2024
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Publication Open Access In vitro influence of PEG functionalized ZnO–CuO nanocomposites on bacterial growth(PubMed ID, 2024-01-14) Thambiliyagodage, C; Jayanetti, M; Liyanaarachchi, H; Ekanayake, G; Mendis, M; Usgodaarachchi, LPolyethyleneglycol-coated biocompatible CuO–ZnO nanocomposites were fabricated hydrothermally varying Zn:Cu ratios as 1:1, 2:1, and 1:2, and their antibacterial activity was determined through the well diffusion method against the Gram-negative Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae, and the Gram-positive Staphylococcus aureus. The minimum inhibitory concentration and the minimum bactericidal concentration values of the synthesized samples were determined. Subsequently, the time synergy kill assay was performed to elucidate the nature of the overall inhibitory effect against the aforementioned bacterial species. The mean zone of inhibition values for all four samples are presented. The inhibitory effect increased with increasing concentration of the nanocomposite (20, 40 and 60 mg/ml) on all the bacterial species except for S. aureus. According to the MBC/MIC ratio, ZnO was found to be bacteriostatic for E. coli and P. aeruginosa, and bactericidal for S. aureus and K. pneumoniae. Zn:Cu 2:1 was bactericidal on all bacterial species. A bacteriostatic effect was observed on E. coli and P. aeruginosa in the presence of Zn:Cu 1:1 whereas, it showed a bactericidal effect on S. aureus and K. pneumoniae. Zn:Cu 1:2 exhibited a bacteriostatic effect on E. coli while a bactericidal effect was observed for E. coli, P. aeruginosa, and K. pneumoniae. The metal oxide nanocomposites were found to be more sensitive towards the Gram-positive strain than the Gram-negative strains. Further, all the nanocomposites possess anti-oxidant activity as shown by the DPPH assay.Publication Embargo 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, JOnychomycosis 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 medicationPublication Open Access Data exploration on the factors associated with cost overrun on social housing projects in Trinidad and Tobago(Elsevier Ltd, 2024-02) Chadee, A. A; Allis, C; Rathnayake, U; Martin, H; Azamathulla, H. MThis data article explores the factors that contribute to cost overrun on public sector projects within Trinidad and Tobago. The data was obtained through literature research, and structured questionnaires, designed using open-ended questions and the Likert scale. The responses were gathered from project actors and decision-makers within the public and private construction industry, mainly, project managers, contractors, engineers, architects, and consultants. The dataset was analysed using frequency, simple percentage, mean, risk impact, and fuzzy logic via the fuzzy synthetic evaluation method (FSE). The significance of the analysed data is to determine the critical root causes of cost overrun which affect public sector infrastructure development projects (PSIDPs), from being completed on time and within budget. The dataset is most useful to project and construction management professionals and academia, to provide additional insight into the understanding of the leading factors associated with cost overrun and the critical group in which they occur (political factors). Such understanding can encourage greater decisions under uncertainty and complexity, thus accounting for and reducing cost overrun on public sector projects. © 2023Publication Embargo Tactical Conflict Prevention Strategies in Public-Private Partnerships: Lessons from Experts(American Society of Civil Engineers (ASCE), 2024-02-01) Liyanapathirana, D; Adeniyi, O; Rathnasiri, PConflicts are frequent in public–private partnerships (PPP) due to the involvement of multidisciplined parties within the process.Individual parties among the numerous stakeholders often work toward achieving their own desired goals. Early identification andelimination of potential conflicts are significant to achieving a smooth delivery process of PPP projects. PPP offers imperative benefitsfor economic development, especially in developing countries. However, despite the huge potentials of PPP in infrastructure develop-ment, limited attention still exists in the early stage on the prevention of conflict. Aside from that, a more focused investigation is requiredacross different locations. Therefore, this study aims to enhance the understanding of conflict occurrenceand articulate steps to conflictprevention in PPPs through preconstruction stage practices. Expert interviews were carried out in two phases. Phase I identified thecurrent process and triggers of conflicts and Phase 2 identified thestrategies, and recommendationsto eliminate the conflicts. Thepurposive sampling method was used for the selection of experts in Sri Lanka and findings were analyzed using the content analysismethod. This study identified and categorized the triggers of conflicts in PPP as political interference and public interruption, lack ofknowledge about PPP, contractual agreement,and commitment of professionals. Further, the strategies to prevent conflicts werediscussed under key headings such as proper professional practice, use of lessons learned, thorough contract drafting, a well-defined program,precontract practices, project-long audits, awareness programs, and government support.DOI:10.1061/JLADAH.LADR-996.