The cohesion and internal friction angle of loess are important macro-mechanical parameters for evaluating the safety and stability of engineering construction. Traditional laboratory measurement methods are time-consuming and difficult to conduct on-site. This study aims to compare the effectiveness of five Machine Learning (ML) methods, namely Random Forest (RF), Support Vector Machine (SVM), Back Propagation Neural Network (BPNN), BPNN optimized by Particle Swarm Optimization (PSO-BPNN) and BPNN optimized by Genetic Algorithm (GA-BPNN), in predicting the macro-mechanical properties of loess. To this end, the study collected data from 89 undisturbed loess samples and 229 remolded loess samples to construct training and testing datasets, and used three correlation analysis methods to analyze the influence of physical parameters on mechanical properties. The study found that the water content has the most significant impact on the mechanical properties of loess. In terms of prediction ability, SVM performs the best among the ML methods used, and the determination coefficient for cohesion of undisturbed loess reaches 0.857. Although the training data is limited, the prediction performance of BPNN is significantly improved after being optimized by PSO or GA. The research results show that ML provides an effective way to study the complex mechanical behavior of loess.

Assessment of seismic deformations of geosynthetic reinforced soil (GRS) walls in literature has dealt with unsolved challenges, encompassing time-consuming analyses, lack of probabilistic-based analyses, ignored inherent uncertainties of seismic loadings and limited investigated scenarios of these structures, especially for tall walls. Hence, a novel multiple analysis method has been proposed, founded on over 257,400 machine learning simulations (trained with 1582 finite element method analyses) and numerous performance-based fragility curves, to promptly evaluate the seismic vulnerability. The conducted probabilistic parametric study revealed that simultaneously considering several intensity measures for fragility curves is inevitable, preventing engineering judgement bias (up to 52% discrepancies in damage possibilities). Up to 75% contrasts between failure possibilities of 8 and 20 m walls, especially under earthquakes with common intensities (e.g. PGA <= 0.3g), raised serious concerns in the application of height-independent designing methods of GRS walls (e.g. AASHTO Simplified Method). Decreases in deformation possibilities were nearly the same due to increasing reinforcement stiffness (J) (1000 to 2000 kN/m) and reinforcement length to wall height ratio (L/H) (0.8 to 1.5); a decisive superiority of J variations over increasing L/H, as a remedial plan. The proposed methodology privileges engineers to swiftly assess the seismic deformations of multiple GRS walls at the design stage.

Heavy metal (HM) pollution in agricultural soils threatens plant growth and food security, underscoring the urgency for sustainable and eco-friendly solutions. This study investigates the potential of endophytic fungi, Fusarium proliferatum SL3 and Aspergillus terreus MGRF2, in mitigating nickel (Ni) and cadmium (Cd) stress in Solanum lycopersicum (tomato). These fungi were evaluated for their plant growth-promoting traits, including the production of indole-3-acetic acid (IAA) and siderophores, offering a sustainable strategy for alleviating HM toxicity. Inoculation with SL3 and MGRF2 significantly reduced metal accumulation in plant tissues by enhancing metal immobilization and modifying root architecture. Microscopic analysis revealed that fungi protected root epidermal cells from Ni- and Cd-induced damage, preserving cellular integrity and preventing plasmolysis. Fungal-treated plants exhibited improved growth and biomass, with SL3 demonstrating superior Cd stress mitigation and MGRF2 excelling under Ni stress. Photosynthetic pigment levels, including chlorophyll-a and carotenoids, were restored, highlighting the role of fungi in maintaining photosynthetic efficiency. Antioxidant activity was also modulated, as reduced glutathione (GSH) levels and increased flavonoid production were observed, contributing to enhanced oxidative stress management. Hormonal profiling revealed that fungal inoculation balanced stress-induced hormonal disruptions, with lower abscisic acid (ABA) levels and improved salicylic acid (SA) and gibberellic acid (GA) pathways. These changes facilitated better stress adaptation, enhanced nutrient uptake, and improved physiological performance. qRT-PCR analysis further revealed differential gene expression patterns, while antioxidant enzyme activity strengthened the plants' defense against HMinduced oxidative damage. Multivariate analyses highlighted shoot and root traits as critical indicators of resilience, with fungal inoculation driving substantial improvements. These findings demonstrate the potential of SL3 and MGRF2 as eco-friendly bioinoculants, offering a sustainable and cost-effective approach to reducing HMs toxicity in contaminated soils while enhancing crop productivity. This work highlights the promising role of plant-microbe interactions in advancing sustainable agriculture and addressing the challenges posed by heavy metal pollution.

