Faculty of Engineering SCOPUS2
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Publication Embargo Non-destructive morphological screening for the assessment of postharvest storage effect on pears stored with apples using optical coherence tomography(Elsevier GmbH, 2024-04) Luna, J.A; Wijesinghe, R.E; Lee, S.Y; Ravichandran, N.K; Saleah, S.A; Seong, D; Jung, H.Y; Jeon, M; Kim, JThe use of a limited and inadequate storage facility for the storage of multiple food items for an extended period of time results in the loss of structural integrity and freshness while storing fruit in confined single storage without adequate individual packaging methods can result in morphological changes and the degradation of the quality of the fruit. In this study, the effects of postharvest storage on pears co-stored with apples were investigated via non-invasive screening of the structural deformation of pears and the respective anatomical changes of the sub-surface. The anatomical changes were monitored for a prolonged time (12 d) under inadequate and confined storage conditions using swept-source optical coherence tomography (SS-OCT) and the results were comparatively analyzed using appropriately stored specimens. In addition, the OCT cross-sectional images were analyzed for the assessment of the dispersed intensity profile using a customized intensity-based image-processing algorithm. The results revealed the internal morphological variations and corresponding intensity fluctuations, thickness variations, and internal gap formations. This confirmed the potential applicability of OCT as a real-time, non-invasive high-resolution assessment technique for determining fruit quality in diverse environments, such as post-harvest storage and transportation systems.Publication Embargo Selecting suitable passive design strategies for residential high-rise buildings in tropical climates to minimize building energy demand(Elsevier Ltd, 2025) Perera, U.S; Weerasuriya A.U; Zhang, X; Ruparathna R; Tharaka M.G.I; Lewangamage C.SPassive design strategies (PDS) are a fitting solution to reduce the ever-growing energy cost of residential high-rise buildings in tropical regions. However, PDSs’ building energy saving potential significantly varies with local climate conditions, but it has been sparsely investigated. Hence, this study investigated the energy-saving efficiency of eight common PDSs integrated into a typical residential high-rise building in three sub-climates: extremely hot humid (0 A), very hot humid (1 A), and warm humid (3 A) defined by ASHRAE for the tropical climate. This study developed a Building Performance Analysis (BPA) workflow with a BIM-based simulation framework and local and global sensitivity analyses for the building energy analysis. The global sensitivity analysis revealed that low e-coating on glasses is the most influential PDS for 0 A and 1 A climates, but it has a negative effect in the sub-climate zone 3 A. The low-conducting exterior walls are the most effective PDS in the sub-climate zone 3 A, but they are poorly performed in the other two sub-climate zones. Based on the energy calculation and sensitivity analysis, this study proposes the best PDS groups, saving up to 40.1 %, 63.5 %, and 31.7 % of average annual building consumption in the sub-climate zones, 0 A, 1 A, and 3 A climates.Publication Open Access Evaluation of mesoscale physical habitats in sediment and water quality improvement – a mesocosm study for urban canals(Inderscience Publishers, 2025) De Zoysa, S; Chandeep, K. A.T; Pathirathne, P.H.D.R; Gomes, P.I.AThis study investigated the applicability of different types of attenuation processes (i.e., aeration and stirring) with and without dilution in nutrients (nitrogen and phosphorous) and sulphide-polluted sediment cleanup via laboratory mesocosms. Attenuation refers to the decline in contaminant concentration, a phenomenon driven by processes like dilution, mixing, and dispersion. Dilution, a remedial method involving the blending of contaminated water with uncontaminated often happens with uncontaminated runoff or a tributary. Regardless of the seasons, aeration, stirring, combined aeration and stirring, and dilution generally resulted in better removal efficiency of pollutants. Aeration combined with stirring showed notable improvements across multiple water quality parameters, and parameters seemed to be treatment type dependent, but without any significant differences. Dilution reduced electrical conductivity and increased dissolved oxygen but did not influence ammoniacal nitrogen and phosphate. The energy consumption for a unit percentage improvement via aeration and stirring was 0.04–0.25 USD and 0.03–0.15 USD, respectively. Therefore, relying solely on attenuation processes without dilution is deemed economically infeasible in real or prototype applications. This research sheds light on potential applications including pros and cons, emphasising the need for a balanced approach, and setting the stage for future studies.Publication 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, KThe 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)Publication Open Access Three-Dimensional Assessment of Dental Enamel Microcrack Progression After Orthodontic Bracket Debonding Using Optical Coherence