, a Dueling Double Deep Q-Network (Dueling DDQN) agent was used to iteratively identify optimal mix ratios that maximize the predicted compressive strength. The proposed framework outperformed ten benchmark models, achieving an MAE of 2.97, RMSE of 4.08, and R² of 0.94. Feature attribution methods—including SHapley Additive exPlanations (SHAP), Elasticity-Based Feature Importance (EFI), and Permutation Feature Importance (PFI)—highlighted the dominant influence of cement content and curing age, as well as revealing non-intuitive effects such as the compensatory role of superplasticizers in low-water mixtures. These findings demonstrate the potential of the proposed approach to support intelligent concrete mix design and real-time optimization in smart construction environments. Full article

African ground squirrels (Xerus spp.), the inhabitants of African arid zones, face extreme heat and water scarcity driving selection for metabolic optimization. We assembled and annotated the first mitogenomes of Xerus inauris and Xerus rutilus (16,525–16,517 bp), revealing conserved vertebrate architecture with genus-specific traits. Key features include Xerus rutilus’s elongated ATP6 (680 vs. 605 bp), truncated ATP8–ATP6 spacers (4 vs. 43 bp), and tRNA-Pro control regions with 78.1–78.3% AT content. Their nucleotide composition diverged from that of related sciurids, marked by reduced T (25.78–26.9%) and extreme GC skew (−0.361 to −0.376). Codon usage showed strong Arg-CGA bias (RSCU = 3.78–3.88) and species-specific elevations in Xerus rutilus’s UGC-Cys (RSCU = 1.83 vs. 1.17). Phylogenetics positioned Xerus as sister to Ratufa bicolor (Bayesian PP = 0.928; ML = 1.0), aligning with African biogeographic isolation. Critically, we identified significant signatures of positive selection in key mitochondrial genes linked to arid adaptation. Positive selection signals in ND4 (ω = 1.8 × background), ND1, and ATP6 (p < 0.0033) correspond to enhanced proton gradient efficiency and ATP synthesis–molecular adaptations likely crucial for optimizing energy metabolism under chronic water scarcity and thermoregulatory stress in desert environments. Distinct evolutionary rates were observed across mitochondrial genes and complexes: Genes encoding Complex I subunits (ND2, ND6) and Complex III (Cytb) exhibited accelerated evolution in arid-adapted lineages, while genes encoding Complex IV subunits (COXI) and Complex V (ATP8) remained highly conserved. These findings resolve the Xerus mitogenomic diversity, demonstrating adaptive plasticity balancing arid-energy optimization and historical diversification while filling critical genomic gaps for this xeric-adapted lineage. Full article

The excellent moisture transport properties of yarns play a crucial role in improving the liquid moisture transfer behavior within textiles and maintaining their thermal-wet comfort. However, the current research on the moisture management performance of fabrics made from yarns with excellent liquid transport properties primarily compares the wicking results, without considering the varying requirements of testing conditions due to differences in human sweating rates during daily activities. Moreover, the understanding of moisture transport mechanisms in yarns within fabrics under different testing conditions remains insufficient. In this study, two types of twisted combination yarns, composed of hydrophobic profiled polyester filaments and hydrophilic spun yarns to form a hydrophobic-hydrophilic gradient along the axial direction of the yarn, were developed and compared with profiled polyester filaments to understand the liquid migration behaviors in the knitted fabrics formed by these yarns. Results showed that hydrophobic profiled polyester filament yarn demonstrated superior liquid transport performance with infinite saturated liquid supply (vertical wicking test). In contrast, the twisted combination yarns exhibited better moisture diffusion properties under limited liquid droplet supply conditions (droplet diffusion test and moisture management test). These contradictory findings indicated that the amount of liquid moisture supply in testing conditions significantly affected the moisture transport performance of yarns within fabrics. It was revealed that the liquid moisture in the twisted combination yarns migrated through capillary wicking for moisture transfer. Under an infinite saturated liquid supply condition, the higher the content of hydrophilic fibers in the spun yarns, the greater the amount of moisture transferred, demonstrating an excellent liquid transport performance. Under the limited liquid droplet supply conditions, both the volume of liquid water and the moisture absorption capacity of the yarn jointly influence internal moisture migration within the yarn. It provided a theoretical reference for testing the internal moisture wicking performance of fabrics under different states of human sweating. Full article

