2023
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Publication Open Access Achieving near-zero carbon dioxide emissions from energy use: The case of Sri Lanka(Elsevier, 2023-07-04) Fernando, G.L; Liyanage, M.H; Anandarajah, G; Attalage, R. A; Karunaratne, SSignatories to the Paris Agreement are to achieve net zero Green House Gas (GHG) emissions during the half-century to pursue the efforts limiting global average temperature increase by 2 °C compared to pre-industrial levels. This study models ambitious to challenging scenarios involving energy demand and supply side actions for energy system transition toward net-zero for Sri Lanka. To analyze these scenarios a least cost optimization-based bottom-up type energy system model was developed from 2015 to 2050. A Business-as-usual (BAU) scenario and four countermeasure (CM) scenarios termed Plausible, Ambitious, Challenging, and Stringent were developed. Four different carbon tax rates were used to fathom the level of carbon tax needed to achieve net-zero emissions. The CM scenarios were formulated considering different technology options and policy measures such as the diffusion of efficient technologies, availability of renewable energy sources, use of cleaner fuels, the introduction of nuclear and carbon capture and storage technologies, and green hydrogen for power generation. The result of this study reveals that the stringent scenario which includes aggressive policy measures in both the energy supply and demand sectors, such as nuclear, and renewable energy for power generation, diffusion of efficient Enduse devices, fuel switching, including the introduction of electric cars, and increased share for public transport achieves the near carbon-neutral scenario at a carbon tax trajectory of 32 US$/tCO2 in 2020 and 562US$/tCO2 in 2050. The Net Energy Import Dependency (NEID) of the country decreases to 13 % in 2050 compared to that of the BAU scenario (65 %) under the near carbon neutral scenario, which is a positive sign from the energy security perspective.Publication Open Access Aggressive strategies of the COVID-19 pandemic on the apparel industry of Sri Lanka using structural equation modeling(PLoS ONE, 2023-06-21) Rajapakshe, W; Karunaratna, D. S. M.; Ariyaratne, W. H. G.; Lakshani Madushika, H. A.; Perera, G. S. K.; Shamila, PDuring the COVID-19 crisis, the apparel industry faced many challenges. Aggressive cost-cutting strategies became a top priority, and in turn, these influenced stressors and adversely affected business sustainability. This study examines the impact of aggressive strategies during the COVID-19 pandemic on business sustainability in the apparel industry of Sri Lanka. Further, it investigates whether the relationship between aggressive cost-cutting strategies and business sustainability was mediated by employee stress, considering aggressive cost reduction strategies and workplace environmental changes. This was a cross-sectional study with data collected from 384 employees in the apparel industry in Sri Lanka. Structural Equation Modelling (SEM) was applied to analyze the direct and indirect effects of aggressive cost reduction strategies and workplace environmental changes on sustainability with mediating effects of stress. Aggressive cost reduction strategies (Beta = 1.317, p = 0.000) and environmental changes (Beta = 0.251, p = 0.000) led to an increase in employee stress but did not affect business sustainability. Thus, employee stress (Beta = -0.028, p = 0.594) was not a mediator in the relationship between aggressive cost-cutting strategies and business sustainability; business sustainability was not a dependent variable. The findings proved that managing workplace stress, particularly improving stressful working environments and aggressive cost reduction strategies, can enhance employee satisfaction. Thus, managing employee stress could be beneficial for policymakers to focus on the area(s) required to retain competent employees. Moreover, aggressive strategies are unsuitable to apply during crisis to enhance business sustainability. The findings provide additional knowledge to the existing literature, enabling employees and employers to predict causes of stress and serve as a significant knowledge base for further studies.Publication Open Access Analysis of Multi-Temporal Shoreline Changes Due to a Harbor Using Remote Sensing Data and GIS Techniques(MDPI, 2023-05-06) Zoysa, S; Basnayake, V; Samarasinghe, J. T.; Gunathilake, M.B.; Kantamaneni, K; Muttil, N; Muttil, U; Rathnayake, UCoastal landforms are continuously shaped by natural and human-induced forces, exacerbating the associated coastal hazards and risks. Changes in the shoreline are a critical concern for sustainable coastal zone management. However, a limited amount of research has been carried out on the coastal belt of Sri Lanka. Thus, this study investigates the spatiotemporal evolution of the shoreline dynamics on the Oluvil coastline in the Ampara district in Sri Lanka for a two-decade period from 