Tomography(Multidisciplinary Digital Publishing Institute (MDPI), 2025-01) Saleah, S.A; Hamdan, A.H; Seong, D; Ravichandran, N. K; Wijesinghe, R.E; Han, S; Kim, J; Jeon, M; Park, H. SThe current study aimed to quantify the length progression of enamel microcracks (EMCs) after debonding metal and ceramic brackets, implementing OCT as a diagnostic tool. The secondary objectives included a three-dimensional assessment of EMC width and depth and the formation of new EMCs. OCT imaging was performed on 16 extracted human premolars before bonding and after debonding. Debonding was conducted with a universal Instron machine, with ARI values recorded. Additionally, 2D and 3D OCT images were employed to detect EMC formation and progression. Enface images quantified the length, width, and number of EMCs, and the length and width were analyzed using Image J (1.54f) and MATLAB (R2014b), respectively. Sagittal cross-sectional images were used for EMC depth analysis. A paired t-test showed significant differences in the length, width, and number of EMCs after debonding (p-value < 0.05), while the Wilcoxon non-parametric test indicated significant EMC depth changes (p-value < 0.05). No significant results were identified for the EMC number in ceramic brackets and EMC depth in metal brackets. Three-dimensional OCT imaging monitored existing EMCs at higher risk of progression and detected new EMCs following orthodontic bracket debonding. This study provides novel insights into EMC progression regarding the length, width, depth, and number after debonding.Publication Embargo Uniaxial compressive response of cement mortar with waste aluminium fibre sourced from electrical distribution cables(Springer Science and Business Media, 2025-01) Perera K.D.Y.G.; Ahamed Y.L.F; Somarathna H.M.C.C; Jayasekara D.A.B.P.M; Mohotti D; Raman S.NElectrical distribution and communication cables cease to function for transmission when their length is insufficient, and it is considered as it approaches the end of their useful lives. Further, the disposal techniques are not eco-friendly. This study aimed to evaluate the feasibility of cement mortar systems with the inclusion of aluminium fibre extracted from electrical distribution cables. Two diameters of 1.35 mm and 1.70 mm and two lengths of 10 mm and 15 mm fibres were used while incorporating four volume ratios, particularly 0.5%, 1.0%, 1.5%, and 2.0% to evaluate the effect of the length, diameter and volume ratios. The compression test and density test were performed to study the behaviour of Metal Fibre Reinforced Mortar (MFRM) systems under both dry and wet states. Compared to conventional mortar, the ultimate compressive strength of MFRM systems was increased up to 39.4% in 1.5% of fibre addition under the 28-day dry state, where the 1.5% volume ratio showed the best performance under compressive loads. Strain at ultimate strength, modulus of elasticity and strain energy also showed improvements with the fibre inclusion up to 74.4%, 87.3%, and 106.6% respectively. Fibres with higher aspect ratios showed significant effectiveness among the aforementioned fibre variations. The overall results highlighted that the MFRM with 1.5% of fibres performed expertly with 15 mm length and 1.35 mm diameter under compression loadsPublication Embargo Machine learning prediction of web-crippling strength in cold-formed steel beams with staggered slotted perforations(Elsevier Ltd, 2025-01) Gatheeshgar, P; Ranasinghe R.S.S; Simwanda, L; Meddage D.P.P.; Mohotti, DThe application of staggered slotted perforations in cold-formed steel (CFS) members is increasingly prominent in modern construction. Understanding the web-crippling strength of CFS beams, especially those with staggered slotted perforations, is crucial in structural engineering. This study employs machine learning (ML) models to predict the web-crippling strength of these beams under one-flange loading conditions, specifically interior-one-flange and end-one-flange loading. The research utilises a comprehensive dataset comprising 576 web-crippling strength results obtained through numerical modelling. The dataset includes parameters such as yield strength, thickness, corner radius, slot length, slot width, and bearing plate length. Four different ML algorithms—k-nearest neighbour (KNN), random forest (RF), support vector regression (SVR), and artificial neural network (ANN)—are developed and evaluated. Performance metrics, including coefficient of determination (R²), mean squared error (MSE), root mean square error (RMSE), mean absolute error (MAE) and mean normalised bias (MNB) are used to assess model accuracy. The random forest model outperforms others in both the training and testing phases. Shapley additive explanation (SHAP) and partial dependence plots further analyse the influence of input features on web crippling strength. This study presents a robust ML-based approach for predicting web crippling strength, providing engineers with a time-efficient alternate method.Publication 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.KIndoor 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.Publication Embargo Feasibility of hydrological modelling for intermittent streams using HEC-HMS: a process evaluation(Taylor and Francis, 2025-01-08) Perera, M. D.D; Gomes, P.I.AThe