Research Publications Authored by SLIIT Staff

Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/4195

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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Now showing 1 - 20 of 2242

Open Access
6-REXOS: Upper limb exoskeleton robot with improved pHRI
(SAGE Publications, 2015-04-29) Gunasekara, M; Gopura, R; Jayawardena, T. S. S
Close interaction can be observed between an exoskeleton robot and its wearer. Therefore, appropriate physical human-robot interaction (pHRI) should be considered when designing an exoskeleton robot to provide safe and comfortable motion assistance. Different features have been used in recent studies to enhance the pHRI in upperlimb exoskeleton robots. However, less attention has been given to integrating kinematic redundancy into upper-limb exoskeleton robots to improve the pHRI. In this context, this paper proposes a six-degrees-of-freedom (DoF) upperlimb exoskeleton robot (6-REXOS) for the motion assistance of physically weak individuals. The 6-REXOS uses a kinematically different structure to that of the human lower arm, where the exoskeleton robot is worn. The 6-REXOS has four active DoFs to generate the motion of the human lower arm. Furthermore, two flexible bellow couplings are attached to the wrist and elbow joints to generate two passive DoFs. These couplings not only allow translational motion in wrist and elbow joints but also a redundancy in the robot. Furthermore, the compliance of the flexible coupling contributes to avoiding misalignments between human and robot joint axes. The redundancy in the 6- REXOS is verified based on manipulability index, mini‐ mum singular value, condition number and manipulability ellipsoids. The 6-REXOS and a four-DoF exoskeleton robot are compared to verify the manipulation advantage due to the redundancy. The four-DoF exoskeleton robot is designed by excluding the two passive DoFs of the 6- REXOS. In addition, a kinematic model is proposed for the human lower arm to validate the performance of the 6- REXOS. Kinematic analysis and simulations are carried out to validate the 6-REXOS and human-lower-arm model.
Open Access
A Comparative Investigation of Infiltration and Channel Roughness of Ephemeral and Perennial Streams in a Mountainous Catchment
(John Wiley, 2025-06) Khaniya, B; Gomes, P.I.A; Perera,M. D.D; Wai, O, W.H
Infiltration and channel roughness, two major factors that govern stream discharge, were studied in similar-sized ephemeral and perennial streams in a mountainous tropical catchment. Seasons were defined based on two ephemeral flow conditions, i.e., with (wet season) and without (dry season) surface flow. A stream was divided transversely into low-flow areas (close to the thalweg) and high-flow areas (close to the channel margin). The highest average infiltration (~50 mm/h) was observed in the low flow areas around the thalweg of ephemeral streams in the dry season and was significantly higher than for any other spatial scale or temporal period. The infiltration in high-flow areas did not show a statistically significant difference between the two stream types, and surprisingly, perennial streams in the dry season showed higher infiltration than ephemeral streams. Since sediment moisture and organic content showed negative and positive correlations with infiltration, respectively, for both stream types and ephemeral streams showed statistically significant negative correlations between litter and infiltration during the dry season, the low infiltration in ephemeral high flow areas was attributed to stream-type dependent litter processing. The litter of ephemeral stream high-flow areas was subject to partial decomposition due to rapid drying and had residue of previously buried litter. Ephemeral channels were two to three times rougher than perennial channels. Standing crop biomass and mean particle size increased stream roughness in both stream types but were less prominent in ephemeral streams due to the presence of litter. The study demonstrated that litter has a special role in defining the infiltration pattern, channel roughness, and flood control potential of ephemeral streams.