1991 to 2021, where the economically significant Oluvil Harbor exists by utilizing remote sensing and geographic information system (GIS) techniques. Shorelines for each year were delineated using Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager images. The Normalized Difference Water Index (NDWI) was applied as a spectral value index approach to differentiate land masses from water bodies. Subsequently, the Digital Shoreline Analysis System (DSAS) tool was used to assess shoreline changes, including Shoreline Change Envelope (SCE), Net Shoreline Movement (NSM), End Point Rate (EPR), and Linear Regression Rate (LRR). The results reveal that the Oluvil coast has undergone both accretion and erosion over the years, primarily due to harbor construction. The highest SCE values were calculated within the Oluvil harbor region, reaching 523.8 m. The highest NSM ranges were recorded as −317.1 to −81.3 m in the Oluvil area and 156.3–317.5 m in the harbor and its closest point in the southern direction. The maximum rate of EPR was observed to range from 3 m/year to 10.7 m/year towards the south of the harbor, and from −10.7 m/year to −3.0 m/year towards the north of the harbor. The results of the LRR analysis revealed that the rates of erosion anomaly range from −3 m/year to −10 m/year towards the north of the harbor, while the beach advances at a rate of 3 m/year to 14.3 m/year towards the south of the harbor. The study area has undergone erosion of 40 ha and accretion of 84.44 ha. These findings can serve as valuable input data for sustainable coastal zone management along the Oluvil coast in Sri Lanka, safeguarding the coastal habitats by mitigating further anthropogenic vulnerabilities.Publication Open Access Cognitive Complexity Applied to Software Development: An Automated Procedure to Reduce the Comprehension Effort(Institute for Research and Community Services, Institut Teknologi Bandung, 2023-05) Wijendra, D.R; Hewagamage, K.PThe cognitive complexity of a software application determines the amount of human effort required to comprehend its internal logic, which results in a subjective measurement. The quantification process of the cognitive complexity as a metric is problematic since the factors representing the computation do not represent the exact human cognition. Therefore, the determination of cognitive complexity requires expansion beyond its quantification. The human comprehension effort related with a software application is associated with each phase of its development process. Correct requirements identification and accurate logical diagram generation prior to code implementation can lead to proper logical identification of software applications. Moreover, human comprehension is essential for software maintenance. Defect identification, correction and handling of code quality issues cannot be maintained without good comprehension. Therefore, cognitive complexity can be effectively applied to demonstrate human understandability inside the respective phases of requirements analysis, design, defect tracking, and code quality optimization. This study involved automation of the above-mentioned phases to reduce the manual human cognitive load and reduce cognitive complexity. It was found that the proposed system could enhance the average accuracy of requirements analysis and class diagram generation by 14.44% and 9.89% average accuracy incrementation through defect tracking and code quality issues compared to manual procedures.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, 2023-10-19) Saleah, S. A; 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 medication.Publication Open Access COMPARATIVE STUDY ON THE STORMWATER RETENTION OF ORGANIC WASTE SUBSTRATES BIOCHAR, SAWDUST AND WOOD BARK RETRIEVED FROM PSIDIUM GUAJAVA L. SPECIES(University of Montenegro, 2023) Kader, S.A; Jaufer, L; Bashir, O; Raimi, M. OThis research compares the stormwater retention performances of an organic waste growing medium extracted from the widely available Psidium guajavala L species in Sri Lanka. Rainfall gauges were manually constructed to outsource accurate precipitation data, and the study was conducted throughout the entire month of January 2023. A stormwater retention curve was constructed for the Biochar, Sawdust and Wood bark substrates and the hotspots were compared. Furthermore, the results were validated using a volumetric comparison of water retention. The experimental outcomes have shown that Biochar exhibits strong water retention ability and enables the overlaying vegetation to acquire nutrients without external obstacles. The main reason for this exceptional performance was biochar's low evaporation levels and high porosity. In contrast, Sawdust was found to be the worst performer in terms of water retention due to its high thermal conductivity. These experimental studies were rationalised by outsourcing the specimen from the same tree. Our recommendations suggest that the biochar manufacturing industry needs to be improved in the future since