use of hydrological software in simulating the rainfall–runoff relationships of intermittent streams is rather unfound due to their dynamic flow regimes. This study assessed the feasibility of using Hydrologic Engineering Center–Hydrologic Modelling System (HEC-HMS), a widely used open-source hydrological modelling software, in discharge simulation of intermittent streams in a dry tropical zone. Individual calibration was required for each stream, even in adjoining sub-catchments with the same geology and climate. Event-based models with transitional periods captured seasonal variations in catchment characteristics. Strong correlations (Pearson’s r > 0.7, P < 0.05) between observed and simulated discharges indicated model success and, after calibration for one season, flows of the next (similar) season could be predicted without further adjustments. Baseflow and channel infiltration were the most sensitive parameters in the wet and dry seasons, respectively. This study demonstrated the possibility of building accurate hydrological models for intermittent streams by incorporating seasonal variations and extensive calibration.Publication Embargo Performance improvement of a cross-flow air turbine for oscillating water column wave energy converter by nozzle and blade optimization(Elsevier Ltd, 2025-01-15) Baddegamage B.H.B.P.D; Bae, S.J; Jang, S.H; Gunawardane S.D.G.S.P; Lee, Y.O; Kim, K; Yoon, MThe global pursuit of renewable energy solutions has highlighted the potential of wave energy converters (WECs), particularly oscillating water column (OWC) systems, as viable clean energy sources. This study focuses on optimizing a cross-flow air turbine (CFAT) through comprehensive numerical simulations, aimed at enhancing its performance as the power take-off system in OWC applications. The influences of various geometrical parameters, such as including nozzle entry arc angle, nozzle starting angle, and angle of attack, on the performance of the CFAT are investigated. The optimized turbine model achieves a peak efficiency (η) of 0.71 in unidirectional flow at a flow coefficient (Φ) of 0.41, representing a significant improvement over the reference model (η = 0.61 at Φ = 0.29). In addition, the performance of the optimized CFAT is evaluated under regular wave conditions, simulating the bidirectional flow typical of real-world OWC applications. Although the peak efficiency in reciprocating flow slightly decreases, shifting to 0.68 at Φ = 0.53, the turbine maintains a high mean efficiency throughout the operating cycle. The results demonstrate that the optimized CFAT model provides robust performance in both unidirectional and bidirectional flows, making it a promising candidate for enhancing the efficiency of OWC-based WECs.Publication Embargo Nonlocal strain gradient modeling of vibration energy harvesting in fluid-immersed bimorph sandwich nanoplates under thermal environment(American Institute of Physics, 2025-02-07) Roodgar Saffari, P; Senjuntichai, T; Rajapakse, NThis research details a method for mathematically simulating and assessing thermal vibration energy harvesting in laminated bimorph nanoplates in fluid contact. The model uses the piezoelectric characteristics of the outer layers and the functionally graded (FG) core material to transform thermal stresses into electrical energy efficiently. Nanostructures' size effects and nonclassical behavior are captured by the nonlocal strain gradient theory (NSGT). Combining the Navier-Stokes equations with the electromechanical equations obtained from Hamilton's principle, first-order shear deformation theory (FSDT), and Gauss's law yields an advanced multi-physics model. The FG core exhibits variations by the power law principle and is composed of both ceramic and metal components. Analytical solutions are obtained for the frequency response functions that relate the electrical power output to the external circuit load resistance by solving the coupled electromechanical-fluid equations. A thorough investigation is conducted to analyze how different elements impact energy harvesting performance using parametric studies. These factors include the configuration of the harvester (either parallel or series piezoelectric connections), nonlocal and strain gradient effects, temperature gradients, fluid depth, electrical load, geometric dimensions, and the material properties of the piezoelectric layers, and functionally graded core.Publication Embargo Durability and mechanical performance of glass and natural fiber-reinforced concrete in acidic environments(Elsevier, 2025-02-28) Justin, S.; Thushanthan, K; Tharmarajah, GThis 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.Publication Embargo Framework for generating high-resolution Hong Kong local climate projections to support building energy simulations(American Institute of Physics, 2025-03-07) Wang, J; Kudagama, B.J; Perera, U.S; Li, S; Zhang, XFiner resolution climate model projections are essential for designing regional building energy consumption and adaptation strategies under changing climate conditions. However, projections from Global Climate Models (GCMs) are typically coarse in resolution and subject to biases and uncertainty. To address this, the present study uses bilinear interpolation and morphing statistical