The quality and freshness of fruits and vegetables are critical factors in consumer acceptance and are significantly affected during transport and storage. This study aimed to evaluate the quality of greenhouse-grown tomatoes stored for 24 days by combining non-destructive image analysis, spectrophotometric assays (including total phenolic content and antioxidant and antiradical activity assessments), and attenuated total reflectance–Fourier transform infrared (ATR-FTIR) spectroscopy. Additionally, water activity, moisture content, total soluble solids, texture, and color were evaluated. Most physicochemical changes occurred between days 14 and 17, without major impact on overall fruit quality. A progressive transition in peel hue from orange to dark orange, and increased surface irregularity of their textural image were noted. Moreover, the combined use of instrumental and image analyses results via multivariate analysis allowed the clear discrimination of tomatoes according to storage days. In this sense, tomato samples were effectively classified by ATR-FTIR spectral bands, linked to carotenoids, phenolics, and polysaccharides. Machine learning (ML) models, including Random Forest and Gradient Boosting, were trained on image-derived features and accurately predicted shelf life and quality traits, achieving R² values exceeding 0.9. The findings demonstrate the effectiveness of combining imaging, spectroscopy, and ML for non-invasive tomato quality monitoring and support the development of predictive tools to improve postharvest handling and reduce food waste. Full article

The prediction of production decline rate in the development of offshore high water-cut reservoirs predominantly relies on the traditional Arps decline curves. However, the solution process is complex, and the interpretation efficiency is low, making it difficult to meet the demand for rapid prediction of production decline rates. To address this, this paper first identifies the key influencing factors of production decline rate through comprehensive feature engineering. Subsequently, it proposes a novel prediction method for the production decline rate in offshore high water-cut reservoirs by integrating Moth–Flame Optimization with Extreme Gradient Boosting Tree (MFO-XGBoost). This method utilizes seven dynamic and static influencing factors, namely vertical thickness, perforated thickness, shale content, permeability, crude oil viscosity, formation flow coefficient, and well deviation angle, to predict the production decline rate. The forecasting outcomes of the MFO-XGBoost method are then compared with those of standard RF, standard DT, the standalone XGBoost model, and the calculated results from the exponential decline model. Additionally, the forecasting capability of the MFO-XGBoost method is benchmarked against Particle Swarm Optimization–XGBoost (PSO-XGBoost) and Bayesian Optimization–XGBoost methods for predicting the production decline rate in offshore high water-cut reservoirs. The findings from the experiments show that the MFO-XGBoost method can achieve accurate prediction of the production decline rate in offshore high water-cut reservoirs, with a coefficient of determination (R²) reaching 0.9128, thereby providing a basis for strategies to mitigate the production decline rate. Full article

To investigate the slope instability caused by differential frost heaving mechanisms from the slope crest to the toe during frost heave processes, this study takes a typical silty clay slope in Xinjiang, China, as the research object. Through indoor triaxial consolidated undrained shear tests, eight sets of natural and frost-heaved specimens were prepared under confining pressure conditions ranging from 100 to 400 kPa. The geotechnical parameters of the soil in both natural and frost-heaved states were obtained, and a spatiotemporal thermo-hydro-mechanical coupled numerical model was established to reveal the dynamic evolution law of anchor rod axial forces and the frost heave response mechanism between the frame and slope soil. The analytical results indicate that (1) the frost heave process is influenced by slope boundaries, resulting in distinct spatial variations in the temperature field response across the slope surface—namely pronounced responses at the crest and toe but a weaker response in the mid-slope. (2) Under the coupled drive of the water potential gradient and gravitational potential gradient, the ice content in the toe area increases significantly, and the horizontal frost heave force exhibits exponential growth, reaching its peak value of 92 kPa at the toe in February. (3) During soil freezing, the reverse stress field generated by soil arching shows consistent temporal variation trends with the temperature field. Along the height of the soil arch, the intensity of the reverse frost heave force field displays a nonlinear distribution characteristic of initial strengthening followed by attenuation. (4) By analyzing the changes in anchor rod axial forces during frost heaving, it was found that axial forces during the frost heave period are approximately 1.3 times those under natural conditions, confirming the frost heave period as the most critical condition for frame anchor design. Furthermore, through comparative analysis with 12 months of on-site anchor rod axial force monitoring data, the reliability and accuracy of the numerical simulation model were validated. These research outcomes provide a theoretical basis for the design of frame anchor support systems in seasonally frozen regions. Full article

