Research Publications Authored by SLIIT Staff
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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.
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Publication Open Access QPred: A Lightweight Deep Learning-Based Web Pipeline for Accessible and Scalable Streamflow Forecasting(Tech Science Press, 2026) Makumbura, R.K; Wijesundara, H; Sajindra, H; Rathnayake, U; Kumar, V; Duraibabu, D; Sen, SAccurate streamflow prediction is essential for flood warning, reservoir operation, irrigation scheduling, hydropower planning, and sustainable water management, yet remains challenging due to the complexity of hydrological processes. Although data-driven models often outperform conventional physics-based hydrological modelling approaches, their real-world deployment is limited by cost, infrastructure demands, and the interdisciplinary expertise required. To bridge this gap, this study developed QPred, a regional, lightweight, cost-effective, web-delivered application for daily streamflow forecasting. The study executed an end-to-end workflow, from field data acquisition to accessible web-based deployment for on-demand forecasting. High-resolution rainfall data were recorded with tipping-bucket gauges and loggers, while river water depth in the Aglar and Paligaad watersheds was converted to discharge using site-specific rating curves, resulting in a daily dataset of precipitation, river water level and discharge. Four DL architectures were trained, including vanilla Long Short-Term Memory (LSTM), stacked LSTM, bidirectional LSTM, and Gated Recurrent Unit (GRU), and evaluated using Nash-Sutcliffe Efficiency (NSE), Coefficient of Determination (R2), Root-Mean-Square-Error-Standard-Deviation Ratio (RSR), and Percentage Bias (PBIAS) metrics. Performance was watershed-specific, as the vanilla LSTM demonstrated the best generalisation for the Aglar watershed (R2 = 0.88, NSE = 0.82, RMSE = 0.12 during validation), while the GRU achieved the highest validation accuracy in Paligaad (R2 = 0.88, NSE = 0.88, RMSE = 0.49). All models achieved satisfactory to excellent performance during calibration (R2 > 0.91, NSE > 0.91 for both watersheds), demonstrating strong capability to capture streamflow dynamics. The highest performing models were selected and embedded into the QPred application. QPred was developed as a lightweight web pipeline, utilising Google Colab as the primary execution environment, Flask as the backend inference framework, Google Drive for artefact storage, and Ngrok for secure HTTPS tunnelling. A user-friendly front end utilises range sliders (bounded by observed minima and maxima) to gather inputs and provides discharge data along with metadata, thereby enhancing transparency. This work demonstrates that accurate, context-aware deep learning models can be delivered through low-cost, web-based platforms, providing a reproducible and scalable pipeline for hydrological applications in other watersheds and for practitioners. CopyrightPublication Open Access Sustainability indicators in a globalised poultry sector: production, consumption, trade openness, and GDP across 126 countries(Elsevier B.V., 2026-02-12) Silva, Y; Perera, N; Mendis, K; Susan, H; Jayathilaka, RThe sustainability of the meat industry relies on consistent demand and the desire for meat. In recent years, chicken was produced around 104.2 million metric tons and expected to increase by 2% in the upcoming years with a record of 109.6 million tons worldwide. Also, global chicken meat export will increase by 3% with a record of around 14.7 million tons. Therefore, this research focuses on investigating the causal relationships that have a significant impact on chicken production, considering independent variables as chicken consumption, trade openness, and GDP. This study is conducted across several income groups, encompassing 126 countries, for a 30-year period from 1993 to 2022. To strengthen the study, the demand theory and international trade theory were utilised. This study employs multiple methodologies, including panel Granger analysis, cross-country Granger causality analysis to identify the direction of causality, and thereafter the Wavelet coherence analysis to determine the time variance and the nature of the coherence between the variables. According to the study, the results have revealed unidirectional relationships between production and trade openness, chicken meat consumption, and GDP. Accordingly, policy suggestions are provided for farmers, policymakers, relevant organisations, and legislators to make an impact on the chicken meat industry by enhancing production, optimising operations, and maintaining high quality to improve nutritional value. All the implementation suggestions are given to support the Sustainable Development Goals, established by the United Nations.Publication Embargo Accessibility and usability of virtual learning platforms: Lived experiences of visually impaired undergraduates in Sri Lanka(Elsevier Ltd, 2026-03-12) Rajapakshe, W; Wickramaarachchi, C; Alwis, M.K. S.S; Amarasinghe, A.A. M.L; Jayasekara, P.N; Jayasekara, P.TThis study