it provides a sustainable and effective alternative in terms of eco-friendly substrates.Publication Embargo Consolidation settlement of vertically loaded pile groups in multilayered poroelastic soils(Elsevier Ltd, 2023-01) Senjuntichai, T; Sornpakdee, N; Keawsawasvong, S; Phulsawat, B; Rajapakse, R.K.N.D.Pile groups are commonly used as the foundations of many structures including those used in transportation infrastructures. Consolidation settlement of a pile foundation is an important design parameter. A theoretical model is developed in this study to estimate the consolidation settlement and axial load transfer of vertically loaded pile groups in multilayered poroelastic soils. The multilayered saturated soil is modeled according to Biot's poroelasticity theory. In order to determine quasi-static response of pile groups, the interaction problem is first formulated in the Laplace transform domain. Vertical displacement compatibility is enforced at the pile-soil interface to simulate the pile group-soil interaction. Axial deformation of each pile is represented by an exponential series with undetermined coefficients, which are obtained from a variational approach. Vertical displacement influence functions due to a buried uniform vertical load applied to the layered soil are required in the formulation. The application of an exact stiffness matrix method yields the required influence functions. Time-domain solutions are obtained by employing a numerical Laplace inversion method. Numerical results for time-dependent vertical stiffness and consolidation settlement are presented for different pile group configurations, layer profiles, pile elastic moduli and pile lengths.Publication Open Access COVID-19 symptom identification using Deep Learning and hardware emulated systems(Elsevier, 2023-06-28) Liyanarachchi, R; Wijekoon, J; Premathilaka, M; Vidhanaarachchi, SThe COVID-19 pandemic disrupted regular global activities in every possible way. This pandemic, caused by the transmission of the infectious Coronavirus, is characterized by main symptoms such as fever, fatigue, cough, and loss of smell. A current key focus of the scientific community is to develop automated methods that can effectively identify COVID-19 patients and are also adaptable for foreseen future virus outbreaks. To classify COVID-19 suspects, it is required to use contactless automatic measurements of more than one symptom. This study explores the effectiveness of using Deep Learning combined with a hardware-emulated system to identify COVID-19 patients in Sri Lanka based on two main symptoms: cough and shortness of breath. To achieve this, a Convolutional Neural Network (CNN) based on Transfer Learning was employed to analyze and compare the features of a COVID-19 cough with other types of coughs. Real-time video footage was captured using a FLIR C2 thermal camera and a web camera and subsequently processed using OpenCV image processing algorithms. The objective was to detect the nasal cavities in the video frames and measure the breath cycles per minute, thereby identifying instances of shortness of breath. The proposed method was first tested on crowd-sourced datasets (Coswara, Coughvid, ESC-50, and a dataset from Kaggle) obtained online. It was then applied and verified using a dataset obtained from local hospitals in Sri Lanka. The accuracy of the developed methodologies in diagnosing cough resemblance and recognizing shortness of breath was found to be 94% and 95%, respectively.Publication Open Access Derivation of Bessel function closed-form solutions in zero dimensional φ4-field theory(Elsevier Ltd, 2022-02) Munasinghe, R. M.The integral is used as an introductory learning tool in the study of Quantum Field Theory and path integrals. Typically, it is analyzed via perturbation theory. Closed-form solutions have been quoted for which I could not find any derivation. Using a simple and elegant transformation, the close form solutions for the integral and its even positive integer moments can be obtained in terms of Bessel functions.Publication Open Access Determining the influence of LPI, GCI and IR on FDI: A study on the Asia and Pacific Region(PLoS ONE, 2023-02) Wannisinghe, P; Jayakody, S; Rathnayake, S; Wijayasinghe, D; Jayathilaka, R; Madhavika, NCompetitiveness Index (GCI) and Interest Rates (IR) on Foreign Direct Investment (FDI) for the Asia & Pacific region. The study is original as extensive evidence on the impact of LPI, GCI and IR on FDI in the Asia & Pacific region are examined initially. For the years 2007, 2010, 2012, 2014, 2016 and 2018, data was gathered for 33 nations in the Asia and Pacific area. Data analysis was performed using a panel regression model and multiple linear regression. The findings of the study reveal that LPI, GCI and IR are the three major factors influencing FDI inflows into the economies. However, the impact of these factors varies from country to country. The results concluded that LPI positively impacts FDI in India, Korea, Lebanon, and Oman. In