Embargo
A Comprehensive Investigation of Microplastic Contamination and Polymer Toxicity in Farmed Shrimps; L. vannamei and P. monodon
(Springer Nature, 2025-02-20) Jayaweera, Y. U; Hennayaka, H. M.A.I; Herath, H.M.L.P.B; Kumara, G. M.P; Mahagamage, M.G.Y.L; Rodrigo, U.D; Manatunga, D. C
Microplastic (MP) pollution poses a significant threat to marine ecosystems, seafood safety, and human health. This study investigates the accumulation of microplastics in two commercially important shrimp species, Litopenaeus vannamei (L. vannamei) and Penaeus monodon (P. monodon), sourced from cluster farming sites in Puttalam, Sri Lanka. Shrimp exoskeletons and edible soft tissues underwent rigorous microplastic analysis, including density separation, alkali digestion, stereo microscopy, and Raman spectroscopy. The results revealed high microplastic contamination, with L. vannamei containing an average of 4.99 ± 1.81 MP particles/g and P. monodon containing 1.87 ± 0.55 MP particles/g. Microplastic sizes varied, with L. vannamei predominantly contaminated with 100–250 µm particles and P. monodon with 500 µm—1000 µm particles. Fiber morphotypes were prevalent in L. vannamei, while blue-colored microplastics were dominant in P. monodon. These comprised polystyrene (PS), nylon 6,6, and polyethylene (PE) which were identified by Raman spectroscopy. Additionally, the study investigated the acute toxicity effects of microplastic polymer combinations using a zebrafish embryo model (FET236 assay). Zebrafish embryos exposed to polyethylene-nylon 6,6 combinations exhibited significant adverse effects on hatching, survival, and heart function at lower concentrations, while polyethylene terephthalate-polystyrene combinations showed no considerable effects. These findings underscore the urgent need for monitoring and managing microplastic contamination in shrimp farming areas. Future research should focus on elucidating the ecological impacts and human health risks associated with microplastic exposure.
Open Access
A Context-Aware Doorway Alignment and Depth Estimation Algorithm for Assistive Wheelchairs
(Multidisciplinary Digital Publishing Institute (MDPI), 2025-07-17) Tennekoon, S; Wedasingha, N; Welhenge, A; Abhayasinghe, N; Murray, I
Navigating through doorways remains a daily challenge for wheelchair users, often leading to frustration, collisions, or dependence on assistance. These challenges highlight a pressing need for intelligent doorway detection algorithm for assistive wheelchairs that go beyond traditional object detection. This study presents the algorithmic development of a lightweight, vision-based doorway detection and alignment module with contextual awareness. It integrates channel and spatial attention, semantic feature fusion, unsupervised depth estimation, and doorway alignment that offers real-time navigational guidance to the wheelchairs control system. The model achieved a mean average precision of 95.8% and a F1 score of 93%, while maintaining low computational demands suitable for future deployment on embedded systems. By eliminating the need for depth sensors and enabling contextual awareness, this study offers a robust solution to improve indoor mobility and deliver actionable feedback to support safe and independent doorway traversal for wheelchair users.
Open Access
A cost effective machine learning based network intrusion detection system using Raspberry Pi for real time analysis
(PLOS ONE, 2025-12-29) Wijethilaka R.W.K.S; Yapa, K; Siriwardena, D
In an increasingly interconnected world, the security of sensitive data and critical operations is paramount. This study presents the development of a Network Intrusion Detection System (NIDS) that analyzes both inbound and outbound network traffic to detect and classify various cyber attacks. The research begins with an extensive review of existing intrusion detection techniques, highlighting the limitations of traditional methods when addressing the unique security challenges posed by distributed networks. To overcome these limitations, advanced machine learning algorithms, including Random Forest, Long Short Term Memory (LSTM) networks, Artificial Neural Networks (ANN), XGBoost, and Naive Bayes, are employed to create a robust and adaptive intrusion detection system. The practical implementation utilizes a Raspberry Pi as the central processing unit for real time traffic analysis, supported by hardware components such as Ethernet cables, LEDs, and buzzers for continuous monitoring and immediate threat response. A comprehensive alert system is developed, sending email notifications to administrators and activating physical indicators to signify detected threats. Our proposed NIDS achieves 96.5 detection accuracy on the NF-UQ-NIDS dataset, with a significantly reduced false positive rate after applying SMOTE. The system processes real time network traffic with an average response time of 50 milliseconds, outperforming traditional IDS solutions in accuracy and efficiency. Evaluation using the NF-UQ-NIDS dataset demonstrates a significant improvement in detection accuracy and response time, establishing the system as an effective tool for safeguarding networks against emerging cyber threats.
