Faculty of Engineering

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Author: search.filters.author.Wijesinghe, R.EStart date: 2024

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Now showing 1 - 6 of 6
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    OcupHI: knowledge-driven colorimetric interpretation framework for high-precision real-time ocular pH diagnostics
    (Springer Nature, 2026-08-12) Kahandawala, B, S; Sandaruwan, H. H. P. B; Liyanage, P; Dassanayake, R.S; Costha, N.P; Liyanage, R.N; Wijenayake, U; Wijesinghe, R.E; Silva, B.N; Manatunga, D.C
    Ocular injuries due to chemical spills pose a substantial concern, representing 10–22% of all ocular trauma. Although precise detection of ocular pH is crucial for determining the optimal medical treatment, many existing methods remain invasive, biased, or insufficiently precise. Reliance on subjective visual assessment of subtle color differences limits the objectivity and hinders high-throughput analysis. Therefore, an advanced colorimetric knowledge-driven ocular pH detection method was developed using a biosensor (OcupHI) based on a Clitoria ternatea (Butterfly Pea) anthocyanin sensing agent. The proposed work delivers fast, high-precision, and easily measurable pH prediction across clinically relevant ranges, while supporting real-time decision support for eye physicians. The pH range from 1 to 12 was tested and compared with six different anthocyanin concentrations: 5, 10, 20, 30, 40, and 50 ppm, and five different machine learning models, namely, Decision Tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machines (SVM). The results revealed that the 40 ppm anthocyanin concentration trained with the XGBoost model produced the most accurate ocular pH values, achieving superior performance with an overall accuracy of 96%, a significantly higher F1-score for early detection. Experimental validation clearly demonstrates strong predictive accuracy, robustness, and interpretability, highlighting the potential for next-generation ocular diagnostics. Further research findings support Sustainable Development Goal (SDG) 3 – good health and well-being through a real-time ocular pH monitoring kit, and SDG 12 – responsible consumption and production by optimizing the use of the natural colorant anthocyanin for sensor development.
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    Bi-directional long short-term memory based ensemble deep learning framework for non-linear steam turbine power forecasting: a biomass fuelled case study
    (Elsevier Ltd, 2026-04-10) Perera, H; Jayasekara, S; Wijesinghe, R.E; Silva, B. N; Cha, H
    In palm oil manufacturing, steam turbines powered by biomass fuel are central to energy generation. However, fluctuating load demands and temporal variations lead to inefficiencies, while limited and variable supply of biomass waste constrains boiler feed flexibility. Current index-based boiler feeding methods overlook actual load demands and waste availability, resulting in significant energy wastage. This study presents a novel ensemble deep learning model combining Bidirectional Long Short-Term Memory (Bi-LSTM) and Gated Recurrent Units (GRU) with Attention Layers, trained on an eight-year operational dataset with structured preprocessing and feature selection, to forecast steam turbine power generation. The model captures complex non-linear temporal patterns more effectively than conventional and standalone ML models, achieving a Root Mean Square Error (RMSE) of 0.0684, Mean Absolute Error (MAE) of 0.0414, and an R-squared (R2) value of 0.9832, which outperformed eight benchmark models by approximately 25% in prediction accuracy. Additionally, the framework incorporates operational parameters such as kVA, total energy, and Fresh Fruit Bunch (FFB) production to dynamically optimise biomass feed rates, balancing energy output with resource availability. This approach minimises energy wastage, reduces grid reliance, and promotes both sustainability and profitability.
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    PublicationOpen Access
    Three-Dimensional Assessment of Dental Enamel Microcrack Progression After Orthodontic Bracket Debonding Using Optical Coherence Tomography
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025-01) Saleah, S.A; Hamdan, A.H; Seong, D; Ravichandran, N. K; Wijesinghe, R.E; Han, S; Kim, J; Jeon, M; Park, H. S
    The current study aimed to quantify the length progression of enamel microcracks (EMCs) after debonding metal and ceramic brackets, implementing OCT as a diagnostic tool. The secondary objectives included a three-dimensional assessment of EMC width and depth and the formation of new EMCs. OCT imaging was performed on 16 extracted human premolars before bonding and after debonding. Debonding was conducted with a universal Instron machine, with ARI values recorded. Additionally, 2D and 3D OCT images were employed to detect EMC formation and progression. Enface images quantified the length, width, and number of EMCs, and the length and width were analyzed using Image J (1.54f) and MATLAB (R2014b), respectively. Sagittal cross-sectional images were used for EMC depth analysis. A paired t-test showed significant differences in the length, width, and number of EMCs after debonding (p-value < 0.05), while the Wilcoxon non-parametric test indicated significant EMC depth changes (p-value < 0.05). No significant results were identified for the EMC number in ceramic brackets and EMC depth in metal brackets. Three-dimensional OCT imaging monitored existing EMCs at higher risk of progression and detected new EMCs following orthodontic bracket debonding. This study provides novel insights into EMC progression regarding the length, width, depth, and number after debonding.