contrast, a negative influence was observed for China, Kuwait and the Philippines. GCI positively impacts FDI in China, Korea, Kuwait, Pakistan and the Philippines, while a negative impact was observed in Armenia, India, Lebanon. Furthermore, IR has a positive impact on FDI flows in China and Egypt while in Korea and Lebanon, a negative impact was observed. Therefore, policymakers should focus more on improving the infrastructural requirements and macroeconomic factors while considering the other country-level variables that influence the FDI in flowPublication Embargo Development of a risk model for different innovator types in textile and apparel industries(Emerald Publishing, 2023-01) Kumarapeli, U; Ratnayake, V; Jayawardana, S. SPurpose – Technological innovation is one of the strongest driving forces in the survival and growth of any organization, including textile and apparel industries. However, technological innovation inherits a wide array of risks due to the uncertainty involved in it. In-depth research reveals the existence of a significant relationship between innovation failures and the approach used to innovate, that is, the organization’s innovator type. However, quantitative evidence supporting this concern is still lacking. Hence, the purpose of this paper is to bridge the existing gap in the literature on effective management of technological innovation risk factors and the innovator type of textile and apparel industries. Design/methodology/approach – The risk factors related to technological innovations are identified under different innovator types. Analytic network process (ANP) has been used to evaluate the contribution of risk factors according to the innovator type of the organization. Data was gathered through the literature review and structured and semi structured interviews with textile and apparel industry experts. The contribution of risk factors was determined through priorities, derived according to the ANP using Super Decision software. Findings – Contribution of risk factors takes different values according to innovator type. This provides comprehensive knowledge on developing a risk management strategy according to the innovator type of the organization. Furthermore, this provides insight into the fact that a generalized risk management strategy will not be effective and sensible for all innovator types. Originality/value – The findings provide a thorough understanding of developing a customized risk management strategy by determining the “most to least” criticality of risks based on the innovator type of the organization. Furthermore, findings can be used to adopt the most appropriate innovator type based on the organization’s key competencies. Moreover, this guides the organization in making the best use of internal resources during risk management. Furthermore, this provides insight into the risk factors that must be addressed prior to embarking on new innovative approachesPublication Embargo Driving Innovative Culture with Emotional Intelligence(IEEE, 2023-06-12) Rizwi, A; Lokuliyana, SThis research aims to examine the relationship between employee innovation and positive and negative contagion within supervising roles. Establishing an innovative culture within the organization and having managers with a high level of Emotional Intelligence are essential. As a result, this enables the study to examine the effects of these factors on employees. The study is evaluated the effects of adopting an innovation culture and working with managers who are emotionally quotient on the performance of the employees. In the corporate sector, innovation takes place under different conditions than in the private sector. Human beings experience emotions daily. An employee survey of 40 items (5-point Likert Scale) is distributed. A total of 200 surveys have been evaluated. The validity and reliability of the data were checked using SPSS, and the results were assessed using regression analysis. It involves constructing a confidence interval based on a single sample and a given level of confidence. The findings indicate that Emotional Intelligence, innovative organizational culture, and employee performance are meaningfully related. In conclusion, organizations must create innovative institution cultures and employ managers that have high levels of Emotional Intelligence to increase their employees' performance using the application of innovation.Publication Embargo Effects of the Organizational Knowledge Management Systems on Psychological Well-Being Among Employees in Private Large-Scale IT Organizations in Sri Lanka(IEEE, 2023-06-12) Aratthanage, K; Wijekoon, JThe goal of this study basically focuses on evaluating the Organizational Knowledge Management Systems (KMS) and their impact on psychological well-being among employees in selected large-scale private IT organizations in Sri Lanka. It evaluates Knowledge Management Systems quality dimensions, KMS adoption of users, and psychological well-being. Knowledge Management is an essential