Background:The aim of this study was to describe the prevalence and risk factors of symptomatic dry eye disease (SDED) in Singapore. Methods: A cross-sectional dry eye survey was carried out using the McMonnies dry eye questionnaire. Members of the public were interviewed at the 46 (out of 62) randomlyselected mass rapid transit (MRT) stations and their vicinity. A total of 1,004 questionnaires were collected from participants aged between 15 and 83 years. Symptomatic dry eye disease (SDED) was defined as at least one of five self-reported symptoms that were reported as often or constantly. Non-dry eye (NDE) subjects were those with no related symptoms reported. Prevalence of symptomatic dry eye disease in the studied population and confidence interval (CI) were calculated. Risk factors were also evaluated using logistic regression analysis at 95% CI. Results: The prevalence for symptomatic dry eye disease was found to be 12.3 per cent with prevalence greater in females than males. Symptomatic dry eye disease was significantly associated with contact lens wear (odds ratio [OR] 2.96, 95% CI: 1.81 to 4.83), those having had previous treatment for dry eye (OR 2.09, 95% CI: 1.33 to 3.29), those taking medication (OR 1.84, 95% CI: 0.99 to 3.44), those with unusual sensitivity of eyes (OR 3.04, 95% CI: 1.92 to 4.83), constant mucous membrane dryness (OR 4.11, 95% CI: 1.62 to 10.45) and irritation on waking (OR 2.38, 95% CI: 1.34 to 4.22). Smoking was not found to be associated with symptomatic dry eye disease. Conclusion: Singapore has a symptomatic dry eye disease prevalence of 12.3 per cent and was associated with contact lens wear, those who had previous treatment for dry eye, medication, those having unusual sensitivity of eyes, mucous membrane dryness and waking irritation. These new data will be of value to the eye-care community in Singapore and elsewhere.

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Realistic prognostic tools are essential for effective condition-based maintenance systems. In this paper, a Duration-Dependent Hidden Semi-Markov Model (DD-HSMM) is proposed, which overcomes the shortcomings of traditional Hidden Markov Models (HMM), including the Hidden Semi-Markov Model (HSMM): (1) it allows explicit modeling of state transition probabilities between the states; (2) it relaxes observations’ independence assumption by accommodating a connection between consecutive observations; and (3) it does not follow the unrealistic Markov chain’s memoryless assumption and therefore it provides a more powerful modeling and analysis capability for real world problems. To facilitate the computation of the proposed DD-HSMM methodology, new forward-backward algorithm is developed. The demonstration and evaluation of the proposed methodology is carried out through a case study. The experimental results show that the DD-HSMM methodology is effective for equipment health monitoring and management.

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An adaptive switching median filter (ASMF) is presented in this paper. Extensive simulation shows that it can provide very high quality restored images for images that are contaminated by "salt & pepper" noise, especially when the noise density is large. Comparing with other median filtering methods, the ASMF can effectively remove "salt & pepper" noise even when the majority of pixels in the filtering window are noise pixel or all of the pixels are noise pixel.