Background: Cervical cancer being a major cause of morbidity and mortality in women in developing countries, its awareness is essential. Aim: The aim of this study is to assess the knowledge, attitude and practices of women regarding the basic screening test for detection of cancer cervix. Settings and Design: Population based cross-sectional study. Materials and Methods: Cross-sectional prospective study was conducted. Information from consenting participants (450) was collected using structured questionnaire. Answers were described in terms of knowledge, attitude and practice and their respective adequacy with respect to Papanicolaou (Pap) test, the most common test used for early detection of cervical cancer. Adequacy was compared between the categories of socio demographic and clinical variables. Statistical Analysis: The data collected was analyzed using statistical package (SPSS version 18.0). Adequacy was compared between the categories of the control variables by χ2 test with a 5% significance level. Results: Knowledge, attitude and practices regarding Pap test were adequate in 32.7%, 18.2% and 7.3% of women respectively. Major impediment to adequate practice was lack of request by physician. Knowledge, attitudes and practices were found to increase significantly with increasing age and education. Conclusion: Effective information, education and communication strategies are required to improve the level of awareness of public. Health-care professional should be proactive in imparting knowledge at every opportunity.

A data-mining and Bayesian learning approach is used to model the reaction network of a low-temperature (150–400 °C) visbreaking process for field upgrading of oil sands bitumen. Obtaining mechanistic and kinetic descriptions for the chemistry involved in this process is a significant challenge because of the compositional complexity of bitumen and the associated analytical challenges. Lumped models based on a preconceived reaction network might be unsatisfactory in describing the key conversion steps of the actual process. Fourier transform infrared spectra of products produced at different operating conditions (temperature and time of processing) of the visbreaking process were collected. Bayesian agglomerative hierarchical cluster analysis was employed to obtain groups of pseudospecies with similar spectroscopic properties. Then, a Bayesian structure-learning algorithm was used to develop the corresponding reaction network. The final reaction network model was compared to the anticipated reaction network of thermal cracking of a model alkyl tricyclic naphthenoaromatic compound, and the agreement was encouraging. The reaction model also indicates that the outcome of thermal processing is the increase in lighter and more aliphatic products, which is consistent with experimental findings. Pseudokinetics were obtained for the reactions between the pseudospecies based on the estimated parameters of the Bayesian network. An attractive feature of the model is that it can be embedded into a process control system to perform real-time online analysis of the reactions both qualitatively and quantitatively.

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