Groundwater is the most important natural resource which cannot be optimally used and sustained unless its quality is properly assessed. In the present study, the spatial and temporal variations in physicochemical quality parameters of groundwater of Araniar River Basin, India were analyzed to determine its suitability for drinking purpose through development of drinking water quality index (DWQI) maps of the post- and pre-monsoon periods. The suitability for drinking purpose was evaluated by comparing the physicochemical parameters of groundwater in the study area with drinking water standards prescribed by the World Health Organization (WHO) and Bureau of Indian Standards (BIS). Interpretation of physicochemical data revealed that groundwater in the basin was slightly alkaline. The cations such as sodium (Na(+)) and potassium (K(+)) and anions such as bicarbonate (HCO3 (-)) and chloride (Cl(-)) exceeded the permissible limits of drinking water standards (WHO and BIS) in certain pockets in the northeastern part of the basin during the pre-monsoon period. The higher total dissolved solids (TDS) concentration was observed in the northeastern part of the basin, and the parameters such as calcium (Ca(2+)), magnesium (Mg(2+)), sulfate (SO4 (2-)), nitrate (NO3 (-)), and fluoride (F(-)) were within the limits in both the seasons. The hydrogeochemical evaluation of groundwater of the basin demonstrated with the Piper trilinear diagram indicated that the groundwater samples of the area were of Ca(2+)-Mg(2+)-Cl(-)-SO4 (2-), Ca(2+)-Mg(2+)-HCO3 (-) and Na(+)-K(+)-Cl(-)-SO4 (2-) types during the post-monsoon period and Ca(2+)-Mg(2+)-Cl(-)-SO4 (2-), Na(+)-K(+)-Cl(-)-SO4 (2-) and Ca(2+)-Mg(2+)-HCO3 (-) types during the pre-monsoon period. The DWQI maps for the basin revealed that 90.24 and 73.46% of the basin area possess good quality drinking water during the post- and pre-monsoon seasons, respectively.
Gearbox is one of the vital components in aircraft engines. If any small damage to gearbox, it can cause the breakdown of aircraft engine. Thus it is significant to study fault diagnosis in gearbox system. In this paper, two deep learning models (Long short term memory (LSTM) and Bi-directional long short term memory (BLSTM)) are proposed to classify the condition of gearbox into good or bad. These models are applied on aircraft gearbox vibration data in both time and frequency domain. A publicly available aircraft gearbox vibration dataset is used to evaluate the performance of proposed models. The results proved that accuracy achieved by LSTM and BLSTM are highly reliable and applicable in health monitoring of aircraft gearbox system in time domain as compared to frequency domain. Also, to show the superiority of proposed models for aircraft gearbox fault diagnosis, performance is compared with classical machine learning models.
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