Faculty of Engineering Q1

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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.
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 cross-category analysis of high impact low occurrence (HILO) disasters
(Elsevier Ltd, 2026-03-19) Samaraweera, U; Kulatunga, U; Dias, P
This paper explores six High Impact Low Occurrence (HILO) disasters, generating insights from five different categories associated with them, namely causes (geophysical, technological, biological, sociological), phases (preparedness, response, recovery), dimensions (socio-economics, governance, equity), sectors (health, education, infrastructure, economy) and national contexts with differing levels of economic development. The process involved the generation of a questionnaire, based on a literature review; and the subsequent analysis and discussion of the questionnaire responses made by six experts nominated by six academies of science in Asia. The findings highlight the limitations of probabilistic, frequency-based risk models for HILO disasters and instead emphasise the importance of scenario-based (worst-case) analyses; mechanisms that preserve inter‐generational knowledge, institutional continuity and community‐based early‐response networks; strengthening community resilience while ensuring equity; and making appropriate investments for increasing preparedness, if not through structural interventions, at least through sustained awareness programs and periodic drills. Theoretical contributions include arguments that institutional capacity, governance quality, and social resilience are more decisive determinants of HILO event outcomes than probabilistic risk analyses; and that effective preparedness depends more on anticipatory planning, scenario-based training and institutionalised memory rather than experiential learning; thus advancing HILO theory beyond event-centred and frequency-driven interpretations towards a more governance- and resilience-oriented understanding.
Open Access
Advancing Object Detection: A Narrative Review of Evolving Techniques and Their Navigation Applications
(Institute of Electrical and Electronics Engineers Inc., 2025-03-17) Tennekoon, S; Wedasingha, N; Welhenge, A; Abhayasinghe, N; Murray Am, I
Object detection plays a pivotal role in advancing computer vision systems by enabling machines to perceive and interact intelligently with their environments. Despite significant advancements, comprehensive exploration of its evolution and applications in navigation remains underrepresented. This review paper examines the evolution of object detection technologies, from early methodologies to contemporary advancements, and their critical role in navigation tasks. The emphasis was on the significance of contextual learning in enhancing object detection performance by leveraging spatial and temporal information. Furthermore, the limitations of conventional approaches that rely heavily on hand-engineered features are examined. It is then demonstrated that contextual learning facilitates automated feature extraction, resulting in improved accuracy exceeding a 50% increase and adaptability in diverse applications. The review concludes by outlining future trends and opportunities for further advancements in object detection and, underscoring its transformative impact on autonomous navigation and beyond. In summary, this review contributes to a comprehensive understanding of object detection technologies by offering insights into their evolution, highlighting their applications in navigation, and providing guidance for future research in context-aware systems.
Open Access
Ambient Backscatter- and Simultaneous Wireless Information and Power Transfer-Enabled Switch for Indoor Internet of Things Systems
(Multidisciplinary Digital Publishing Institute (MDPI), 2025-01-06) Sooriarachchi, V.P; Perera, T.D.P; Jayakody, D.N.K
Indoor Internet of Things (IoT) is considered as a crucial component of Industry 4.0, enabling devices and machine to communicate and share sensed data leading to increased efficiency, productivity, and automation. Increased energy efficiency is a significant focus within Industry 4.0, as it offers numerous benefits. To support this focus, we developed a hybrid switching mechanism to switch between energy harvesting techniques, ambient backscattering and Simultaneous Wireless Information and Power Transfer (SWIPT), which can be utilized within cooperative communications. To implement the proposed switching mechanism, we consider an indoor warehouse environment, where the moving sensor node transmits sensed data to the fixed relay located on the roof, which is then transmitted to an IoT gateway. The relay is equipped with the proposed switch to energize its communication capabilities while maintaining the expected quality of service at the IoT gateway. Simulation results illustrate the improved energy efficiency within the indoor communication setup while maintaining QoS at varying signal-to-noise (SNR) conditions.