© 2023American Society of Civil EngineersPublication Open Access Towards a greener future: examining carbon emission dynamics in Asia amid gross domestic product, energy consumption, and trade openness(Springer Nature, 2024-02-10) Dharmapriya, N; Edirisinghe, S; Gunawardena, V; Methmini, D; Jayathilaka, R; Dharmasena, T; Wickramaarachchi, C; Rathnayake, NThe purpose of this study is to examine the impact of gross domestic product, energy consumption, and trade openness on carbon emission in Asia. Among the 48 countries in Asia, 42 were included in the analysis, spanning a period of 20 years. Given that Asia is the predominant contributor, accounting for 53% of global emissions as of 2019, a comprehensive examination at both continental and individual country levels becomes imperative. Such an approach aligns with local, regional, and global development agendas, contributing directly and indirectly to climate change mitigation. The analytical techniques employed in this study encompassed panel regression and multiple linear regression, illuminating the specifc contributions of each country to the study variables and their impact on carbon emissions. The fndings suggest that gross domestic product (13 out of 42 countries), energy consumption (21 out of 42 countries), and trade openness (eight out of 42 countries) have a highly signifcant impact (p<0.01) on carbon emissions in Asia. Energy consumption plays a vital role in increasing carbon emissions in Asia, driven by rising populations, urbanisation, and oil and gas production. Policymakers can take several actions such as adopting a carbon pricing system, using sustainable transportation, renewable energy development,and international cooperation within Asia to reach the goal of being carbon neutral by 2050.Publication Open Access Image processing techniques to identify tomato quality under market conditions(Elsevier B.V., 2024-03) Abekoon, T; Sajindra, H; Jayakody, J．A．D．C．A．; Samarakoon, E．R．J; Rathnayake, UTomatoes are essential in both agriculture and culinary spheres, demanding rigorous quality assessment. It is highly advantageous to discern the maturity level and the time range post-harvesting of tomatoes in the market through visual analysis of their images. This research endeavors to forecast tomato quality by accurately determining the maturity level and the duration post-harvest, specifically tailored to Sri Lankan market conditions, with a particular focus on Padma tomatoes. It identifies maturity stages (Green, Breakers, Turning, Pink, Light Red, Red) and post-harvest dates using image processing techniques. Greenhouse-grown Padma tomatoes mimic market conditions for image capture, and Convolutional Neural Networks facilitate this analysis. Model 1, using ReLU and sigmoid activation functions, accurately classifies tomatoes with 99 % training and validation accuracy. Model 2, with seven classes, achieves 99 % training and 98 % validation accuracy using ReLU and softmax activation functions. Integration of the IPGRI/IITA 1998 classification method enhances tomato categorization. Efficient tomato image screening optimizes resource use. This study successfully determines Padma tomato post-harvest dates based on maturity stages, a significant contribution to tomato quality assessment under market conditions.Publication Open Access Unveiling Sri Lanka’s brain drain and labour market pressure: A study of macroeconomic factors on migration(Public Library of Science, 2024-03) Kaluarachchi, S; Jayathilaka, RThe purpose of this study is to explore the impact of GDP per capita income (GDPPCI), unemployment, higher education (HE), and economic growth (EG) on migration in Sri Lanka. Numerous global and local studies have explored the influence of macroeconomic and socioeconomic factors on migration. In the Sri Lankan context, fewer studies have probed the impact of GDPPCI, unemployment, HE, and EG on migration, particularly concerning brain drain and domestic labour market pressure. An applied research methodology was adopted, utilising annual data from 1986 to 2022. The statistical data were sourced from reports by the Sri Lanka Bureau of Foreign Employment (SLBFE), the Central Bank of Sri Lanka (CBSL), Labor Force Survey Data from the Department of Census and Statistics (LFSDCS), and University Grants Commissions (UGC). This study utilised the Vector Error Correlation model (VECM), Vector Auto-regression (VAR), and Granger Causality test through STATA. The empirical findings of the VAR model highlighted that GDPPCI and EG negatively impact migration, whereas unemployment and HE positively affect migration. The study’s implications demonstrated that GDPPCI, unemployment, HE, and EG were the primary factors influencing the country’s migration decisions. These findings will hopefully inform and guide the Sri Lankan government and policymakers for more effective decision-making. © 2024 Kaluarachchi, Jayathilaka. