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Bio-cementation of weathered soils of hilly regions using native ureolytic bacteria is a practical approach to improve soil strength. This study aimed to isolate and characterize native urease-producing bacterial strains from different slope failure regions of Uttarakhand, India, through multi-step screening, and showcased their potential in soil improvement. A total of 80 bacterial strains were isolated from thirteen landslide-prone sites to screen for potent native urease-producing strains. Interestingly, nine native strains exhibited high calcium carbonate precipitation (158–187 mg/10 ml) and urease activity (657–3495 µM/mL) compared to Sporosarcina pasteurii (NCIM 2477), a potent bacterium commonly used for bio-cementation. These strains belonged to various genera, including Lysinibacillus sp., Sporosarcina sp., Pseudochrobactrum sp., and Cytobacillus sp. Cohesionless soil samples treated with the native strains exhibited high unconfined compressive strength values ranging from 0.1 MPa to 1 MPa, whereas those treated with S. pasteurii were 0.15 MPa. The urease-producing bacterial strains with CaCO3 precipitation activity influenced the soil strength and failure patterns. FESEM, XRD, and acid-washing tests revealed that native bacterial strains precipitated CaCO3 more than S. pasteurii strain. The study explores, for the first time, the potential of native ureolytic bacteria from different regions of Uttarakhand, India, for soil strength enhancement. [ABSTRACT FROM AUTHOR]

Introduction: The Indian Himalayas' susceptibility to landslides, particularly as a location where climate change effects may be event catalysts, necessitates the development of dependable landslide susceptibility maps (LSM). Method: This study diverges from traditional binary classification models, framing LSM as a positive-unlabeled learning problem. This approach acknowledges that regions without recorded landslides are not necessarily at low risk but could simply have not experienced landslides yet. The study utilizes novel positive-unlabeled learning-enhanced algorithms—Random Forest, K-Nearest Neighbor, and Decision Tree—to create LSM for Chamoli district, India. Eleven causative factors for landslides are identified, including elevation, aspect, slope, geology, geomorphology, distance to lineament, lithology, NDVI, distance to river, distance to road and residential land use. To address spatial correlation biases, instead of randomly splitting the dataset, the study adopts spatial splitting to get the training and testing datasets. Conclusion: The study reveals that positive-unlabeled learning substantially improves the Area Under Curve and recall, leading to a more conservative LSM compared to binary classification methods. Analysis shows that the southern region of Chamoli exhibits high recall but lower accuracy, suggesting a latent high landslide susceptibility despite a lack of historical landslides in this region. The study also quantifies the impact of human activity on landslide risk, indicating an elevated threat to life and the local economy, especially in Chamoli's southwestern areas. [ABSTRACT FROM AUTHOR]

Silicate materials such as quartz and silica glass are inherently brittle in nature. Here, using atomistic simulations, we demonstrate that the quartz crystal exhibits a brittle‐to‐ductile transition when irradiated. We show that the nanoscale plasticity observed in the irradiated structure is reminiscent of metal‐like plasticity, which is neither observed in the isochemical crystalline or glassy structure. Invoking an energy landscape approach, we demonstrate that the local atomic self‐organization facilitated by the shallow energy landscape is at the origin of this plastic behavior. Overall, the results suggest that the irradiation could be a methodology to induce a ductile transition in materials that are otherwise brittle. [ABSTRACT FROM AUTHOR]

Quantification of particle shape features to characterize granular materials remains an open problem till date, owing to the complexity involved in obtaining the geometrical parameters necessary to adequately compute the shape components (sphericity, roundness and roughness). A new computational method based on image analysis and filter techniques is proposed in this paper to overcome this difficulty. In this method, operations are performed on binary images of particles obtained from raster images (collection of pixels) by the process of image segmentation. The boundary of particles captured in 2D images consist of micro, meso and macro scale features on which filter techniques are applied to remove the micro level features for the quantification of particle roughness and to obtain a roughness free boundary. A robust algorithm is then written and implemented in MATLAB to obtain the complete geometry of the particle boundary (free from roughness features) and to identify the precise corner and non-corner regions along the boundary. This information is used to quantify the roundness (as per Wadell in J Geol 40:443-451, 1932) and sphericity of particles. The proposed methodology to measure roundness and sphericity is compared against standard visual charts provided by earlier researchers. Finally, the methodology is demonstrated on real soil particles falling across a wide range of sizes, shapes and mineralogical compositions. Also, an idea to comprehend the kinematics of particle motion based on its concavo-convex features is discussed with two proposed novel descriptors and a visual classification chart. [ABSTRACT FROM AUTHOR]