BACKGROUND: There is a burgeoning body of evidence suggesting that arginine vasopressin (AVP) acts as a neuromodulator of the stress response. AVP stimulates the release of adrenocorticotropic hormone, synergistic to corticotropin-releasing hormone, which might explain AVP's role in resilience. Personal hardiness is the bedrock of resilience. Numerous studies have demonstrated elevated plasma levels of AVP in patients with major depressive disorder (MDD), suggesting an etiopathogenetic role as well as a novel therapeutic target.

OBJECTIVE: The aim of this study was to examine the relationship between AVP and resilience in patients with MDD and to determine AVP levels in serum of patients with MDD.

METHODS: Forty patients with MDD and 40 healthy control subjects were studied using the Dispositional Resilience (Hardiness) Scale by Barton, the Quality of Life Scale, the Social Readjustment Rating Scale, and the Beck Depression Inventory. Biochemical analysis of plasma levels of AVP, using the enzyme-linked immunosorbent assay (ELISA), was performed for all participants.

RESULTS: Levels of AVP were statistically significantly elevated in patients with MDD compared with healthy controls. Psychological hardiness was decreased in patients with MDD compared with healthy controls, a finding also statistically significant. There was a negative correlation between plasma AVP level and psychological hardiness.

CONCLUSION: AVP and psychological hardiness are negatively correlated, reflecting lower stress resilience. AVP levels are indeed higher in patients struggling with MDD.

SPOT is a network optimization tool that performs Consistency Check for both 2G and 3G Communication systems in mobile operators providing the ability to expand and work on new technologies such as the LTE based 4G system. The network optimization tool, SPOT, is done in cooperation with Vodafone Egypt. It aims at providing an automated tool that keeps hundreds of thousands of parameters under control all the time saving tons of precious working hours, saving manpower and helps engineers to automatically keep track of the network at any time. It can also operate offline which makes it easy to be used anywhere. Having this tool in hands, engineers will no longer have to go through the long process of checks to conclude the network status in any area to get the defects, “SPOT” does this whole job in a few seconds and provides a simple way to store the results on the personal computer or even share them via e-mail. The tool doesn’t just spot the defect and show it to the optimizer, but rather a future update of the tool will send a detailed report to the specialized team after generating the correct script needed to fix the errors in the network providing the ability to send the script through email easily. Apart from keeping track of such a massive amount of parameters, SPOT also performs many of the network optimizers’ everyday job by providing 4 different relations checks for the 2G system with just a single click, and other 8 different relations mismatches for the 3G system, and other fixed and customizable rule to prevent the errors caused by missing cell definitions and eventually improve the handover success rate and reduce the call drop rate, thus improving the GSM and CDMA network quality and the user experience. The tool is to be used by network optimizers in mobile operators on a daily basis, as it helps minimize the everyday repetitive work and keeps the system under control 24/7 spotting the errors anywhere on the system before experiencing a network failure, without the need for the dive testing process that takes time and consumes money. All results can be exported as Excel files to be sent to the proper sub-technology department responsible for taking action. The user-friendly frontend has been coded in java which makes it accessible for all kinds of computers. The tool is totally customizable giving the optimizer the space to check whichever rule he believes could better serve the network beside the built-in default settings. To conclude, simplicity and speed are what defining “SPOT”.

This thesis presents an automatic soccer video summarization system using machine learning (ML) techniques. The proposed system is composed of ve phases. Namely; in the pre-processing phase, the system segments the whole video stream into small video shots. Then, in the shot processing
phase, it applies two types of classication (shot type classication and play / break classification) to the video shots resulted from the pre-processing phase. Afterwards, in the replay detection phase, the proposed system applies two machine learning algorithms, namely; support vector machine (SVM) and articial neural network (ANN), for emphasizing important segments with championship logo appearance. Also, in the excitement event detection phase, the proposed system uses both machine learning algorithms for detecting the scoreboard which contain an information about the score of the game. The proposed system also uses k-means algorithm and Hough line transform for detecting vertical goal posts and Gabor lter for detecting goal net. Finally, in the event detection and summarization phase, the proposed system highlights the most important events during the match. Experiments on real soccer videos demonstrate encouraging results. The event detection and summarization has attained recall 94% and precision 97.3% for soccer match videos from ve international soccer championships.