Open Access
A Deep Learning-Based Dual-Model Framework for Real-Time Malware and Network Anomaly Detection with MITRE ATT&CK Integration
(Science and Information Organization, 2025) Migara H.M.S; Sandakelum M.D.B; Maduranga D.B.W.N; Kumara D.D.K.C; Fernando, H; Abeywardena, K
The contemporary world of high connectivity in the digital realm has presented cybersecurity with more advanced threats, such as advanced malware and network attacks, which in most cases will not be detected using traditional detection tools. Static cybersecurity tools, which are traditional, often fail to deal with dynamic and hitherto unseen attacks, including signature-based antivirus systems and rule-based intrusion detection. To ad-dress this issue, we would suggest a two-part, AI-powered solution to cybersecurity which would allow real-time threat detection on an endpoint and a network level. The first element uses a Feedfor-ward Neural Network (FNN) to categorize Windows Portable Ex-ecutable (PE) files, whether they are benign or malicious, by using structured static features. The second component improves net-work anomaly detection with a deep learning model that is aug-mented by Generative Adversarial Networks (GAN) and effec-tively addresses the data imbalance issue and sensitivity to rare cyber-attacks. To enhance its performance further, the system is integrated with the MITRE ATT&CK adversarial tactics and techniques, which correlate real-time detection results with adver-sarial tactics and techniques, thus offering actionable context to incident response teams. Tests based on open-source datasets pro-vided accuracies of 98.0 per cent of malware detection and 96.2 per cent of network anomaly detection. Data augmentation using GAN was very effective in improving the detection of less popular attacks, including SQL injections and internal reconnaissance. Moreover, the system is horizontally scalable and responsive in real-time due to Docker-based deployment. The suggested frame-work is an effective, explainable and scalable cybersecurity de-fense system, which is perfectly applicable to Managed Security Service Providers (MSSPs) and Security Operations Centers (SOCs), greatly increasing the precision rate and contextual in-sight of threat detection. © (2025), (Science and Information Organization)
Open Access
A novel application with explainable machine learning (SHAP and LIME) to predict soil N, P, and K nutrient content in cabbage cultivation
(Elsevier B.V., 2025-03-06) Abekoon, T; Sajindra, H; Rathnayake, N; Ekanayake, I, U; Jayakody, A; Rathnayake, U
Cabbage (Brassica oleracea var. capitata) is commonly cultivated in high altitudes and features dense, tightly packed leaves. The Green Coronet variety is well-known for its robust growth and culinary versatility. Maximizing yield is crucial for food sustainability. It is essential to predict the soil’s major nutrients (nitrogen, phosphorus, and potassium) to maximize the yield. Artificial intelligence is widely used for non-linear predictions with explainability. This research assessed the predictive capabilities of soil nitrogen, phosphorus, and potassium levels with explainable machine learning methods over an 85-day cabbage growth period. Experiments were conducted on cabbage plants grown in central hills of Sri Lanka. SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) were used to clarify the model’s predictions. SHAP analysis showed that high feature values of the number of days and plant average leaf area negatively impacted for nutrient predictions, while high feature values of leaf count and plant height had a positive effect on the nutrient predictions. To validate the results, 15 greenhouse-grown cabbage plants at various growth stages were selected. The nitrogen, phosphorus, and potassium levels were measured and compared with the predicted values. These insights help refine predictive models and optimize agricultural practices. A user-friendly application was developed to improve the accessibility and interpretation of predictions. This tool is a user-friendly platform for end-users, enabling effective use of the model’s predictive capabilities.