part in IT industry. Gaining domain knowledge from one starting point to sharing knowledge among organizations is a very complex process, therefore, different types of Knowledge Management systems are implemented within IT organizations. There are several quality dimension factors introduced to determine better knowledge management systems. This research evaluates eight quality dimensions against four psychological aspects. In this study, statistical analysis was used to determine the statistically significant levels, correlations and relationships between the independent and dependent variables. Ultimately the results can be used to improve the Human-centered Knowledge Management System's design approach with balanced employee psychological well-being. For further improvements, the results can be used to build interactive knowledge management software-based solutions.Publication Open Access Evaluating expressway traffic crash severity by using logistic regression and explainable & supervised machine learning classifiers(Elsevier, 2023-07-09) Shashiprabha, M.J.P.S; Kelum, S.R.M; Meddage, D.P.P; Pasindu, H.R; Gomes, P.I.AThe number of expressway road accidents in Sri Lanka has significantly increased (by 20%) due to the expansion of the transport network and high traffic volume. It is crucial to identify the causes of these crashes for effective road safety management. However, traditional statistical methods may be insufficient due to their inherent assumptions. This study utilized explainable machine learning to investigate the factors that affect the severity of traffic crashes on expressways. The study evaluated two groups of traffic crashes: fatal or severe crashes, and other crashes that included non-severe injuries or only property damage. Five factors that contribute to crashes were analyzed: road surface condition, road alignment, location, weather condition, and lighting effect. Four machine learning models (Random Forest (RF), Decision Tree (DT), extreme gradient boosting (XGB), K-Nearest Neighbor (KNN)) were developed and compared with Logistic Regression (LR) using 223 training and 56 testing data instances. The study revealed that the machine learning algorithms provided more accurate predictions than the LR model. To explain the machine learning models, Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) were used. These methods revealed that all five features decreased the possibility of occurrence of fatal accidents. SHAP and LIME explanations confirmed the known interactions between factors influencing crash severity in expressway operational conditions. These explanations increase the trust of end-users and domain experts on machine learning models. Furthermore, the study concluded that using explainable machine learning methods is more effective than traditional regression analysis in evaluating safety performance. Additionally, the results of the study can be utilized to improve road safety by providing accurate explanations for decision-making processes for black-box models. © 2023Publication Open Access Evaluation of the Impact of Land Use Changes on Soil Erosion in the Tropical Maha Oya River Basin, Sri Lanka(MDPI, 2023-01) Palliyaguru, C; Basnayake, V; Makumbura, R. K; Gunathilake, M. B; Muttil, N; Wimalasiri, E. M; Rathnayake, USoil degradation is a serious environmental issue in many regions of the world, and Sri Lanka is not an exception. Maha Oya River Basin (MORB) is one of the major river basins in tropical Sri Lanka, which suffers from regular soil erosion and degradation. The current study was designed to estimate the soil erosion associated with land use changes of the MORB. The Revised Universal Soil Loss Equation (RUSLE) was used in calculating the annual soil erosion rates, while the Geographic Information System (GIS) was used in mapping the spatial variations of the soil erosion hazard over a 30-year period. Thereafter, soil erosion hotspots in the MORB were also identified. The results of this study revealed that the mean average soil loss from the MORB has substantially increased from 2.81 t ha−1 yr−1 in 1989 to 3.21 t ha−1 yr−1 in 2021, which is an increment of about 14.23%. An extremely critical soil erosion-prone locations (average annual soil loss > 60 t ha−1 yr−1) map of the MORB was developed for the year 2021. The severity classes revealed that approximately 4.61% and 6.11% of the study area were in high to extremely high erosion hazard classes in 1989 and 2021, respectively. Based on the results, it was found that the extreme soil erosion occurs when forests and vegetation land are converted into agricultural and bare land/farmland. The spatial analysis further reveals that erosion-prone soil types, steep slope areas, and reduced forest/vegetation cover in hilly mountain areas contributed to the high soil erosion risk (16.56 to 91.01 t ha−1 yr−1) of the MORB. These high soil erosional areas should be prioritized according to the severity classes, and appropriate land use/land cover (LU/LC) management and