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Many approaches have been developed to face the estimation problems in
panel data; such as Generalized Least Square (GLS) technique which is used in
Swamy (1970), and Generalized Method of Moment (GMM) which is used in
Hansen (1982) and Verbeek (2004). Generalized Least Square is a known
procedure used in estimating the unknown parameters in the linear regression
model and it can be used in situations where Ordinary Least Squares (OLS) is
statistically inefficient, or gives misleading inferences. The GMM is a very general
statistical technique for obtaining estimates of parameters of statistical models.
Many estimators are known as special cases of (GMM) such as (OLS),
Instrumental Variables (IV) and two Stage Least Squares (2-SLS). The study is
concerned with solving the problem of the negative variance concerning the (GLS)
method and hence a comparative study of (GLS) and GMM procedures with
Simple Panel Data (SPD) and Multiple Panel Data (MPD) is introduced and
discussed simulated data from several models that we used to compare the two
procedures under different conditions of panel data such as: ample sizes, models,
parameters values, and standard deviation. For comparison, we applied the bias,
the Mean Square Error (MSE), the Variances and the rate of Negative Variances.
We found from the above mentioned approaches that (GMM) is more capable and
accurate in estimation than (GLS) in case of random coefficients and nonnegative
definite. Finally, a criminal statistics data from ministry of interior (MOI) in state
of Kuwait were used. We first have to test the coefficients variation to proved that
the coefficients was random or fixed in the real data and we found that the
coefficients are random and (GMM) was better in sense of (MSE) than (GLS) in
case of random coefficients which support our simulation study.

Objective: To evaluate the presence of suicidal behavior and depressive symptoms and the factors contributing to them in female patients with systemic lupus erythematosus (SLE).
Method: Forty female patients attending the Rheumatology and Rehabilitation department in Kasr Al Aini for treatment of SLE where patients fulfilled the Modified 1997 American College of Rheumatology criteria for the classification of SLE. They were assessed using psychiatric assessment, Rheumatology sheet including SLE Disease Activity Index (SLEDAI) and psychometric tools; Suicide probability scale (SPS) (Arabic version), Patient Health Questionnaire (PHO-9) for assessment of depression, MiniMental State Examination(MMSE) for assessment of cognitive ability and Pain Visual Analogue Scale.
Results: 52.5% of the patients of the sample were depressed, 25% of the sample fulfilled the criteria of major depressive disorder (MDD) according to DSM-IV while the other 27.5% patients were diagnosed as depressive disorder NOS, 90% of the sample had SLE disease activity, 70% of the sample had a suicide probability and 17.5% of the sample had severe suicide probability and 82.5% had a pain score of 5 or more on the pain visual analogue scale.
Suicide probability is related to pain intensity, disfigurement and physical disability but not related to disease activity. SPS and the hostility subscale are predicted by the increased depression (P=0.000) the higher cognitive ability (P=0.028) and the presence of disability/ disfigurement. SLE disease activity is related to depression but not to suicide probability.
Conclusion: Depression and suicide are significant problems in SLE. Pain is a predictor of depression. Younger age with higher cognitive abilities and presence of physical disability/ disfigurement predict suicide. Psychiatric assessment for SLE female patients should be considered as part of their clinical assessment.
Key Words: Systemic lupus erythematosus- depression- suicide.

Underwater Wireless Sensor Networks (UWSN) are revolutionizing our ability to monitor and explore underwater environments. The applications of UWSN span a wide spectrum of scientific, commercial and defense domains, such as oceanographic data collection and tactical surveillance. Due to various physical limitations that face EM waves in water, acoustic communication has emerged as the most practical form of underwater wireless communication. The unique characteristics of underwater acoustic channels confront the design of UWSN with grand challenges, among which are the long propagation delays and the low available bandwidth, which makes communication efficiency of paramount importance. Moreover, nodes in wireless sensor networks are usually powered by batteries, which are difficult to replace or recharge. For long-term applications such as environment monitoring, a node is expected to work continuously for a long time. Thus, energy efficiency becomes one of the most important design considerations.
To deal with these challenges at the architectural level, a layer of radio-capable gateway nodes can be deployed at the surface level. This heterogeneous architecture integrates underwater acoustic communication and aerial radio communication to substantially improve the performance characteristics of underwater sensor networks, provided that gateway nodes are placed in suitable locations. In this work, we study the problem of gateway placement for maximizing the cost-benefit of this UWSN architecture.

Firstly, we develop a mixed integer programming (MIP) gateway deployment optimization (GDO) framework. We investigate the performance advantages of the surface-gateway architecture in the optimal case. We develop various heuristic algorithms for efficiently finding an approximate solution to this NP-hard optimization problem, and compare the complexity and quality of the developed algorithms.

Secondly, after noting the limitations of using a regular mesh of candidate gateway locations, we develop a novel technique for enhancing the problem formulation, by deriving candidate gateway locations from the geometry of the underwater network deployment.

Thirdly, we extend the GDO framework to solve the gateway deployment problem for maximizing network lifetime and present a method for balancing this goal with other performance optimization objectives, such as the minimization of average end-to-end delay.

Lastly, we show how our gateway deployment optimization framework can be practicality extended to a dynamic redeployment scenario. In addition to improving the performance of existing underwater sensor network deployments, the methods developed in this dissertation provide guidance for improving the performance of deployments of autonomous underwater and water-surface robots.