Open Access
Anthocyanin (ATH)-incorporating polyvinylpyrrolidone-ethyl cellulose-(2-hydroxypropyl)-β-cyclodextrin (PVP–EC–BCD) nanofiber-based pH sensor for ocular pH detection during accidental chemical spills
(Royal Society of Chemistry, 2026-02-03) Sandaruwan, B; Liyanage, R; Costha, P; Dassanayake, Rohan S; Wijesinghe, R, E; Herath H.M.L.P.B; Nalin de Silva K.M.; de Silva, Rohini M; Rajapaksha, Suranga M; Wijenayake, U; Manatunga, Danushika C
The existing ocular pH detection methods encounter numerous limitations, including low accuracy, poor sensitivity across a wide pH range, and patient discomfort, highlighting the need for innovative approaches. A novel biosensor for ocular pH detection has been developed to assess ocular health and chemical injuries in clinical settings. This study uses the pH-sensitive properties of anthocyanins (ATHs), natural pigments extracted from butterfly pea flowers, to develop a novel pH-responsive nanofiber mat. ATHs are integrated into a polymer blend containing polyvinylpyrrolidone (PVP), ethyl cellulose (EC), and (2-hydroxypropyl)-β-cyclodextrin (BCD) to fabricate electrospun nanofibers. The acquired characterization, employing scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and thermogravimetric analysis (TGA), confirmed the successful fabrication of the ATH-infused nanofibers with a mean diameter ranging from 121 to 396 nm. Four formulations were tested: PVP:EC:BCD:ATH (18 ppm), PVP:EC:BCD:ATH (25 ppm), PVP:EC:BCD:ATH (35 ppm), and PVP:EC:BCD:ATH (50 ppm). Among them, the 50 ppm ATH-incorporating nanofiber mat exhibited the best performance in terms of color clarity, response time, and pH sensitivity. The fabricated 50 ppm ATH incorporating nanofiber mat demonstrated a rapid pH response time of less than 5 seconds (s) while exhibiting a color variation from pink to blue to green across the pH range of 1 to 12, providing a rapid and accurate method for visual pH detection. Based on the color performance of the 50 ppm ATH-incorporating system, a standardized color reference chart was developed to serve as a practical and visual guide for estimating pH levels in clinical applications. Zebrafish toxicity assays were conducted further to validate the safety and biocompatibility of the developed sensor, revealing no significant toxic effects across the range of ATH concentrations.
Open Access
Automated design of reinforced concrete dapped-end connections using hybrid deep learning and generative AI augmentation
(Elsevier Ltd, 2026-04-15) Dharmawansha, S; Herath, S; Fernando P.L.N; Meddage D.P.P.; Rajapakse, C
Dapped-end connections, also known as half-joints or Gerber beams, are widely used yet structurally vulnerable elements in precast concrete structures due to high stress concentrations near the re-entrant corner. Therefore, a comprehensive assessment of the load-bearing capacity of dapped-end connections is important to ensure structural integrity and mitigate the risk of failure. Although prior studies have explored their behaviour through analytical and experimental methods, the application of data-driven approaches remains limited due to the availability of limited experimental data, which constrains the predictive accuracy and generalisation of Machine Learning (ML) models. This study presents a novel approach that integrates numerical simulation with Conditional Tabular Generative Adversarial Network (CTGAN)-based data augmentation to enhance prediction accuracy and model generalisation. A numerical database containing 720 results was developed, which was expanded with 680 augmented data using CTGAN. The combined dataset of 1400 instances was used to train Artificial Neural Network (ANN), Genetic Algorithm-ANN (GA-ANN), and Particle Swarm Optimisation-ANN (PSO-ANN) models. The hybrid models outperformed the standalone ANN, with GA-ANN achieving the highest accuracy (testing R2 = 0.961). The trained models were separately validated using 64 unseen experimental datasets, which shows the improved generalisation of the models through augmentation. Shapley Additive Explanations analysis reveals that the GA-ANN model predictions aligned with the principles underlying the compatibility of deformations of dapped-ends. Further, a novel ML-assisted design model was developed, which predicts multiple solutions for a given design problem, assisting in the optimisation of connection design.
