Faculty of Applied Science

Inference-Driven Logistic Regression Approach for Identifying Risk Factors of Myopia

Thu, 01 Jan 2026 00:00:00 GMT

Inference-Driven Logistic Regression Approach for Identifying Risk Factors of Myopia Kayathiri, T.; Kayanan, M.; Wijekoon, P. Myopia is a prevalent refractive error among children and adolescents and represents an increasing global public health issue. The present study aims to identify key risk and protective factors associated with myopia onset through statistical inference. A real-world myopia dataset from the R statistical package, which has been referenced in prior research for theoretical validation and factor identification, was analyzed. This investigation extends previous work by utilizing statistical inference to distinguish between variables that elevate myopia risk and those that confer protection. The dataset comprises 618 individuals who were non-myopic at baseline and were observed over a period of at least five years. During this period, 17 clinical and behavioral variables were recorded. Predictor variables include age, spherical equivalent refraction (SPHEQ), axial length (AL), anterior chamber depth (ACD), lens thickness (LT), vitreous chamber depth (VCD), and time spent on activities such as sports (SPORTHR), reading (READHR), computer use (COMPHR), studying (STUDYHR), and watching television (TVHR). The binary response variable was coded as 1 for myopic and 0 otherwise. Logistic regression coefficients were estimated using maximum likelihood estimation (MLE). Findings indicated that age, SPHEQ, AL, SPORTHR, STUDYHR, and TVHR exhibited negative coefficients, suggesting that increases in these variables are associated with a reduced risk of developing myopia. Conversely, positive coefficients for ACD, LT, VCD, READHR, and COMPHR point to elevated myopia risk. At the 5% significance level, SPHEQ and SPORTHR emerged as statistically significant predictors, while READHR and STUDYHR achieved significance at the 10% level. At the 90% confidence interval for odds ratios, SPHEQ, SPORTHR, and STUDYHR show protective effects (odds ratios < 1), whereas READHR is linked to greater risk; these protective effects remain significant at the 95% level. In summary, reduced reading time, increased participation in sports and studying, along with specific ocular measurements, may mitigate the risk of myopia development.

On the development of the adjusted multinomial logistic Liu estimator

Thu, 01 Jan 2026 00:00:00 GMT

On the development of the adjusted multinomial logistic Liu estimator Kayathiri, T.; Kayanan, M.; Wijekoon, P. The multinomial logistic regression (MNLR) model is a widely used statistical tool for predicting categorical response variables with more than two outcomes, based on multiple predictor variables. It finds applications across various fields, including healthcare, social sciences, and marketing. The maximum likelihood estimator (MLE) is the standard method for estimating parameters in MNLR models. However, when predictor variables exhibit multicollinearity, the MLE becomes inefficient, resulting in inflated variances and unstable coefficient estimates. To address this issue, a limited number of biased estimators have been proposed in the literature. This study aimed to develop an efficient estimator that reduces the impact of multicollinearity in MNLR models by introducing the adjusted multinomial logistic Liu estimator (AMLLE), which improves estimation accuracy and stability. A Monte Carlo simulation study was conducted to evaluate the performance of the MLE, multinomial logistic ridge estimator (MLRE), multinomial logistic Liu estimator (MLLE), almost unbiased multinomial logistic Liu estimator (AUMLLE), and AMLLE under moderate to high multicollinearity. The simulations considered a wide range of sample sizes and varying levels of the response variable. The findings indicated that the proposed estimator, AMLLE, outperformed the existing estimators in all scenarios considered. The relative efficiency of AMLLE compared to MLE, based on SMSE, showed substantial improvement across different correlation values. For correlations of 0.5, 0.7, 0.9, and 0.99, AMLLE achieves efficiencies of 38.25, 46.62, 68.73, and 96.79% for n=50; 21.56, 27.03, 46.25, and 90.80% for n=100; and 2.85, 3.80, 9.04, and 41.21% for n=1000, with three response levels. The corresponding efficiencies with five response levels are 43.47, 52.16, 61.86, and 97.95%; 27.66, 33.39, 52.84, and 93.95%; and 3.76, 5.23, 12.41, and 47.05%, respectively. Moreover, increasing the sample size further enhanced the performance of the proposed estimator, while higher correlation and additional response levels tend to reduce its effectiveness. In conclusion, the adjusted multinomial logistic Liu estimator provides a reliable and computationally efficient alternative for parameter estimation in multinomial logistic regression models affected by multicollinearity. It shows strong potential for practical applications, and future research could explore its extension to high-dimensional predictor settings.

