Mechanical properties and disintegration behavior of EICP-reinforced sea sand subjected to drying-wetting cycles

Enzyme-induced carbonate precipitation(EICP)has emerged promising in various geotechnical applications,and has been presented as an alternative to the traditional cementitious materials-based ground improvement method.However,the study on mechanical properties and disintegration behavior of EICP-reinforced sea sand subjected to drying-wetting cycles are limited.This study investigated the mechanical properties and disintegration behavior of EICP-reinforced sea sand against the impact of drying-wetting(D-W)cycles.The uniaxial compressive strength(UCS)tests were performed to discuss the effect of drying-wetting cycles on the mechanical behavior of EICP-treated sea sand.The disintegration tests were conducted on EICP-treated sea sand to investigate the disintegration resistance of bio-cemented samples with various cementation levels.The microstructures of samples before and after disintegration were examined to disclose the disintegration mechanisms of EICP-reinforced sea sand.D-W cycles significantly affect the mechanical properties of EICP-reinforced sea sand,with UCS decreasing by 63.7%after undergoing 15 D-W cycles.The disintegration resistance index of specimens with a lower cementation level decreases significantly under the effect of D-W treatment.The higher disintegration resistance of specimens with higher cementation can be attributed to more crystals with better crystallinity formed in the contact point between sand particles within specimen.The crystals formed by soybean husk urease are mainly calcite and the crystallinity of spherical calcites would gradually change into larger rhombic calcite with further bio-grouting.The crystal with poor crystallinity is susceptible to the effect of D-W treatment,resulting in the obvious disintegration of EICP-reinforced sea sand.Overall,this study is expected to provide useful guidance on the long-term stability and drying-wetting disintegration mechanisms of EICP-reinforced sea sand.

SNCDM: Spinal Tumor Detection from MRI Images Using Optimized Super-Pixel Segmentation

Conferring to the American Association of Neurological Surgeons(AANS)survey,85%to 99%of people are affected by spinal cord tumors.The symptoms are varied depending on the tumor’s location and size.Up-to-the-min-ute,back pain is one of the essential symptoms,but it does not have a specific symptom to recognize at the earlier stage.Numerous significant research studies have been conducted to improve spine tumor recognition accuracy.Nevertheless,the traditional systems are consuming high time to extract the specific region and features.Improper identification of the tumor region affects the predictive tumor rate and causes the maximum error-classification problem.Consequently,in this work,Super-pixel analytics Numerical Characteristics Disintegration Model(SNCDM)is used to segment the tumor affected region.Estimating the super-pix-els of the affected region by this method reduces the variance between the iden-tified pixels.Further,the super-pixels are selected according to the optimized convolution network that effectively extracts the vertebral super-pixels features.Derived super-pixels improve the network learning and training process,which minimizes the maximum error classification problem also the efficiency of the system was evaluated using experimental results and analysis.