Research Publications Authored by SLIIT Staff
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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.
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Publication Embargo Socio-Economic and Environmental Drivers of Food Security in Low-Income Countries: A 32-Year Cross-National Analysis for Sustainable Development(John Wiley and Sons Ltd, 2026-04-06) Pulle, N; Sampath, P; Wijayaweera, D; Perera, S; Jayathilaka, R; Dabare, UEnsuring countries meet their basic food security needs has been a long-standing challenge. Despite numerous attempts to tackle this issue, it remains unresolved. Hence, considering the broad reach of this concern, our study aims to collectively analyse how urbanisation, greenhouse gas emissions, renewable energy consumption, population growth, agricultural land, and gross domestic product per capita impact food security across countries. This study tests a stepwise panel-ordered probit model, utilising data from 17 low-income countries from 1992 to 2023. The research categorises the food production index into three categories: low, moderate, and high food security, allowing for the analysis of the likelihood of falling into a category of the food production index. The outcomes reveal that in low-income countries, urbanisation, greenhouse gas emissions, population growth, and agricultural land have a positive impact on the food production index, whereas renewable energy consumption has an insignificant negative impact. This analysis contributes distinctively to the existing body of knowledge by comparatively analysing the probabilities over the given period, together with the financial crisis and the COVID-19 pandemic, thus providing insights into both events. Furthermore, this report highlights the effectiveness of policies implemented, focusing on the efficient use of agricultural land through technological advancements, promoting modern farming techniques, encouraging youth engagement in agriculture, managing population growth, and innovating climate-resilient strategies, which are directly aligned with the research findings. These recommendations address specific challenges identified in the highlighted countries, helping improve their food security and promote sustainable development.Publication Open Access Global hunger risk projections and the factors shaping future vulnerabilities(Elsevier B.V., 2026-06) Pulle, N; Sampath, P; Wijayaweera, D; Perera, S; Jayathilaka, RHunger remains one of the most persistent barriers to human development, with progress uneven across regions and increasingly exposed to economic instability, climate pressure and structural vulnerabilities. This study investigates the key drivers of cross-country variation in hunger severity and develops forward looking projections to support evidence based global policy planning. Using a comprehensive global panel dataset covering 146 countries over the period, 1992 to 2024 the analysis identifies the socio-economic and environmental factors more strongly associated with changes in hunger outcomes over time. The results reveal that hunger severity is not shaped by a single pathway but by a combination of development related conditions, demographic pressures, and external shocks with substantial heterogeneity across country groups. Building on the estimated relationships the study generates projections from 2025 to 2029, highlighting regions where hunger risks may remain elevated or worsened in the absence of targeted interventions. The findings underscore that substantial progress in hunger reduction requires integrated methods that extend beyond food supply alone including strengthening resilience, improving social protection and supporting inverse growth by combining long run global evidence with near turn projections. This study contributes to the literature on hunger dynamics and offers timely insights for international organisations and national policymakers working towards the achievement of food security and related Sustainable Development Goals.Publication Open Access Achieving zero hunger: A global policy lens on food security drivers and income group disparities(Elsevier B.V., 2026-03) Pulle, N; Sampath, P; Perera, S; Wijayaweera, D; Jayathilaka, RMany countries struggle to meet their daily dietary requirements despite numerous attempts to address the existing demand. Consequently, this study collectively analyses the impact of urbanisation, renewable energy, greenhouse gas emissions, population growth, gross domestic product per capita and agricultural land on food production relying on Sen's Entitlement Theory, thus providing insights to resolve the long-standing issue of food insecurity, and support the achievement of the Sustainable Development Goals. The study utilises a stepwise panel ordered Probit model on 146 countries, for the years 1993 to 2023. It further categorises the food production index into three categories of food security as; low, moderate and high, thereby enabling discussion of the likelihood of a country falling into one of the aforementioned food security categories over the years. Urbanisation, agricultural land, and the dummy variables introduced to represent the income groups have been identified to have a significant and favourable relationship with the food production index. In contrast, the greenhouse gas emissions and renewable energy variables have a significantly inverse impact on the food production index. This makes a unique contribution to the existing body of literature, especially by comparing odds over the years, across different food secure categories, countries, and their specific income levels. This study enables policymakers to gain a comprehensive