Advances in Science and Technology Vol. 177

Abstract: This study investigates the flammability characteristics of composites developed from waste materials, notably integrating Palmyra fibre and waste LDPE. The research systematically assessed the flammability properties of Palmyra Fibre-Reinforced Composites (PFRCs) across seventeen distinct variations. PFRCs were synthesized employing a variety of techniques, including the hot-press method, the cold-press method, and the hand lay-up method. The analysis spanned various dimensions such as the treatment condition of the fibres, fibre lengths, volume fractions, and orientation, aiming to evaluate their impact on the composite's flammability properties comprehensively. Among the variations considered, 40 mm length alkali-treated fibre with 20% (w/w) inclusion in random orientation provided the best overall density and flammability characteristics. The results highlight the capability of Palmyra fibre to serve as an effective alternative for reinforcing composite sheets. The research indicates that these materials demonstrate not only favourable density and improved resistance to fire but also add to the overall durability and wider usage possibilities of the composites. Together, these findings emphasise the field of sustainable and alternative materials research, emphasizing the practicality of utilizing waste-derived composites in a range of applications.

Abstract: This study explores the variation of compressive stress-strain behavior of concrete incorporating waste tire aggregates as a partial replacement for conventional coarse aggregates, addressing the global challenge of tire waste management. Concrete mixes with 0%, 10%, and 20% rubber replacement were tested under varying loading conditions after curing for 28 days. The research aims to provide insights into the trade-offs between strength and flexibility in rubberized concrete to support sustainable construction practices. Experimental results demonstrated that the control mix (0% rubber) exhibited the highest compressive strength but showed brittle behavior with minimal strain tolerance. The 10% rubber mix achieved a balance, retaining substantial strength while improving strain capacity and energy absorption, making it suitable for applications requiring both strength and ductility. The 20% rubber mix had the greatest strain tolerance and energy absorption but the lowest compressive strength, indicating its potential for impact-resistant and flexible structures. These findings align with existing literature, emphasizing the material's suitability for applications in seismic zones, noise barriers, and vibration-dampening structures. This study highlights the potential of rubberized concrete as a sustainable alternative, offering environmental benefits by reusing waste tires and reducing dependence on natural aggregates. However, challenges such as reduced strength with higher rubber content need to be addressed through optimized mix designs and pretreatment methods. Rubberized concrete provides a promising pathway for balancing sustainability with structural performance, particularly for dynamic and non-load-bearing applications.

Abstract: Fish markets in Sri Lanka, vital to the national economy, lack essential hygiene facilities such as washable floors, dedicated cutting and gutting stations, and enclosed drainage. These deficiencies, coupled with poor wastewater handling, severely impact surrounding environments, ecosystems, and public health. In most markets, waste management relies on a collect-and-dump approach, while wastewater often flows through clogged, foul-smelling drains, worsening hygiene and environmental issues. These conditions highlight the urgent need for an integrated water management strategy that addresses market operations and adapts to functional zones. This research develops a scalable framework for water and wastewater management in Sri Lankan fish markets. It draws on case studies of the Sydney Fish Market as a global best practice and the Peliyagoda Fish Market as a local practice, adapting international standards to the Sri Lankan context. Stakeholder engagement and field assessments revealed critical gaps, including the absence of greywater separation, inadequate drainage, poor solid waste filtering, limited reuse systems, and lack of smart monitoring The framework suggests dedicated zones for market functions—washing and sorting, wholesale and retail, cutting bays, and loading spaces—while integrating water supply and wastewater systems within each zone to ensure hygiene, spatial organization, and sustainability. It further recommends seawater ice for storage, rainwater harvesting for non-potable use, bunded wash zones with enclosed drainage, biofiltration units for treatment, and affordable smart technologies for monitoring. This systematic organization offers a replicable framework to upgrade Sri Lankan fish markets into hygienic, resource-efficient, and environmentally responsible urban facilities.