Embargo
Balancing Maintenance Costs and Degradation Levels in Building Elements
(American Society of Civil Engineers (ASCE), 2025-10-06) Dissanayake, D; Dias W.P.S
Maintenance strategies require a balance between maintaining assets at acceptable levels of service and minimizing maintenance costs. The latter is imperative especially for building elements, since they require less maintenance than machinery, and carrying out such maintenance can be disruptive and costly. This paper reports an inquiry into optimal maintenance strategies for 12 building elements, ranging from structural through finishes and opening to services ones, each with a differing service life. Element degradation was predicted using Markov chain models, while repair costs were based on lost value ratios associated with five discrete condition ratings, separately defined for each of the elements. The strategies considered were based on three different criteria, namely, minimizing maintenance cost, achieving the best the overall condition rating at end of service life, and optimizing a rating that combined both cost and quality. The maintenance cycles considered were 5, 10, and 20 years, and also the zero-maintenance case, costed at both zero and 5% net discount rates. The repair combinations involved upgrading elements in a single given Condition 2, 3, 4, or 5 to the as new Condition 1 or upgrading elements in all such conditions to Condition 1. It was found that performing regular maintenance on concrete slabs, floor tiles, timber windows, and especially ceiling fans and fan regulators was in fact cheaper than not maintaining them (and having to renovate at the end of service life), while a 20 year cycle was found to be optimal for the other elements studied.
Embargo
Bi-directional long short-term memory based ensemble deep learning framework for non-linear steam turbine power forecasting: a biomass fuelled case study
(Elsevier Ltd, 2026-04-10) Perera, H; Jayasekara, S; Wijesinghe, R.E; Silva, B. N; Cha, H
In palm oil manufacturing, steam turbines powered by biomass fuel are central to energy generation. However, fluctuating load demands and temporal variations lead to inefficiencies, while limited and variable supply of biomass waste constrains boiler feed flexibility. Current index-based boiler feeding methods overlook actual load demands and waste availability, resulting in significant energy wastage. This study presents a novel ensemble deep learning model combining Bidirectional Long Short-Term Memory (Bi-LSTM) and Gated Recurrent Units (GRU) with Attention Layers, trained on an eight-year operational dataset with structured preprocessing and feature selection, to forecast steam turbine power generation. The model captures complex non-linear temporal patterns more effectively than conventional and standalone ML models, achieving a Root Mean Square Error (RMSE) of 0.0684, Mean Absolute Error (MAE) of 0.0414, and an R-squared (R2) value of 0.9832, which outperformed eight benchmark models by approximately 25% in prediction accuracy. Additionally, the framework incorporates operational parameters such as kVA, total energy, and Fresh Fruit Bunch (FFB) production to dynamically optimise biomass feed rates, balancing energy output with resource availability. This approach minimises energy wastage, reduces grid reliance, and promotes both sustainability and profitability.
Open Access
CALCOM: An integrated techno-economic and life-cycle environmental framework for electric vehicle assessment in Sri Lanka
(Elsevier B.V., 2026-08-01) Abeygunawardena, N; Wijayapala, A; Jathunga, T
Electric vehicle (EV) adoption is accelerating worldwide, creating a need for comprehensive frameworks that assess economic feasibility and environmental performance. This study presents a context-adaptive levelized cost of mileage (CALCOM) framework to evaluate the economic and environmental performance of battery electric vehicles (BEVs), hybrid electric vehicles (HEVs), and internal combustion engine vehicles (ICEVs) under Sri Lankan conditions. The framework integrates discounted life-cycle cost (LCC), net present value (NPV), and greenhouse gas (GHG) emissions into a unified assessment model. Real-world operational ad cost data were collected from owners representing Nissan Leaf (BEV), Toyota Aqua (HEV), and Toyota Vitz (ICEV). The analysis included purchase cost, energy consumption, maintenance, battery replacement, salvage value, and environmental costs over a 10-year ownership period. The BEV achieved the lowest levelized cost of mileage of 0.080 USD/km which further decreased to 0.050 USD/km under renewable charging. Life-cycle GHG emissions were 59% lower than those of ICEV. Sensitivity analysis identified electricity price, annual distance travelled, and charging efficiency as the primary determinants of BEV competitiveness. Threshold analysis indicated that BEVs remain economically attractive when domestic electricity tariffs are maintained below 0.32 USD/kWh. The findings demonstrate that BEVs offer the greatest economic and environmental benefits and offer evidence-based guidance for policies supporting electric mobility in developing countries.