Voronoi tessellations are a mathematical concept that appears in many examples in nature, such as the skin of giraffes, dry soil, and vegetable cells. In the context of biomimicry, these tessellations have been used to build impressive structures worldwide that are both aesthetically pleasing and structurally efficient. This paper proposes a methodology based on genetic algorithms (GA) to determine the structural topology of Voronoi flat roofs with tubular steel cross sections and a given boundary. The design variables correspond to the number and position of the Voronoi centers that form the tessellations within the roof, as well as the dimensions of the structural elements. This representation of the design variables creates an unstructured optimization problem. Such characteristic is addressed by an implicit redundant representation of possible solutions, which generates chromosomes with varying numbers of variables. The objective function relates to the weight of the roof, considering constraints raised in technical and constructive issues. The methodology was applied to four different roof boundaries: triangular, pentagonal, square, and rhombic. In general, the results provide optimal aesthetic solutions with a few Voronoi tessellations, based on the algorithm configuration and the multimodal nature of the search space. Convergence analysis indicates the possibility of the algorithm getting stuck in an optimum local and shows the progressive reduction of Voronoi centers. Lastly, it is observed that the maximum displacement constraint leads to the shape of the optimal roof.

Heavy metal (HM) pollution has become a major environmental concern due to increased anthropogenic activities. The persistence and toxicity of HMs pose significant risks to ecosystems, biodiversity, and human health. This review highlights the pressing issue of HM contamination, its impact on ecosystems, and the potential risks of bio-magnification. Addressing these issues requires sustainable and cost-effective solutions. Among various remediation strategies, phytoremediation stands out as a promising green technology for mitigating environmental damage by using plants to extract or detoxify contaminants. A key challenge in phytoremediation, however, is the management of large volumes of contaminated biomass. This study explores the integration of phytoremediation with biofuel production, which not only addresses biomass management but also offers a sustainable solution within the framework of the circular economy. The dual role of specific plant species in both phytoremediation and biofuel production is evaluated, providing reduced environmental waste, lowering remediation costs, and promoting energy security. Future advancements in plant engineering, biotechnology, and process optimization hold the potential to enhance phytoremediation efficiency and biofuel yields. Expanding research into metal-tolerant, high-biomass crops can further improve scalability and economic feasibility. The review also critically assesses challenges such as the safe handling of contaminated biomass, sustainability concerns, and existing research gaps. By merging environmental remediation with bioenergy production, this interdisciplinary approach presents a viable pathway toward sustainable development.

Global climate change and permafrost degradation have significantly heightened the risk of geological hazards in high-altitude cold regions, resulting in severe casualties and property damage, particularly in the Qinghai-Tibet Plateau of China. To mitigate the risk of geological disasters, it is crucial to identify the primary disaster-inducing factors. Therefore, to address this issue more effectively, this study proposes a spatiotemporal-scale approach for detecting disaster-inducing factors and investigates the disaster-inducing factors of geological hazards in high-altitude cold regions, using the Kanchenjunga Basin as a case study. As the world's third-highest peak, Kanchenjunga is highly sensitive to climate fluctuations. This study first integrates the frost heave model and multitemporal interferometric synthetic aperture radar techniques to monitor ascending and descending track line-of-sight deformation of the frozen active layer in the study area. Subsequently, the surface parallel flow constrained model is employed to decompose the 3-D time-series deformation of geological hazards in the basin, with remote sensing imagery and field surveys used to identify a total of 94 disaster sites. In parallel, a database of potential conditioning factors is constructed by leveraging Google Earth Engine remote sensing inversion technology and relevant data provided by the China Geological Survey. Finally, by integrating monitoring results with a database of potential geological conditioning factors, the spatiotemporal-scale approach for detecting disaster-inducing factors proposed in this study is applied to investigate the disaster-inducing factors in the Kanchenjunga Basin. The research results highlight that surface temperature is the primary driving factor of geological hazards in the Kanchenjunga Basin. This research helps bridge the data gap in the region and offers critical support for local government decision-making in disaster prevention, risk assessment, and related areas.

Floods accompanied by thunderstorms in developed cities are hazardous, causing damage to infrastructure. To secure infrastructure, it is important to employ an integrated approach, combining remote sensing, GIS and precipitation data. The model was developed based on the estimation of event-based runoff and investigated the relationship between runoff and impervious surfaces. The novel approach of combining Hydrologic Engineering Center's River Analysis System (HEC-GeoRAS) along with satellite imagery was utilized, where spatial data was combined with real-time values to run the model. As a first step, the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) model was fed with information about precipitation, slope, soil type, as well as land use and land cover. The results reveal that the subbasins of Deira, Nief and Jumeirah have the largest impervious area and, thus, a higher probability of flood occurrence. The model was calibrated and validated using previous runoff events and by comparing observed and simulated streak flow and peak discharge against those reported in previous studies. It was found that the model is efficient and can be used in similar regions.

