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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, U; Manatunga, D.CThe 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 A Numerical Investigation of Valve Timing and Intake Pressure Effects on Performance and Emissions in a Hydrogen Port Fuel Injection Engine(Faculty of Engineering, 2025-09-09) Wickramaarachchi, I; Nissanka, I. D; Wijeyakulasuriya, SHydrogen internal combustion engines (H2ICEs) offer a viable low-emission alternative for decarbonizing transport, especially where full electrification is not practical. Among fueling strategies, port fuel injection (PFI) is particularly attractive due to its compatibility with existing engine platforms and simplicity compared to direct injection (DI). Performance and emissions in hydrogen PFI engines are strongly influenced by valve timing and intake boosting strategies. This study presents a computational framework to investigate the coupled effects of valve timing and intake pressure on the performance, thermal efficiency, and NOx emissions of a hydrogen PFI engine under fuel-lean conditions (ϕ = 0.59). A modified Sandia optical engine geometry was simulated using CONVERGE CFD v4.1, employing detailed chemistry and adaptive mesh refinement. Latin Hypercube Sampling (LHS) was employed to generate 373 design cases that span a wide parametric space. Results show that intake boosting significantly improves performance, achieving a 220% increase in indicated power (up to 43.55 kW) and an 11% improvement in thermal efficiency (up to 48.7%) over the baseline configuration. However, these gains are accompanied by elevated NOx emissions, particularly at higher valve overlaps. Conversely, the configuration that achieved the lowest NOx emissions reduced them by 76% compared to the baseline, albeit at the expense of lower power and efficiency. The three configurations representing the most favorable outcomes for power, efficiency, and emissions within the studied parameter space highlight the inherent trade-offs among these objectives. These results provide practical guidance for calibrating hydrogen PFI engines and establish a solid foundation for future studies incorporating formal optimization methods.Publication Open Access A Numerical Investigation of Valve Timing and Intake Pressure Effects on Performance and Emissions in a Hydrogen Port Fuel Injection Engine(Faculty of Engineering, 2025-09-09) Wickramaarachchi, I; Nissanka, I.D; Wijeyakulasuriya, SHydrogen internal combustion engines (H2ICEs) offer a viable low-emission alternative for decarbonizing transport, especially where full electrification is not practical. Among fueling strategies, port fuel injection (PFI) is particularly attractive due to its compatibility with existing engine platforms and simplicity compared to direct injection (DI). Performance and emissions in hydrogen PFI engines are strongly influenced by valve timing and intake boosting strategies. This study presents a computational framework to investigate the coupled effects of valve timing and intake pressure on the performance, thermal efficiency, and NOx emissions of a hydrogen PFI engine under fuel-lean conditions (ϕ = 0.59). A modified Sandia optical engine geometry was simulated using CONVERGE CFD v4.1, employing detailed chemistry and adaptive mesh refinement. Latin Hypercube Sampling (LHS) was employed to generate 373 design cases that span a wide parametric space. Results show that intake boosting significantly improves performance, achieving a 220% increase in indicated power (up to 43.55 kW) and an 11% improvement in thermal efficiency (up to 48.7%) over the baseline configuration. However, these gains are accompanied by elevated NOx emissions, particularly at higher valve overlaps. Conversely, the configuration that achieved the lowest NOx emissions reduced them by 76% compared to the baseline, albeit at the expense of lower power and efficiency. The three configurations representing the most favorable outcomes for power, efficiency, and emissions within the studied parameter space highlight the inherent trade-offs among these objectives. These results provide practical guidance for calibrating hydrogen PFI engines and establish a solid foundation for future studies incorporating formal optimization methods.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.Item Embargo Wet-Neuromorphic Computing: A New Paradigm for Biological Artificial Intelligence(Institute of Electrical and Electronics Engineers, 2025-03-31) Perera, J; Balasubramaniam, S; Somathilaka, S; Wen, Q; Li, X; Kasthurirathna, D; Roohi, A; Nelson, M. TAs we delve into a life governed by artificial intelligence (AI), ongoing research continues to discover new forms of intelligence that are efficient and closely mimic an organism’s brain in terms of performance. This article presents a new concept termed wet-neuromorphic computing, in which biological cells or organisms are leveraged to perform computational tasks using their natural molecular functions. We map key neuromorphic properties to natural biological computing observed in bacteria, 3-D organoids, and Caenorhabditis elegans. To expand beyond the inspiration of the brain to create conventional neuromorphic computing, the study presents a case study that demonstrates bacterial AI computing using the gene regulatory neural network derived from Escherichia coli’s gene regulatory network for pattern recognition, validated through wet lab experiments. Finally, challenges and future directions are discussed.Publication Open Access The Effect of TPACK on ESL Learners' Performance: An Investigation