Open Access
Aberrant chromatin landscape following loss of the H3. 3 chaperone Daxx in haematopoietic precursors leads to Pu. 1-mediated neutrophilia and inflammation
(Nature Publishing Group, 2021-12) Gerber, J. P; Russ, J; Chandrasekar, V; Offermann, N; Lee, H. M; Spear, S; Guzzi, N; Maida, S; Pattabiraman, S; Zhang, R; Kayvanjoo, A. H; Datta, P; Kasturiarachchi, J. C; Sposito, T; Izotova, N; Händler, K; Adams, P. T; Marafioti, T; Enver, T; Wenzel, J; Beyer, M; Mass, E; Bellodi, C; Schultze, J. L; Capasso, M; Nimmo, R; Salomoni, P
Defective silencing of retrotransposable elements has been linked to inflammageing, cancer and autoimmune diseases. However, the underlying mechanisms are only partially understood. Here we implicate the histone H3.3 chaperone Daxx, a retrotransposable element repressor inactivated in myeloid leukaemia and other neoplasms, in protection from inflammatory disease. Loss of Daxx alters the chromatin landscape, H3.3 distribution and histone marks of haematopoietic progenitors, leading to engagement of a Pu.1-dependent transcriptional programme for myelopoiesis at the expense of B-cell differentiation. This causes neutrophilia and inflammation, predisposing mice to develop an autoinflammatory skin disease. While these molecular and phenotypic perturbations are in part reverted in animals lacking both Pu.1 and Daxx, haematopoietic progenitors in these mice show unique chromatin and transcriptome alterations, suggesting an interaction between these two pathways. Overall, our findings implicate retrotransposable element silencing in haematopoiesis and suggest a cross-talk between the H3.3 loading machinery and the pioneer transcription factor Pu.1.
Embargo
Absorbing Markov Chain Approach to Modelling Disruptions in Supply Chain Networks
(IEEE, 2019-08) Perera, S; Bell, M; Kurauchi, F; Kasthurirathna, D
Recent developments in the area of network science has encouraged researchers to adopt a topological perspective in modelling Supply Chain Networks (SCNs). While topological models can provide macro level insights into the properties of SCN systems, the lack of specificity due to high level of abstraction in these models limit their real-world applicability, especially in relation to assessing the impact on SCNs arising due to individual firm or supply channel level disruptions. In particular, beyond the topological structure, a more comprehensive method should also incorporate the heterogeneity of various components (i.e. firms and inter-firm links) which together form the SCN. To fill the above gap, this work proposes using the idea of absorbing Markov chains to model disruption impacts on SCNs. Since this method does not require path enumeration to identify the number of supply chains which form the SCN, it is deemed more efficient compared to the other traditional methods.
Open Access
Abstract concepts: A contemporary requirement for Rich Internet Applications engineering
(2016) Dissanayake, N. R; Dias, G. K. A
Rich Internet Applications are very advanced and complex systems, and for their development there are numerous tools, frameworks, libraries, techniques, and technologies are available. The underplaying concepts of the Rich Internet Applications are still have not been defined well, and the tools, frameworks, or libraries do not improve these underlying concepts; instead they might use their own forms of the concepts. If we can understand the abstract fundamental concepts of Rich Internet Applications, we can gain some advantages like: increased realization, knowledge sharing, and lower learning curves. These aspects have not being much discussed or researched within the domain; therefore, we attempt to pinpoint the importance of having abstract concepts for Rich Internet Applications engineering, as a contemporary requirement. This knowledge will help to look at the researching in Rich Internet Application engineering in a different perspective, and will lead to introduce abstract concepts, for Rich Internet Applications.
Embargo
Academic Depression Detection Using Behavioral Aspects for Sri Lankan University Students
(2021 3rd International Conference on Advancements in Computing (ICAC) -SLIIT, 2021-12-09) Gamage, M.A.; Matara Arachchi, R.; Naotunna, S.; Rubasinghe, T.; Silva, C.; Siriwardana, S.