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    Brewing plastics: OCT reveals microplastic release from nylon tea bags in simulated brewed tea infusions
    (Royal Society of Chemistry, 2026-02-12) Jayasekara, P.M; Abhishek, P; Kahandawala,B.S; Damith, N; Weerasinghe, M; Kahatapitiya, N.S; Silva, B.N; Karunaratne, S; Wijesinghe, R.E; Wijenayake, U
    The release of microplastics (MPs) from nylon tea bags poses a critical concern for human exposure; however,their detection and quantification remain challenging especially in beverage matrices, and hence, this study pioneers the use of high-resolution optical coherence tomography (OCT) integrated with an image processing algorithm to rapidly detect and quantify the size and count of the MPs directly in the water extractions simulating tea brewing. The water extractions prepared by simulating tea brewing conditions, hot (100 °C, 1–5min), cold (2 °C, 1 h), and ambient (30 °C, 1 h), were observed employing OCT imaging and validated through Nile Red (NR) staining and digital microscopy. The nylon tea bags steeped in hot water for 5 minutes released 16 000 to 24 000 LMPs (>30 mm) and SMPs (12–30 mm) per millilitre. The estimated daily intake (EDI) of MPs indicates a higher exposure for children (ranging from 0.201 to 0.349 mm3 kg−1 day−1 ) compared to adults (0.046 to 0.080 mm3 kg−1 day−1 ). In contrast, cold brewing for 1 hour released fewer LMPs but an equal quantity of small MPs (SMPs) compared to hot brewing. This OCT-based approach offers a rapid, versatile platform for the detection and quantification of MPs from diverse packaging materials and provides a powerful tool for comprehensive risk assessment when combined with chemical and toxicological analyses.
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    Comparative quantifications and morphological monitoring of the topical treatment approach for onychomycosis-affected in vivo toenail using optical coherence tomography: A case study
    (Elsevier Ltd, 2024-02) Saleah, S.S; Gu, Y; Wijesinghe, R.E; Seong, D; Cho, H; Jeon, M; Kim, J
    Onychomycosis is one of the most common toenail fungal infections that affect the quality of life of many patients. Long-term and noninvasive monitoring of morphological changes of onychomycosis-affected nail plate aids the medication process and provides comfort for patients. However, existing medical and dermatological imaging methods have various types of limitations in nail investigation due to low resolution, lack of volumetric data, the necessity of highly trained personnel for image analysis, and the variety of protocols. In this study, qualitative monitoring-based quantitative assessments were performed to assess the morphological changes of onychomycosis-affected toenail for 15 consecutive weeks using high-resolution optical coherence tomography (OCT). Layer intensity and surface roughness measuring algorithms were applied to two-dimensional OCT cross-sectional images to detect gradual changes in the morphological structure of the diseased toenail. A depth intensity profile and the angle formed between the nail plate and nail fold were also used to analyze the thickness and shape of the toenail plates, respectively. The quantitative and morphological monitoring results revealed significant changes in the toenail structure before and during the treatment process, confirming the healing of the diseased toenail. Therefore, the proposed noninvasive optical analysis approach can be applied to monitor nail abnormalities and evaluate the process of diseased toenail medication
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    Non-destructive morphological screening for the assessment of postharvest storage effect on pears stored with apples using optical coherence tomography
    (Elsevier GmbH, 2024-04) Luna, J.A; Wijesinghe, R.E; Lee, S.Y; Ravichandran, N.K; Saleah, S.A; Seong, D; Jung, H.Y; Jeon, M; Kim, J
    The use of a limited and inadequate storage facility for the storage of multiple food items for an extended period of time results in the loss of structural integrity and freshness while storing fruit in confined single storage without adequate individual packaging methods can result in morphological changes and the degradation of the quality of the fruit. In this study, the effects of postharvest storage on pears co-stored with apples were investigated via non-invasive screening of the structural deformation of pears and the respective anatomical changes of the sub-surface. The anatomical changes were monitored for a prolonged time (12 d) under inadequate and confined storage conditions using swept-source optical coherence tomography (SS-OCT) and the results were comparatively analyzed using appropriately stored specimens. In addition, the OCT cross-sectional images were analyzed for the assessment of the dispersed intensity profile using a customized intensity-based image-processing algorithm. The results revealed the internal morphological variations and corresponding intensity fluctuations, thickness variations, and internal gap formations. This confirmed the potential applicability of OCT as a real-time, non-invasive high-resolution assessment technique for determining fruit quality in diverse environments, such as post-harvest storage and transportation systems.