Computer Sceince

The theory of asymptotic speeds of spread and monotone traveling waves is established for a class of monotone discrete and continuous-time semiflows and is applied to a functional differential equation with diffusion, a time-delayed lattice population model and a reaction-diffusion equation in an infinite cylinder. © 2005 Wiley Periodicals, Inc.

Delineation of the groundwater’s potential zones is a growing phenomenon worldwide due to the high demand for fresh groundwater. Therefore, the identification of potential groundwater zones is an important tool for groundwater occurrence, protection, and management purposes. More specifically, in arid and semi-arid regions, groundwater is one of the most important natural resources as it supplies water during the drought period. The present research study focused on the delineation of potential groundwater zones in Saveh City, the northern part of the Markazi Province in Iran. The groundwater potential mapping was prepared using hybrid deep learning and machine learning algorithm of the boosted tree (BT), artificial neural network (ANN), deep learning neural network (DLNN), deep learning tree (DLT), and deep boosting (DB). This study was carried out by using fourteen groundwater potential conditioning factors and 349 each for springs and non-springs points. The performance of each model was validated through statistical analysis of sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV), and receiver operating characteristic (ROC)-area under curve (AUC) analysis. The validation result showed that the success rate of AUC is very good for the DB model (0.87–0.99) and other models are also good i.e. BT (0.81–0.90), ANN (0.77–0.82), DLNN (0.84–0.86), and DLT (0.83–0.91). Among the several factors used in this study altitude, rainfall, distance to fault and soil types are the more important conditioning factors for groundwater potential modeling. Finally, all the models in this study had high efficiency in groundwater potential mapping, but it is recommended to use the Deep Boost model due to the better results in future studies. The result of this work will be useful to planners for optimal use and future planning of groundwater.

In this research a special type of meter is developed which can measure various important parameters of electrical energy and as well as give users the access to cntrol the appliances which are connected to it through the designed android application. Initially measurement of the current is performed with the help of Hall Effect sensor, as this method is very accurate. In second part supply voltage is calculated with the help of transformer and DC biasing method. This method is also very accurate for measurement of voltage. By multiplying the above measured current and voltage, apparent power is obtained. Finally, the energy parameter is obtained. In this research, a special type of meter is designed which can measure electrical current, voltage, power and energy with accuracy around 99%. These obtained parameter results will be directly uploaded to Google firebase which is connected to our android app. Any user having this application installed will take this reading live to their smart phones as well as their web browser.