of Teacher Competency in Technology Integration in the Colombo District(School of Education, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Colombage, L.U.; Abeywickrama,K.R.W. K.H.Given the growing use of technology in education, it is essential to comprehend how Technological Pedagogical Content Knowledge (TPACK) contributes to teachers’ successful learning. This study investigates how the TPACK of secondary-level English as a Second Language (ESL) teachers affects the performance of ESL students byanalysing data gathered from secondary-level ESL teachers in seven schools in the Colombo District using a mixed method approach.Publication Open Access Conceptual Design of Short Range - Low Altitude Fixed Wing Unmanned Aerial Vehicle For Landmine Detection(Sri Lanka Institute of Information Technology, 2023-03-25) Daisan, GOver the past five decades, landmines are creating serious problems all over the world. Even though many UAVs are being developed for demining, many of them are not available to the humanitarian activities. Designing a UAV for demining from the air is challenging and there are only a few UAVs employed in landmines search in Sri Lanka. This research designs a fixed-wing UAV with a total mass of 47 kg, with 10 kg dedicated to payload. It can fly with a maximum speed of 30 m/s continuously for 40 minutes. This UAV detect landmines using ground penetrating radar in no wetted areas. Several manual calculations and software such as Microsoft Excel, XFLR 5, X-Foil, CATIA V5, and ANSYS 19 were used to complete the conceptual design.Publication Embargo Performance Comparison of Sea Fish Species Classification using Hybrid and Supervised Machine Learning Algorithms(IEEE, 2022-10-04) Nalmi, R; Rathnayake, N; Mampitiya, L.IIn the domain of autonomous underwater vehicles, the classification of objects underwater is critical. The hazy effect of the medium causes this obstacle, and these effects are directed by the dissolved particles that lead to the reflecting and scattering of light during the formation process of the image. This paper mainly focuses on exploring the best possible image classifier for the underwater images of the different fish species. The classifications were carried out by different hybrid and supervised machine learning algorithms such as Support Vector Machine (SVM), Random Forest (RF), Neural Networks (NN), Logistic Regression (LR), Decision Tree (DT), and Naive Bayes (NB). This study compares the algorithms’ accuracy and time and analyzes crucial features to decide the most optimal algorithm. Furthermore, the results of this paper depict that using dimension reduction methods such as PCA and LDA increases the accuracy of some algorithms. Random Forest was able to outperforms with a higher accuracy of 99.89% with the proposed feature extraction methods.Publication Embargo Intelligent System to Verify the Effectiveness of Proposed Teacher Transfers Incorporating Human Factors(IEEE, 2022-02-23) Karunanayake, V; Wanniarachchi, J. C; Karunanayake, p; Rajapaksha, SA data center architecture can be determined as the basic structure of a cloud computing data center. However, the expected outcome cannot be obtained even though the tools, technologies, and elements are advanced. It is crucial to select the right architecture as architecture used, which directly affects the operational cost and power consumption of the data center. The SDN, Software Defined Networking, is a novel technology of cloud computing. This SDN is used to separate the control panel and forwarding plane as well, as it enables programmable network components. Therefore, the components of the SDN can easily control and managed. This research aimed to select an SDN-based architecture for a data center. Here, there are two parameters of average throughput and average packet delay on the three existing architectures that are tested using simulation. Then the results are compared with each other. In this research, the accuracy correlations among average packet delay and average throughput are investigated. The Obtained results show that Fat-Tree architecture is the best one among VL2 and hybrid architecture consists with flat tree and VL2 technology in terms of their performances.Publication Embargo Investigating the Performance in SDN Based Data Centers Under Different Network Topologies(IEEE, 2022-02-23) Rankothge, W; Hemachandra, K. G. R. P.; Jayasena, K. P. N; Wijesiri, M. P. MA data center architecture can be determined as the basic structure of a cloud computing data center. However, the expected outcome cannot be obtained even though the tools, technologies, and elements are advanced. It is crucial to select the right architecture as architecture used, which directly affects the operational cost and power consumption of the data center. The SDN, Software Defined Networking, is a novel technology of cloud computing. This SDN is used to separate the control panel and forwarding plane as well, as it enables programmable network components. Therefore, the components of the SDN can easily control and managed. This research aimed to select an SDN-based architecture for a data center. Here, there are two parameters of average throughput and average packet delay on the three existing architectures that are tested using simulation. Then the results are compared with each other. In this research, the accuracy correlations among average packet delay and average throughput are investigated. The Obtained results show that Fat-Tree architecture is the best one among VL2 and hybrid architecture consists with flat tree and VL2 technology in terms of their performances.