Application of electrical resistance tomography in groundwater pollution

Distance relays are wide used for defense of transmission lines. Historically used mechanical device distance relays for defense of transmission lines are liable to effects of fault resistance. Every fault condition corresponds to a selected pattern. Therefore use of a pattern recognizer will improve the relay performance. This system presents a brand new approach, referred to as artificial neural network (ANN) to beat the impact of fault resistance on relay mal-operation. During this technique, impact of fault resistance for single line to ground kind of fault is taken into account. When roaring simulation of LG fault remaining styles of fault like LL, LLG, and 3 part faults are going to be simulate and analyze for various fault resistance. The theme utilizes the magnitudes of resistance and electrical phenomenon as inputs. Once trained with an outsized variety of patterns comparable to numerous conditions, it will classify unknown patterns. In this technique, we have a tendency to take into account the impact of fault resistance for cable protection that influences the standard distance relay protection. In order that for removing this issue we have a tendency to introduced ANN based mostly cable protection by considering the impact of ground resistance. During this approach ANN classify the precise fault location and send the trip signal to it zone relay same as distance relay zone protection. For this, we are going to initial analyzed the voltage and current signals measured at reference bus bar and send this signals to riffle rework energy standardization and details signal constant analysis then send to ANN for zone identification for generation of trip signal for the electrical fuse. The project implementation is going to be done victimization MATLAB Simulink computer code surroundings victimization MATLAB 2013b version in which ANN tool cabinet, installation tool cabinet and riffle tool cabinet used for Simulink block modeling.

The aim of this study was to study the diagnostic parameters in
brain tuberculosis (meningeal and parenchymal)
Material and Methods: This study was conducted in the
department of Neurosurgery and Neurology SKIMS for a period
of two years. A total of 61 patients presenting with brain
tuberculosis admitted at skims during these two years were
included in the study.
Results: The most presenting symptom in our study was headache
found in 95.10% followed by vomiting found in 86.90% of
subjects, fever in 78.70%, altered sensorium in 49.20%, seizures in
19.70% and diplopia in18%. Out of 61 patients cranial nerve
involment was found in 34(55.73%) with 11 having more than two
cranial nerves involved. The most common cranial nerve involved
were 3rd and 6th. ADA was positive in 36 of 53 patients of TBM
with a sensitivity of 67.9% and a specificity of 75%. PCR proved
to be highly specific CT scan of head was abnormal in 56 out of
61 patients (91.8%). 12 (19.70%) were in stage I (meningeal
involvement only), 29 (47.50%) were in stage II (parenchymal
involvement only) and 15 (24.60%) were in stage III (both
parenchymal and meningeal invlolvement). The most common
finding in CT head was meningeal enhancement in 43 patients,
hydrocephalus in 37 patients and tuberculomas in 14 patients. The
most common sites of tuberculomas were frontal lobe (n=6;
42.8%), parietal lobe (n=4; 28.5%), followed by cerebellum in 2
patients and occipital in two. Nine patients had single and five
multiple tuberculomas. Of the 14 patients with tuberculomas,
hydrocephalus on CT was seen in 6 patients.
Conclusion: CT scan is a useful diagnostic tool even in very early
stages of TBM. Abnormalities reported on CT scan done are
hydrocephalus, infarcts, basal enhancement, and tuberculomas.
Normal study is reported in up to 20% of the cases.

Electrical resistivity tomography is a geophysical method that was used to characterize the soil in Ahamdu Bello University Phase II, Zaria. Ahmadu Bello University site II lies between longitudes 7° 37.98' E and 7° 39.07' E and latitudes 11° 7.86' N and 11° 8.50' N. A multi-electrode resistivity meter (ABEM Terrameter SAS 4000) was used for data acquisition on the field using a dipole-dipole array and the data were processed and interpreted using RES2DINV. The results obtained from the resistivity tomograms of the three profiles were used to correlate with the borehole data and the correlation indicates four distinct layers (Topsoil, weathered basement, fractured and fresh basement). The most competent layer for building a foundation in the study area is the fresh basement which is about 15.9m deep from the earth surface. In this study, electrical resistivity tomography has been successfully used to determine the depth to basement and the extent to which the bedrock was delineated. Weak zones that may be prone to subsidence and the competent areas that can support massive engineering were identified.