water conservation practices should be implemented as recommended by this study to restore degraded lands.Publication Embargo An exact stiffness matrix method for nanoscale beams(CRC Press/Balkema, 2023-01) Wijesinghe, R.A.R; De Silva, K. K. V.; Sapsathiarn, Y.; Rajapakse, N.Conventional continuum theories are inapplicable to nanoscale structures due to their high surface- to-volume ratios and the effects of surface energy and inter-atomic forces. Although atomistic simulations are more realistic and accurate for nanostructures, their use in practical situations is constrained by the high computational cost. Modified continuum methods accounting for the surface energy are therefore considered computationally efficient engineering approximations for nanostructures. The modified continuum theory of Gurtin and Murdoch accounting for the surface energy effects has received considerable attention in the literature. This paper focuses on developing an exact stiffness matrix method for nanoscale beams based on the Gurtin-Murdoch theory. Past research has presented a classical finite element formulation to analyze nanoscale beams using the Galerkin weighted residual method. The proposed approach is based on the analytical solutions to the governing partial differential equations of nanobeams. These governing equations are established by using the Gurtin-Murdoch continuum theory. The general analytical solutions are used to derive the exact stiffness matrix and mass matrix of a beam finite element in closed form. The study examines the static and time-harmonic dynamic response of thin nanoscale beams. Normalized deflections and bending moments under concentrated and distributed loads are obtained for aluminum and silicon thin beams subjected to simply supported, cantilevered and clamped-clamped edges. Our results were compared with the available solutions in the literature, and close agreement was observed. Therefore, the method presented in this study serves as an efficient and accurate scheme to analyze nanobeams under static and dynamic loading compared to the conventional finite element schemesPublication Open Access Fabrication of r-GO/GO/α-Fe2O3/Fe2TiO5 Nanocomposite Using Natural Ilmenite and Graphite for Efficient Photocatalysis in Visible Light(MDPI, 2023-01) Usgodaarachchi, L; Jayanetti, M; Thambiliyagodage, C; Liyanaarachchi, H; Vigneswaran, SHematite (α-Fe2O3) and pseudobrookite (Fe2TiO5) suffer from poor charge transport and a high recombination effect under visible light irradiation. This study investigates the design and production of a 2D graphene-like r-GO/GO coupled α-Fe2O3/Fe2TiO5 heterojunction composite with better charge separation. It uses a simple sonochemical and hydrothermal approach followed by L-ascorbic acid chemical reduction pathway. The advantageous band offset of the α-Fe2O3/Fe2TiO5 (TF) nanocomposite between α-Fe2O3 and Fe2TiO5 forms a Type-II heterojunction at the Fe2O3/Fe2TiO5 interface, which efficiently promotes electron-hole separation. Importantly, very corrosive acid leachate resulting from the hydrochloric acid leaching of ilmenite sand, was successfully exploited to fabricate α-Fe2O3/Fe2TiO5 heterojunction. In this paper, a straightforward synthesis strategy was employed to create 2D graphene-like reduced graphene oxide (r-GO) from Ceylon graphite. The two-step process comprises oxidation of graphite to graphene oxide (GO) using the improved Hummer’s method, followed by controlled reduction of GO to r-GO using L-ascorbic acid. Before the reduction of GO to the r-GO, the surface of TF heterojunction was coupled with GO and was allowed for the controlled L-ascorbic acid reduction to yield r-GO/GO/α-Fe2O3/Fe2TiO5 nanocomposite. Under visible light illumination, the photocatalytic performance of the 30% GO/TF loaded composite material greatly improved (1240 Wcm−2). Field emission scanning electron microscopy (FE-SEM) and high-resolution transmission electron microscopy (HR-TEM) examined the morphological characteristics of fabricated composites. X-ray photoelectron spectroscopy (XPS), Raman, X-ray diffraction (XRD), X-ray fluorescence (XRF), and diffuse reflectance spectroscopy (DRS) served to analyze the structural features of the produced composites. © 2022 by the authors.Publication Open Access Factors affecting job performance of Sri Lankan IT professionals working from home(PLOS ONE, 2023-12-12) Jayanandana, N; Jayathilaka, RThis study investigated the influence of the physical work environment, work life balance, work flexibility, and effective communication on the job performance of IT professionals in Sri Lanka’s IT industry who work from home (WFH). A standard questionnaire was used to collect data from 293 IT specialists in 50 different IT organizations in Sri Lanka, and a stepwise probit model was employed for data analysis. According to the findings, both the physical work environment and work life balance had a significantly positive effect on job performance. A one-unit increase in the physical work environment