Browsing by Author "Liyanage, K"

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Prevalence of Computer Vision Syndrome among the Academic Staff of SLIIT, Malabe Campus amidst COVID-19 Pandemic
(Faculty of Humanities and Sciences,SLIIT, 2021-09-25) Jayakody, L. W; Bandara, P.C; Liyanage, K; Wijekoon, W. M. P. D. S; Anthony, D. K.
Computer Vision Syndrome (CVS) is considered as an occupational hazard in the 21st century resulting from high utilization of computers, laptops and mobile phones in the workplace. Current study aimed to determine the prevalence of computer vision syndrome and its associated factors among the academic staff of SLIIT, Malabe campus amidst the COVID-19 pandemic. A descriptive cross-sectional institution-based study was conducted among 145 academic staff members. Data were collected through a self-administered questionnaire that consisted demographic questions and a standard questionnaire validated to measure CVS. Data were analyzed using SPSS version 22. The participants of the study comprised 50.3% of female respondents and 49.7% of male respondents while majority of the participants were in 20-30 years age category. The prevalence of CVS among the participants was 59.3%. Dryness in the eyes (52.4%), itching (54.4%), eye pain (65.5%) and headache (76.5%) were the most common symptoms reported by the staff members, while coloured halos around objects (20.7%) and double vision (21.4%) were experienced by a limited number of participants. Laptops and mobile phones are used by the majority of the academic staff employees (91.5%) while 6-8 working hours in front of a digital screen was reported by 42.1% of academics. Awareness of CVS was identified among 136 employees. Taking breaks in-between the working time (26.2%), adjusting the screen (21.5%) and adjusting the chair and posture (20.7%) were the most common methods used by the participants to minimize CVS. A significant association was observed between age categories and the prevalence of CVS (p= 0.006). A high prevalence of Computer Vision Syndrome was observed among the academic staff of SLIIT. Further, institutional activities to raise awareness on CVS and ergonomic practices are recommended to reduce the prevalence of CVS among the academic staff.
Open Access
Queue Length Prediction at Un-Signalized Intersections with Heterogeneous Traffic Conditions
(SLIIT, 2022-02-11) Rathnayake, I; Amarasinghe, N; Wickramasinghe, V; Liyanage, K
Increasing queue lengths while reducing average vehicle speeds is a notable criterion in intersections with heterogeneous traffic conditions. Such queue lengths vary with different intersection controls. Thisstudy aimed to estimate the queue length at un-signalized intersections with heterogeneous traffic conditions. The study was done for un-signalized intersections in Peradeniya and Weliwita, Sri Lanka and the data were collected through video recordings. The queue lengths in an un-signalized intersection with mixed traffic conditions have an instantaneous aggressive variation due to the uncontrolled movements. Thus, a time series analysis with the aid of Vector Auto Regression (VAR) model was used in order to estimate the queue length. Variables considered in this study were arrival flow rate, discharge flow rate, number of conflicts for 15 seconds time intervals as independent variables and queue length at the end of each 15 seconds as the dependent variable. For the modelling, the procedure of “Box-Jenkins” method was followed. After the confirmation of the variables are stationary, Cointegration check and Granger causality tests were done to check the cointegration between variables and the granger causality between variables. Then, VAR models were developed using 80% data from the total data set for both locations. The remaining 20% of the data set was used to validate the model using the MAE, MAPE, and RMSE error values between the actual and predicted queues. Among both models, 0.94 of higher R2 value and Durbin Watson value as 2 was obtained for the developed model using raw variables for Weliwita junction. Furthermore, the observed MAE, MAPE, and RMSE values for Weliwita model were 3,5 and 6%, respectively. Thus, the results of this study can be used to reduce traffic congestion while enhancing the safety of the users at un-signalized intersections in Sri Lanka.