Forests are a key terrestrial carbon sink, storing carbon in biomass, the forest floor, and the mineral soil (SOC). Since Pinus radiata D. Don is the most widely planted forest species in Chile, it is important to understand how environmental and soil factors influence these carbon pools. Our objective was to evaluate the effects of climate and site variables on carbon stocks in adult radiata pine plantations across contrasting water and nutrient conditions. Three 1000 m² plots were installed at 20 sites with sandy, granitic, recent ash, and metamorphic soils, which were selected along a productivity gradient. Biomass carbon stocks were estimated using allometric equations, and carbon stocks in the forest floor and mineral soil (up to 1 m deep) were assessed. SOC varied significantly, from 139.9 Mg ha⁻¹ in sandy soils to 382.4 Mg ha⁻¹ in metamorphic soils. Total carbon stocks (TCS) per site ranged from 331.0 Mg ha⁻¹ in sandy soils to 552.9 Mg ha⁻¹ in metamorphic soils. Across all soil types, the forest floor held the lowest carbon stock. Correlation analyses and linear models revealed that variables related to soil water availability, nitrogen content, precipitation, and stand productivity positively increased SOC and TCS stocks. In contrast, temperature, evapotranspiration, and sand content had a negative effect. The developed models will allow more accurate estimation estimates of C stocks at SOC and in the total stand. Full article

Wildfires, whether natural or human-caused, significantly alter soil properties and increase soil erosion susceptibility, particularly through changes in rill detachment capacity (Dc). This study aimed to evaluate the influence of fire intensity on key soil properties and to recognize their relationships with Dc under controlled laboratory conditions. The research was conducted in the Darestan Forest, Guilan Province, northern Iran, a region characterized by a Mediterranean semi-arid climate. Soil samples were collected from three fire-affected conditions: unburned (NF), low-intensity fire (LF), and high-intensity fire (HF) zones. A total of 225 soil samples were analyzed using flume experiments at five slope gradients and five flow discharges, simulating rill erosion. Soil physical and chemical characteristics were measured, including hydraulic conductivity, organic carbon, sodium content, bulk density, and water repellency. The results showed that HF soils significantly exhibited higher rill detachment capacity (1.43 and 2.26 times the values compared to the LF and NF soils, respectively) and sodium content and lower organic carbon, hydraulic conductivity, and aggregate stability (p < 0.01). Strong correlations were found between Dc and various soil properties, particularly a negative relationship with organic carbon. The multiple linear equation had good accuracy (R² > 0.78) in predicting rill detachment capacity. The findings of the current study show the significant impact of fire on soil degradation and rill erosion potential. The study advocates an urgent need for effective post-fire land management, erosion control, and the development of sustainable soil restoration strategies. Full article

The increasing concentration of greenhouse gases is amplifying the global risk of drought on crop productivity. This study sought to investigate the effects of drought on the growth of maize (Zea mays L.) seedlings. A total of 78 maize hybrids were employed in this study to replicate drought conditions through the potting method. The maize seedlings were subjected to a 10-day period of water breakage following a standard watering cycle until they reached the third leaf collar (V3) stage. Parameters including plant height, stem diameter, chlorophyll content, and root number were assessed. The eight phenotypic traits include the fresh and dry weights of both the aboveground and underground parts. Three machine learning methods—random forest (RF), K-nearest neighbor (KNN), and extreme gradient boosting (XGBoost)—were employed to systematically analyze the relevant traits of maize seedlings’ drought tolerance and to assess their predictive performance in this regard. The findings indicated that plant height, aboveground weight, and chlorophyll content constituted the primary indices for phenotyping maize seedlings under drought conditions. The XGBoost model demonstrated optimal performance in the classification (AUC = 0.993) and regression (R² = 0.863) tasks, establishing itself as the most effective prediction model. This study provides a foundation for the feasibility and reliability of screening drought-tolerant maize varieties and refining precision breeding strategies. Full article

