Preface

N.A

Heart Transplantation

The book is an overview on current indications, treatment strategies, outcome data and ethical issues related with the heart transplantation practice worldwid

Chikungunya virus; Review of Epidemiology and Outbreak in Pakistan

Chikungunya virus (CHIKV) is mosquito-borne, alpha virus. It causes acute fever and acute and chronic musculoskeletal pain in humans. CHIKV has spread to 22 countries including Pakistan resulting in hundreds of thousands of death across the world. International travel stands out as one of the major risk factors for rapid global spread of the disease. CHIKV can be highly debilitating and large epidemic have severe economic consequences. Reemergence of CHIKV is serious public health concern. In the past 10 years, after decades of infrequent, specific outbreaks, the virus has caused major epidemic outbreaks in Africa, Asia, the India Ocean, and more recently the Caribbean and Americas. Chikungunya virus represents a substantial health burden to affected population, with symptoms that include severe joint and muscle pain, rashes, and fever, as well as prolonged periods of disability in some patients. Entry of virus into tissues causes infiltration of innate immune cells, monocytes, macrophages, neutrophils, natural killer cells and adaptive immune cells. Macrophages bearing the replicating virus, in turn, secrete pro-inflammatory cytokines IL-1B, TNF-a, IL-17. Together, this pro-inflammatory milieu induces osteoclastogenesis, bone loss, and erosion. Understanding the mechanisms of host immune responses is essential for the development of diagnosis, treatments and vaccines. Viral culture and isolation from blood cells of infected patients are the Gold standards for diagnosis. Early diagnosis of CHIKV is possible by nucleic acid detection techniques. Thus there is urgent need for continued research into the epidemiology, pathology, prevention and treatment of this disease. In this article, we have provided and update on Chikungunya virus with its recent epidemiology, molecular virology and antiviral therapies and vaccines

Numerical Analysis of Transfer of Heat by Forced Convection in a Wavy Channel

Convective heat transfer of laminar forced convection in a wavy channel is studied in this paper. Numerical simulations of the 3D steady flow of Newtonian fluid and heat transfer characteristics are obtained by the finite element method. The effects of the Reynolds number (10 ≤Re≤1000), number of oscillations (0 ≤N≤5) and amplitude of the wall (0.05 ≤A≤0.2) on the heat transfer have been analyzed. The results show that the average Nusselt number is elevated as the Reynolds number is raised, showing high intensity of heat transfer, as a result of the intensified effects of the inertial and zones of recirculation close to the hot wavy wall. The rate of heat transfer increases about 0.28% with the rise of the number of oscillations. In the transfer of heat along a wavy surface, the number of oscillations and the wave amplitude are important factors. With an increment in the number of oscillations, the maximal value of the average velocity is elevated, and its minimal value occurs when the channel walls are straight. The impact of the wall amplitude on the average Nusselt number and dimensionless temperature tends to be stronger compared to the impact of the number of oscillations. An increase of the wall amplitude improves the rate of heat transfer about 0.91% when the Reynolds number is equal 100. In addition, when the Reynolds number is equal 500, the rate of heat transfer grows about 1.1% with the rising of the wall amplitude

Application of Machine Learning and Multivariate Statistics to Predict Uniaxial Compressive Strength and Static Young’s Modulus Using Physical Properties under Different Thermal Conditions

Uniaxial compressive strength (UCS) and the static Young’s modulus (Es) are fundamental parameters for the effective design of engineering structures in a rock mass environment. Determining these two parameters in the laboratory is time-consuming and costly, and the results may be inappropriate if the testing process is not properly executed. Therefore, most researchers prefer alternative methods to estimate these two parameters. This work evaluates the thermal effect on the physical, chemical, and mechanical properties of marble rock, and proposes a prediction model for UCS and ES using multi-linear regression (MLR), artificial neural networks (ANNs), random forest (RF), and k-nearest neighbor. The temperature (T), P-wave velocity (PV), porosity (η), density (ρ), and dynamic Young’s modulus (Ed) were taken as input variables for the development of predictive models based on MLR, ANN, RF, and KNN. Moreover, the performance of the developed models was evaluated using the coefficient of determination (R2) and mean square error (MSE). The thermal effect results unveiled that, with increasing temperature, the UCS, ES, PV, and density decrease while the porosity increases. Furthermore, ES and UCS prediction models have an R2 of 0.81 and 0.90 for MLR, respectively, and 0.85 and 0.95 for ANNs, respectively, while KNN and RF have given the R2 value of 0.94 and 0.97 for both ES and UCS. It is observed from the statistical analysis that P-waves and temperature show a strong correlation under the thermal effect in the prediction model of UCS and ES. Based on predictive performance, the RF model is proposed as the best model for predicting UCS and ES under thermal conditions

Research on Leakage Detection at the Joints of Diaphragm Walls of Foundation Pits Based on Ground Penetrating Radar

It is significant to monitor the leakage at the joints of the diaphragm walls of subway station foundation pits to check the weak links in the waterproof quality of the diaphragm wall structure. It is essential to take effective waterproof measurements timely to improve the overall waterproof quality of the diaphragm wall in the foundation pit to prevent accidents and reduce the operation and maintenance costs. This paper used ground penetrating radar (GPR) to detect the Lishan North Road Station section of Jinan Rail Transit Line R2 during construction. The abnormal waveform image is obtained after processing radar detection data with Reflexw software. This abnormal waveform image is used to identify the abnormal area. In order to accurately predict the location of leakage at the joint of diaphragm wall, MATLAB is used to calculate the average wave velocity amplitude and single channel signal of the electromagnetic wave velocity of geological radar at different mileages and draw the trend chart of average wave velocity amplitude with mileage and the corresponding relationship curve of electromagnetic wave amplitude and depth of radar. It is proposed that sudden changes in the area of the average wave velocity amplitude cause a change in the trend chart. Furthermore, the radar electromagnetic wave velocity amplitude curve is taken as the area where seepage may occur at the joints of the diaphragm wall, so as to determine the corresponding mileage and depth of the leakage area. On this basis, the grey correlation analysis for the analysis of the source of the water leakage at the joints of the diaphragm wall of the subway foundation pit is proposed. The research results show that the leakage water at the joints of the diaphragm wall of the subway foundation pit is not connected to the rivers around the foundation pit, which confirms that the construction of the subway station has not affected the groundwater resources around the station. The proposed approach has successfully predicted the location of the foundation pit leakage disaster and has been verified on the project site. The research results provide a reference for the monitoring and early warning of leakage at the joints of diaphragm walls in foundation pits with similar geological conditions