Prediction And Optimization Of En8 Mild Steel Material Removal Rate And Surface Roughness Using Response Surface Methodology

The demand for EN8 mild steel in the industry is high due to its integral mechanical properties. However, conventional machining of EN8 mild steel is a challenging task. In this research work, prediction and optimization of EN8 mild steel Material Removal Rate (MRR) and Surface Roughness (Ra) using Response Surface Methodology (RSM) was investigated. The dimension of the EN8 mild steel material was 120 mm diameter and 80 mm in length. The turning operation of the ENS mild steel was carried out using a M42 HSS single point cutting tool. To minimize any form of error, the machining operation was done in a dry environment. A TR 100 Surface Roughness Tester was used to carry out the surface roughness measurement of the EN8 mild steel in a transverse direction. This process was repeated three times and the average value of three measurements recorded. The data generated was analyzed using Response Surface Methodology. The results obtained revealed an R2 value of 0.9985 and 0.9978 for Material Removal Rate (MRR) and Surface Roughness (Ra) respectively. Besides, it was observed that the feed rate, spindle speed, and depth of cut, had significant influence on material removal rate.  Nevertheless, unlike the other parameters evaluated, it was only feed rate that had significant influence on surface roughness. The results obtained from the numerical optimization solution revealed that optimum machining setting of spindle speed of 220 rpm, feed rate of 0.14 mm/min and a depth of cut of 1.5 mm will result in a turning process with an optimum material removal rate of 12598.5 mm3/min and surface roughness of 0.87785 µm, and with a composite desirability value of 98.9%

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Interfacial IMC evolution and shear strength of MWCNTs-reinforced Sn–5Sb composite solder joints: Experimental characterization and artificial neural network modelling

The continuous miniaturization of electronic products and the constantly rising functionality demand by final users necessitate major challenges for the academia and the industry to produce reliable lead-free solders for long-term service. In the present study, an analysis on the influence of MWCNTs (multi-walled carbon nanotubes) on the interfacial IMC (intermetallic compound) evolution and shear strength of Sn–5Sb solder joint was performed. The composite solder joint samples were developed through the reflow soldering process and thereafter subjected to isothermal aging at different temperatures (120 °C, 150 °C and 170 °C). Given the promising properties of MWCNTs, empirical findings showed that inhibited interfacial IMC evolution and enhanced shear strength were markedly achieved due to the presence of MWNCTs in the Sn–5Sb solder alloy. Artificial neural network (ANN) model was developed by making use of the experimental data to characterize the composite solder joints. Various influential parameters that affect the thickness of the IMC layer and the shear strength performance of the composite solder joints including the MWCNTs content, aging temperature and aging time were considered as the input parameters for the ANN model. Having used the statistical parameters such as the coefficient of determination (R2) and root mean square error (RMSE) to evaluate the model, the ANN model developed in this study adequately predicted the IMC layer thickness (R2 = 0.9913; RMSE = 0.0234) and shear strength (R2 = 0.9798; RMSE = 0.0314) of the composite solder joints

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

The past 2 years, during which waves of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants swept the globe, have starkly highlighted health disparities across nations. Tegally et al. show how the coordinated efforts of talented African scientists have in a short time made great contributions to pandemic surveillance and data gathering. Their efforts and initiatives have provided early warning that has likely benefited wealthier countries more than their own. Genomic surveillance identified the emergence of the highly transmissible Beta and Omicron variants and now the appearance of Omicron sublineages in Africa. However, it is imperative that technology transfer for diagnostics and vaccines, as well the logistic wherewithal to produce and deploy them, match the data-gathering effort