Urban change monitoring using GIS and remote sensing tools in Kathmandu valley (Nepal)

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies

On the hydrogeology of the Lower Tagus Basin and its Cenozoic geologic evolution

The chemical features of the ground water in the Lower Tagus Cenozoic deposits are strongly influenced by lithology, by the velocity and direction of the water movement as well as by the localization of the recharge and discharge zones. The mineralization varies between 80 and 900 mg/l. It is minimal in the recharge zones and in the Pliocene sand and maximum in the Miocene carbonated and along the alluvial valley. Mineralization always reflects the time of permanence, the temperature and the pressure. The natural process of water mineralization is disturbed in agricultural areas because the saline concentration of the infiltration water exceeds that of the infiltrated rainwater. In the discharge zones, the rise of the more mineralized, some times thermal deep waters related to tectonic accidents give rise to anomalies in the distribution of the aquiferous system mineralization model. The diversity of the hydrochemical facies of the ground water may be related to several factors whose identification is some times difficult.

Cognitive assessment of topological movement Patterns and direction turns: An influence of scale

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Groundwater quality, vulnerability and potential assessment in Kobo Valley development project, Ethiopia

This study deals with investigating the groundwater quality for irrigation purpose, the vulnerability of the aquifer system to pollution and also the aquifer potential for sustainable water resources development in Kobo Valley development project. The groundwater quality is evaluated up on predicting the best possible distribution of hydrogeochemicals using geostatistical method and comparing them with the water quality guidelines given for the purpose of irrigation. The hydro geochemical parameters considered are SAR, EC, TDS, Cl-, Na+, Ca++, SO4 2- and HCO3 -. The spatial variability map reveals that these parameters falls under safe, moderate and severe or increasing problems. In order to present it clearly, the aggregated Water Quality Index (WQI) map is constructed using Weighted Arithmetic Mean method. It is found that Kobo-Gerbi sub basin is suffered from bad water quality for the irrigation purpose. Waja Golesha sub-basin has moderate and Hormat Golena is the better sub basin in terms of water quality. The groundwater vulnerability assessment of the study area is made using the GOD rating system. It is found that the whole area is experiencing moderate to high risk of vulnerability and it is a good warning for proper management of the resource. The high risks of vulnerability are noticed in Hormat Golena and Waja Golesha sub basins. The aquifer potential of the study area is obtained using weighted overlay analysis and 73.3% of the total area is a good site for future water well development. The rest 26.7% of the area is not considered as a good site for spotting groundwater wells. Most of this area fall under Kobo-Gerbi sub basin.

On an organizational level: How can the emergency department at Rouge Valley Centenary hospital improve their wait times?

The prolonged wait times may arguably put into question the Canadian Health Act of 1984. Statistics show throughput wait times are 5.5 hours and output wait times for admitted patients are 32.4 hours. After probing and analyzing best practices through a qualitative/quantitative Value Stream Mapping and a qualitative SWOT Analysis; Team Triage and an Overcapacity Protocol is suggested to improve non-admitted patients wait times by 1.89 hours and admitted patients wait times by 16 hours by eliminating wasteful steps in the patient process and upon overcapacity, effectively sharing already stabilized and admitted patients with all wards in the hospital.

Predicting market movement with Artificial Neural Networks

This paper presents an application of an Artificial Neural Network (ANN) to the prediction of stock market direction in the US. Using a multilayer perceptron neural network and a backpropagation algorithm for the training process, the model aims at learning the hidden patterns in the daily movement of the S&P500 to correctly identify if the market will be in a Trend Following or Mean Reversion behavior. The ANN is able to produce a successful investment strategy which outperforms the buy and hold strategy, but presents instability in its overall results which compromises its practical application in real life investment decisions.