Energy and flux measurements of laser-induced silver plasma ions by using Faraday cup

Silver(Ag)plasma has been generated by employing Nd∶YAG laser(532 nm,6 ns)laser irradiation.The energy and flux of ions have been evaluated by using Faraday cup(FC)using time of flight(TOF)measurements.The dual peak signals of fast and slow Ag plasma ions have been identified.Both energy and flux of fast and slow ions tend to increase with increasing irradiance from 7 GW cm-2 to 17.9 GW cm-2 at all distances of FC from the target surface.Similarly a decreasing trend of energies and flux of ions has been observed with increasing distance of FC from the target.The maximum value of flux of the fast component is 21.2×1010cm-2,whereas for slow ions the maximum energy and flux values are 8.8 keV,8.2×1012 cm-2 respectively.For the analysis of plume expansion dynamics,the angular distribution of ion flux measurement has also been performed.The overall analysis of both spatial and angular distributions of Ag ions revealed that the maximum flux of Ag plasma ions has been observed at an optimal angle of～15°.In order to confirm the ion acceleration by ambipolar field,the self-generated electric field(SGEF)measurements have also been performed by electric probe;these SGEF measurements tend to increase by increasing laser irradiance.The maximum value of 232 V m-1 has been obtained at a maximum laser irradiance of 17.9 GW cm-2.     

Auger Depth Profiling of Carbonized SiC/Si Samples

Silicon carbide (SiC) has been prepared by passing natural gas over (100) oriented hot Si substrate at different temperatures in the range 930~1000℃. Reaction times of 60 and 90 min are used.Depth profile, using Auger Electron Spectroscopy, shows the formation of SiC under a thin coating of carbon for the samples prepared at 930 and 950℃. Annealing, at 1050℃ for 12 h,results in a more pronounced formation of SiC. It is found that at the temperature of 1000℃and reaction times of 60 and 90 min, a hard diamond-like coating is formed.     

Analysis of the Smart Player’s Impact on the Success of a Team Empowered with Machine Learning

The innovation and development in data science have an impact in all trades of life. The commercialization of sport has encouraged players, coaches, and other concerns to use technology to be in better position than r their opponents. In the past, the focus was on improved training techniques for better physical performance. These days, sports analytics identify the patterns in the performance and highlight strengths and weaknesses of potential players. Sports analytics not only predict the performance of players in the near future but it also performs predictive modeling for a particular behavior of a player in the past. The impact of a smart player on the success of a team is always a big question mark before the start of a match. The fans always want to know performance analysis of these superstar players and they always are interested to get to know more about their favorite player and they always have high hopes from their favorite player. Machine learning (ML) based techniques help in predicting the performance of an individual player as well as for the whole team. The statistics are very vital and useful for management, fans, and expert analysis. In our proposed framework, the adaptive back propagation neural network (ABPNN) model is used for the prediction of a player’s performance. The data is collected from football websites, and the results are stored in the cloud for fast fetching of data. They can be retrieved anywhere in the world through cloud storage. The results are computed with 94% accuracy and the performance of the smart player is formulated for the success of a team.     

Is tumor cell specificity distinct from tumor selectivity in vivo?A quantitative NIR molecular imaging analysis of nanoliposome targeting

The significance and ability for receptor targeted nanoliposomes(tNLs)to bind to their molecular targets in solid tumors in vivo has been questioned,particularly as the efficiency of their tumor accumulation and selectivity is not always predictive of their efficacy or molecular specificity.This study presents,for the first time,in situ near-infrared(NIR)molecular imaging-based quantitation of the in vivo specificity of tNLs for their target receptors,as opposed to tumor selectivity,which includes influences of enhanced tumor permeability and retention.Results show that neither tumor delivery nor selectivity(tumor-to-normal ratio)of cetuximab and IRDye conjugated tNLs correlate with epidermal growth factor receptor(EGFR)expression in U251,U87,and 9L tumors,and in fact underrepresent their imaging-derived molecular specificity by up to 94.2%.Conversely,their in vivo specificity,which we quantify as the concentration of tNL-reported tumor EGFR provided by NIR molecular imaging,correlates positively with EGFR expression levels in vitro and ex vivo(Pearson’s r=0.92 and 0.96,respectively).This study provides a unique opportunity to address the problematic disconnect between tNL synthesis and in vivo specificity.The findings encourage their continued adoption as platforms for precision medicine,and facilitates intelligent synthesis and patient customization in order to improve safety profiles and therapeutic outcomes.     