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Publication Open Access A novel deep learning model to predict the soil nutrient levels (N, P, and K) in cabbage cultivation(Elsevier, 2024-03) Sajindra, H; Abekoon, T; Jayakody, J.A.D.C.A.; Rathnayake, UCabbage (Brassica oleracea) is a green cruciferous vegetable. Major nutrients (nitrogen, phosphorus, and potassium) are frequently applied to the soil due to low fertility levels. However, optimizing required fertilizer levels are extremely important to avoid any overuse and underuse. Therefore, it is important to develop a comprehensive methodology for evaluating the major nutrients in the soil. In this research, a deep learning model was introduced to predict the nitrogen, phosphorus, and potassium content of the soil by analyzing the growing characteristics of the plants, such as plant height, the number of leaves, and the average leaf area of the plant. To achieve this, the growing characteristics of the cabbage plants were recorded weekly along with the respective soil nitrogen, phosphorus, and potassium content of the nearby soil. After the data was trained using the Levenberg–Marquardt algorithm and tested with different transfer functions such as logarithmic sigmoid, pure linear, and tangent sigmoid, better predictions were obtained through the model. According to the Pearson correlation values, pure linear and tangent sigmoid showed higher values, ranging from 0.99 for training, testing, validation, and all data points from the model, indicating a strong relationship between the actual and predicted values. According to the Mean Square Error values, the tangent sigmoid transfer function outperformed the others, giving a value of 1.0813, indicating better predictions of the soil nitrogen, phosphorus, and potassium content from the modelPublication Embargo Exploring the evolving landscape: Urban horticulture cropping systems–trends and challenges(Elsevier, 2024-03-01) Sashika, M.A.N; Gammanpila, H.W; Priyadarshani, S.V.G.N.Urban horticulture cropping systems offer a promising solution for food security and sustainable agriculture in rapidly urbanizing areas. This paper explores their evolving landscape, emphasizing trends and challenges shaping their development and impact on urban environments. Vertical farming, rooftop gardens, hydroponics, aeroponics, Internet of Things (IoT), integration of optimized space, resources, and year-round cultivation are considered as key trends in urban horticulture. These innovations reflect the growing interest in sustainable urban agriculture and technology's role in boosting productivity and resilience. However, urban horticulture faces challenges that demand attention. Limited space requires creative land-use solutions, while soil quality and contamination concerns necessitate remediation strategies for crop safety. Similarly, access to water is crucial, driving the adoption of water-saving technologies. The urban heat island effect poses another challenge, urging heat stress mitigation for crop health. Zoning and regulations play a vital role, requiring supportive policies and secure land tenure. High costs must be managed with innovative financial approaches to ensure urban farming's viability. Finally, public perception and awareness play a critical role. Advocacy, education, and community engagement are vital to dispel misconceptions, garner support, and encourage involvement in urban horticulture. Even with challenges, urban horticulture helps to provide food for the growing population, creates business opportunities, and contributes to a greener environment for sustainable development.Publication Open Access Forecasting weekly dengue incidence in Sri Lanka: Modified Autoregressive Integrated Moving Average modeling approach(PLoS ONE, 2024-03-08) Karasinghe, N; Peiris, S; Jayathilaka, R; Dharmasena, TDengue poses a significant and multifaceted public health challenge in Sri Lanka, encompassing both preventive and curative aspects. Accurate dengue incidence forecasting is pivotal for effective surveillance and disease control. To address this, we developed an