Quantifying the localized relationship between vector containment activities and dengue incidence in a real-world setting: A spatial and time series modelling analysis based on geo-located data from Pakistan.

Increasing urbanization is having a profound effect on infectious disease risk, posing significant challenges for governments to allocate limited resources for their optimal control at a sub-city scale. With recent advances in data collection practices, empirical evidence about the efficacy of highly localized containment and intervention activities, which can lead to optimal deployment of resources, is possible. However, there are several challenges in analyzing data from such real-world observational settings. Using data on 3.9 million instances of seven dengue vector containment activities collected between 2012 and 2017, here we develop and assess two frameworks for understanding how the generation of new dengue cases changes in space and time with respect to application of different types of containment activities. Accounting for the non-random deployment of each containment activity in relation to dengue cases and other types of containment activities, as well as deployment of activities in different epidemiological contexts, results from both frameworks reinforce existing knowledge about the efficacy of containment activities aimed at the adult phase of the mosquito lifecycle. Results show a 10% (95% CI: 1-19%) and 20% reduction (95% CI: 4-34%) reduction in probability of a case occurring in 50 meters and 30 days of cases which had Indoor Residual Spraying (IRS) and fogging performed in the immediate vicinity, respectively, compared to cases of similar epidemiological context and which had no containment in their vicinity. Simultaneously, limitations due to the real-world nature of activity deployment are used to guide recommendations for future deployment of resources during outbreaks as well as data collection practices. Conclusions from this study will enable more robust and comprehensive analyses of localized containment activities in resource-scarce urban settings and lead to improved allocation of resources of government in an outbreak setting

Optimization of antireflection coating design using pc1d simulation for c − si solar cell application

Minimizing the photon losses by depositing an anti-reflection layer can increase the conversion efficiency of the solar cells. In this paper, the impact of anti-reflection coating (ARC) for enhancing the efficiency of silicon solar cells is presented. Initially, the refractive indices and reflectance of various ARC materials were computed numerically using the OPAL2 calculator. After which, the reflectance of SiO2, TiO2, SiNx with different refractive indices (n) were used for analyzing the performance of a silicon solar cells coated with these materials using PC1D simulator. SiNx and TiO2 as single-layer anti-reflection coating (SLARC) yielded a short circuit current density (Jsc ) of 38.4 mA/cm2 and 38.09 mA/cm2 respectively. Highest efficiency of 20.7% was obtained for the SiNx ARC layer with n = 2.15. With Double-layer anti-reflection coating (DLARC), the Jsc improved by ∼0.5 mA/cm2 for SiO2 /SiNx layer and hence the efficiency by 0.3%. Blue loss reduces significantly for the DLARC compared with SLARC and hence increase in Jsc by 1 mA/cm2 is observed. The Jsc values obtained is in good agreement with the reflectance values of the ARC layers. The solar cell with DLARC obtained from the study showed that improved conversion efficiency of 21.1% is obtained. Finally, it is essential to understand that the key parameters identified in this simulation study concerning the DLARC fabrication will make experimental validation faster and cheaper