Open Access
Channel Estimation of full-duplex relay-assisted RSMA-OFDM based wireless networks
(Frontiers Media SA, 2025-10-03) Chaudhary, U; Rajkumar, S; Jayakody, D. N.K
This paper analyzes the channel estimation of rate splitting multiple access (RSMA) wireless network through the full-duplex amplify-and-forward (AF) relay. Basically, full-duplex transmission can improve temporal efficiency, however the loop interference is an unavoidable problem that occurs in the strong user of this proposed network. The orthogonal frequency division multiplexing (OFDM) system is used to provide high data rate communication, assuming the presence of phase noise (PN) in local oscillators. Using the least square (LS) estimate, the channel coefficients of the proposed RSMA relay network are estimated. In addition, convex optimization techniques are applied to estimate the phase noise components of this network. The problem is formulated by optimizing phase noise under transmit power constraints. We analyze the Bit Error Rate (BER) performance of the proposed network under binary phase shift keying (BPSK) modulation and 16-quadrature amplitude modulation (QAM). Simulation results demonstrate that channel estimation achieves better performance after the PN compensation
Open Access
Circular Valorization of Post-Industrial Textile Waste in Thermal-Insulating Cementitious Ceiling Sheets
(Multidisciplinary Digital Publishing Institute (MDPI), 2026-02-27) Fernando, K. V; Dodangodage, C.A; Seneviratne, V.M; Jayasinghe, S.M; Dharmaratne, D.D; Gamage, G.N; Halwatura, R. H; Gunasekera U.S.W; Halwatura, R.U
The construction sector faces increasing pressure to reduce the embodied energy of building materials while valorizing industrial waste streams. This study evaluates the direct incorporation of post-industrial textile waste (100% cotton and cotton–polyester blends) in its native form to develop high-performance cementitious ceiling sheets. Composites were fabricated under a controlled hydraulic compaction pressure of 2.0 MPa, optimized to achieve matrix densification while preserving the integrity of the fibrous network. Viscoelastic recovery of the compressed fibers induced a hierarchical double-porosity architecture characterized by macro-voids and hollow fiber lumens. This microstructural evolution reduced thermal conductivity to 0.091 W/m·K, approximately 50% lower than commercial cement–fiber benchmarks—without compromising mechanical compliance. Scanning Electron Microscopy (SEM) revealed a mechanistic decoupling between water absorption and dimensional stability. Although the CP15 formulation (15 wt.% cotton–polyester) exhibited high moisture uptake (~21%), thickness swelling remained limited to 1.35%. This dimensional stability is attributed to the hydrophobic polyester framework, which bridges microcracks and constrains hygroscopic expansion within the cellulosic phase. The optimized CP15 composite achieved a Modulus of Rupture (MOR) of 8.75 MPa, exceeding ISO 8336 Category C, Class 2 requirements. Despite increased thickness, the areal density (10.84 kg/m2) remains compatible with standard gypsum-grade suspension systems, eliminating the need for structural modification. These findings establish a scalable, direct-valorization strategy for circular construction materials delivering enhanced thermal insulation and robust performance under tropical climatic conditions.
Embargo
Classification, subsurface and surface sediment physical properties, and bank stability of non-perennial and perennial headwater streams of a tropical climate
(Springer Nature Link, 2025-11-20) Gomes, P.I.A; Perera,M. D. D; Karunarathna J.M.G.L.D.