Academic Depression is a widespread problem among undergraduate students in Sri Lanka. It is exhausting and has a detrimental impact on students' academic life. Therefore, the development of a technique to estimate the probability of depression among undergraduates is a blessed respite. Depression is mostly caused by a failure to check students' psychological well-being on a regular basis. Identifying depression at the college level, leading the students to get proper therapy treatments. If a counselor detects depression in a student early enough, he/she can successfully assist the student in overcoming depression. However, keeping track of the substantial changes that occur in students because of depression becomes challenging for the counselor with a considerable number of undergraduates. The advancement of image processing and machine learning fields has contributed to the creation of effective algorithms capable of identifying depression probability. Depression Possibility Detection Tool (DPDT) is considered an effective automated tool that brings the depression probability of a certain student. In DPDT, the result is generated by concerning four main strategies. They are facial expressions, eye movements, behavior changes (step count and phone usage), and physical conditions (heart rate and sleep rate). Convolutional Neural Network (CNN) with Visual Geometry Group 16 (VGG16) model, Residual Neural Network (ResNet-50), Random Forest (RF) classifier is the main models and techniques used in the system. More than 93% of accuracy was generated in every trained model. The paper concludes the system overview along with four strategies, literature review, methodologies, conclusion, and future works.
Open Access
ACADEMIC SUCCESS OF PERSONS WITH VISUAL IMPAIRMENT AND BLINDNESS IN THE TERTIARY SECTOR: EXPLANATORY MODEL
(Available on-line at: www.oapub.org/edu, 2022-01-14) Suraweera, T; Bandara, S; Wickramarachchi, C; Dewage, N; Gunawardana, T; Nanayakkara, N; Yapa, E; Thelijjagoda, S; Jayathilaka, R
Ensuring universal access to education is an effective and sustainable means of empowering people with visual impairment and blindness. Literature confirms that blindness of a person is not a barrier for learning, yet past research brings about a range of diverse obstacles for productive engagement in education due to universities being designed for the sighted persons. Given that the persons with impairment are looked after comparatively better in the western world than in the developing nations for realising their challenging academic goals. Purpose of this paper is to present the findings of a comprehensive study on the academic work of persons with visual impairment and blindness in the tertiary sector. This population included recent undergraduates and those who have completed a university degree. The two distinct outcomes presented are; (first phase) the critical factors influencing the academic performance of persons with visually impairment and blindness and, (second phase) an explanatory model that characterizes the construct ‘the academic performance’. Using a semi-structured questionnaire, purposely selected eleven key informant interviews were utilized for the first phase for the qualitative investigation. Thematic analysis was used as the main method of data analysis. The Second phase employed a sample survey. Fifty respondents who had studied in universities during the 5-year period from 2015-2020 were selected through snow-ball sampling. Exploratory factor analysis was used as the main data analysis technique. The key findings of phase one revealed that external support, physical environment, motivation to learn, instructional strategies, ICT and English literacy are major contributory factors to academic performance. The second phase of the quantitative analysis derived five composite factors. Of these, the factor labeled “Motivating influences” appears to be mostly contributing to the academic performance of persons with visual impairment and blindness. While education is a lifelong endeavor of a person, these findings can contribute to make a substantial change in the quality of life of this community in the long run.
Open Access
Accelerated membrane durability testing of heavy duty fuel cells
(IOP Publishing, 2014-11-19) Macauley, N; Alavijeh, A. S; Watson, M; Kolodziej, J; Lauritzen, M; Knights, S; Wang, G; Kjeang, E
Regular durability testing of heavy duty fuel cell systems for transit bus application requires several thousand hours of operation, which is costly and time consuming. Alternatively, accelerated durability tests are able to generate failure modes observed in field operation in a compressed time period, by applying enhanced levels of stress. The objective of the present work is to design and validate an accelerated membrane durability test (AMDT) for heavy duty fuel cells under bus related conditions. The proposed AMDT generates bus relevant membrane failure modes in a few hundred hours, which is more than an order of magnitude faster than for regular duty cycle testing. Elevated voltage, temperature, and oxidant levels are used to accelerate membrane chemical stress, while relative humidity (RH) cycling is used to induce mechanical stress. RH cycling is found to significantly reduce membrane life-time compared to constant RH conditions. The role of a platinum band in the membrane is investigated and membranes with Pt bands demonstrate a considerable life-time extension under AMDT conditions, with minimal membrane degradation. Overall, this research serves to establish a benchmark AMDT that can rapidly and reliably evaluate membrane stability under simulated heavy duty fuel cell conditions.
