Pseudomonas fluorescens is one of the main causes of septicemic diseases among freshwater fish, causing severe economic losses and decreasing farm efficiency. Thus, this research was aimed to investigate the occurrence of P. fluorescens in Nile Tilapia (O. niloticus) fish in Egypt, gene sequencing of 16SrDNA gene, and antimicrobial susceptibility. P. fluorescens strains were detected in 32% (128\400) of apparently healthy (9%; 36\400) and diseased (23%; 92\400) Nile tilapia fish. The highest prevalence was observed in gills of fish, 31.3% followed by intestine 26.9%, liver 24.2%, and kidneys 17.6%. The PCR results for the 16SrDNA gene of P. fluorescens showed 16SrDNA gene in 30% of examined isolates. Moreover, Homogeny and a strong relationship between strains of P. fluorescens was confirmed using 16SrDNA sequences. Beside the responsibility of 16SrDNA gene on the virulence of P. fluorescens. The results of antimicrobial susceptibility tests revealed that all strains were resistant to piperacillin (100%), followed by ceftazidime (29.7%), and cefepime (25.8%). The strains of P. fluorescence were highly sensitive to cefotaxime (74.2%), followed by ceftriaxone and levofloxacin (70.3% each). Interestingly, 29.7% of strains of P. fluorescens were multiple antimicrobial-resistant (MAR).

Geometric correction is used to correct the registration errors in remotely sensed images. These images are often compared to ground control points (GCPs) either by using an accurate map (image to map) or using another geo-referenced image (image to image) and then resampled. Accordingly, the exact locations and the appropriate pixel values can be calculated in more accurate, time-wise and effortless manner. In the traditional methods, the GCPs are manually selected and then the transformation models are applied which yield time consuming and less accurate processes. The objective of this work is to develop an automatic approach for image registration based on another georeferenced image using five feature extraction models. They are Scale Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), Discrete Wavelet Transforms (DWT), (SIFT & DWT), and (SURF & DWT). The GCPs were selected based on the least-squares adjustments as the basis for improving the spatial accuracy of all the linking points in both images. The obtained results showed that models have higher accuracy in image registration with Root Mean Square Error (RMSE) less than 0.5. The developed automated image registration method provides more accurate results and saves time, money and effort.

Building new cities at the fringes of old ones is a mandatory nowadays to lower the over increasing population in old cities