Over 100 reaches of perennial streams (PS) and non-perennial streams (NPS) were classified based on the Rosgen stream classification. NPS were mainly type B (39%), characterized by moderate entrenchment and low sinuosity. The remainder were almost equally split between three different classes, highlighting the morphological diversity of NPS. Fifty-nine percent of PS belonged to type C; such streams are slightly entrenched, less sinuous and have a sequential riffle-pool configuration. Surface particles were significantly coarser than the subsurface in both stream types in thalweg and low flow areas, whereas in NPS, this was prominent, showing 4–5 times more armoring than PS. Even though the NPS had a significantly coarser surface sediment layer than PS in thalweg and low flow areas, its subsurface sediment showed similar particle sizes to PS; this is an indication of surface armoring and provision of more infiltration of fine particles in NPS. A two-year return period flow event did not result in a change of the cross-section profiles. In both stream types, the horizontal force required to uproot herbaceous vegetation with unexposed roots under moist conditions manually was higher than the tractive force at high flows; however, at bankfull flows, it was lower. Also, the uprooting force in PS was lower than that of NPS, indicating that NPS banks are more stable, and numerical analyses showed they are stable under the self-weight.
Open Access
Distributed solar generation forecasting using attention-based deep neural networks for cloud movement prediction
(Elsevier Ltd, 2026-10-01) Perera, M; De Hoog, J; Bandara, K; Weeratunge, H; Halgamuge, S
Accurate forecasts of distributed solar generation are necessary to maintain grid stability amid the increased uptake of distributed solar photovoltaic (PV) systems. However, the high variability of solar generation over short time intervals (seconds to minutes) caused by cloud movement makes this forecasting task difficult. To address this, using cloud images, which capture the second-to-second changes in cloud cover affecting solar generation, has shown promise. Recently, deep neural networks with attention that focus on important regions of an image have been applied with success in many computer vision applications. However, whether such methods provide meaningful benefits for cloud movement forecasting, and how such improvements propagate through to downstream solar generation forecasting accuracy, remains under-explored. In this study, we conduct a large-scale empirical investigation of the impact of attention-based cloud forecasting on solar generation forecasting, addressing a gap that has been overlooked in the literature. To this end, we develop a pipeline that incorporates an attention-enhanced convolutional long short-term memory network and an existing self-attention-based video prediction method to forecast cloud movement using satellite imagery. The effectiveness of the resulting cloud forecasts is evaluated through their downstream impact on solar forecasting across 50 PV sites in Australia. We further provide insights into the cloud conditions under which attention-based cloud forecasting methods yield the most significant improvements in downstream solar forecasting accuracy. We find that for clouds at high altitudes, the cloud predictions obtained using attention-based methods result in solar forecast skill score improvements of 5.86% or more compared to non-attention-based methods.
Embargo
Durability and mechanical performance of glass and natural fiber-reinforced concrete in acidic environments
(Elsevier Ltd, 2025-02-28) Justin, S; Thushanthan, K; Tharmarajah, G
This study investigates the mechanical and durability characteristics of fiber-reinforced concrete when exposed to acidic environments. The research focuses on the effects of adding 1 % of treated coir fibers (TCF), treated rice husk fibers (TRH), and glass fibers (GF), along with 5 % silica fume (SF), to concrete. Experimental results show that the inclusion of these fibers and SF enhances both compressive and tensile strengths, with the most significant improvements observed in GF-reinforced concrete. The durability of the concrete was tested by immersing samples in acidic solutions with pH values of 3 and 5 for 28 days. Ultrasonic Pulse Velocity (UPV) tests indicated that the concrete’s quality remained stable, while compressive strength tests revealed an increase in strength, particularly in samples exposed to pH 5. Sorptivity tests, which measure water absorption, indicated higher initial absorption rates due to the porous nature of fiber-reinforced concrete. However, as hydration progressed, the rate decreased. SEM images show that incorporating silica fume improves the microstructure of the specimens benefitting the strength of the structure. The study concludes that concrete reinforced with GF and SF exhibits superior mechanical properties and durability in acidic environments, making it a promising material for use in harsh conditions
Open Access
Efficient Hotspot Detection in Solar Panels via Computer Vision and Machine Learning
(Multidisciplinary Digital Publishing Institute (MDPI), 2025-07-15) Fernando, N; Seneviratne, L; Weerasinghe, N; Rathnayake, N; Hoshino, Y