downscaling to obtain high spatial (around 10 km) and temporal (hourly) resolution weather data, for more accurate estimations of future residential building energy consumption under climate change. An empirical quantile mapping bias-correction technique is applied to adjust the projection data from 44 GCMs under four representative Shared Socioeconomic Pathways (SSPs): SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The bias-corrected data are validated against meteorological observations from the Hong Kong Observatory's King's Park station. The hourly data are then converted to typical meteorological year data and used as input for EnergyPlus to predict future energy consumption patterns in public rental housing in Hong Kong. Case studies under the four SSPs show that climate change will significantly impact residential building energy use. Energy consumption is projected to increase by up to 14.0% for harmony-type buildings, 12.8% for trident-type buildings, and 12.4% for slab-type buildings by the end of the century under the SSP5-8.5 scenario, highlighting the urgent need for adaptive building design and energy policy measures.Publication Embargo Mechanical and durability analysis of eco-friendly light weight cement blocks using raw rice husk as a partial replacement for manufactured sand(Elsevier Ltd, 2025-03-08) Thavarajah, L; Sundaralingam, K; Tharmarajah, GThe study investigates the mechanical and durability properties of cement blocks made by partially replacing manufactured sand (M-sand) with raw rice husk (RRH). The rising demand for sand in construction, coupled with the environmental impact of its extraction, has prompted the exploration of alternative materials. RRH, a byproduct of rice milling, offers an eco-friendly substitute for sand. The research examines blocks containing varying proportions of RRH (20 %, 40 %, and 60 %) and compares treated and untreated husks. Key parameters, including compressive strength, tensile strength, density, performance when exposed to heat, and water absorption, were analyzed. The results show that up to 40 % of RRH can be used to replace sand without compromising the blocks' structural integrity. Treated RRH blocks demonstrated better bonding with cement, leading to higher compressive and tensile strengths compared to untreated ones. A 40 % RRH replacement achieved an average compressive strength of 3.57 MPa, surpassing the minimum requirements for non-load-bearing masonry units as per Sri Lankan and Australian standards. However, increasing RRH content to 60 % significantly reduced strength and durability. Additionally, RRH blocks exhibited a decrease in density, offering advantages in terms of transportation and handling. Water absorption increased with higher RRH content due to its porous nature yet remained within acceptable limits for treated blocks. These findings suggest that RRH can be a sustainable alternative to sand in masonry applications, especially in rural and eco-conscious construction.Publication Open Access Multi-User Sparse Vector Coding for eXtreme Ultra-Reliable Low-Latency Communication in Beyond 5G(Institute of Electrical and Electronics Engineers Inc., 2025-03-14) Sabapathy, S; Maruthu, S; Jayakody, D. N.KShort A short packet transmission scheme, such as Sparse Vector Coding (SVC), is a primary candidate for achieving ultra-low latency and high-reliability communication (URLLC). This paper proposes a spectral-efficient multi-user SVC (MU-SVC) scheme for achieving next-generation URLLC or eXtreme URLLC (xURLLC) in beyond 5G (B5G) communications. The key idea is to transmit multiple user information within a single sparse vector where the users are segregated into far users (FU) and near users (NU) depending on the distance from the base station. The classification into FU and NU paves way to optimize resource allocation, user fairness, manage interference, ensure reliable communication and quality of service requirements. Firstly, the FU binary data is converted into a sparse vector and secondly, the NU data is modulated and embedded into the non-zero positions of the sparse vector to form an MU-SVC. On transmission, the FU data is obtained through sparse demapping, while the NU adopts symbol detection techniques like the maximum likelihood detector. A new performance metric, called position error rate (PoER), is introduced to study the performance of the FU since it is based on the correct identification of the non-zero positions. Theoretical analyses of PoER and symbol error rate (SER) were carried out for FU and NU, respectively and the results are also validated through Monte-Carlo simulations. Further, the bit error rate, complexity, spectral and latency analyses are performed for MU-SVC and compared with the SVC and enhanced SVC schemes. The simulation results demonstrate an improved spectral efficiency and low latency with high reliability for the proposed MU-SVC scheme, thus, achieving xURLLC with reduced complexity in the multi-user scenario for B5G.Publication Open Access Facial identity recognition using StyleGAN3 inversion and improved tiny YOLOv7 model(www.nature.com, 2025-03-17) Kumar, A; Bhattacharjee, S; Kumar, A; Jayakody, D. N. KFacial 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.Publication 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, IObject 