Post-grouting pile technology has gained extensive application in collapsible loess regions through the injection of slurry to compress and consolidate the soil at the pile base, thereby forming an enlarged base that enhances the foundation's bearing capacity and reduces settlement. Despite the prevalent unsaturated state of loess in most scenarios, the conventional design methodologies for piles in collapsible loess predominantly rely on saturated soil mechanics principles. The infiltration of water can significantly deteriorate the mechanical properties of loess due to the reduction in matric suction and the occurrence of collapsible deformation, leading to a substantial degradation in the bearing behavior of piles. To explore the variations in load transfer mechanisms of post-grouting piles in collapsible loess under conditions of intense precipitation, a comprehensive large-scale model test was conducted. The findings revealed that the post-grouting technique effectively mitigates the adverse effects of negative pile shaft friction in saturated zones on the pile's bearing behavior. Furthermore, the failure criteria for piles may shift from the shear failure of the base soil to excessive pile settlement. By incorporating principles of unsaturated soil mechanics, modified load transfer curves were developed to describe the mobilization of both pile shaft friction and base resistance. These curves facilitate the extension of the traditional load transfer method to post-grouting piles in collapsible soils under extreme weather conditions. The proposed revised load transfer method is characterized by its simplicity, requiring only a few soil indices and mechanical properties, making it highly applicable in engineering practice.

Seasonal freezing and thawing significantly influence the migration and distribution of soil hydrothermal salts. Understanding the dynamics of hydrothermal salt forces in canal foundation soils is crucial for effective canal disease control and optimization. However, the impact on rectangular canals remains poorly understood. Therefore, field-scale studies on water-heat-salt-force-displacement monitoring were conducted for the canal. The study analyzed the changes and interaction mechanisms of water-heat-salt-force in the soil beneath the canal, along with the damage mechanisms and preventive measures. The results indicate that the most rapid changes in temperature, moisture, and salt occur in the subsoil on the canal side, with the greatest depth of freezing. Heat transfer efficiency provides an intuitive explanation for the sensitivity of ground temperature at the junction of the canal wall and subsoil to air temperature fluctuations, as well as the minimal moisture migration in this region under the subcooling effect. The temperature-moisture curve suggests that current waterheat-force and water-heat-salt-force models exhibit a delay in accurately predicting water migration within the subsoil. Rectangular canals are more susceptible to damage under peak freezing conditions, requiring a combined approach of freezing restraint and frost-heaving force to mitigate damage. These findings offer valuable insights for canal design, maintenance, and further research.

This study integrates a dynamic plant growth model with a three-dimensional (3D) radiative transfer model (RTM) for maize traits retrieval using high spatial-spectral resolution airborne data. The research combines the Discrete Anisotropic Radiative Transfer (DART) model with the Dynamic L-System-based Architectural maize (DLAmaize) growth model to simulate field reflectance. Comparison with the 1D RTM SAIL revealed limitations in representing row structure effects, field slope, and complex light-canopy interactions. Novel Global Sensitivity Analyses (GSA) were carried out using dependence-based methods to overcome limitations traditional variance-based approaches, enabling better characterization of hyperspectral sensitivity to changes in leaf biochemistry, canopy architecture, and soil moisture. GSA provided complementary results to assess estimation uncertainties of the proposed traits retrieval method across growth stages. A hybrid inversion framework combining DART simulations with an active learning strategy using Kernel Ridge Regression was implemented for traits estimation. The approach was validated using ground data and HyPlant-DUAL airborne hyperspectral images from two field campaigns in 2018 and achieved high retrieval accuracy of key maize traits: leaf area index (LAI, R2=0.91, RMSE=0.42 m2/m2), leaf chlorophyll content (LCC, R2=0.61, RMSE=3.89 mu g/cm2), leaf nitrogen content (LNC, R2=0.86, RMSE=1.13 x 10-2 mg/cm2), leaf dry matter content (LMA, R2=0.84, RMSE=0.15 mg/cm2), and leaf water content (LWC, R2=0.78, RMSE=0.88 mg/cm2). The validated models were used to generate two-date 10 m resolution maps, showing good spatial consistency and traits dynamics. The findings demonstrate that integrating 3D RTMs with dynamic growth models is suited for maize trait mapping from hyperspectral data in varying growing conditions.

Energy piles, which serve the dual functions of load-bearing and geothermal energy exchange, are often modeled with surrounding soil assumed to be either fully saturated or completely dry in existing design and computational methods. These simplifications neglect soil saturation variability, leading to reduced predictive accuracy of the thermomechanical response of energy piles. This study proposes a novel theoretical framework for predicting the thermo-hydro-mechanical (THM) behavior of energy piles in partially saturated soils. The framework incorporates the effects of temperature and hydraulic conditions on the mechanical properties of partially saturated soils and pile-soil interface. A modified cyclic generalized nonlinear softening model and a cyclic hyperbolic model were developed to describe the interface shear stress-displacement relationship at the pile shaft and base, respectively. Governing equations for the load-settlement behavior of energy piles in partially saturated soils were derived using the load transfer method (LTM) and solved numerically using the matrix displacement method. The proposed approach was validated against experimental data from both field and centrifuge tests, demonstrating strong predictive performance. Specifically, the average relative error (ARE) was less than 15% for saturated soils and below 23% for unsaturated soils when evaporation effects were considered. Finally, parametric analyses were conducted to assess the effects of flow rate, groundwater table position, and softening parameters on the THM behavior of energy piles. This framework can offer a valuable tool for predicting THM behavior of energy piles in partially saturated soils, supporting their broader application as a sustainable foundation solution in geotechnical engineering.