Nanosecond Laser Induced Surface Structuring of Cadmium after Ablation in Air and Propanol Ambient

In the present study KrF Excimer laser has been employed to irradiate the Cadmium (Cd) targets for various number of laser pulses of 500, 1000, 1500 and 2000, at constant fluence of 3.6 J cm⁻². Scanning Electron Microscopy (SEM) analysis was utilized to reveal the formation of laser induced nano/micro structures on the irradiated target (Cd) surfaces. SEM results show the generation of cavities, cracks, micro/nano wires/rods, wrinkles along with re-deposited particles during irradiation in air, whereas subsurface boiling, pores, cavities and Laser Induced Periodic Surface Structures (LIPSS) on the inner walls of cavities are revealed at the central ablated area after irradiation in propanol. The ablated volume and depth of ablated region on irradiated Cd targets are evaluated for various number of pulses and is higher in air as compared to propanol ambient. Fast Fourier Transform Infrared spectroscopy (FTIR), Energy Dispersive X-ray Spectroscopy (EDS) and X-ray Diffraction (XRD) analyses show the presence of oxides and hydro-oxides of Cd after irradiation in propanol, whereas the existence of oxides is observed after irradiation in air ambient. Nano-hardness tester was used to investigate mechanical modifications of ablated Cd. It reveals an increase in hardness after irradiation which is more pronounced in propanol as compared to air.     

Forecast the Influenza Pandemic Using Machine Learning

Forecasting future outbreaks can help in minimizing their spread. Influenza is a disease primarily found in animals but transferred to humans through pigs. In 1918, influenza became a pandemic and spread rapidly all over the world becoming the cause behind killing one-third of the human population and killing one-fourth of the pig population. Afterwards, that influenza became a pandemic several times on a local and global levels. In 2009, influenza ‘A’ subtype H1N1 again took many human lives. The disease spread like in a pandemic quickly. This paper proposes a forecasting modeling system for the influenza pandemic using a feed-forward propagation neural network (MSDII-FFNN). This model helps us predict the outbreak, and determines which type of influenza becomes a pandemic, as well as which geographical area is infected. Data collection for the model is done by using IoT devices. This model is divided into 2 phases: The training phase and the validation phase, both being connected through the cloud. In the training phase, the model is trained using FFNN and is updated on the cloud. In the validation phase, whenever the input is submitted through the IoT devices, the system model is updated through the cloud and predicts the pandemic alert. In our dataset, the data is divided into an 85% training ratio and a 15% validation ratio. By applying the proposed model to our dataset, the predicted output precision is 90%.     

Laser-assisted ablation and plasma formation of titanium explored by LIBS,QCM, optical microscopy and SEM analyses along with mechanical modifications under different pressures of Ar and Ne

This study deals with the investigation of Nd:YAG laser-assisted ablation and plasma formation of Ti at irradiance of 0.85 GW cm^-2 under Ar and Ne environment at various pressures ranging from 10-120 Torr.Laser-induced breakdown spectroscopy is used to evaluate plasma parameters,whereas quartz crystal microbalance is used for ablation yield measurements.The crater depth is evaluated by optical microscopy.The surface features are explored by scanning electron microscope(SEM) analysis a...     

L-Shaped Schottky Barrier MOSFET for High Performance Analog and RF Applications

Silicon - This work presents the design and simulation of a novel double-gate L-shaped Schottky barrier MOSFET (DG-LS-SB-MOSFET). The device uses a low work function metal near source-channel...     

Numerical sensitivity analysis of temperature-dependent reaction rate constants for optimized thermal conversion of high-density plastic waste into combustible fuels

The use of experimental rate constants for producing a high yield of liquid fuels from the pyrolysis of plastic waste is not widely accepted owing to a lack of compatibility between the different kinetic rate constants responsible for successful conversion reactions. In R software, the Arrhenius law can forecast the ideal combination of reaction rate constants and frequency factors and then perform sensitivity analysis on individual rate constants to estimate the selectivity and quantity of primary pyrolysis products. Sensitivity analysis is a way of determining the effectiveness of individual rate constants in the reaction. This research element is currently lacking in the literature for the cost-effective valorization of plastics into combustible fuels. We are the first to use R software to perform sensitivity analysis on specific rate constants by reducing or raising their initial values to a point where maximum oil yield is attainable in the temperature range of 340–370°C. The primary focus was to save time and cost of extracting empirical rate constants from experiments to produce commercial-scale pyrolytic oil. The H-abstraction, chain fission, polymerization, and scission reactions were chosen due to the high availability of free radicals for maximum oil production. The oil recovery rate improved drastically to 90% at the end of processing time, while the number of by-products gradually decreased. The k8 rate constant driven reaction is the best-suited condition for industrial-scale pyrolysis of high-density plastics into liquid fuels, with 74% improvement in oil production and 14% improvement in light wax during sensitivity analysis.