and work life balance increased the likelihood of high job performance by 0.21% and 0.19%, respectively. In contrast, work flexibility had a negative effect on job performance, with an increase of one unit resulting in a 0.18% decrease in the likelihood of high job performance. The positive impact of effective communication on job performance was less significant. The study emphasises the significance of providing a conducive work environment and promoting work life balance to enhance the job performance of IT professionals in Sri Lanka’s IT industry who WFH.Publication Open Access From short to long term: Dynamic analysis of FDI and net export in global regions(PLoS ONE, 2023-09-14) Lakshani, S; Sandaruwan, C; Fernando, C; Vidyapathirana, G; Jayathilaka, R; Munasinghe, SIt is crucial to examine the impact between foreign direct investment (FDI) and net exports (NE) for unveiling international trade dynamics, and the economic development of different geographical regions. It yields sharp insights into how FDI inflows, driven by theories such as backward linkage, export platform, and knowledge transfer, enhance a host country’s export capacity and contribute to economic growth. Moreover, studying the reciprocal linkages between FDI and NE helps recognise the aspects of domestic factors, such as productivity and the product life cycle, in attracting FDI and increasing export performance. Based on those theories, the study aims to ascertain the dynamic causality or correlation between FDI and NE across all regions with the utilisation of panel data gathered from 110 countries, considering the period from 2002 to 2020. The Wavelet coherence method is used to investigate the relationship between these variables across different frequencies and periods, followed by a Granger causality test. The findings demonstrated that FDI and NE have a significant relationship in most regions, with a bidirectional relationship between FDI and NE across all continents. The results could assist respective governments and policymakers in formulating policies related to FDI flows and offer insights into how a host country can attract more FDI and boost NE.Publication Open Access A Graph Pointer Network-Based Multi-Objective Deep Reinforcement Learning Algorithm for Solving the Traveling Salesman Problem(MDPI, 2023-01-13) Perera, J; Liu, S.H; Mernik, M; Črepinšek, M; Ravber, MTraveling Salesman Problems (TSPs) have been a long-lasting interesting challenge to researchers in different areas. The difficulty of such problems scales up further when multiple objectives are considered concurrently. Plenty of work in evolutionary algorithms has been introduced to solve multi-objective TSPs with promising results, and the work in deep learning and reinforcement learning has been surging. This paper introduces a multi-objective deep graph pointer network-based reinforcement learning (MODGRL) algorithm for multi-objective TSPs. The MODGRL improves an earlier multi-objective deep reinforcement learning algorithm, called DRL-MOA, by utilizing a graph pointer network to learn the graphical structures of TSPs. Such improvements allow MODGRL to be trained on a small-scale TSP, but can find optimal solutions for large scale TSPs. NSGA-II, MOEA/D and SPEA2 are selected to compare with MODGRL and DRL-MOA. Hypervolume, spread and coverage over Pareto front (CPF) quality indicators were selected to assess the algorithms’ performance. In terms of the hypervolume indicator that represents the convergence and diversity of Pareto-frontiers, MODGRL outperformed all the competitors on the three well-known benchmark problems. Such findings proved that MODGRL, with the improved graph pointer network, indeed performed better, measured by the hypervolume indicator, than DRL-MOA and the three other evolutionary algorithms. MODGRL and DRL-MOA were comparable in the leading group, measured by the spread indicator. Although MODGRL performed better than DRL-MOA, both of them were just average regarding the evenness and diversity measured by the CPF indicator. Such findings remind that different performance indicators measure Pareto-frontiers from different perspectives. Choosing a well-accepted and suitable performance indicator to one’s experimental design is very critical, and may affect the conclusions. Three evolutionary algorithms were also experimented on with extra iterations, to validate whether extra iterations affected the performance. The results show that NSGA-II and SPEA2 were greatly improved measured by the Spread and CPF indicators. Such findings raise fairness concerns on algorithm comparisons using different fixed stopping criteria for different algorithms, which appeared in the DRL-MOA work and many others. Through these lessons, we concluded that MODGRL indeed performed better than DRL-MOA in terms of hypervolumne, and we also urge researchers on fair experimental designs and comparisons, in order to derive scientifically sound conclusions.
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