2024

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Applicability of machine learning techniques to analyze Microplastic transportation in open channels with different hydro-environmental factors
(Elsevier Ltd, 2024-09-15) Fazil, A. Z; Gomes, P. I.A.; Sandamal, R.M. K
This research utilized machine learning to analyze experiments conducted in an open channel laboratory setting to predict microplastic transport with varying discharge, velocity, water depth, vegetation pattern, and microplastic density. Four machine learning (ML) models, incorporating Random Forest (RF), Decision Tree (DT), Extreme Gradient Boost (XGB) and K-Nearest Neighbor (KNN) algorithms, were developed and compared with the Linear Regression (LR) statistical model, using 75% of the data for training and 25% for validation. The predictions of ML algorithms were more accurate than the LR, while XGB and RF provided the best predictions. To explain the ML results, Explainable artificial intelligence (XAI) was employed by using Shapley Additive Explanations (SHAP) to predict the global behavior of variables. RF was the most reliable model, with a coefficient of correlation of 0.97 and a mean absolute percentage error of 1.8% after hyperparameter tuning. Results indicated that discharge, velocity, water depth, and vegetation all influenced microplastic transport. Discharge and vegetation enhanced and reduced microplastic transport, respectively, and showed a response to different vegetation patterns. A strong linear positive correlation (R2 = 0.8) was noted between microplastic density and retention. In the absence of dedicated microplastic transport analytical models and infeasibility of using classical sediment transport models in predicting microplastic transport, ML proved to be helpful. Moreover, the use of XAI will reduce the black-box nature of ML models with effective interpretation enhancing the trust of domain experts in ML predictions. The developed model offers a promising tool for real-world open channel predictions, informing effective management strategies to mitigate microplastic pollution.
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, 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
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.
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, U
The 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.
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. M
This 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. © 2023
Open Access
Dense Convolutional Neural Network-Based Deep Learning Pipeline for Pre-Identification of Circular Leaf Spot Disease of Diospyros kaki Leaves Using Optical Coherence Tomography
(Multidisciplinary Digital Publishing Institute (MDPI), 2024-08) Kalupahana, D; Kahatapitiya, N.S; Silva, B.N; Kim, J; Jeon, M; Wijenayake, U; Wijesinghe, R. E
Circular leaf spot (CLS) disease poses a significant threat to persimmon cultivation, leading to substantial harvest reductions. Existing visual and destructive inspection methods suffer from subjectivity, limited accuracy, and considerable time consumption. This study presents an automated pre-identification method of the disease through a deep learning (DL) based pipeline integrated with optical coherence tomography (OCT), thereby addressing the highlighted issues with the existing methods. The investigation yielded promising outcomes by employing transfer learning with pre-trained DL models, specifically DenseNet-121 and VGG-16. The DenseNet-121 model excels in differentiating among three stages of CLS disease (healthy (H), apparently healthy (or healthy-infected (HI)), and infected (I)). The model achieved precision values of 0.7823 for class-H, 0.9005 for class-HI, and 0.7027 for class-I, supported by recall values of 0.8953 for class-HI and 0.8387 for class-I. Moreover, the performance of CLS detection was enhanced by a supplemental quality inspection model utilizing VGG-16, which attained an accuracy of 98.99% in discriminating between low-detail and high-detail images. Moreover, this study employed a combination of LAMP and A-scan for the dataset labeling process, significantly enhancing the accuracy of the models. Overall, this study underscores the potential of DL techniques integrated with OCT to enhance disease identification processes in agricultural settings, particularly in persimmon cultivation, by offering efficient and objective pre-identification of CLS and enabling early intervention and management strategies. © 2024 by the authors.
Open Access
Eco-friendly mix design of slag-ash-based geopolymer concrete using explainable deep learning
(Elsevier, 2024-09) Ranasinghe, R.S.S.; Kulasooriya, W.K.V.J.B; Perera, U.S; Ekanayake, I.U.; Meddage, D.P.P.; Mohotti, D; Rathanayake, U
Geopolymer concrete is a sustainable and eco-friendly substitute for traditional OPC (Ordinary Portland Cement) based concrete, as it reduces greenhouse gas emissions. With various supplementary cementitious materials, the compressive strength of geopolymer concrete should be accurately predicted. Recent studies have applied deep learning techniques to predict the compressive strength of geopolymer concrete yet its hidden decision-making criteria diminish the end-users’ trust in predictions. To bridge this gap, the authors first developed three deep learning models: an artificial neural network (ANN), a deep neural network (DNN), and a 1D convolution neural network (CNN) to predict the compressive strength of slag ash-based geopolymer concrete. The performance indices for accuracy revealed that the DNN model outperforms the other two models. Subsequently, Shapley additive explanations (SHAP) were used to explain the best-performed deep learning model, DNN, and its compressive strength predictions. SHAP exhibited how the importance of each feature and its relationship contributes to the compressive strength prediction of the DNN model. Finally, the authors developed a novel DNN-based open-source software interface to predict the mix design proportions for a given target compressive strength (using inverse modeling technique) for slag ash-based geopolymer concrete. Additionally, the software calculates the Global Warming Potential (kg CO2 equivalent) for each mix design to select the mix designs with low greenhouse emissions.