A novel framework for nonlinear thermal elastic-viscoplastic (TEVP) constitutive relationships was proposed in this study, incorporating three distinct thermoplasticity mechanisms. These four TEVP formulations, combined with an existing TEVP constitutive equation presented in the companion paper, were integrated into a coupled consolidation and heat transfer (CHT) numerical model. The CHT model accounts for large strain, soil selfweight, creep strains, thermal-induced strains, the relative velocity of fluid and solid phases, varying hydraulic conductivity and compressibility during consolidation process, time-dependent loading, and heat transfer, including thermal conduction, thermo-mechanical dispersion, and advection. The performance of CHT model, incorporating different TEVP constitutive equations, was evaluated through comparing the simulation results with measurements from laboratory oedometer tests. Simulation results, including settlement, excess pore pressure and temperature profiles, showed good agreement with the experimental data. All four TEVP constitutive relationships produced identical results for the consolidation behavior of soil that in the oedometer tests. The TEVP constitutive equations may not have a significant effect on the heat transfer in soil layers because of the identical performance on simulating soil compression. The CHT model, incorporating the four TEVP constitutive equations, was then used to investigate the long-term consolidation and heat transfer behavior of a four layer soil stratum under seasonally cyclic thermal loading in a field test, with excellent agreement observed between simulated results and measured data.

Calculation and prediction of the uplift capacity of squeezed branch piles (SBP) are still immature. This study develops a method to predict the load-displacement relationship and ultimate capacity of SBP under pullup load by using a hyperbolic model to describe the nonlinear load transfer between pile-soil and plate-soil. The uplift bearing behaviors of SBP are analyzed through six sets of indoor model tests in homogeneous soils. The results, along with field tests of single-plate piles in layered soils and the indoor tests, confirm the high accuracy of the theoretical prediction method. The effects of three factors, including the pile side soil damage ratio (Rf), the horizontal earth pressure coefficient (k) and the damage angles of the soil under plate (psi), on the prediction results are analyzed. The results show that these factors significantly affect the second half of the loaddisplacement curve of SBP. Furthermore, as the Rf rises, the anticipated ultimate uplift capacity of SBP decreases linearly; as the k rises, it increases linearly; and as the psi rises, it increases nonlinearly.

The environmental threat, pollution and damage posed by heavy metals to air, water, and soil emphasize the critical need for effective remediation strategies. This review mainly focuses on microbial electrochemical technologies (MET) for treating heavy metal pollutants, specifically includes Chromium (Cr), Copper (Cu), Zinc (Zn), Cadmium (Cd), Lead (Pb), Nickel (Ni), and Cobalt (Co). First, it explores the mechanisms and current applications of MET in heavy metal treatments in detail. Second, it systematically summarizes the key microbial communities involved, analyzing their extracellular electron transfer (EET) processes and summarizing strategies to enhance the EET efficiencies. Next, the review also highlights the synergistic microbial interactions in bioelectrochemical systems (BES) during the recovery and removal (remediation) processes of heavy metals, underscoring the crucial role of microorganisms in the transfer of the electrons. Then, this paper discussed how factors including pH values, applied voltages, types and concentrations of electron donors, electrode materials, biofilm thickness and other factors affect the treatment efficiencies of some specific metals in BES. BES has shown its great superiority in treating heavy metals. For example, for the treatments of Cr6+, under low pH conditions, the recovery and removal rate of Cr-6(+) by double chambers microbial fuel cell (DCMFC) can generally reach 98-99%, with some cases even achieving 100% (Gangadharan & Nambi, 2015). For the treatments of heavy metal ions such as Cu2+, Zn2+ and Cd2+, BES can also achieve the rates of treatments of more than 90% under the corresponding conditions of appropriate pH values and applied voltages(Choi, Hu, & Lim, 2014; W. Teng, G. Liu, H. Luo, R. Zhang, & Y. Xiang, 2016; Y. N. Wu et al., 2019; Y. N. Wu et al., 2018). After that, the review outlines the future challenges and the research opportunities for understanding the mechanisms of BES and microbial EET in heavy metal treatments. Finally, the prospect of future BES researches are pointed out, including the combinations with existing wastewater treatment systems, the integrations with the wind energy and the solar energy, and the application of machine learning (ML) in future BES. This article has certain significance and value for readers to better understand the working principles of BES and better operate and control BES to deal with heavy metal pollutants.