Aerosols affect Earth's climate both directly and indirectly, which is the largest uncertainty in the assessment of radiative forcings affecting anthropogenic climate change. The standard Aerosol Robotic Network (AERONET) aerosol products have been widely used for more than 30 years. Currently, there is strong community interest in the possibility of determining aerosol composition directly from remote sensing observations. This work presents the results of applying such a recently developed approach by Li et al. to extended datasets of the directional sky radiances and spectral aerosol optical depth (AOD) measured by AERONET for the retrievals of aerosol components. First, the validation of aerosol optical properties retrieved by this component approach with AERONET standard products shows good agreement. Then, spatiotemporal variations of the obtained aerosol component concentration are characterized globally, especially the absorbing aerosol species (black carbon, brown carbon, and iron oxides) and scattering aerosol species (organic carbon, quartz, and inorganic salts). Finally, we compared the black carbon (BC) and dust column concentration retrievals to the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), products in several regions of interest (Amazon zone, Desert, and Taklamakan Desert) for new insights on the quantitative assessment of MERRA-2 aerosol composition products (R = 0.60-0.85 for BC; R = 0.75-0.90 for dust). The new value-added and long-term aerosol composition product globally is available online (https://doi.org/10.6084/ m9.figshare.25415239.v1), which provides important measurements for the improvement and optimization of aerosol modeling to enhance estimation of the aerosol radiative forcing. SIGNIFICANCE STATEMENT: In the assessment of climate change, the uncertainty associated with aerosol radiative forcing is the largest one. The purpose of this study is to provide a new value-added and long-term aerosol composition (including absorbing and scattering aerosol species) inversion dataset derived from Aerosol Robotic Network (AERONET) measurements for characterizing their spatiotemporal variations at global scale. We find some new insights on the quantitative assessment of black carbon and dust column concentration products in the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). Our results and aerosol composition inversion dataset will provide robust support for the overall improvement and optimization of aerosol modeling to better understand the aerosol radiative forcing.

Rice is the primary grain crop in China, and the quality of rice is closely related to the external environment, such as soil characteristics, climate, sunshine time, and irrigation water. The high-quality rice-origin area has certain regional limitations. Therefore,the rice can be seen as an apparent geographical marker. There are often some counterfeits or branded famous high-quality rice in the market, which can damage the rice brand, reduce the rice quality guarantee of consumers, and disturb the market stability, so rapid identification technology of rice origin is needed. The rice origin identification models of five sources in Jilin Province (Daan, Gongzhuling, Qianguo, Songyuan and Taoerhe) are done by laser-induced breakdown spectroscopy and machine learning algorithms. The principal component analysis (PCA) algorithm, combined with four machine learning algorithms, Bagged Trees, Weighted KNN, Quadratic SVM, and Coaster Gaussian SVM, has been established. A total of 450 groups of LIBS data are selected. The spectral data of rice LIBS are pretreated with Savitzky-Golay smoothing (SG smoothing) is used for noise reduction and normalisation. The principal component analysis uses the rice LIBS data, which shows that the rice origins had an excellent cluster distribution of clustering spaces. Still, there is spatial overlap in some rice origins. Utilising5x cross-validation, the identification accuracy of rice origins can reachmore than 91.8% by adopting PCA-Bagged Trees, PCA-Weighted KNN, PCA-Quadratic SVM and PCA-Coarse Gaussian SVM, and the recognition accuracy of PCA-Quadratic SVM model is as high as 97.3%. The results show that the combination of LIBS technology and machine learning algorithms can identify rice origin with high precision and high efficiency.

New flood records are being set across the world as precipitation patterns change due to a warming climate. Despite the presence of longstanding water management infrastructure like levees and reservoirs, this rise in flooding has been met with property damage, loss of life, and hundreds of billions in economic impact, suggesting the need for new solutions. In this work, the authors suggest the active management of distributed networks of ponds, wetlands and retention basins that already exist across watersheds for the mitigation of flood damages. As an example of this approach, we investigate optimal control of the gated outlets of 130 such locations within a small watershed using linear programming, genetic algorithms, and particle swarm optimization, with the objective of reducing downstream flow and maximizing basin storage. When compared with passive operation (i.e., no gated outlets) and a uniformly applied active management scheme designed to store water during heavy rainfall, the optimal control techniques (1) reduce the magnitudes of peak flow events by up to 10%, (2) reduce the duration of flood crests for up to several days, and (3) preserve additional storage across the watershed for future rainfall events when compared with active management. Combined, these findings provide both a better understanding of dynamically controlled distributed storage as a flood fighting technique and a springboard for future work aimed at its use for reducing flood impacts.