Open Access
Eco-friendly mix design of slag-ash-based geopolymer concrete using explainable deep learning
(Elsevier, 2024-09) Ranasinghe, R.S.S.; Kulasooriya, W.K.V.J.B.; Perera, U S; Ekanayake, I.U.; Meddage, D.P.P.; Mohotti, D; Rathanayake, U
Geopolymer concrete is a sustainable and eco-friendly substitute for traditional OPC (Ordinary Portland Cement) based concrete, as it reduces greenhouse gas emissions. With various supplementary cementitious materials, the compressive strength of geopolymer concrete should be accurately predicted. Recent studies have applied deep learning techniques to predict the compressive strength of geopolymer concrete yet its hidden decision-making criteria diminish the end-users’ trust in predictions. To bridge this gap, the authors first developed three deep learning models: an artificial neural network (ANN), a deep neural network (DNN), and a 1D convolution neural network (CNN) to predict the compressive strength of slag ash-based geopolymer concrete. The performance indices for accuracy revealed that the DNN model outperforms the other two models. Subsequently, Shapley additive explanations (SHAP) were used to explain the best-performed deep learning model, DNN, and its compressive strength predictions. SHAP exhibited how the importance of each feature and its relationship contributes to the compressive strength prediction of the DNN model. Finally, the authors developed a novel DNN-based open-source software interface to predict the mix design proportions for a given target compressive strength (using inverse modeling technique) for slag ash-based geopolymer concrete. Additionally, the software calculates the Global Warming Potential (kg CO2 equivalent) for each mix design to select the mix designs with low greenhouse emissions.
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, L
Background 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.
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.
Open Access
Feasibility of Sediment Budgeting in an Urban Catchment with the Incorporation of an HEC—HMS Erosion Model: A Case Study from Sri Lanka
(Springer, 2024-09-16) Abeysiriwardana, H. D.; Pattiyage, I. A. Gomes
This study aimed at studying the feasibility of using a sediment model built in HEC – HMS incorporating Modified Universal Soil Loss Equation (MUSLE) in aiding the separation of sediment contribution as point and non-point, an important aspect in sediment pollution control. The model was developed and verified using a representative sub-catchment and a canal reach of a tropical climate. The field observations and model developed had a good agreement and indicated about 16% and 35% of total sediments in the canal may be from nonpoint sources for the dry and wet seasons, respectively. Results suggested that a major fraction of eroded sediment ended up in the main canal through the dense drainage network across the catchment. This meant sediment trapping should focus tributary drainage ditches or at point source inputs to canal rather than the main canal banks. The study recognized that HEC – HMS is also capable of simulating sediment generation with acceptable errors. Being a free software package, HEC – HMS would be an effective sediment modelling tool for jurisdictions where sediment analysis has been constrained by cost.
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, T
Dengue 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.
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, U
Tomatoes 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.
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, L
Polyethyleneglycol-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.
Open Access
The interconnectedness of energy consumption with economic growth: A granger causality analysis
(Elsevier Ltd, 2024-09-15) Perera, N; Dissanayake, H; Samson, D; Abeykoon, S; Jayathilaka, R; Jayasinghe, M; Yapa, S
In considering today's energy challenges, the link between the usage of renewable and non-renewable energy sources and economic growth has gained substantial policy attention. This research examines the complex relationship between these three variables to understand how non-renewable energy consumption and renewable energy consumption interact and what that means for economic growth. This study uses the Granger causality approach to explore the relationships between non-renewable energy consumption, renewable energy consumption, and economic development. It draws on a comprehensive dataset from the Word Bank database, including 152 nations from 1990 to 2019. The analysis is further disaggregated by four subgroups of countries; least developed, developed, transitional economies and developing countries. The result of this study provides valuable empirical evidence of uni-directional causality running from renewable energy consumption to economic growth and non-renewable energy consumption to economic growth in transitional economies. Furthermore, policymakers should focus on both variables when making decisions because the results show that energy consumption and economic growth are interconnected. Implementing global energy efficiency standards, reducing fossil fuel usage, and adopting regulatory measures are all viable policies for limiting adverse effects on the environment while encouraging economic development.