IMPACTS OF BIOCHAR ON EROSION POTENTIAL OF SOIL IN SLOPE LAND

Tue, 01 Jan 2019 00:00:00 GMT

IMPACTS OF BIOCHAR ON EROSION POTENTIAL OF SOIL IN SLOPE LAND Madhushani, K.G.S.; Nanthakumaran, A. The study was conducted to evaluate the soil erosion potential in the slope land incorporated with biochar produced by Pinus pinaster (Pinus), Eucalyptus tereticornis (Eucalyptus) and Camellia sinensis (Tea). This study was conducted in Kandy district between latitudes 7.2906 N, 80.6337 E during 27th of August to 20th of November, 2017. There were sixteen plots of land prepared with the plot size of 1.5 mx 2.0 mand each type of biochar made from pinus, eucalyptus and tea incorporated into the 5 cm top soilin each plotseparately. The Complete Randomized Design was used with four treatments such as soil with tea biochar, soil with eucalyptus biochar, soil with pinus biochar and the control with each of four replicates. At the bottom of these plots, a ditch was excavated and covered with polythene and collected the eroded soil once in two weeks after the incorporation of different biochar with soil. Duncan‟ s multiple range tests were carried out using SPSS 25.0. The estimated soil losses in the plots with tea biochar, pinus biochar, eucalyptus biochar and control were 98.81 g, 219.77 g, 218.96 g and 781.83 g respectively. There was a significant reduction in the rate of soil erosion in the plots with tea biochar as 2.74 gm-2week-1. Plots with pinus and with eucalyptus biochar had almost similar reduction in the soil erosion rate as 6.08 gm-2week-1 and 6.12 gm-2week-1 respectively while the erosion rate in the control plot recorded as 21.72 gm-2week-1. Hence, application of tea biochar as a soil amendment could be recommended to reduce the soil erosion significantly in slope land in the hilly area.

Utilization of Alligator Weed as Organic Liquid Fertilizer: A Comparative Study on Okra (Abelmoschus esculentus) Growth and Yield

Wed, 01 Jan 2025 00:00:00 GMT

Utilization of Alligator Weed as Organic Liquid Fertilizer: A Comparative Study on Okra (Abelmoschus esculentus) Growth and Yield Kabisha, P.; Nanthakumaran, A. Alligator weed (Alternanthera philoxeroides) is a highly invasive species causing severe ecological and economic consequences by affecting water quality, hydrological flow, and the growth of native flora and fauna due to its rapid growth and dense mat formation. It thrives in both aquatic and terrestrial habitats and has become a significant ecological concern in Sri Lanka. In Trincomalee, a two-hectare farm pond in Anpuvalipuram has been infested annually, typically from November to May (up to 1.5-hectare level), disrupting fishing and irrigation activities. Although local communities have attempted control measures such as mulching and feeding it to poultry because of its high nutrient availability, these practices have further encouraged its spread into surrounding terrestrial areas. This study was conducted to evaluate the potential of alligator weed as an organic liquid fertilizer and to compare its effectiveness on the growth and yield of the okra crop with that of a conventional organic fertilizer mix and an inorganic fertilizer. Liquid fertilizers were prepared using four concentrations of alligator weed (25%, 50%, 75% and 100%). A traditional organic mixture of Gliricidia and neem leaves, an inorganic fertilizer treatment, and an untreated control were also included. The experiment was arranged in a Completely Randomized Design with four replicates. Fertilizer samples were analyzed for nitrogen, phosphorus, potassium (NPK), pH, and electrical conductivity (EC). Growth and yield performance were evaluated by measuring crop height, stem circumference, total pod number and total pod weight. Despite relatively low NPK concentrations, the 50% alligator weed liquid fertilizer treatment achieved the highest yield performance (1,145.26 g and 64 pods), which was comparable to inorganic fertilizer and superior to both the organic mixture and the control. Utilizing alligator weed not only provides a sustainable alternative to inorganic fertilizers but also offers an eco-friendly strategy for managing a problematic invasive species while recycling nutrients within agricultural areas.