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.Publication Open Access Robust Distributed Collaborative Beamforming for WSANs in Dual-Hop Scattered Environments with Nominally Rectangular Layouts(Multidisciplinary Digital Publishing Institute (MDPI), 2025-03-19) Ben Smida, O; Affes, S; Jayakody, D; Nizam, YWe introduce a robust distributed collaborative beamforming (RDCB) approach for addressing channel estimation challenges in dual-hop transmissions within wireless sensor and actuator networks (WSANs) of K nodes. WSANs enhance wireless communication by reducing data transmission, latency, and energy consumption while optimizing network load through integrated sensing and actuation. The source S transmits signals to the WSAN, where nodes relay them to the destination D using beamforming weights to minimize noise and preserve signal integrity. These weights depend on channel state information (CSI), where estimation errors degrade performance. We develop RDCB solutions for three first-hop propagation scenarios—monochromatic [line-of-sight (LoS)] or “M”, bichromatic (moderately scattered) or “B”, and polychromatic (highly scattered) or “P”—while assuming a monochromatic LoS or “M” link for the second hop between the nodes and the far-field destination. Termed MM-RDCB, BM-RDCB, and PM-RDCB, respectively (“X” and “Y” in XY-RDCB—for X (Formula presented.) and Y (Formula presented.) —refer to the chromatic natures of the first- and second-hop channels, respectively, to which a specific RDCB solution is tailored), these solutions leverage asymptotic approximations for large K values and the nodes’ geometric symmetries. Our distributed solutions allow local weight computation, enhancing spectral and power efficiency. Simulation results show significant improvements in the signal-to-noise ratio (SNR) and robustness versus WSAN node placement errors, making the solutions well suited for emerging 5G and future 5G+/6G and Internet of Things (IoT) applications for different challenging environments.Publication Embargo Hydrodynamic adjustment of mean flow and turbulence around a sinking boulder during local scouring(Springer Science and Business Media Deutschland GmbH, 2025-04) Ye, C; Zhang, Q. Y; Wang, X. K; Lei, M; Gomes, P.I.A; Yan, X, FThe fact that on a live bed, boulders tend to sink during scouring is usually ignored, weakening the true understanding of hydrodynamics in boulder beds. In this paper, flume experiments were conducted to investigate the hydrodynamics around a boulder over a movable bed with a particle tracking velocimetry (PTV) system. By measuring the velocity field, the major flow characteristics, such as velocity distribution, turbulent kinetic energy (TKE) and bed shear stress, were analyzed. The results show that the sinking boulder apparently mediates the local flow structure and turbulence pattern. The near wake region is located in the range of 2D (D is the particle size of the boulder) downstream of the boulder. There is a near-bed countercurrent in the near wake region, the TKE increase sharply, and the velocity distribution deviates from the logarithmic distribution. Compared with the flat bed, the turbulent kinetic energy extreme point of the boulder riverbed in the near wake area deviate from the bed surface to the water depth at the top of the boulder, and the direction reversal and extreme point appear at the top of the boulder. The bed shear stress increases sharply in the near wake region of 1.5 ~ 2D.Publication Open Access Forecasting renewable energy for microgrids using machine learning(Springer Nature, 2025-05-03) Sudasinghe, P; Herath, D; Karunarathne, I; Weeratunge, H; Jayasuriya, LMicrogrids, comprised of interconnected loads and distributed energy resources, function as single controllable entities with respect to the main grid. However, the inherent variability of distributed wind and solar generation within microgrids presents operational stability challenges concerning voltage regulation and frequency stability. Accurate forecasting of renewable generation is crucial for mitigating these challenges. This work proposes a one-dimensional Convolutional Neural Network (1-D CNN) based approach to forecast photovoltaic (PV) generation and wind energy, using data from the University of California, San Diego microgrid and San Diego Airport weather records. The proposed method is evaluated against various forecasting methods using key metrics: Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared value. Results show that the 1-D CNN model achieves an improvement of up to 229.8 times in MSE and a 24.47 fold improvement in MAE compared to baseline models that use traditional statistical methods in forecasting. This demonstrates the potential of machine learning for enhancing microgrid management, particularly in short-term forecasting of renewable generation. © The Author(s) 2025. Evaluated ML-based renewable energy forecasting models by implementing 1-D CNN and LSTM models using real-world data. Proposed 1-D CNN performs better than LSTM and baseline models, achieving higher accuracy and computational efficiency. Accurate forecasting of PV and wind energy generation enhances grid stability, reduces backup power dependency, and supports sustainable energy integration.