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, J
This 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.
Embargo
Kinetic study of in vitro release of curcumin from chitosan biopolymer and the evaluation of biological efficacy
(Elsevier B.V., 2024-09) Wijayawardana, S; Thambiliyagodage, C; Jayanetti, M
Sustained release of curcumin from the polymeric carrier system chitosan, a natural biopolymer material derived from chitin originated from natural shrimp shell waste, was studied. Six kinetic models, zero order, first order, Korsmeyer–Peppas (KP), Peppas – Sahlin (PS), Higuchi, and Hixson–Crowell, were applied to study the drug release kinetics. The release mechanism of the drug from the curcumin-chitosan composite was evaluated by changing the pH, ionic strength of the release media, and drug concentration. KP and PS models were selected among the studied models to investigate the drug release mechanism from the chitosan biopolymer based on the R2 values (R2 > 0.99). The model constants m in the PS model and n in the KP model stand for the case II relaxation and Fickian diffusion contribution, respectively. The n being < 0.43 in the KP model suggests that the Fickian diffusion governs the drug release. Furthermore, there is a noticeable difference between the values obtained for model parameters m and n in the PS and KP models, indicating that Case II relaxation and Fickian diffusion play crucial roles in the curcumin release mechanism from chitosan. Polymer relaxation has been proven to play a predominant role in releasing curcumin from the composite at lower ionic strengths and higher pH values. Anti-inflammatory activity was tested using the egg-albumin denaturation assay, and the diphenyl-2-picrylhydrazyl assay was carried out to determine the antioxidant activity of the composite. The composite material showed IC50 values of 0.29 mg/ mL and 1.08 mg/ mL for anti-inflammatory and anti-oxidant activities, respectively. The drug composite has shown antibacterial activity against Pseudomonas aeruginosa, Klebsiella pneumoniae, and Staphylococcus aureus, which are highly effective against S.aureus. The resulting inhibition zones for S.aureus were 13.34 ± 0.34 mm, 16.34 ± 0.60 mm, and 13.34 ± 0.73 mm for 5, 10, and 20 mg/ml concentrations, respectively. The drug composite’s minimum inhibitory concentration/ minimum bactericidal concentration ratio for S.aureus, K. pneumoniae, and P.aeruginosa was greater than 4, suggesting that they cause bacteriostatic effects.
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.H
Embargo
Navigating economic crisis: Factors shaping resilience in Sri Lankan constructionSME supply chains
(Taylor and Francis, 2024-10-05) Madhavika, N; Bandara, M; Manchanayake, M; Perera, C; Bandara, W; Jayasinghe, P; Ehalapitiya, S
In today’s construction industry, supply chains are subject to much greater disruption than they were inthe past, resulting in a greater need for resilience. However, there is a gap in the literature that examinesthe resilience of construction small and medium scale Enterprises (SMEs) specifically focusing on develop-ing countries. This article is a step towards identifying the factors influencing the resilience of construc-tion SME supply chains taking the case of Sri Lanka: a developing country which is currently amidst amajor economic crisis. This research study adopted a mixed-method approach, employing 08 structuredinterviews with employees ranging from executive level to top level management of 08 constructionSMEs followed by a questionnaire survey considering a sample of 195 construction SMEs also with execu-tive level to top level management of each construction SME. The findings indicated that Collaboration,Entrepreneurial Orientation (EO), Internal Integration, and Outsourcing have a positive significant impacton the resilience of Sri Lankan construction SMEs’ supply chains during an economic crisis, while‘collaboration’ and ‘EO’ are the most influential factors respectively. Therefore, construction SMEs mustprioritize and enhance collaboration and EO when devising supply chain strategies to strengthen resili-ence during economic crises. This paper contributes to filling the research gap by investigating factorsinfluencing construction SME supply chains in a developing country during an economic crisis. Moreover,it contributes to the knowledge by being one of the latest empirical studies focusing on the constructionSME supply chains in Sri Lanka. The findings provide a valuable reference for both policymakers and prac-titioners seeking to improve the resilience of construction SME supply chains
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, 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.