In data analytics, missing data is a factor that degrades performance. Incorrect imputation of missing values could lead to a wrong prediction. In this era of big data, when a massive volume of data is generated in every second, and utilization of these data is a major concern to the stakeholders, efficiently handling missing values becomes more important. In this paper, we have proposed a new technique for missing data imputation, which is a hybrid approach of single and multiple imputation techniques. We have proposed an extension of popular Multivariate Imputation by Chained Equation (MICE) algorithm in two variations to impute categorical and numeric data. We have also implemented twelve existing algorithms to impute binary, ordinal, and numeric missing values. We have collected sixty-five thousand real health records from different hospitals and diagnostic centers of Bangladesh, maintaining the privacy of data. We have also collected three public datasets from the UCI Machine Learning Repository, ETH Zurich, and Kaggle. We have compared the performance of our proposed algorithms with existing algorithms using these datasets. Experimental results show that our proposed algorithm achieves 20% higher F-measure for binary data imputation and 11% less error for numeric data imputations than its competitors with similar execution time.
Natural gas is the major indigenous source of energy in Bangladesh and accounts for almost one-half of all primary energy used in the country. Per capita and total energy use in Bangladesh is still very small, and it is important to understand how energy, and natural gas, demand will evolve in the future. We develop a dynamic econometric model to understand the natural gas demand in Bangladesh, both in the national level, and also for a few sub-sectors. Our demand model shows large long run income elasticity around 1.5 for aggregate demand for natural gas. Forecasts into the future also show a larger demand in the future than predicted by various national and multilateral organizations. Even then, it is possible that our forecasts could still be at the lower end of the future energy demand. Price response was statistically not different from zero, indicating that prices are possibly too low and that there is a large suppressed demand for natural gas in the country.
In order to avoid an unacceptably large efficiency loss when moving towards thinner silicon materials, the near-term challenge in the c-Si PV industry is to implement an effective passivation method for both cell surfaces. This paper discussed several suitable passivation schemes available. While the efficiency potential of industrially produced thin film poly-Si cells on foreign substrates cannot yet reliably be predicted, it is clear that wafer-based c-Si solar cells will allow to maintain (or even improve) today's efficiency levels while at the same time reducing the consumption of (expensive) crystalline silicon by up to 50 %. Given the trend towards these Si materials, the most promising surface passivation methods are identified to date. The key issues to be considered are cost-effectiveness, added complexity, additional benefits, reliability, and efficiency potential. The efficiency increase for best cells is around 0.5-0.6 %abs and the current efficiency potential already demonstrated for all technologies is around 19.0 %. Average efficiencies in industrial mass production for selected technologies are 18.5-18.6 % for Cz and 17.1 % for mc-Si.
A rapid (≤2 min) and high-yield low-temperature synthesis has been developed for the in situ growth of gold nanoparticles (NPs) with controlled sizes in the interior of halloysite nanotubes (HNTs). A combination of HAuCl in ethanol/toluene, oleic acid, and oleylamine surfactants and ascorbic acid reducing agent with mild heating (55 °C) readily lead to the growth of targeted nanostructures. The sizes of Au NPs are tuned mainly by adjusting nucleation and growth rates. Further modification of the process, through an increase in ascorbic acid, allows for the formation of nanorods (NRs)/nanowires within the HNTs. This approach is not limited to gold-a modified version of this synthetic strategy can also be applied to the formation of Ag NPs and NRs within the clay nanotubes. The ability to readily grow such core-shell nanosystems is important to their further development as nanoreactors and active catalysts. NPs within the tube interior can further be manipulated by the electron beam. Growth of Au and Ag could be achieved under a converged electron beam suggesting that both Au@HNT and Ag@HNT systems can be used for the fundamental studies of NP growth/attachment.
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