historical perspective on each case. This study further promotes the Sustainable Development Goals, highlighting areas where these goals have been negatively impacted. Additionally, the study discusses optimised investment allocations, agricultural research and development, agricultural technology, climate resilient farming, and sustainable urbanisation planning as solutions for extreme cases.Publication Open Access Optimizing Asset Transfer Process in ERP Using Business Process Management Technique(Science and Information Organization, 2025-10-30) Yasarathne, R; Ranatunga, N; Herath, V; Chalinda, L; Kahandawaarachchi, C; Perera, S; Randula, CEnterprise Resource Planning (ERP) systems are critical for managing enterprise-wide business processes, including asset management. Yet, many ERP platforms lack efficient mechanisms for bulk asset transfers, leading to high manual effort, increased costs, and data inconsistencies. This study applies Business Process Reengineering (BPR) techniques as the methodology to optimize ERP asset management, focusing on workflow optimization and automation, contributing both practical and methodological insights. A mixed-method approach was adopted, analyzing a financial organization with 256 branches and over 450 Oracle ERP users. Data from 51 representative branches identified inefficiencies such as manual transfer delays, approval bottlenecks, and synchronization issues. The proposed solution introduces automated bulk asset transfers, optimized approval workflows, and real-time data synchronization, along with new metrics for evaluating efficiency, compliance, risk, and asset utilization. Compared to the As-Is system, the reengineered framework achieved a 100% reduction in operational costs per user ($7,500 annual saving), an 80% reduction in compliance incidents, a 67% reduction in asset transaction errors, and a 20% improvement in asset utilization. These results demonstrate a scalable, adaptable, and effective framework that enhances ERP operational efficiency, strengthens data integrity, and advances both academic understanding and industrial practice of asset management process reengineering.Publication Open Access Achieving zero hunger: A global policy lens on food security drivers and income group disparities(Elsevier B.V., 2026-01-19) Pulle, N; Sampath, P; Perera, S; Wijayaweera, D; Jayathilaka, RMany countries struggle to meet their daily dietary requirements despite numerous attempts to address the existing demand. Consequently, this study collectively analyses the impact of urbanisation, renewable energy, greenhouse gas emissions, population growth, gross domestic product per capita and agricultural land on food production relying on Sen’s Entitlement Theory, thus providing insights to resolve the long-standing issue of food insecurity, and support the achievement of the Sustainable Development Goals. The study utilises a stepwise panel ordered Probit model on 146 countries, for the years 1993 to 2023. It further categorises the food production index into three categories of food security as; low, moderate and high, thereby enabling discussion of the likelihood of a country falling into one of the aforementioned food security categories over the years. Urbanisation, agricultural land, and the dummy variables introduced to represent the income groups have been identified to have a significant and favourable relationship with the food production index. In contrast, the greenhouse gas emissions and renewable energy variables have a significantly inverse impact on the food production index. This makes a unique contribution to the existing body of literature, especially by comparing odds over the years, across different food secure categories, countries, and their specific income levels. This study enables policymakers to gain a comprehensive historical perspective on each case. This study further promotes the Sustainable Development Goals, highlighting areas where these goals have been negatively impacted. Additionally, the study discusses optimised investment allocations, agricultural research and development, agricultural technology, climate resilient farming, and sustainable urbanisation planning as solutions for extreme casesPublication Embargo Supply and Demand Planning of Electricity Power: A Comprehensive Solution(IEEE, 2019-12-06) Perera, S; Dissanayake, S; Fernando, D; De Silva, S; Rankothge, WElectrical energy is one of the fastest growing energy demands in the world. Uncertainty in supplying the demand can threaten the social economic aspects of a country. The biggest driver of electrical demand is weather. Climatic changes not only affect the demand but also renewable energy supply. Wind and Solar are two alternative energy sources with less pollution. We have proposed a platform which helps energy providers, energy traders with services related to electricity supply and demand planning, with following modules. (1) Forecasting electricity consumption patterns (2) Forecasting wind power generation (3) Optimizing Load Shedding. Our platform has been implemented using statistical and machine learning techniques: Multi-Linear Regression for consumption prediction, Random forest regression for wind power forecast, and genetic algorithm to optimize load shedding. Our results show that, using our proposed module, we can minimize the imbalance between the supply and demand of electricity by predicting the consumption patterns of consumers, predicting the wind power generation and by selecting the best feeder to be selected for load shedding under given constraints.Publication Open Access A Conceptual Framework for the Association between Relationship Conflicts and Employees’ Intention to Leave(Canadian