Artificial Neural Networks to Solve the Singular Model with Neumann-Robin, Dirichlet and Neumann Boundary Conditions.

The aim of this work is to solve the case study singular model involving the Neumann-Robin, Dirichlet, and Neumann boundary conditions using a novel computing framework that is based on the artificial neural network (ANN), global search genetic algorithm (GA), and local search sequential quadratic programming method (SQPM), i.e., ANN-GA-SQPM. The inspiration to present this numerical framework comes through the objective of introducing a reliable structure that associates the operative ANNs features using the optimization procedures of soft computing to deal with such stimulating systems. Four different problems that are based on the singular equations involving Neumann-Robin, Dirichlet, and Neumann boundary conditions have been occupied to scrutinize the robustness, stability, and proficiency of the designed ANN-GA-SQPM. The proposed results through ANN-GA-SQPM have been compared with the exact results to check the efficiency of the scheme through the statistical performances for taking fifty independent trials. Moreover, the study of the neuron analysis based on three and 15 neurons is also performed to check the authenticity of the proposed ANN-GA-SQPM.

Numerical Study of the Environmental and Economic System through the Computational Heuristic Based on Artificial Neural Networks.

In this study, the numerical computation heuristic of the environmental and economic system using the artificial neural networks (ANNs) structure together with the capabilities of the heuristic global search genetic algorithm (GA) and the quick local search interior-point algorithm (IPA), i.e., ANN-GA-IPA. The environmental and economic system is dependent of three categories, execution cost of control standards and new technical diagnostics elimination costs of emergencies values and the competence of the system of industrial elements. These three elements form a nonlinear differential environmental and economic system. The optimization of an error-based objective function is performed using the differential environmental and economic system and its initial conditions. The optimization of an error-based objective function is performed using the differential environmental and economic system and its initial conditions.

Anxiety and depression in doctors undergoing postgraduate training courses at Armed Forces Postgraduate Medical Institute Rawalpindi.

BACKGROUND: In the recent past doctors own physical and mental health care has been a focus of research worldwide. The estimated suicide rate among doctors is approximately one per day. Various other studies reveal high scores of anxiety, depression, job dissatisfaction and burnouts. The study was carried out to determine the prevalence of anxiety and depression in doctors undergoing various postgraduate training courses at AFPGMI, Rawalpindi. METHODS: This cross-sectional study was carried out from June to December 2010 at Armed Forces Postgraduate Medical Institute, Rawalpindi. All willing available trainee doctors were included in the study. Urdu version of Beck's anxiety and depression scale was used. RESULTS: Average anxiety score of all docctors was (8.05). The anxiety score was 15.7 in ENT, 12.0 in Pathology, 8.5 in Medicine, 7 in Basic Medical Siciences, 5.7 in Ophthalmology, 3.61 in Surgery, 3.3 in Radiology, and 3.05 in General Duty Medical Officers. With overall average score of (9.2), the average score on depression was highest in Anaesthesiology (20), followed by Radiology (10.05), Pathology (10.02), Basic Medical Sciences (9) Ophthalmology (8.2), Medicine (7),and General Duty Medical Officers (4.1). CONCLUSION: This study reveals an important area of doctors' own health which remains relatively neglected. Rectification measures are required to reduce anxiety/depression, and mortality and morbidity among doctors.