This study investigated the prediction of unconfined compressive strength (UCS), a common measure of soil’s undrained shear strength, using fundamental soil characteristics. While traditional pavement subgrade design often relies on parameters like the resilient modulus and California bearing ratio (CBR), researchers are exploring the potential of incorporating more easily obtainable strength indicators, such as UCS. To evaluate the potential effectiveness of UCS for pavement engineering applications, a dataset of 152 laboratory-tested soil samples was compiled to develop predictive models. For each sample, geotechnical properties including the Atterberg limits, liquid limit (LL), plastic limit (PL), water content (WC), and bulk density (determined using the Harvard miniature compaction apparatus), alongside the UCS, were measured. This dataset served to train various models to estimate the UCS from basic soil parameters. The methods employed included multi-linear regression (MLR), multi-nonlinear regression (MNLR), and several machine learning techniques: backpropagation artificial neural networks (ANNs), gradient boosting (GB), random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN). The aim was to establish a relationship between the dependent variable (UCS) and the independent basic geotechnical properties and to test the effectiveness of each ML algorithm in predicting UCS. The results indicate that the ANN-based model provided the most accurate predictions for UCS, achieving an R² of 0.83, a root-mean-squared error (RMSE) of 1.11, and a mean absolute relative error (MARE) of 0.42. The performance ranking of the other models, from best to worst, was RF, GB, SV, KNN, MLR, and MNLR. Full article

Prefabricated components inevitably generate numerous assembly joints during installation, with each 1 mm increase in joint width correlating to a 15–20% elevation in the annual occurrence frequency of the frost formation risk. In the Inner Mongolia Region, the water migration at wall connection interfaces during winter significantly exacerbates freeze–thaw damage due to persistent thermal gradients. A coupled heat–moisture transfer model incorporating gas–liquid–solid phase transitions was developed, with the liquid moisture content and temperature gradient as dual driving forces. A validation against internationally recognized BS EN 15026:2007 benchmark cases confirmed the model robustness. The prefabricated sandwich insulation walls reconstructed with region-specific volcanic ash materials underwent a comparative evaluation of temperature and relative humidity distributions under varied winter conditions. Furthermore, we analyze and assess the potential for freezing at connection points and identify the specific areas at risk. Synergistic effects between assembly gaps and indoor–outdoor environmental interactions on wall performance degradation were systematically assessed. The results indicated that, across all working conditions, both the temperature and relative humidity at each wall measurement point underwent periodic variations influenced by the outdoor environment. These fluctuations decreased in amplitude from the exterior to the interior, accompanied by a noticeable delay effect. Specifically, at Section 2, the wall temperatures at points B2–B8 were higher compared to those at A2–A8 of Section 1. The relative humidity gradient remained relatively stable at each measurement point, while the temperature fluctuation amplitude was smaller by 2.58 ± 0.3 °C compared to Section 1. Under subfreezing conditions, Section 1 demonstrates a marked reduction in relative humidity (Cases 1-3 and 2-3) compared to reference cases, which is indicative of internal ice crystallization. Conversely, Section 2 maintains higher relative humidity values under identical therma. These findings suggest that prefabricated building joints significantly impact indoor and outdoor wall temperatures, potentially increasing the indoor heat loss and accelerating temperature transfer during winter. Full article

