WETTING AND DRYING RESISTANCE OF LIME-STABILIZED EXPANSIVE SOILS MODIFIED WITH NANO-ALUMINA

Weak soil at construction sites necessitates ground improvement. Chemical stabilization is typically carried out using either lime or cement. The primary objective of this study was to assess the strength and durability of lime-stabilized soils modified with nano-alumina (NA). This study adopted the scientifically established initial consumption of lime (ICL) content for soil stabilization. In addition, nano-alumina was added in varying percentages as an auxiliary additive. It was observed that 0.5 % of nano-alumina was optimal with respect to the ICL for maximizing the soil stabilization. The stabilized soils were cured for 0, 7, 14, and 28 days. Post-curing testing revealed that the strength increased sixfold for the optimal combination, compared with the virgin soil. To understand the durability behavior of the optimal combination, the stabilized soil specimens were subjected to wetting and drying cycles after 28 days of curing. The optimal combination was nearly as durable as that of the lime-stabilized soil subjected to five cycles of wetting and drying

The role of cinnamon as a modulator of the expression of genes related to antioxidant activity and lipid metabolism of laying quails

Since cinnamon has vitamins and minerals in addition to antioxidants compounds in its chemical composition studies have shown the potential of cinnamon supplementation on some important characteristics in the performance of birds. Thus, this study was conducted under the hypothesis that the inclusion of cinnamon in the laying quail diet could influence the performance of the birds through the expression of genes related to antioxidant activity and lipid metabolism. To test this hypothesis, 144 Japanese quail (Coturnix japonica) with an initial age of 18 weeks and average weight of 133g were distributed in a completely randomized design with two treatments: no cinnamon supplementation (NCS—control group) and with supplementation of 9g/kg of cinnamon powder (CPS). The experiment lasted for 84 days. At the end of the experimental period, six animals from each treatment were euthanized by cervical dislocation, blood was collected and organs weighed. Liver tissue was collected for gene expression and biochemical analyses. We observed a significant effect of cinnamon inclusion on the weight of the pancreas (P = 0.0418), intestine (P = 0.0209) and ovary (P = 0.0389). Lower weights of the pancreas and intestine, and a higher ovary weight was observed in birds receiving the CPS diet. Quails fed with cinnamon supplementation also had better feed conversion per egg mass (2.426 g /g, P = 0.0126), and higher triglyceride (1516.60 mg/dL, P = 0.0207), uric acid (7.40 mg/dL, P = 0.0003) and VLDL (300.40 mg/dL, P = 0.0252) contents. A decreased content of thiobarbituric acid reactive substances (TBARS) and lower catalase activity was observed in the liver of quails from the CPS diet (0.086 nmoles/mg PTN, and 2.304 H2O2/min/mg PTN, respectively). Quails from the CPS group presented significantly greater expression of FAS (fatty acid synthase, 36,03 AU), ACC (Acetyl-CoA Carboxylase, 31.33 AU), APOAI (apolipoprotein A-I, 803,9 AU), ESR2 (estrogen receptor 2, 0.73 AU) SOD (superoxide dismutase, 4,933.9 AU) and GPx7 (glutathione peroxidase 7, 9.756 AU) than quails from the control group. These results allow us to suggest that cinnamon powder supplementation in the diet of laying quails can promote balance in the metabolism and better performance through the modulation of antioxidant activity and the expression of genes related to lipid metabolism

Ensemble Merit Merge Feature Selection for Enhanced Multinomial Classification in Alzheimer’s Dementia

The objective of this study is to develop an ensemble classifier with Merit Merge feature selection that will enhance efficiency of classification in a multivariate multiclass medical data for effective disease diagnostics. The large volumes of features extracted from brain Magnetic Resonance Images and neuropsychological tests for diagnosis lead to more complexity in classification procedures. A higher level of objectivity than what readers have is needed to produce reliable dementia diagnostic techniques. Ensemble approach which is trained with features selected from multiple biomarkers facilitated accurate classification when compared with conventional classification techniques. Ensemble approach for feature selection is experimented with classifiers like Naïve Bayes, Random forest, Support Vector Machine, and C4.5. Feature search is done with Particle Swarm Optimisation to retrieve the subset of features for further selection with the ensemble classifier. Features selected by the proposed C4.5 ensemble classifier with Particle Swarm Optimisation search, coupled with Merit Merge technique (CPEMM), outperformed bagging feature selection of SVM, NB, and Random forest classifiers. The proposed CPEMM feature selection found the best subset of features that efficiently discriminated normal individuals and patients affected with Mild Cognitive Impairment and Alzheimer’s Dementia with 98.7% accuracy