Solar power generation is rapidly emerging within renewable energy due to its cost-effectiveness and ease of deployment. However, improper inspection and maintenance lead to significant damage from unnoticed solar hotspots. Even with inspections, factors like shadows, dust, and shading cause localized heat, mimicking hotspot behavior. This study emphasizes interpretability and efficiency, identifying key predictive features through feature-level and What-if Analysis. It evaluates model training and inference times to assess effectiveness in resource-limited environments, aiming to balance accuracy, generalization, and efficiency. Using Unmanned Aerial Vehicle (UAV)-acquired thermal images from five datasets, the study compares five Machine Learning (ML) models and five Deep Learning (DL) models. Explainable AI (XAI) techniques guide the analysis, with a particular focus on MPEG (Moving Picture Experts Group)-7 features for hotspot discrimination, supported by statistical validation. Medium Gaussian SVM achieved the best trade-off, with 99.3% accuracy and 18 s inference time. Feature analysis revealed blue chrominance as a strong early indicator of hotspot detection. Statistical validation across datasets confirmed the discriminative strength of MPEG-7 features. This study revisits the assumption that DL models are inherently superior, presenting an interpretable alternative for hotspot detection; highlighting the potential impact of domain mismatch. Model-level insight shows that both absolute and relative temperature variations are important in solar panel inspections. The relative decrease in “blueness” provides a crucial early indication of faults, especially in low-contrast thermal images where distinguishing normal warm areas from actual hotspot is difficult. Feature-level insight highlights how subtle changes in color composition, particularly reductions in blue components, serve as early indicators of developing anomalies.
Embargo
Elastodynamic Fundamental Solutions of a Transversely Isotropic Unsaturated Half-Space Subjected to Axisymmetric Buried Vertical and Fluid Loading
(Springer Science and Business Media B.V., 2025-06-17) Yoonirundorn K; Senjuntichai T; Keawsawasvong S.; Rajapakse R.K.N.D
This paper presents solutions for a transversely isotropic unsaturated half-space subjected to axisymmetric time-harmonic vertical and fluid pressure loading. The Biot’s coupled poroelastodynamic equations are modified to include the unsaturated case by adding the air phase into the balance equations. The resulting fully coupled field equations of the three-phase medium are analytically solved to obtain the frequency-domain general solutions by using Hankel integral transforms in the radial direction. A boundary value problem is then formulated to obtain explicit expressions for the solutions of a half-space subjected to internally applied time-harmonic vertical loading and applied fluid pressure. To validate the accuracy of the proposed formulation, a comparison is made with existing solutions for surface loading on an unsaturated half-space. Numerical results are presented to illustrate the influence of the degree of saturation and the frequency of excitation on the dynamic response of the unsaturated half-space under internal excitations. The analysis reveals significant differences in profiles of displacement, stress, and pore pressure for the unsaturated soils compared to fully saturated cases, highlighting the importance of incorporating partial saturation in dynamic poroelastic modelling for geotechnical and earthquake engineering applications.
Open Access
Electromagnetic Continuously Variable Transmission (EMCVT) System for Precision Torque Control in Human-Centered Robotic Applications
(Multidisciplinary Digital Publishing Institute (MDPI), 2025-09-08) Madusankha, I; Jayaweera, P. N; Kahatapitiya, N. S; Sampath, P; Weeraratne, A; Subasinghage, K; Liyanage, C; Wijethunge, A; Ravichandran, N. K; Wijesinghe, R. E
In human-centered robotic applications, safety, efficiency, and adaptability are critical for enabling effective interaction and performance. Incorporating electromagnetic continuously variable transmission (EM-CVT) systems into robotic designs enhances both safety and precise, adaptable motion control. The flexible power transmission offered by CVTs allows robots to operate across diverse environments, supporting various tasks, human interaction, and safe collaboration. This study presents a CVT-based mechanical subsystem developed using two cones and an intermediate belt-driven transmission mechanism, providing efficient power and motion transfer. The control subsystem consists of six strategically positioned electromagnets energized by signals from a microcontroller. This electromagnetic actuation enables rapid and precise adjustments to the transmission ratio, enhancing overall system performance. A linear relationship between slip percentage and gear ratio was observed, indicating that the control system achieves stable and efficient operation, with a measured power consumption of 2.95 W per electromagnet. Future work will focus on validating slip performance under dynamic loading conditions, integrating the system into robotic platforms, and optimizing materials and control strategies to enable broader real-world deployment.