Plants dynamically regulate ions in the tree to defend against abiotic stresses such as drought and saline-alkali, However, it is not clear how ‘Junzao’ jujube regulates ions to maintain a normal life cycle under saline-alkali stress. Therefore, in this study, the roots of 10-year old steer jujube trees were watered using a saline and alkaline gradient solution simulating the main salt (NaCl) and alkali (NaHCO₃) of Aral with NaCl:NaHCO₃ = 3:1 gradient of 0, 60, 180, and 300 mM, and three jujube trees with uniform growth were taken as samples in each treatment plot, and the ion contents of potassium (K), sodium (Na), calcium (Ca), magnesium (Mg), iron (Fe), manganese (Mn), zinc (Zn) and carbon (C) in each organ of the fruit at the dot red period (S1) and full-red period (S2) were determined, in order to elucidate the relationship between physiological adaptation mechanisms of saline-alkali tolerance and the characteristics of mineral nutrient uptake and utilisation in jujube fruit. The results showed that under saline-alkali stress, Na was stored in large quantities in the roots, Ca and Mg in the perennial branches at S1, Na and Fe in the leaves at S2, and K, Mg and Mn in the perennial branches. There was no significant difference in the distribution of C content in various organs of ‘Junzao’. Compared with CK (0 mM), under salinity stress, the K content in the leaves was significantly reduced at S1 and S2, and the K/Na ratios remained > 1.0. At S2, under medium and high concentrations of saline-alkali stress (180–300 mM), the K/Na is less than 1, and the ionic homeostasis was disrupted, and the leaves die and fall off, and the Na is excreted from the body. The selective transport coefficients SK/Na, SCa/Na and SMg/Na from root to leaf showed a downward trend at S1, but still maintained positive transport capacity. At S2, this stage is close to leaf fall, the nutrient transport coefficient is less than 1, and a large amount of nutrients are returned to the perennial branches and roots occurred. These results indicated that the mechanism of nutrient regulation and salt tolerance in jujube trees was different at different growth stages. Full article

Soil moisture content (SMC) is a critical parameter for agricultural productivity, particularly in semi-arid regions, where irrigation practices are extensively used to offset water deficits and ensure decent yields. Yet, the socio-economic and remote context of these regions prevents sufficiently dense SMC monitoring in space and time to support farmers in their work to avoid unsustainable irrigation practices and preserve water resource availability. In this context, our study addresses the challenge of high spatial resolution (i.e., 20 m) SMC estimation by integrating remote sensing datasets in machine learning models. For this purpose, a dataset made of 166 soil samples’ SMC along with corresponding SMC, precipitation, and radar signal derived from Soil Moisture Active Passive (SMAP), Integrated Multi-satellitE Retrievals for GPM (IMERG), and Sentinel-1 (S1), respectively, was used to assess four machine learning models’ (Decision Tree—DT, Random Forest—RF, Gradient Boosting—GB, Extreme Gradient Boosting—XGB) reliability for SMC mapping. First, each model was trained/validated using only the coarse spatial resolution (i.e., 10 km) SMAP SMC and IMERG precipitation estimates as independent features, and, second, S1 information (i.e., 20 m) derived from single scenes and/or composite images was added as independent features to highlight the benefit of information (i.e., S1 information) for SMC mapping at high spatial resolution (i.e., 20 m). Results show that integrating S1 information from both single scenes and composite images to SMAP SMC and IMERG precipitation data significantly improves model reliability, as R² increased by 12% to 16%, while RMSE decreased by 10% to 18%, depending on the considered model (i.e., RF, XGB, DT, GB). Overall, all models provided reliable SMC estimates at 20 m spatial resolution, with the GB model performing the best (R² = 0.86, RMSE = 2.55%). Full article

Seawater desalination is considered the first option to meet the domestic and industrial requirements of freshwater in desert areas, such as the Atacama Desert (Chile). However, its environmental implications remain poorly characterized. This study evaluated the effects of brine discharge from a desalination plant located in Caldera Bay, where fishing and tourism coexist. Sampling was conducted at increasing distances from the outfall to assess physicochemical parameters, sediment metal content, and nutrient concentrations. The results revealed a clear spatial gradient: salinity decreased from 57.75 to 34.87 PSU and nitrate from 10.49 to 4.05 µM. The sediment samples near the outfall showed elevated concentrations of Al, Fe, and Cr(VI). These findings suggest that brine discharge alters water chemistry and sediment quality. This study highlights the need for long-term environmental monitoring and regulatory frameworks to ensure sustainable desalination in sensitive coastal systems. Full article