Abstract: Leachate treatment is a critical component of municipal solid waste management due to the complex and variable nature of leachate composition. This study investigates a dilution-based strategy to address insufficient leachate volume during a mandated 21-day reliability test at the Kelaniya Transfer Station leachate treatment plant, part of the Metro Colombo Solid Waste Management Project. The primary objective was to evaluate the plant's capacity to treat leachate under continuous operational conditions at its design flow rate of 160 m³/day, despite a limited leachate supply caused by non-functional compactors. A dilution ratio of 50–60% was applied to available leachate to meet volume requirements, with the underlying assumption that such dilution would not significantly alter leachate variability. Baseline and incoming leachate samples were analyzed for key parameters, chemical oxygen demand (COD), ammonia nitrogen (NH₄–N), total nitrogen (TN), and total suspended solids (TSS). Statistical tests, including two-sample t-tests and Levene’s tests, were conducted to assess variability before and after dilution. The results revealed significant variation between undiluted samples from the same source, confirming the inherent variability of leachate. However, Levene’s tests showed no statistically significant differences in variance for NH₄–N, TN and TSS before and after dilution, indicating that the dilution process preserved the natural variability of leachate characteristics. The findings support the use of controlled dilution as a valid strategy for leachate volume supplementation during performance testing, without compromising the reliability of treatment plant assessment. Nevertheless, dilution reduces nutrient loading, particularly carbon input, and may not reflect peak loading scenarios. To address this, a supplementary a separate testing phase was conducted using undiluted leachate with high-strength characteristics over a period, specifically to evaluate the treatment plant’s capacity to handle peak loading scenarios.

Abstract: This study explores the adoption of Artificial Intelligence (AI) within the research component of business processes and its potential to enhance client acquisition effectiveness in IT services companies. In an environment where traditional research methods remain dominant yet inefficient, AI offers opportunities to streamline data collection, improve insight generation, and reduce time-to-strategy. However, AI adoption across IT research teams remains inconsistent due to barriers such as data privacy concerns, skill gaps, and limited organizational readiness. The research applies a qualitative approach using semi-structured interviews with nine professionals from a global IT services organization. Thematic analysis reveals that while AI tools such as ChatGPT and Microsoft Copilot are increasingly used for summarization and trend analysis, their integration remains exploratory rather than systematic. Barriers for AI adoption include limited AI literacy, lack of structured training, leadership hesitation, and concerns about trust and compliance. Conversely, enablers such as pilot testing, internal AI platforms, peer learning, and strong leadership support are identified as critical facilitators of successful adoption. A five-phase AI Adoption Framework is then proposed, offering a practical roadmap for organizations to transition from ad-hoc AI use to structured, outcome-driven integration. The findings contribute to academic literature by addressing gaps in B2B AI adoption research and offering practical implications for IT service organizations seeking to enhance research efficiency and client acquisition performance through AI.

Abstract: Deforestation is a significant threat to the sustainability of the ecosystem, leading to adverse effects such as climate change, biodiversity loss, and socio-economic consequences. Timely monitoring of forest destruction enables effective implementation of preventive mechanisms supported by law enforcement. Advancements in remote sensing, coupled with enhanced deep learning techniques, boost efficient deforestation monitoring as these technologies support real-time analysis of complex satellite images. Thus, this study aimed to develop a classification model to identify forest areas from non-forest areas using Landsat-8 data acquired for Wilpattu park, Sri Lanka, between 2015 to 2024. We explored model building using minimal input of two bands in satellite data, facilitating low resource needs. Seven deep learning models were explored, progressing from Convolution Neural Networks to Transformer-based models to build the classifier using a set of patches of size 100×100. The results were evaluated using standard metrics such as accuracy, precision, recall, F1 score, and Kappa index. We found that SegNet outperformed the remaining models with an overall accuracy of 96.36%, F1 score of 0.97, and Kappa index of 0.92, demonstrating excellent ability to distinguish the classes. However, the efficiency of the model needs further improvement. The proposed system will contribute to deforestation detection, offering a simpler model development approach with minimum input requirements. The proposed method can be adopted to other domains where the chosen band combination supports effective detection, such as water body identification.

Abstract: Plastic pollution remains a critical environmental and public health challenge. Bioplastics have emerged as a promising alternative to reduce the adverse impacts of petroleum-based plastics. Among renewable biomass sources, macroalgae, particularly seaweeds, stand out due to their high biomass yields, cost-effectiveness, and ease of cultivation. For an island nation like Sri Lanka, seaweed-based bioplastics present a unique opportunity to advance sustainability while strengthening the economy. Sri Lanka already has an established seaweed farming industry, primarily exporting dried seaweeds, which could be expanded into value-added bioplastic production. Several studies project a significant global increase in bioplastic demand by 2028, underscoring the potential market. With its year-round cultivation potential, rich marine biodiversity, and proximity to major Asian markets, Sri Lanka is well-positioned to become a competitive player in the regional bioplastics industry. This review examines bioplastic production from seaweeds, with a focus on its applicability, benefits, and strategic relevance for Sri Lanka as a developing country.