Autoregressive Integrated Moving Average (ARIMA) model tailored for predicting weekly dengue cases in the Colombo district. The modeling process drew on comprehensive weekly dengue fever data from the Weekly Epidemiological Reports (WER), spanning January 2015 to August 2020. Following rigorous model selection, the ARIMA (2,1,0) model, augmented with an autoregressive component (AR) of order 16, emerged as the best-fitted model. It underwent initial calibration and fine-tuning using data from January 2015 to August 2020, and was validated against independent 2000 data. Selection criteria included parameter significance, the Akaike Information Criterion (AIC), and Schwarz Bayesian Information Criterion (SBIC). Importantly, the residuals of the ARIMA model conformed to the assumptions of randomness, constant variance, and normality affirming its suitability. The forecasts closely matched observed dengue incidence, offering a valuable tool for public health decision-makers. However, an increased percentage error was noted in late 2020, likely attributed to factors including potential underreporting due to COVID-19-related disruptions amid rising dengue cases. This research contributes to the critical task of managing dengue outbreaks and underscores the dynamic challenges posed by external influences on disease surveillance.Publication Open Access Unveiling Sri Lanka’s brain drain and labour market pressure: A study of macroeconomic factors on migration(PLoS ONE, 2024-03-11) Kaluarachchi, S; Jayathilaka, RThe purpose of this study is to explore the impact of GDP per capita income (GDPPCI), unemployment, higher education (HE), and economic growth (EG) on migration in Sri Lanka. Numerous global and local studies have explored the influence of macroeconomic and socioeconomic factors on migration. In the Sri Lankan context, fewer studies have probed the impact of GDPPCI, unemployment, HE, and EG on migration, particularly concerning brain drain and domestic labour market pressure. An applied research methodology was adopted, utilising annual data from 1986 to 2022. The statistical data were sourced from reports by the Sri Lanka Bureau of Foreign Employment (SLBFE), the Central Bank of Sri Lanka (CBSL), Labor Force Survey Data from the Department of Census and Statistics (LFSDCS), and University Grants Commissions (UGC). This study utilised the Vector Error Correlation model (VECM), Vector Auto-regression (VAR), and Granger Causality test through STATA. The empirical findings of the VAR model highlighted that GDPPCI and EG negatively impact migration, whereas unemployment and HE positively affect migration. The study’s implications demonstrated that GDPPCI, unemployment, HE, and EG were the primary factors influencing the country’s migration decisions. These findings will hopefully inform and guide the Sri Lankan government and policymakers for more effective decision-making.Publication Open Access Examining the influence of global smoking prevalence on stroke mortality: insights from 27 countries across income strata(Springer link, 2024-03-19) Abeysekera, I; De Silva, R; Silva, D; Piumika, L; Jayathilaka, R; Rajamanthri, LBackground This study investigates the influence of Global Smoking Prevalence (GSP) on Stroke Death Rates (SDR) across 27 countries categorized into High-Income Countries (HIC), Upper Middle-Income Countries (UMIC), Lower Middle-Income Countries (LMIC), and Low-Income Countries (LIC). Methods Analysing data from two distinct periods (1990–1999 and 2010–2019), countries exhibiting an increased SDR were selected. The study uses a polynomial regression model, treating income groups as cross-sectional and years as time series data. Results Results from the regression model reveal that 17 countries observed a significant impact of GSP on SDR, with only Turkey, Solomon Islands, and Timor-Leste resulting in negative values. However, the study emphasises that out of all 27 countries, the highest occurrence of the impact of GSP on SDR has been reported in the LMIC stratum for the period under review. Conclusion It is evident that GSP affects the risk of incidence of stroke death, specifically in the LMIC stratum. Furthermore, it has been identified that GSP is a major preventable risk factor affecting global mortality. To mitigate the risk of stroke death attributable to smoking prevalence, necessary preventive steps should be adopted to encourage smoking cessation, and essential policies should be implemented to reduce the burden of SDR.Publication Embargo 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, JThe 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.Publication Open Access Personal well-being index as a measure of quality of life of diverse groups of people with visual impairment and blindness(Springer Science and Business Media B.V., 