Center of Science and Education, 2018) Weerarathna, R. S; Perera, SThe main objective of this research paper to conceptualize a model to test the association between relationship conflicts and employees’ intention to leave with the support of past theories and research findings. This research paper contains the definitions for variables related to relationship conflicts and employees’ intention to leave from the organization and also it explains models which explain the interconnections among two variables. The conceptualization of this research includes a logically developed model that leads to identify the relationships between the independent variable and dependent variable. The proposed model suggested that there is an association between relationship conflicts and employees’ intention to leave.Publication Embargo A Real-Time Cardiac Arrhythmia Classifier(IEEE, 2019-10-08) Abayaratne, H; Perera, S; De Silva, E; Atapattu, P; Wijesundara, MCardiovascular diseases (CVD) have increased drastically among Non-Communicable diseases, which have peaked over the past recent years. In 2018, around 17.9 million which is an estimated 31% of the people have died worldwide due to CVDs. A novel machine learning algorithm for continuous monitoring, identification and classification of cardiac arrhythmias from Electrocardiogram (ECG) data is presented here. The proposed solution has two stages where the first stage is a rule based cardiac abnormality identification which has an individual 97.55% ± 0.3% of accuracy (Acc) for a dataset of 705,000 and the second stage is a Neural Network (NN) based classification model which is trained and tested to identify 15 different classes recommended by ANSI/AAMI standard [1], and has 97.1% of individual accuracy for MIT-BIH Arrhythmia dataset [2] of 96265 beat samples. The combined real-time cardiac arrhythmia classifier is parallelized with CUDA in order to utilize the GPU and increase the execution speed by 4.86 times.Publication Open Access Influence of Supply Chain Network Topology on the Evolution of Firm Strategies(Institute of Transport and Logistic Studies (ITLS), 2020-01-01) Perera, S; Kasthurirathna, D; Bell, MThis study investigates the influence of the topological structure of a supply chain network (SCN) on the evolution of cooperative and defective strategies adopted by the individual firms. First, a range of topologies representative of SCNs was generated using a fitness-based network growth model, which enabled cross comparisons by parameterising the network topologies with the power law exponent of their respective degree distributions. Then, the inter-firm links in each SCN were considered as repeated strategic interactions and were modelled by the Prisoner’s Dilemma game to represent the self-interested nature of the individual firms. This model is considered an agent-based model, where the agents are bound to their local neighbourhood by the network topology. A novel strategy update rule was then introduced to mimic the behaviour of firms. In particular, the heterogeneously distributed nature of the firm rationality was considered when they update their strategies at the end of each game round. Additionally, the payoff comparison against the neighbours was modelled to be strategy specific as opposed to accumulated payoff comparison analysis adopted in past work. It was found that the SCN topology, the level of rationality of firms and the relative strategy payoff differences are all essential elements in the evolution of cooperation. In summary, a tipping point was found in terms of the power law exponent of the SCN degree distribution, for achieving the highest number of cooperators. When the connection distribution of an SCN is highly unbalanced (such as in hub and spoke topologies) or well balanced (such as in random topologies), more difficult it is to achieve higher levels of co-operation among the firms. It was concluded that the scale-free topologies provide the best balance of hubs firms and lesser connected firms. Therefore, scale-free topologies are capable of achieving the highest proportion of cooperators in the firm population compared to other network topologies.Publication Open Access Consumer Surplus based Method for Quantifying and Improving the Material Flow Supply Chain Network Robustness(2018-06-01) Perera, S; Bell, M. G. H; Kurauchi, F; Bliemer, M. C. J; Kasthurirathna, DRecent advances in network science has encouraged researchers to adopt a topological view when characterising the robustness of supply chain networks (SCNs). However, topology based characterisations, without considering the heterogeneity among the supply chains which form the SCN, can only provide a partial understanding of robustness. Hitherto, focus of robustness studies have been on cyclic SCNs, with unweighted and undirected links representing general inter-firm interactions. Here, we consider the specific case of a material flow SCN with multi-sourcing, which is characterised by a tiered structure with directed and weighted links. The proposed method uses the multinomial logit model to estimate the utility levels of supply chains within the SCN, as perceived by a focal firm which is indicative of the SCN consumers. The robustness of the SCN is characterised by considering the degree to which supply chains overlap with each other as a cost in the logit formulation. Finally, using a randomisation scheme to generate ensembles of SCN configurations which preserve the number of connections at each firm, the configuration which maximises the consumer surplus for the focal firm is identified. The proposed method is implemented on a real world SCN to identify the optimal configuration in terms of robustness.