explores the accessibility and usability of virtual learning platforms of visually impaired undergraduate students in Sri Lanka, focusing on their lived experiences, use of assistive technologies, and institutional support mechanisms. As online learning becomes increasingly prevalent, accessibility and inclusive challenges persist, particularly in developing countries with limited infrastructure and institutional support. Despite the availability of assistive technologies, visually impaired learners frequently encounter barriers, including poorly designed platforms, limited usability of screen readers, and inadequate institutional guidance. Addressing a critical research gap, this study investigates how visually impaired undergraduates experience and navigates virtual learning environments to identify accessibility barriers, enabling practices, and context-specific strategies for inclusive digital learning. Using a qualitative approach, semi-structured interviews were conducted with fifteen visually impaired university students across Sri Lanka. Thematic analysis revealed five core themes: barriers and challenges to effective virtual learning, preferred virtual platforms, accessibility features and tools, facilitators of learning success, and strategies to optimise the learning environment. These findings illuminate how systemic inequalities, infrastructural limitations, and institutional neglect collectively constrain the digital learning experience for visually impaired students, while also highlighting enabling practices that foster access and inclusion. The study's originality lies in foregrounding student-led insights in a developing country context and integrating practical, context-specific recommendations for platform developers, educators, and policymakers. By centering the voices of visually impaired learners, this research contributes unique and actionable knowledge to the field of inclusive digital education.Publication Open Access The impact of video game addiction on aggressive behaviour among tertiary students in Sri Lanka(Discover, 2026-01-30) Fonseka, W.Y.S; Hathurusinghe, B.M; Weerarathna, R.S; Rathnayake, R.M.N.M; Samindika, H.R.T; Ramasingha, L.T.D; Jayasuriya, N.U; Kumarapperuma, C; Dayapathirana, NThis research examines the impact of video game addiction on aggressive behaviour of tertiary students in Sri Lanka. Video game addiction, with its potential adverse impacts has raised concerns among the public, especially regarding its link to aggressive behaviour. Data was gathered from a sample of 382 undergraduates of local non-state universities selected employing cluster sampling technique to examine the correlation between video game addiction and aggression. The survey was conducted to collect data on video game addiction and aggression. Research findings reveal how gaming addiction leads to aggressive behaviour in addicted gamers indicating a strong positive relationship between video game addiction and aggressive behaviour. Therefore, researchers recommend balanced time management practices between game usage and other daily essential activities and promoting awareness about negative behavioural implication of game addiction. It is expected that this study provides insights to stakeholders including teachers, parents, and administrators to better understand the effects of video game addiction, and mitigation strategies to minimise the negative impact on students’ behaviour.Publication Embargo Enhancing the effectiveness of satellite precipitation products with topographic and seasonal bias correction(Elsevier B.V., 2026-02) Wanniarachchi, S; Sarukkalige, R; Hapuarachchi, H.A. P; Gomes, P.I.A; Rathnayake, UEstimating precipitation distribution across large regions is crucial for understanding water availability, planning infrastructure, and forecasting flood hazards. Traditional gauge-based methods face challenges, particularly with sparse gauge networks. In response, satellite-based, near-real-time (NRT) precipitation data has gained popularity, especially in poorly gauged watersheds. However, satellite precipitation data quality is often compromised by latency, atmospheric complexities, and topographic effects, resulting in nonlinear errors. To overcome the research gap, this study introduces the Heavy Rain Peak Adjustment (HRPA) method alongside the well-established Seasonal Autoregressive Integrated Moving Average (SARIMA) model for satellite precipitation bias correction. The analysis utilised Global Satellite Mapping of Precipitation (GSMaP-NRT) data and hourly precipitation records from 31 rain gauges in the Ovens River region of Australia. On average, the mean residual of observed and GSMaP-NRT precipitation was −0.02 mm. Additionally, the HRPA method yielded better linear regression R2(0.911), NSE (log) (−0.847), and RMSE (0.628) compared to SARIMA. The results indicate that HRPA outperforms SARIMA, particularly at lower elevations, whereas SARIMA struggles at higher elevations, underscoring its limitations in those areas. Additionally, autocorrelation and partial autocorrelation plots for some stations in hilly areas show significant wave-like