Incorporating waste byproducts into concrete is an innovative and promising way to minimize the environmental impact of waste material while maintaining and/or improving concrete’s mechanical characteristics and strength. The proper application of sawdust as a pozzolan in the building industry remains a significant challenge. Consequently, this study conducted an experimental evaluation of sawdust as a fill material. In particular, sawdust as a fine aggregate in concrete offers a realistic structural and economical possibility for the construction of lightweight structural systems. Failure under four-point loads was investigated for six concrete-filled steel tube (CFST) specimens. The results indicated that recycled lightweight concrete performed similarly to conventional concrete when used as a filler material in composite steel tube beams. The structural effects of sawdust substitution on ultimate load and initial stiffness were less substantial than the relative changes in the material properties, and the ultimate capacity of the tested samples decreased moderately as the substitution percentage of sawdust increased. Moreover, the maximum load capacity was observed to decrease by 6.43–30.71% for sawdust replacement levels between 5% and 45.1% across all tested samples. Additionally, when using lightweight concrete with 5% sawdust, the moment value of the CFST sample was reduced by 6.4%. Notably, the sawdust CFST samples exhibited a flexural behavior that was relatively comparable to that of the standard CFST samples.
The Yamama Formation belongs to the late Berriasian-Aptian succession, which was deposited during the Lower Cretaceous period within the main shallow marine depositional environment.
Petrographic study and microfacies analysis enabled the recognition of six main microfacies for three association facies. These are the Semi-restricted, Shallow open marine and Shoal environments. The study succession represents deposition of three third order cycles, these cycles where deposited during successive episodes of relative sea level rises and still stand.
The presence of shoal association facies (oolitic packstone microfaces) between the Sulaiy and Yamama formations refer to continue the deposition during the same stage, and may s
... Show MoreData mining has the most important role in healthcare for discovering hidden relationships in big datasets, especially in breast cancer diagnostics, which is the most popular cause of death in the world. In this paper two algorithms are applied that are decision tree and K-Nearest Neighbour for diagnosing Breast Cancer Grad in order to reduce its risk on patients. In decision tree with feature selection, the Gini index gives an accuracy of %87.83, while with entropy, the feature selection gives an accuracy of %86.77. In both cases, Age appeared as the most effective parameter, particularly when Age<49.5. Whereas Ki67 appeared as a second effective parameter. Furthermore, K- Nearest Neighbor is based on the minimum err
... Show MoreThe hydrolysis of urea by the enzyme urease is significant for increasing the irroles in human pathogenicity, biocementation, soil fertilizer, and subsequently in soil improvement. This study devoted to the isolation of urease from urea-rich soil samples collected from seven different locations. Isolation of the various bacterial species was conducted using nutrient agar. The identity of isolated urease was based on morphological characteristics and standard microbiological and biochemical procedures. The urease producing strains of bacteria were obtained using the urease hydrolysis test. The bacterial isolates produced from soil samples collected from different environments and treat
Data mining has the most important role in healthcare for discovering hidden relationships in big datasets, especially in breast cancer diagnostics, which is the most popular cause of death in the world. In this paper two algorithms are applied that are decision tree and K-Nearest Neighbour for diagnosing Breast Cancer Grad in order to reduce its risk on patients. In decision tree with feature selection, the Gini index gives an accuracy of %87.83, while with entropy, the feature selection gives an accuracy of %86.77. In both cases, Age appeared as the most effective parameter, particularly when Age<49.5. Whereas Ki67 appeared as a second effective parameter. Furthermore, K- Nearest Neighbor is based on the minimu
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