Open Access
Accessbim model for environmental characteristics for vision impaired indoor navigation and way finding
(2012 International Conference on Indoor Positioning and Indoor Navigation, 2012-11) Jayakody, J. A. D. C. A; Abhayasinghe, N; Murray, I
Most blind people require assistance to navigate within buildings as there is often insufficient information about the buildings available to them. To address this problem, this paper describes the “AccessBIM” model as an approach to facility management in which a digital representation of the indoor building features is used to facilitate the exchange and interoperability of real-time information in digital format which can assist blind people to independently access unfamiliar building indoor environments. This paper discusses conceptual communication model driven architecture that can be implemented for way finding and data synchronization, generating, in real-time, an AccessBIM for a remote user.
Embargo
Accommodation Finder: An Augmented Reality Based Mobile Application Integrated with Smart Contracts
(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Parameswaran, G.; Perera, M.J.F.R.; Aluthgedara, C.R.B.; Amanda, E.D.N.; Ishara, W.G.A.; Ganegoda, D.
Accommodation is one of the basic needs for travelers, tourists, students, and employees. Accommodations range from low-budget lodges to world-class luxury hotels, but finding the preferable accommodation is undoubtedly a tedious task. And due to the COVID-19 pandemic, it has become problematic state to visit each accommodation property to check whether it's suitable for the accommodation seeker, considering the location, environment, and to check if the property matches the user’s preferences. There have been incidents reported where thousands of people have been victimized because of contract breaches in the accommodation and real estate sectors, recurring from contract alterations. Considering these problems, we have proposed a system to provide solutions using Natural Language Processing (NLP), Automatic Speech Recognition (ASR), Augmented Reality (AR), Block-chain, and K-Nearest Neighbor (KNN). This system provides an efficient approach to viewing the exterior and interior of an accommodation using 360-degree views, providing recommendations to the user based on user preferences using KNN and cosine similarity, providing security in a digital agreement using blockchain technology, and a map navigation system using ASR. With the aid of the previously mentioned techniques, a mobile application prototype is created with the possibility of future testing and implementation.
Open Access
Accounting Education towards Sustainable Labour Markets in Sri Lanka
(researchgate.net, 2021-01-11) Perera, K. A. J. O; Perera, U. L. N. L; Guruge, N. H. G; Subashini, S; Madhavika, W. D. N; Weerarathna, R. S
The quality of Accounting Education determines the quality of Professionals in the field of Accounting. During last few decades with the effect of globalization, many technological advancements occurred in every industry. It directly affects the job profile of sustainable labor market of Accounting field where work becomes challenging and complex to achieve. Babike [1] proved that as Accounting Academics have become more important in the re-contextualization of the new global. The purpose of this article is to identify which qualifications are preferred by the employers out of Professional Accounting Qualification and Academic Accounting Qualification in Sri Lankan sustainable labor market. The present study adopted interpretation in philosophy and the inductive approach. The data collection was based on primary data and was collected through online interview method. Researchers selected convenience sampling method since more than 80% of listed companies in Colombo Stock Exchange are in Western Province. Based on the convenience sampling technique researchers have selected ten listed companies and interview ten Finance Managers in respective companies. Thematic Analysis technique was used to analyze the data by using the NVivo software application. The findings of the present study suggest that many employers prefer Professional Accounting Qualifications rather than Academic Accounting Qualifications. The main reason for the choice is candidates with Professional Accounting Qualifications are more capable in handling tasks and the assigned job role due to the training offered through Professional Accounting Qualification when compared to Academic Accounting Qualifications. As an implication the respected authorities in Sri Lankan educational sector can implement Accounting trainings component for Academic Accounting Education. This may also be useful to future researchers to identify the perceptions of the employers.
