• Asian Pacific Journal of Cancer Prevention
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• v.13 no.10
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• pp.5195-5199
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• 2012

Objectives: Global cancer statistical data show that India has one of the highest incidence rates of oral cancer worldwide. Early detection is extremely important as it results in lower morbidity and death rates. The present study was undertaken to assess awareness of oral cancer and knowledge of its early signs and risk factors in the general public of the semi-urban Gorakhpur area of Uttar Pradesh (India). It was also intended to educate the same population for early detection by increasing their ability to recognize signs and risk factors. Method: A questionnaire-based household survey was conducted over a period of one month in different parts of Gorakhpur district, a region where tobacco use is apparently very high. A total of 2,093 persons participated in the survey. The collected data were analyzed using SPSS software to assess and associate oral cancer awareness with the prevalence, and abstract risk factors, as well as other confounding variables. Results: The general awareness, knowledge of signs and risk factors of oral cancer were found to be proportionate to the literacy level with the highest rate of awareness being among high school and graduates and lowest among illiterates. It was also observed that on most of these dimensions the younger age groups (<30 years) were significantly more knowledgeable. Conclusion: Overall, the awareness of oral cancer in the high-risk population of Gorakhpur was not satisfactory, pointing to a need for further dissemination of information on this issue and its associated risks. This is especially important for the youngsters, as this may possibly help them keep away from the deleterious habit of tobacco indulgence in any form. If necessary risk factor cessation counselling should be provided.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.6
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• pp.3991-3992
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• 2013

• Advances in Energy Research
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• v.6 no.2
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• pp.131-143
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• 2019

Global Solar Radiation (GSR) is the key element for performance estimation of any Solar Power Plant (SPP). Its forecasting may help in estimation of power production from a SPP well in advance, and may also render help in optimal use of this power. Seasonal Auto-Regressive Moving Average (SARMA) and Artificial Neural Network (ANN) models are combined in order to develop a hybrid model (SARMA-ANN) conceiving the characteristics of both linear and non-linear prediction models. This developed model has been used for prediction of GSR at Gorakhpur, situated in the northern region of India. The proposed model is beneficial for the univariate forecasting. Along with this model, we have also used Auto-Regressive Moving Average (ARMA), SARMA, ANN based models for 1 - 6 day-ahead forecasting of GSR on hourly basis. It has been found that the proposed model presents least RMSE (Root Mean Square Error) and produces best forecasting results among all the models considered in the present study. As an application, the comparison between the forecasted one and the energy produced by the grid connected PV plant installed on the parking stands of the University shows the superiority of the proposed model.