Bio-inspired algorithm integrated with sequential quadratic programming to analyze the dynamics of hepatitis B virus

Abstract Background There are a variety of lethal infectious diseases that are seriously affecting people's lives worldwide, particularly in developing countries. Hepatitis B, a fatal liver disease, is a contagious disease spreading globally. In this paper, a new hybrid approach of feed forward neural networks is considered to investigate aspects of the SEACTR (susceptible, exposed, acutely infected, chronically infected, treated, and recovered) transmission model of hepatitis B virus disease (HBVD). The combination of genetic algorithms and sequential quadratic programming, namely CGASQP, is applied, where genetic algorithm (GA) is used as the main optimization algorithm and sequential quadratic programming (SQP) is used as a fast-searching algorithm to fine-tune the outcomes obtained by GA. Considering the nature of HBVD, the whole population is divided into six compartments. An activation function based on mean square errors (MSEs) is constructed for the best performance of CGASQP using proposed model. Results The solution's confidence is boosted through comparative analysis with reference to the Adam numerical approach. The results revealed that approximated results of CGASQP overlapped the reference approach up to 3–9 decimal places. The convergence, resilience, and stability characteristics are explored through mean absolute deviation (MAD), Theil’s coefficient (TIC), and root mean square error (RMSE), as well as minimum, semi-interquartile range, and median values with respect to time for the nonlinear proposed model. Most of these values lie around 10−10–10−4 for all classes of the model. Conclusion The results are extremely encouraging and indicate that the CGASQP framework is very effective and highly feasible for implementation. In addition to excellent reliability and level of precision, the developed CGASQP technique also stands out for its simplicity, wider applicability, and flexibility

Threads of Vulnerability: A Cross-sectional Study on Factors Associated with Suicide and Self-harm in Pakistan

Abstract Background: Globally, over a million people commit suicide every year. Although suicide rates are more in high-income countries, many countries do not report suicide cases regularly to the World Health Organization (WHO). Therefore, this study aimed to determine the factor associated with suicide and self-harm in Pakistan. Method: This cross-sectional study was conducted at Peoples Medical College Hospital (PMCH) in Shaheed Benazirabad, Sindh, Pakistan, from July to December 2019. A total of 131 cases of suicide/self-harm were included using a convenience sampling technique. Data were collected on a predesigned questionnaire consisting of 14 close-ended questions. A chi-square test was used to determine the association between different categorical variables. Results: The majority of the subjects were males (53.4%), young adults aged between 16 and 30 years (69.5%), single (51.9%), and uneducated (57.3%). More than half (51.9%) of the subjects who attempted suicide or self-harm were unemployed. There was a significant association between education level ( χ 2 =13.149, P = 0.001) and age groups ( χ 2 = 15.554, P = 0.001) with health outcomes (suicide or self-harm) only. Moreover, gender ( χ 2 = 20.776, P = 0.004), marital status ( χ 2 = 69.047, P < 0.001), level of education ( χ 2 = 63.144, P < 0.001), age groups ( χ 2 = 69.848, P < 0.001), and employment status ( χ 2 = 28.677, P = 0.012) were also associated with the reasons of suicide and self-harm. Conclusion: Our study concluded that mostly single, unemployed males with low literacy and with marital and family issues are determined as factors associated with a high risk of self-harm and suicide

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

Ultrasound-Guided Transversus Abdominis Plane Block versus Land Mark Technique in Lower Abdominal Surgery

OBJECTIVE: To determine the efficacy of pain for Ultrasound-Guided Transversus Abdmominis Plane Block versus landmark technique in lower abdominal surgery. METHODOLOGY: This Randomized Control trial study was conducted from July to December 2018 at the Department of Anesthesiology, Peoples Medical University & Hospital Shaheed Benazirabad. The sample techniques were used randomly through envelopes, and the sample size was 120. RESULTS: The study findings revealed that age distribution among L and U groups regarding gender was 70% females 66.7% females. Moreover, for ASA status in the L group, 58.3% of the participants stand in 2-4 ASA status, whereas in the U group, 50% of the participants were in ASA status 1 and 2-4 each. The bilateral block was performed among 85% of the participants in the L group and 96.7% of the participants in the U group. In the surgical procedure in the L group, 15% underwent appendectomy, whereas, in the U group, 28.3% underwent lower c- section. The overall rate of postoperative pain at 60 minutes was observed in 10 women among both groups. Statistically significant results among both groups with a p-value of 0.001. CONCLUSION: Our study results showed that surgeries performed using Ultrasound-Guided Transversus Abdominis Plane Block are more effective with less postoperative analgesia

A design of predictive computational network for transmission model of Lassa fever in Nigeria

This paper presents an innovative artificial neural networks (ANNs) based hybrid algorithm of genetics optimization and sequential quadratic programming (AGOSQP) to construct the mathematical model for the dynamics of Lassa fever (DLF) in Nigeria. The model designated by the transmission of disease between two populations: human population i.e. susceptible Sh, exposed Eh, infectious Ih and recovered Rh humans and rodent population i.e. susceptible Sr and infectious Ir rodents. The log sigmoid function as an objective function based on mean squared error is constructed to optimize AGOSQP where genetic algorithm work as global searching optimization and SQP serve as the local searching optimization. To assess the correctness, robustness and convergence stability, the comparison between state of art Adam method and proposed AGOSQP is established. The Theil’s inequality coefficient (TIC), root mean square error (RMSE) and mean absolute deviation (MAD) are also computed to authenticate the efficiency of proposed AGOSQP to solve the model for the dynamics of Lassa fever

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

Entropy Optimized Second Grade Fluid with MHD and Marangoni Convection Impacts: An Intelligent Neuro-Computing Paradigm

Artificial intelligence applications based on soft computing and machine learning algorithms have recently become the focus of researchers&rsquo; attention due to their robustness, precise modeling, simulation, and efficient assessment. The presented work aims to provide an innovative application of Levenberg Marquardt Technique with Artificial Back Propagated Neural Networks (LMT-ABPNN) to examine the entropy generation in Marangoni convection Magnetohydrodynamic Second Grade Fluidic flow model (MHD-SGFM) with Joule heating and dissipation impact. The PDEs describing MHD-SGFM are reduced into ODEs by appropriate transformation. The dataset is determined through Homotopy Analysis Method by the variation of physical parameters for all scenarios of proposed LMT-ABPNN. The reference data samples for training/validation/testing processes are utilized as targets to determine the approximated solution of proposed LMT-ABPNN. The performance of LMT-ABPNN is validated by MSE based fitness, error histogram scrutiny, and regression analysis. Furthermore, the influence of pertinent parameters on temperature, concentration, velocity, entropy generation, and Bejan number is also deliberated. The study reveals that the larger &beta; and Ma, the higher f&prime;(&eta;) while M has the reverse influence on f&prime;(&eta;). For higher values of &beta;,&nbsp;M,&nbsp;Ma, and Ec, &theta;(&eta;) boosts. The concentration &#981;(&eta;) drops as Ma and Sc grow. An augmentation is noticed for NG&nbsp;for higher estimations of &beta;,M, and Br. Larger &beta;,M and Br decays the Bejan number