2024-04) Jayathilaka, R; Dunuwila, V; Attale, D; De Seram, H; Sudusinghe, D; Abeyrathna, I; Suraweera, T; Thelijjagoda, SToday, the world adopts various assessment tools and indices to measure quality of life (QoL) of different persons. The Personal Well-being Index (PWI) is a popular and validated tool used by developed countries to assess the QoL of their citizens. The PWI consists of seven major domains that define people’s QoL. Thus, the main purpose of this study is to explore the application of PWI in measuring the QoL of the visually impaired and blind (VI and B) persons in Sri Lanka, and to identify how QoL varies with their demographic characteristics. Primary data revealed among 64 VI&B, 34 blind and 30 visually impaired people from Hambanthota, was analysed based on vision level, age, gender, marital status, and the level of education. Results indicated that visually impaired (VI) respondents had a higher PWI value than that of the blind. Accordingly, the age group of 40–59 contributes to a higher PWI value than that of others; while the results signify that the PWI values basically depend on the levels of education the participants received. It is significant that the blind and the partially sighted people are concerned about their future security to a greater extent compared to the other domains in the PWI. Also, QoL was perceived to deteriorate with age. Thus, it is evident that efforts to improve QoL of people with visual disabilities requires priority to secure a fruitful and secure future for them.Publication Open Access Making competent decisions in sport and exercise science and sports medicine: Preliminary practical guidelines on sex and gender(Elsevier, 2024-04) Fraser, K. K; Williams, A.G; de Silva, T.T.A; Stebbings, G.K; Backhouse, S.HPublication Embargo IoT-Based Solution for Fish Disease Detection and Controlling a Fish Tank Through a Mobile Application(IEEE, 2024-04-05) Bodaragama, B.D.T; Miyurangana, E.H.A.D.M; Jayakod, Y.T.W.S.L; Vipulasiri, D.M.H.D; Rajapaksha, U. U. S; Krishara, JThis research project seeks to enhance fish tank management and improve the well-being of aquatic life by leveraging modern technological solutions. It focuses on four key areas: monitoring water quality, detecting fish diseases, preventing algae growth, and developing an automatic fish feeder with remote control capabilities. The project’s first goal is to establish a comprehensive water quality monitoring and control system that predicts future water conditions, continuously assesses key parameters, and provides real-time data to users for proactive interventions. Additionally, the research project aims to develop an image-processing-based mobile application for early detection of fish diseases, eliminating the need for manual inspection and improving overall fish health management. The project also involves the creation of a mobile app to predict and prevent algae growth by analyzing factors like lighting, nutrient levels, and water flow, providing personalized recommendations for algae control. Lastly, an automatic fish feeder with remote control capabilities will be designed, allowing fish owners to schedule and adjust feeding times and portion sizes through a mobile app. This innovative approach ensures fish receive consistent and appropriate nutrition even when owners are away from home.Publication Embargo Corrigendum to “Meta-heuristic optimization based cost efficient demand-side management for sustainable smart communities” [Energy Build. (2024) 113599] (Energy & Buildings (2024) 303, (S0378778823008290),(Elsevier Ltd, 2024-04-15) Silva, B.N; Khan, M; Wijesinghe, R.E; Wijenayake, UThe monetary value of grid electricity is inflating significantly due to the staggeringly broadening gap between electricity demand and supply, which arise from the unceasing growth of consumption demands. Although heuristic optimization based demand side management has its merits, incorporating Ant Colony Optimization remains disputable due to its tendency to converge at a local optimum. Therefore, this work presents a hybridized algorithm of Ant Colony Optimization and Genetic Algorithm, which alleviates the drawbacks of Ant Colony Optimization through Genetic Algorithm. The proposed work promotes sustainable energy utilization simultaneously with demand-side optimization. The performance of the proposed algorithm is compared with no scheduling instance, Ant Colony Optimization based energy management controller, and mutated Ant Colony Optimization based appliance scheduling. The proposed algorithm successfully curtails 35.4% from community peak load demand and achieves 33.67% cumulative cost saving for the community. In other words, comparative analysis confirms the supremacy of the proposed algorithm in terms of minimizing peak load, total cost, peak-to-average