Extractive Titrimetric Determination of Hg(II), Cu(II) & Ag(I) Using Lead Diethyldithiocarbamate

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A kinetic scheme with variable velocities and relative entropy

A new kinetic model is proposed where the equilibrium distribution with bounded support has a range of velocities about two average velocities in 1D. In 2D, the equilibrium distribution function has a range of velocities about four average velocities, one in each quadrant. In the associated finite volume scheme, the average velocities are used to enforce the Rankine-Hugoniot jump conditions for the numerical diffusion at cell-interfaces, thereby capturing steady discontinuities exactly. The variable range of velocities is used to provide additional diffusion in smooth regions. Further, a novel kinetic theory based expression for relative entropy is presented which, along with an additional criterion, is used to identify expansions and smooth flow regions. Appropriate flow tangency and far-field boundary conditions are formulated for the proposed kinetic model. Several benchmark 1D and 2D compressible flow test cases are solved to demonstrate the efficacy of the proposed solver.Comment: 53 page

Large nonlinear absorption and refraction coefficients of carbon nanotubes estimated from femtosecond Z-scan measurements

Nonlinear transmission of 80 and 140 femtosecond pulsed light with $0.79 \mu m$ wavelength through single walled carbon nanotubes suspended in water containing sodium dodecyl sulphate is studied. Pulse-width independent saturation absorption and negative cubic nonlinearity are observed, respectively, in open and closed aperture Z-scan experiments. The theoretical expressions derived to analyze the z-dependent transmission in the saturable limit require two photon absorption coefficient $\beta_0\sim$ $1.4 cm/MW$ and a nonlinear index $\gamma \sim -5.5 \times10^{-11} cm^2/W$ to fit the data.Comment: 10 pages, 2 figures. Accepted and to appear in Applied Physics Letter

An Adaptive Technique to Predict Heart Disease Using Hybrid Machine Learning Approach

cardiovascular disease is amongby far prevalent fatalities in today's society. Cardiovascular disease is extremely hard to predict using clinical data analysis. Machine learning (ML) hasproved to be useful for helping in judgement and predictions with the enormous amount data produced by the healthcare sectorbusiness. Furthermore, latest events in other IoT sectors have demonstrated that machine learning is used (IOT). Several studies have examined the use of MLa heart disease prediction. In this research, we describe a novel method that, by highlighting essential traits, can improvethe precision of heart disease prognosis. Numerous data combinations and well-known categorization algorithms are used to create the forecasting models. Using a decent accuracy of 88.7%, we raise the level of playusing a heart disease forecasting approach that incorporates a88.7% absolute certainty in a combination random forest and linear model. (HRFLM)

LSGDM Two Stage Consensus Reaching Process for Autocratic Decision Making using Group Recommendations

The decision making is a general and significant action in day-to-day life. In some cases, experts cannot express their preferences using precise value due to inherent unreliability. The utilization of linguistic labels creates expert judgement more informative and consistent for decision making. The group recommendation is considered as a significant factors of e-commerce domain due to their direct impact on profit. The personalized experiments improve the engagement and the count of purchases of the customer when the recommended products are matched to the current interest.In this paper, the Large-Scale Group Decision Making (LSGDM) two stage consensus reaching process is proposed by using three various Amazon real world dataset.This proposed method permits an autocratic decision maker to utilize a different group recommendation for a sequence of decisions at highest level of consensus. The performance of the model is estimated by applying parameters like Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Precision and Recall. The obtained result shows that proposed methodology provides better result while comparing various other methods

Modelling of DMNB Content for Marked Plastic Explosives

2,3-dimethyl-2,3-dinitrobutane (DMNB) has been internationally accepted as an additive forthe purpose of marking, as it has desired vapour pressure for reliable detection. It is reportedto be compatible with known explosive formulations and has a good shelf life. Explosivecompositions with DMNB as marking agent can be detected in the temperature range –20 ºC to+ 50 ºC. This paper describes modelling for quantifying activation energy for depletion of  DMNBin the marked explosives, period for definite detection of the marked explosives and optimuminitial concentration needed for the detection of DMNB content in the marked  plastic explosives

A novel approach for iceberg query evaluation on multiple attributes using set representation

Iceberg query (IBQ) can be an really identifying kind of aggregation question that calculate aggregations up-on user given threshold(T). In data mining field, effective investigation of compounding queries was because of by the majority of investigators because the tremendous generation of information outside of industrial and businesses industries. Conclusion assist database and discovery of the majority of information connected systems largely calculate the worthiness of most fascinating features having an critical level of information from data foundations that may be tremendous. By means of the paper, we propose that an initial Manner of calculating IBQ, which builds a choice for every attribute nicely value, but additionally includes a One of a Kind events Inside the attribute column also plays specify operations for creating closing Outcomes. We formulated highly effective GUI software for just 2 characteristics, numerous traits employing egotistical prepare and several features utilizing lively plan. If data collection comprises two traits, then it truly is substantially more advanced than apply just two traits. In the event of information collection comprises multiple traits, predicated up on anyone choice suitable module could potentially be decided on. If characteristic uniqueness changes from characteristic in to the following characteristic, then vibrant variety approach is very powerful. This strategy somewhat reduces performance memory and time space contrast with additional processes. A experiment using artificial Statistics collection and actual info demonstrates our strategy will be considerably more effective compared to present apps for Nearly Every threshold

Violation of Traffic Rules and Detection of Sign Boards

Today's society has seen a sharp rise in the number of accidents caused by drivers failing to pay attention to traffic signals and regulations. Road accidents are increasing daily as the number of automobiles rises. By using synthesis data for training, which are produced from photos of road traffic signs, we are able to overcome the challenges of traffic sign identification and decrease violations of traffic laws by identifying triple-riding, no-helmet, and accidents, which vary for different nations and locations. This technique is used to create a database of synthetic images that may be used in conjunction with a convolution neural network (CNN) to identify traffic signs, triple riding, no helmet use, and accidents in a variety of view lighting situations. As a result, there will be fewer accidents, and the vehicle operator will be able to concentrate more on continuing to drive but instead of checking each individual road sign. Also, simplifies the process to recognize triple driving, accidents, but also incidents when a helmet was not used

An Adaptive Technique for Crime Rate Prediction using Machine Learning Algorithms

Any country must give the investigation and preventive of crime top priority. There are a rising amount of cases that are still pending due to the rapid increase in criminal cases in India and elsewhere. It is proving difficult to classify and address the rising number of criminal cases. Understanding a place's trends in criminal activity is essential to preventing it from occurring. Crime-solving organisations will be more effective if they have a clear awareness of the patterns of criminal behavior that are present in a particular area. Women's safety and protection are of highest importance despite the serious and persistent problem of crime against them. This study offers predictions about the kinds of crimes that might occur in a particular location using ensemble methods. This facilitates the categorization of criminal proceedings and subsequent action in a timely manner. We are applying machine learning methods like KNN, Linear regression, SVM, Lasso, Decision tree and Random forest in order to assess the highest accuracy