patterns, indicating greater uncertainty in satellite precipitation estimates over complex terrain. For several stations, autocorrelations at 24 and 48-hour lags suggest a systematic influence of past residuals on future ones, emphasizing the need for further refinement in satellite precipitation correction methods for these regions.Publication Open Access Anthocyanin (ATH)-incorporating polyvinylpyrrolidone-ethyl cellulose-(2-hydroxypropyl)-β-cyclodextrin (PVP–EC–BCD) nanofiber-based pH sensor for ocular pH detection during accidental chemical spills(Royal Society of Chemistry, 2026-02-03) Sandaruwan, B; Liyanage, R; Costha, P; Dassanayake, R.S; Wijesinghe, R.E; Herath H.M.L.P.B; Nalin de S.K.M; de Silva, R.M; Rajapaksha, S.M; Wijenayake, UThe existing ocular pH detection methods encounter numerous limitations, including low accuracy, poor sensitivity across a wide pH range, and patient discomfort, highlighting the need for innovative approaches. A novel biosensor for ocular pH detection has been developed to assess ocular health and chemical injuries in clinical settings. This study uses the pH-sensitive properties of anthocyanins (ATHs), natural pigments extracted from butterfly pea flowers, to develop a novel pH-responsive nanofiber mat. ATHs are integrated into a polymer blend containing polyvinylpyrrolidone (PVP), ethyl cellulose (EC), and (2-hydroxypropyl)-β-cyclodextrin (BCD) to fabricate electrospun nanofibers. The acquired characterization, employing scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and thermogravimetric analysis (TGA), confirmed the successful fabrication of the ATH-infused nanofibers with a mean diameter ranging from 121 to 396 nm. Four formulations were tested: PVP:EC:BCD:ATH (18 ppm), PVP:EC:BCD:ATH (25 ppm), PVP:EC:BCD:ATH (35 ppm), and PVP:EC:BCD:ATH (50 ppm). Among them, the 50 ppm ATH-incorporating nanofiber mat exhibited the best performance in terms of color clarity, response time, and pH sensitivity. The fabricated 50 ppm ATH incorporating nanofiber mat demonstrated a rapid pH response time of less than 5 seconds (s) while exhibiting a color variation from pink to blue to green across the pH range of 1 to 12, providing a rapid and accurate method for visual pH detection. Based on the color performance of the 50 ppm ATH-incorporating system, a standardized color reference chart was developed to serve as a practical and visual guide for estimating pH levels in clinical applications. Zebrafish toxicity assays were conducted further to validate the safety and biocompatibility of the developed sensor, revealing no significant toxic effects across the range of ATH concentrations.Publication Open Access Self-starting characteristics and dynamic response of a free-spinning cross-flow air turbine for oscillating water columns under irregular wave conditions(Elsevier Ltd, 2026-02-24) Baddegamage B.H.B.P.D; Bae, S.J; Gunawardane S.D.G.S.P.; Lee, Y.H; Kim, K; Yoon, MThe cross-flow air turbine (CFAT) has been proposed as a self-rectifying device for oscillating water column (OWC) wave energy converters as an alternative to conventional Wells and impulse turbines. While previous studies have primarily focused on steady or regular flow conditions, the self-starting behavior and transient response of a free-spinning CFAT under irregular, bidirectional inflow representative of realistic sea states have not yet been investigated. This study presents a fully transient computational fluid dynamics analysis of a free-spinning CFAT operating under irregular airflow conditions derived from the JONSWAP spectrum. The simulations were performed under no-load conditions to isolate the intrinsic aerodynamic torque generation and evaluate self-starting capability. The effects of significant wave height and spectral peak period on turbine startup and unsteady aerodynamic response were systematically examined in both the time and frequency domains. The CFAT consistently initiates rotation without external assistance and reaches quasi-steady operation within 25–30 oscillation cycles. For significant wave heights ranging from 0.0375 m to 0.05 m, the mean instantaneous efficiency varies between 0.24 and 0.52, while efficiencies between 0.30 and 0.59 are obtained for spectral peak periods from 1.50 s to 1.88 s. Furthermore, wave-grouping effects play a decisive role in accelerating the turbine toward its equilibrium speed. Torque and pressure fluctuations closely follow the inflow velocity profile, with hysteresis-like behavior observed during flow reversals. These findings confirm the CFAT's suitability for practical OWC applications, demonstrating robust self-starting and stable performance under irregular conditions.Publication Embargo Data-centric single teacher guided knowledge distillation for alleviating sub-optimal supervision in image classification(Elsevier Ltd, 2026-02-23) Sharma, K; Silva, B. NIn recent years, larger, deeper, and more complex deep learning models have emerged as a result of advancements in deep learning techniques. Nevertheless, the computational costs have also increased with the growing model size. Thus, Knowledge Distillation has evolved