ratio, and waiting time, while providing prevailing insights about proposed algorithm as a sustainable solution approach.Publication Open Access Persulfate assisted photocatalytic and antibacterial activity of TiO2–CuO coupled with graphene oxide and reduced graphene oxide(https://www.nature.com, 2024-05-31) Thambiliyagodage, C; Liyanaarachchi, H; Jayanetti, M; Ekanayake, G; Mendis, A; Samarakoon, U; Vigneswaran, SPhotocatalysts of TiO2–CuO coupled with 30% graphene oxide (GO) were hydrothermally fabricated, which varied the TiO2 to CuO weight ratios to 1:4, 1:2, 1:1, 2:1 and 4:1 and reduced to form TiO2–CuO/reduced graphene oxide (rGO) photocatalysts. They were characterized using XRD, TEM, SEM, XPS, Raman, and DRS technologies. TiO2–CuO composites and TiO2–CuO/GO degrade methylene blue when persulfate ions are present. Persulfate concentration ranged from 1, 2, 4 to 8 mmol/dm−3 in which the highest activity of 4.4 × 10–2 and 7.35 × 10–2 min−1 was obtained with 4 mmol/dm−3 for TiO2–CuO (1:4) and TiO2–CuO/GO (1:1), respectively. The presence of EDTA and isopropyl alcohol reduced the photodegradation. TiO2–CuO coupled with rGO coagulates methylene blue in the presence of persulfate ions and such coagulation is independent of light. The catalyst dosage and the concentration of the dye were varied for the best-performing samples. The antibacterial activity of the synthesized samples was evaluated against the growth of Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa and Klebsiella pneumonia. Ti:Cu (1:2)-GO and Ti:Cu (1:4)-GO had the highest antibacterial activity against K. pneumoniae (16.08 ± 0.14 mm), P. aeruginosa (22.33 ± 0.58 mm), E. coli (16.17 ± 0.29 mm) and S. aureus (16.08 ± 0.88).Publication Open Access Real-time Multi-spectral Iris Extraction in Diversified Eye Images Utilizing Convolutional Neural Networks(IEEE, 2024-07-03) Rathnayake, R; Madhushan, N; Jeeva, A; Darshani, D; Pathirana, I; Ghosh, S; Subasinghe, A; Silva, B N; Wijenayake, UIris extraction has gained prominence due to its application versatility across many domains. However, achieving real-time iris extraction poses challenges due to several factors. Learning-based algorithms outperform non-learning-based iris extraction methods, delivering superior accuracy and performance. In response, this article proposes a Convolutional Neural Networks (CNN)-based, accurate direct iris extraction mechanism for a broad spectrum of eye images. The innovation of our approach lies in its proficiency with varied image types, including those where the iris is partially obscured by the eyelid. We enhance the method’s reliability by introducing a modified Circular Hough Transform (CHT). Extensive testing demonstrates our method’s excellent real-time performance across diverse image types, even under challenging conditions. These findings underscore the proposed method’s potential as a cost-effective and computationally efficient solution for real-time iris extraction in varied application domains.Publication Open Access A comprehensive review to evaluate the synergy of intelligent food packaging with modern food technology and artificial intelligence field(Springer link, 2024-07-22) Abekoon A; Sajindra, H; Samarakoon, E. R. J.; Jayakody, J.A.D.C.A; Kantamaneni, K; Rathnayake, U; Buthpitiya, B. L. S. K.This study reviews recent advancements in food science and technology, analyzing their impact on the development of intelligent food packaging within the complex food supply chain. Modern food technology has brought about intelligent food packaging, which includes sensors, indicators, data carriers, and artificial intelligence. This innovative packaging helps monitor food quality and safety. These innovations collectively aim to establish an unbroken chain of food safety, freshness, and traceability, from production to consumption. This research explores the components and technologies of intelligent food packaging, focusing on key indicators like time–temperature indicators, gas indicators, freshness indicators, and pathogen indicators to ensure optimal product quality. It further incorporates various types of sensors, including gas sensors, chemical sensors, biosensors, printed electronics, and electronic noses. It integrates data carriers such as barcodes and radio-frequency identification to enhance the complexity and functionality of this system. The review emphasizes the growing influence of artificial intelligence. It looks at new advances in artificial intelligence that are driving the development of intelligent packaging, making it better at preserving food freshness and quality. This review explores how modern food technologies, especially artificial intelligence integration, are revolutionizing intelligent packaging for food safety, quality, reduced waste, and enhanced traceability.