Abstract: Alzheimer’s disease (AD) (a progressive neurodegenerative disorder) is mostly predominant cause of dementia in the elderly. Prolonging the function of acetylcholine by inhibiting both acetylcholinesterase and butyrylcholinesterase is most effective treatment therapy of AD. Traditionally Pterocarpus santalinus L. is widely known for its medicinal use. In this study, in vitro acetylcholinesterase inhibitory activity was investigated and methanolic extract of the plant showed significant activity. To confirm this activity (in vivo), learning and memory enhancing effects were tested in mice. For the test, memory impairment was induced by scopolamine (cholinergic muscarinic receptor antagonist). Anti-amnesic effect of the extract was investigated by the passive avoidance task in mice. The study also includes brain acetylcholinesterase activity. Results proved that scopolamine induced cognitive dysfunction was significantly decreased by administration of the extract solution, in the passive avoidance task and inhibited brain acetylcholinesterase activity. These results suggest that bark extract of Pterocarpus santalinus can be better option for further studies on AD via their acetylcholinesterase inhibitory actions.
Abstract: In this research, Response Surface Methodology (RSM) is used to investigate the effect of four controllable input variables namely: discharge current, pulse duration, pulse off time and applied voltage Surface Roughness (SR) of on Electrical Discharge Machined surface. To study the proposed second-order polynomial model for SR, a Central Composite Design (CCD) is used to estimation the model coefficients of the four input factors, which are alleged to influence the SR in Electrical Discharge Machining (EDM) process. Experiments were conducted on AISI D2 tool steel with copper electrode. The response is modeled using RSM on experimental data. The significant coefficients are obtained by performing Analysis of Variance (ANOVA) at 5% level of significance. It is found that discharge current, pulse duration, and pulse off time and few of their interactions have significant effect on the SR. The model sufficiency is very satisfactory as the Coefficient of Determination (R2) is found to be 91.7% and adjusted R2-statistic (R2 adj ) 89.6%.
Abstract: Perth will run out of available sustainable natural
water resources by 2015 if nothing is done to slow usage rates,
according to a Western Australian study [1]. Alternative water
technology options need to be considered for the long-term
guaranteed supply of water for agricultural, commercial, domestic
and industrial purposes. Seawater is an alternative source of water for
human consumption, because seawater can be desalinated and
supplied in large quantities to a very high quality.
While seawater desalination is a promising option, the technology
requires a large amount of energy which is typically generated from
fossil fuels. The combustion of fossil fuels emits greenhouse gases
(GHG) and, is implicated in climate change. In addition to
environmental emissions from electricity generation for desalination,
greenhouse gases are emitted in the production of chemicals and
membranes for water treatment. Since Australia is a signatory to the
Kyoto Protocol, it is important to quantify greenhouse gas emissions
from desalinated water production.
A life cycle assessment (LCA) has been carried out to determine
the greenhouse gas emissions from the production of 1 gigalitre (GL)
of water from the new plant. In this LCA analysis, a new desalination
plant that will be installed in Bunbury, Western Australia, and known
as Southern Seawater Desalinization Plant (SSDP), was taken as a
case study. The system boundary of the LCA mainly consists of three
stages: seawater extraction, treatment and delivery. The analysis
found that the equivalent of 3,890 tonnes of CO2 could be emitted
from the production of 1 GL of desalinated water. This LCA analysis
has also identified that the reverse osmosis process would cause the
most significant greenhouse emissions as a result of the electricity
used if this is generated from fossil fuels
Abstract: In the current research, neuro-fuzzy model and regression model was developed to predict Material Removal Rate in Electrical Discharge Machining process for AISI D2 tool steel with copper electrode. Extensive experiments were conducted with various levels of discharge current, pulse duration and duty cycle. The experimental data are split into two sets, one for training and the other for validation of the model. The training data were used to develop the above models and the test data, which was not used earlier to develop these models were used for validation the models. Subsequently, the models are compared. It was found that the predicted and experimental results were in good agreement and the coefficients of correlation were found to be 0.999 and 0.974 for neuro fuzzy and regression model respectively