The wheat powdery mildew (WPM) is one of the most severe crop diseases worldwide, affecting wheat growth and causing yield losses. The WPM was a bottom-up disease that caused the loss of cell integrity, leaf wilting, and canopy structure damage with these symptoms altering the crop's functional traits (CFT) and canopy spectra. The unmanned aerial vehicle (UAV)-based hyperspectral analysis became a mainstream method for WPM detection. However, the CFT changes experienced by infected wheats, the relationship between CFT and canopy spectra, and their role in WPM detection remained unclear, which might blur the understanding for the WPM infection. Therefore, this study aimed to propose a new method that considered the role of CFT for detecting WPM and estimating disease severity. The UAV hyperspectral data used in this study were collected from the Plant Protection Institute's research demonstration base, Xinxiang city, China, covering a broad range of WPM severity (0-85 %) from 2022 to 2024. The potential of eight CFT [leaf structure parameter (N), leaf area index (LAI), chlorophyll a + b content (Cab), carotenoids (Car), Car/Cab, anthocyanins (Ant), canopy chlorophyll content (CCC) and average leaf angle (Deg)] obtained from a hybrid method combining a radiative transfer model and random forest (RF) and fifty-five narrow-band hyperspectral indices (NHI) was explored in WPM detection. Results indicated that N, Cab, Ant, Car, LAI, and CCC showed a decreasing trend with increasing disease severity, while Deg and Car/Cab exhibited the opposite pattern. There were marked differences between healthy samples and the two higher infection levels (moderate and severe infection) for Cab, Car, LAI, Deg, CCC, and Car/Cab. N and Ant only showed significant differences between the healthy samples and the highest infection level (severe infection). As Cab, Car, and Ant decreased, the spectral reflectance in the visible light region increased. The decrease in N and LAI was accompanied by a reduction in reflectance across the entire spectral range and the near-infrared area, which was exactly the opposite of Deg. The introduction of CFT greatly improved the accuracy of the WPM severity estimation model with R2 of 0.92. Features related to photosynthesis, pigment content, and canopy structure played a decisive role in estimating WPM severity. Also, results found that the feature importance showed a remarkable interchange as increasing disease levels. Using features that described canopy structure changes, such as optimized soil adjusted vegetation index, LAI, visible atmospherically resistant indices, and CCC, the mild infection stage of this disease was most easily distinguished from healthy samples. In contrast, most severe impacts of WPM were best characterized by features related to photosynthesis (e.g., photochemical reflectance index 515) and pigment content (e.g., normalized phaeophytinization index). This study help deepen the understanding of symptoms and spectral responses caused by WPM infection.

Multiangle helical piles are used to support multidirectional loads. The load transfer behavior of inclined piles may differ from that of vertical piles. Vertical compressive and oblique uplift load field tests were conducted on a multiangle helical pile group and two single helical piles embedded in silty clay. The load-bearing capacities, group effects, load transfer behavior, earth pressure, and excess pore water pressure were investigated. The results show that the vertical compressive and oblique uplift capacities of the 10 degrees-inclined single helical pile were improved by 12% and 95% compared to those of the vertical single helical pile, respectively. The inclined installation of helical piles significantly optimized the load transfer mechanism of the piles under oblique loads. The group efficiency of the multiangle helical pile group was approximately 102%, attributed to the increased pile spacing resulting from the inclined installation. During loading, the helices and pile toe together contribute more than 50% of the bearing capacities of helical piles. The earth pressure and excess pore water pressure around the grouped helical pile, particularly near the bottom helix, exhibited less variation than those around the single pile, suggesting a smaller disturbance in the surrounding soil.

Component temperature and emissivity are crucial for understanding plant physiology and urban thermal dynamics. However, existing thermal infrared unmixing methods face challenges in simultaneous retrieval and multicomponent analysis. We propose Thermal Remote sensing Unmixing for Subpixel Temperature and emissivity with the Discrete Anisotropic Radiative Transfer model (TRUST-DART), a gradient-based multi-pixel physical method that simultaneously separates component temperature and emissivity from non-isothermal mixed pixels over urban areas. TRUST-DART utilizes the DART model and requires inputs including at-surface radiance imagery, downwelling sky irradiance, a 3D mock-up with component classification, and standard DART parameters (e.g., spatial resolution and skylight ratio). This method produces maps of component emissivity and temperature. The accuracy of TRUST-DART is evaluated using both vegetation and urban scenes, employing Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images and DART-simulated pseudo-ASTER images. Results show a residual radiance error is approximately 0.05 W/(m2 & sdot;sr). In absence of the co-registration and sensor noise errors, the median residual error of emissivity is approximately 0.02, and the median residual error of temperature is within 1 K. This novel approach significantly advances our ability to analyze thermal properties of urban areas, offering potential breakthroughs in urban environmental monitoring and planning. The source code of TRUSTDART is distributed together with DART (https://dart.omp.eu).