A framework for the analysis of skin sores disease using evolutionary intelligent computing approach.

The most common and contagious bacterial skin disease i.e. skin sores (impetigo) mostly affects newborns and young children. On the face, particularly around the mouth and nose area, as well as on the hands and feet, it typically manifests as reddish sores. In this study, a neuro-evolutionary global algorithm is introduced to solve the dynamics of nonlinear skin sores disease model (SSDM) with the help of an artificial neural network. The global genetic algorithm is integrated with local sequential quadratic programming (GA-LSQP) to obtain the optimal solution for the proposed model. The designed differential model of skin sores disease is comprised of susceptible (S), infected (I), and recovered (R) categories. An activation function based neural network modeling is exploited for skin sores system through mean square error to achieve best trained weights. The integrated approach is validated and verified through the comparison of results of reference Adam strategy with absolute error analysis. The absolute error results give accuracy of around 10-11 to 10-5, demonstrating the worthiness and efficacy of proposed algorithm. Additionally, statistical investigations in form of mean absolute deviation, root mean square error, and Theil's inequality coefficient are exhibited to prove the consistency, stability, and convergence criteria of the integrated technique. The accuracy of the proposed solver has been examined from the smaller values of minimum, median, maximum, mean, semi-interquartile range, and standard deviation, which lie around 10-12 to 10-2.

Assessment of anaesthesia workforce capacity in district and tehsil (taluka) hospitals in Sindh province of Pakistan: a survey.

OBJECTIVES: Our objective was to determine the current availability of human resource at secondary care hospitals in Sindh province and to identify gaps in term of appropriate number of anaesthesiologists available for delivery of safe anaesthesia care. DESIGN: A cross-sectional survey of anaesthesia workforce. SETTING: All district and taluka hospitals in the Sindh province of Pakistan. PARTICIPANTS: Administrative anaesthesia leaders in the hospitals. OUTCOME MEASURES: Standard descriptive statistics (percentages and numbers) of anaesthesia workforce in these hospitals including both full-time and part-time physician anaesthesiologists, and non-specialist physicians providing anaesthesia services as well as technician support. RESULTS: Only 54 (75%) hospitals had a full-time anaesthesia physician, and 32 of these had only one. Two hundred and one operating rooms were present in 72 (80%) hospitals with an average of three operating rooms/hospital. CONCLUSIONS: This study has identified a deficit of anaesthesiology personnel in district-level and tehsil-level hospitals of Sindh province of Pakistan.

A design of neuro-computational approach for double-diffusive natural convection nanofluid flow.

The artificial intelligence based neural networking with Back Propagated Levenberg-Marquardt method (NN-BPLMM) is developed to explore the modeling of double-diffusive free convection nanofluid flow considering suction/injection, Brownian motion and thermophoresis effects past an inclined permeable sheet implanted in a porous medium. By applying suitable transformations, the PDEs presenting the proposed problem are transformed into ordinary ones. A reference dataset of NN-BPLMM is fabricated for multiple influential variants of the model representing scenarios by applying Lobatto III-A numerical technique. The reference data is trained through testing, training and validation operations to optimize and compare the approximated solution with desired (standard) results. The reliability, steadiness, capability and robustness of NN-BPLMM is authenticated through MSE based fitness curves, error through histograms, regression illustrations and absolute errors. The investigations suggest that the temperature enhances with the upsurge in thermophoresis impact during suction and decays for injection, whereas increasing Brownian effect decreases the temperature in the presence of wall suction and reverse behavior is seen for injection. The best measures of performance in form of mean square errors are attained as 7.1058 × 10 - 10 , 2.9262 × 10 - 10 , 1.1652 × 10 - 08 , 1.5657 × 10 - 10 and 5.5652 × 10 - 10 against 969, 824, 467, 277 and 650 iterations. The comparative study signifies the authenticity of proposed solver with the absolute errors about 10-7 to 10-3 for all influential parameters results.