into a cornerstone in contemporary machine learning, facilitating the transfer of knowledge from cumbersome teacher models to more compact student models. However, student learning is persistently challenged by sub-optimal supervision caused by erroneous and ambiguous teacher predictions. Moreover, the learning process is further deteriorated by the complications introduced through frequently encountered noisy labels in real-world datasets. Existing methods often resort to the ensemble of teachers, introducing additional complexity. We propose a novel, simple, and efficient learning method, Corrective Knowledge Distillation (CKD), to alleviate these drawbacks while relying solely on a single-teacher model. The proposed work employs a two-phase learning paradigm. In the initial phase, the teacher selectively teaches extremely confident knowledge to the student, and in the subsequent phase, the student leverages its own past learning experiences, conditioning its knowledge acquisition on the guidance of the teacher. The proposed method consistently exhibits superior performance in addressing sub-optimal supervision, as evidenced by comprehensive experiments on benchmark datasets such as CIFAR-100, CIFAR-100N-Fine, and ImageNet-1K. Notably, CKD surpasses established baselines, achieving substantial accuracy gains of up to 3.53% in real-world scenarios. Furthermore, CKD exhibits exceptional robustness in highly noisy environments, outperforming ensemble techniques by a significant margin of up to 5.18%. Our code is available at https://github.com/Karthick47v2/ckd.Publication Open Access Coconut Shell Waste-Derived Porous Carbon-Supported Sn Catalysts for Efficient Electrochemical CO2Reduction to Formic Acid and Deuterated Formic Acid(American Chemical Society, 2025-11-05) Qin, C; Masakorala, G; Mohideen, M; Samarasekara, T; Zhang, L; Zhu, W; Zhou, Y; Thambiliyagodage, CIndustrial-level electrochemical CO2 reduction reaction (CO2RR) to form HCOO– and DCOO– requires robust Sn catalysts with high performance. In this study, the hydrothermal method was employed to load varying amounts of Sn precursors onto waste biomass-derived porous carbon to investigate the structure–activity relationship between Sn loading forms and HCOO– selectivity. Through comprehensive ex/in situ characterizations, we discovered that with 5% Sn precursor addition, highly dispersed SnO2 nanoparticles formed on the carbon support, enabling the catalyst to exhibit exceptional HCOO– activity (Faradaic efficiency exceeding 90%) across a broad potential window. In situ attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) and in situ Raman spectroscopy revealed that the highly dispersed SnO2 nanoparticles enhance the stability of the *OCHO intermediate. Furthermore, when H2O was replaced with D2O, the generation of DCOO– was observed, and good selectivity was maintained. This study provides a facile strategy for waste biomass conversion and the design of Sn-based catalysts for DCOO– production.Publication Embargo From Tourism Growth to Sustainable Development: A Causality Analysis of Tourism, Exchange Rates, and Economic Growth in Asia(John Wiley and Sons Ltd, 2026-02-04) Wickramaarachchi, C; Jayathilaka, RTourism is widely recognised as a catalyst for sustainable development, particularly in regions where it supports employment, foreign exchange earnings and local entrepreneurship. However, the extent to which tourism contributes to sustainable development depends on macroeconomic stability and policy environments that enable long-term investment rather than short-term revenue maximisation. This study examines the causal relationships between tourism receipts, per capita GDP (PGDP), and exchange rates across 46 Asian countries from 2000 to 2020, while controlling for trade openness to account for broader external sector exposure. Employing a panel data framework that accounts for cross-sectional dependence, heterogeneity, and mixed integration properties, the analysis combines second-generation unit root and cointegration tests with country-specific Granger causality techniques. The findings reveal substantial heterogeneity in causal dynamics across countries. In some economies, tourism-led growth emerges, where expanding tourism receipts stimulate economic growth. In others, economy-driven tourism dominates, indicating that rising income levels facilitate tourism development through improved infrastructure and destination competitiveness. Exchange rate stability plays an important conditioning role, shaping the extent to which tourism revenues translate into sustained development gains. Countries characterised by stable exchange rate environments are better positioned to channel tourism income toward long-term, sustainability-oriented investments. The study offers actionable policy insights by demonstrating that macroeconomic stability is a prerequisite for sustainable tourism development. Strengthening exchange rate governance, promoting eco-friendly tourism investment, and enhancing regional cooperation can support a transition from growth-oriented tourism strategies toward sustainable development pathways.