Embargo
Environmental forensics of the X-press pearl disaster: Uncovering the internal micro-structural transformations in marine microplastics
(Elsevier B.V., 2025-07-15) Jayasekara, P.M; Abhishek, P; Kahatapitiya, N. S; Weerasinghe, M; Kahandawala, B. S; Silva, B. N; Wijenayake, U; Rajapaksha, A.U; Wijesinghe, R. E; Vithanage, M
The MV X-Press Pearl (XPP) maritime disaster on May 25, 2021, released approximately 75 billion microplastic (MP) nurdles into the Indian Ocean and degraded due to the elevated temperatures, a cocktail of chemicals, physical abrasions, and environmental factors. While degradation-induced surface-level chemical and morphological changes were well documented, internal degradation remains largely unexplored. This study highlights the utilization of high-resolution optical coherence tomography (OCT) as a purely non-destructive imaging modality to discover profound internal alterations in the micrometer range, such as internal hollow regions, cracks, and voids in MP nurdles subjected to different degrees of degradation. The dark pixel intensity probability density corresponds to the degraded areas, increased from 0.0019 (pristine nurdle) to 0.0135–0.5252 for thermal degradation, 0.0878–0.3134 for chemical degradation, and 0.1291–0.2179 for mechanical degradation, indicating progressive internal degradation. Attenuated total reflectance fourier transform infrared (ATR-FTIR) spectroscopy analysis confirmed that all the nurdles are polyethylene (PE) and revealed that extreme conditions lead to the formation of new functional groups, including hydroxyl bands and carbonyl bands, even though PE is highly resistant to degradation. The integration of high-resolution OCT imaging with FTIR analysis provides novel insights into the interconnection between micrometer-scale internal physical alterations and associated chemical modifications of MP nurdles resulting from environmental degradation. These findings highlight the potential of this OCT-FTIR integrated approach for advancing the understanding of MP degradation and its long-term environmental impacts.
Open Access
Facial identity recognition using StyleGAN3 inversion and improved tiny YOLOv7 model
(Nature Research, 2025-03-17) Kumar, A; Bhattacharjee, S; Kumar, Ambrish; Jayakody, D. N.K
Facial identity recognition is one of the challenging problems in the domain of computer vision. Facial identity comprises the facial attributes of a person’s face ranging from age progression, gender, hairstyle, etc. Manipulating facial attributes such as changing the gender, hairstyle, expressions, and makeup changes the entire facial identity of a person which is often used by law offenders to commit crimes. Leveraging the deep learning-based approaches, this work proposes a one-step solution for facial attribute manipulation and detection leading to facial identity recognition in few-shot and traditional scenarios. As a first step towards performing facial identity recognition, we created the Facial Attribute Manipulation Detection (FAM) Dataset which consists of twenty unique identities with thirty-eight facial attributes generated by the StyleGAN3 inversion. The Facial Attribute Detection (FAM) Dataset has 11,560 images richly annotated in YOLO format. To perform facial attribute and identity detection, we developed the Spatial Transformer Block (STB) and Squeeze-Excite Spatial Pyramid Pooling (SE-SPP)-based Tiny YOLOv7 model and proposed as FIR-Tiny YOLOv7 (Facial Identity Recognition-Tiny YOLOv7) model. The proposed model is an improvised variant of the Tiny YOLOv7 model. For facial identity recognition, the proposed model achieved 10.0% higher mAP in the one-shot scenario, 30.4% higher mAP in the three-shot scenario, 15.3% higher mAP in the five-shot scenario, and 0.1% higher mAP in the traditional 70% − 30% split scenario as compared to the Tiny YOLOv7 model. The results obtained with the proposed model are promising for general facial identity recognition under varying facial attribute manipulation.