Research Publications

Now showing 1 - 3 of 3

Open Access
Biocompatibility vs antibacterial activity: chitosan-mediated nanosilver/PCL/gelatin nanofibers
(Taylor and Francis Ltd., 2026) Chandraguptha, D; Fernando, L; Herath, L; Godakanda, V. U; Perera, N; Samarakoon, S; de Silva, K. M. N; Williams, G.R; de Silva, W. R.M
Electrospinning is an efficient approach to prepare nanofiber scaffolds that mimic local tissue environments. While many reported scaffolds incorporate nanoparticles, detailed assessments of how nanosilver distribution affects antibacterial activity and biocompatibility remain limited. In this study, we developed an electrospun biopolymer scaffold composed of polycaprolactone and gelatin with chitosan-mediated nanosilver (C-AgNPs), introduced either by bulk surface coating or by dispersing the NPs within the electrospinning solution. The C-AgNP surface-coated scaffold exhibited antibacterial activity against Staphylococcus aureus and Escherichia coli, whereas the dispersed scaffold did not. However, the dispersed scaffold promoted higher dermal fibroblast viability (82.7%) compared with the coated scaffold (60.9%). Zebrafish embryo assays further revealed mild developmental toxicity from the coated scaffold but no observable toxicity from the dispersed formulation. These findings demonstrate a distinct trade-off between antibacterial efficacy and cytocompatibility depending on nanoparticle distribution. Understanding this relationship is critical for designing electrospun nanofiber scaffolds with balanced biological properties.
Embargo
WORDEX: Early Dyslexia Detection and Support
(Institute of Electrical and Electronics Engineers Inc., 2025) Ganegoda, S.H; Dissanayake, O; Samarakoon, S; Jayawardana, N; Thelijjagoda, S; Gunathilake, P
Dyslexia is a prevalent and complex learning disability that affects approximately 5% of primary school students worldwide. It often manifests as persistent difficulties in reading, writing, spelling, and overall academic performance, which can lead to long-term educational and psychological impacts if not addressed early. To facilitate the early identification and support of dyslexic learners aged 7 to 10, this paper introduces Wordex, an innovative and adaptive educational platform. Wordex is designed to screen for multiple dyslexia subtypes and provide targeted interventions through engaging, interactive, and personalized learning activities. The platform features an integrated machine learning-based screening system that analyzes user interactions and performance metrics to assess the risk of dyslexia. Upon identification, the platform delivers tailored remedial exercises that align with national school curricula, aiming to strengthen specific cognitive and linguistic skills. Wordex is developed using a modern technology stack including Spring Boot, Flutter, Python libraries, Firebase, and MongoDB, and incorporates capabilities such as image processing, supervised learning algorithms, real-time progress tracking, and cloud-based data management. A user-centered design approach and iterative testing cycles were employed to ensure the platform is accessible, intuitive, and pedagogically effective. Wordex contributes significantly to the field of educational technology by offering a scalable, research-informed intervention tool. Future enhancements include multilingual support, broader age group coverage, and integration with classroom learning environments.
Embargo
MindBridge: Early Identification of Learning Difficulties in Children as a Supporting Tool for Teachers
(Institute of Electrical and Electronics Engineers Inc., 2025) Mapa, N; Deshapriya, M; Premathilake, M; Samarakoon, S; Thelijjagoda, S; Vidanaralage, A.J
Learning difficulties in children significantly impede academic success by affecting information processing, mathematical performance, and the learning of proper reading and writing. This paper proposes a Progressive Web Application (PWA) based on artificial intelligence (AI) and machine learning (ML) for identifying potential learning barriers. In contrast with standard diagnostic instruments, the proposed system is designed as a prediction tool with the potential for teachers to conduct timely and focused interventions. By automating feature extraction and reducing manual processing, the system overcomes the limitations of existing learning systems and improves early detection accuracy. Preliminary evaluations indicate that the PWA can effectively identify at-risk students and improve intervention methods and overall academic performance. This research contributes to the integration of computational methods and pedagogy, offering a scalable and low-cost solution for helping slow learners overcome their learning challenges.

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