Material characterization studies on the laser beam formed AISI 1008 mild steel

Laser Beam Forming is a new non-contact method without the use of a die, to achieve deformation in metals, which traditionally involved the application of mechanical forces to change the shape and form of the material permanently. Laser forming causes deformation by introducing thermal stresses from an external heat source as opposed to the simple application of forces in mechanical forming. In this study, samples were formed mechanically by using a dynamic press brake machine, whereby, a punch and die apply the force. A 4.4 kW Nd:YAG laser system was used to form a second set of samples made from cold rolled AISI 1008 mild steel using laser forming. In this collaborative work involving researchers from the USA, South Africa and India, the mechanical and metallurgical properties of the unformed, mechanically formed and laser formed samples were experimentally investigated. The objective is to compare these properties amongst the different samples in order to analyze the impact of the varying methodologies especially the laser energy effects on the samples. The conclusions from these tests have provided valuable information on the applicability of laser forming to attain the appropriate surface modifications yielding the desired mechanical and metallurgical properties of the metal

BB-ML: Basic Block Performance Prediction using Machine Learning Techniques

Recent years have seen the adoption of Machine Learning (ML) techniques to predict the performance of large-scale applications, mostly at a coarse level. In contrast, we propose to use ML techniques for performance prediction at a much finer granularity, namely at the Basic Block (BB) level, which are single entry, single exit code blocks that are used for analysis by the compilers to break down a large code into manageable pieces. We extrapolate the basic block execution counts of GPU applications and use them for predicting the performance for large input sizes from the counts of smaller input sizes. We train a Poisson Neural Network (PNN) model using random input values as well as the lowest input values of the application to learn the relationship between inputs and basic block counts. Experimental results show that the model can accurately predict the basic block execution counts of 16 GPU benchmarks. We achieve an accuracy of 93.5% in extrapolating the basic block counts for large input sets when trained on smaller input sets and an accuracy of 97.7% in predicting basic block counts on random instances. In a case study, we apply the ML model to CUDA GPU benchmarks for performance prediction across a spectrum of applications. We use a variety of metrics for evaluation, including global memory requests and the active cycles of tensor cores, ALU, and FMA units. Results demonstrate the model's capability of predicting the performance of large datasets with an average error rate of 0.85% and 0.17% for global and shared memory requests, respectively. Additionally, to address the utilization of the main functional units in Ampere architecture GPUs, we calculate the active cycles for tensor cores, ALU, FMA, and FP64 units and achieve an average error of 2.3% and 10.66% for ALU and FMA units while the maximum observed error across all tested applications and units reaches 18.5%.Comment: Accepted at the 29th IEEE International Conference on Parallel and Distributed Systems (ICPADS 2023

Mycobacterium indicus pranii is a potent immunomodulator for a recombinant vaccine against human chorionic gonadotropin

The objective of this work was to identify a human use-permissible adjuvant to enhance significantly the antibody response to a recombinant anti-hCG vaccine. Previous Phase II efficacy trials in sexually active women have demonstrated the prevention of pregnancy at hCG bioneutralization titers of 50 ng/ml or more. Mycobacterium indicus pranii (MIP), a non-pathogenic Mycobacterium employed as an autoclaved suspension in aqueous buffer, significantly increased antibody titers in the FVB strain of mice. Three other genetic strains of mice: SJL, C3H, and C57Bl/6 responded with antibody titers several-fold higher than 50 ng/ml, which is the protective threshold in women, although there were differences in the peak titers attained. In addition, the duration of the antibody response was lengthened. The vaccine hCGβ-LTB, given together with MIP, induces both a Th1 and Th2 response, which is reflected in the production of not only IgG1, but also a high proportion of IgG2a and IgG2b antibodies

High-Sensitivity Characterization of Ultra-Thin Atomic Layers using Spin-Hall Effect of Light

Magnetic/non-magnetic/heterostructured ultra-thin films' characterisation is highly demanding due to the emerging diverse applications of such films. Diverse measurements are usually performed on such systems to infer their electrical, optical and magnetic properties. We demonstrate that MOKE-based spin-Hall effect of light (SHEL) is a versatile surface characterization tool for studying materials' magnetic and dielectric ordering. Using this technique, we measure magnetic field dependent complex Kerr angle and the coercivity in ultra-thin films of permalloy (Py) and at molybdenum disulphide (MoS$_2$) - permalloy (MSPy) hetero-structure interfaces. The measurements are compared with standard magneto-optic Kerr effect (MOKE) studies to demonstrate that SHEL-MOKE is a practical alternative to the conventional MOKE method, with competitive sensitivity. A comprehensive theoretical model and simulation data are provided to further strengthen the potential of this simple non-invasive optical method. The theoretical model is applied to extract the optical conductivity and susceptibility of non-magnetic ultra-thin layers such as MoS$_2$ .Comment: 15 pages, 7 figure, one supplementary documen

Intrinsic insights to antimicrobial effects of Nitrofurantoin to multi drug resistant Salmonella enterica serovar Typhimurium ms202

Emerging multidrug resistant (MDR) serovar of Salmonella has raised the concern of their impactful effect on pathogenic infection and mortality in human lead by the enteric diseases. In order to combat the battle against these MDR Salmonella pathogen, new drug molecules need to be evaluated for their potent antibacterial application. This study evaluates the mechanistic antimicrobial effect of nitrofurantoin against a MDR strain of Salmonella named S. enterica Typhimurium ms202. The antimicrobial effect of nitrofurantoin was studied through experimental and computational approach using standard microbiological and molecular techniques like growth curve analysis, live-dead analysis, oxidative stress evaluation using high throughput techniques like flow cytometry and fluorescent microscopy. The result showed a potent dose dependent antibacterial effect of nitrofurantoin against S. enterica Typhimurium ms202 with a MIC value of 64 & mu;g/ml. Moreover, the mechanistic excavation of the phenomenon described the mechanism as an effect of molecular interaction of nitrofurantoin molecule with membrane receptor proteins OmpC of S. enterica Typhimurium ms202 leading to internalization of the nitrofurantoin heading towards the occurrence of cellular physiological disturbances through oxidative stress impeded by nitrofurantoin-Sod1 C protein interaction. The results indicated towards a synergistic effect of membrane damage, oxidative stress and genotoxicity for the antibacterial effect of nitrofurantoin against S. enterica Typhimurium ms202. The study described the potent dose-dependent application of nitrofurantoin molecule against MDR strains of Salmonella and guided towards their use in further discovered MDR strains