Open Access
Accounting Education towards Sustainable Labour Markets in Sri Lanka
(researchgate.net, 2021-01-11) Perera, K. A. J. O; Perera, U. L. N. L; Guruge, N. H. G; Subashini, S; Madhavika, W. D. N; Weerarathna, R. S
The quality of Accounting Education determines the quality of Professionals in the field of Accounting. During last few decades with the effect of globalization, many technological advancements occurred in every industry. It directly affects the job profile of sustainable labor market of Accounting field where work becomes challenging and complex to achieve. Babike [1] proved that as Accounting Academics have become more important in the re-contextualization of the new global. The purpose of this article is to identify which qualifications are preferred by the employers out of Professional Accounting Qualification and Academic Accounting Qualification in Sri Lankan sustainable labor market. The present study adopted interpretation in philosophy and the inductive approach. The data collection was based on primary data and was collected through online interview method. Researchers selected convenience sampling method since more than 80% of listed companies in Colombo Stock Exchange are in Western Province. Based on the convenience sampling technique researchers have selected ten listed companies and interview ten Finance Managers in respective companies. Thematic Analysis technique was used to analyze the data by using the NVivo software application. The findings of the present study suggest that many employers prefer Professional Accounting Qualifications rather than Academic Accounting Qualifications. The main reason for the choice is candidates with Professional Accounting Qualifications are more capable in handling tasks and the assigned job role due to the training offered through Professional Accounting Qualification when compared to Academic Accounting Qualifications. As an implication the respected authorities in Sri Lankan educational sector can implement Accounting trainings component for Academic Accounting Education. This may also be useful to future researchers to identify the perceptions of the employers
Open Access
Accurate control position of belt drives under acceleration and velocity constraints
(Institute of Control, Robotics and Systems, 2003) Jayawardena, T. S. S; Nakamura, M; Goto, S
Belt drives provide freedom to position the motor relative to the load and this phenomenon enables reduction of the robot arm inertia. It also facilitates quick response when employed in robotics. Unfortunately, the flexible dynamics deteriorates the positioning accuracy. Therefore, there exists a trade-off between the simplicity of the control strategy to reject time varying disturbance caused by flexibility of the belt and precision in performance. Resonance of the system further leads to vibrations and poor accuracy in positioning. In this paper, accurate positioning of a belt driven mechanism using a feed-forward compensator under maximum acceleration and velocity constraints is proposed. The proposed method plans the desired trajectory and modifies it to compensate delay dynamics and vibration. Being an offline method, the proposed method could be easily and effectively adopted to the existing systems without any modification of the hardware setup. The effectiveness of the proposed method was proven by experiments carried out with an actual belt driven system. The accuracy of the simulation study based on numerical methods was also verified with the analytical solutions derived.
Open Access
Accurate Pedometer for Smartphones
(2013) Jayalath, S; Abhayasinghe, N; Murray, I
Accuracy of step counting is one of the main problems that exist in current Pedometers, especially when walking slowly on flat lands and performing different activities, such as climbing up and down stairs and walking on inclined planes. Although accelerometer based pedometers provide a reasonable accuracy when walking at higher speeds, the accuracy of them are not sufficient at slow walking speeds and performing different activities. This paper proposes a novel algorithm to detect steps using single-point gyroscopic sensors embedded in mobile devices. Preliminary analysis of data collected in different environments with the involvement of male and female volunteers indicated that gyroscope alone provides sufficient information necessary for accurate step detection. Algorithm was developed based on the gyroscopic data in conjunction with zero crossing and threshold detection techniques. The results proved that gyroscope based step detection algorithm provide a high accuracy when performing different activities and at slow paced walking.
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, R
Many 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