Recent Submissions
Dynamic linkages between chicken meat production, consumption, income and trade: Evidence from Wavelet coherence and Granger causality in Asia
(Elsevier Inc., 2026) Silva, Y; Susan, H; Perera, N; Mendis, K; Jayathilaka, R; Dabare, U
The poultry industry has become one of the fastest-growing agricultural sectors in Asia, driven by rising incomes, and shifting food preferences. Therefore, this study aims to examine the relationship between chicken meat production and key determinants, including chicken meat consumption, gross domestic product, and trade openness, over 30 years (1993-2022) across 28 Asian countries. This study's foundation was based on the theories of consumer demand and international trade. Wavelet coherence and Granger causality analysis were utilised to identify the direction of causality of the variables. The Wavelet results reveal that chicken consumption and GDP become most significant with the production in the Asian continent, while Granger results reveal that most Asian countries showed unidirectional causal flows from trade openness to chicken meat production and from chicken meat production to gross domestic product and consumption. Furthermore, this study provides novel insights that inform policy considerations for policymakers, international and domestic organisations, and governments, aligning with the Sustainable Development Goals established by the United Nations.
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.
Investigation of inelastic response ratios for buildings with damping subjected to near-fault ground motions using numerical simulations and transformer-based models
(Elsevier Ltd, 2026-03-09) Konara, L; Deshika, T; Gobirahavan, R; Alahakoon, Y; Ekanayake I.U; Meddage D.P.P
Inelastic responses are used in seismic design to estimate inelastic seismic demand from known elastic demand, yet current provisions remain limited, especially when damping and displacement ductility are considered. This study investigated the inelastic displacement ratio and inelastic velocity ratio for single degree of freedom (SDOF) systems subjected to near-fault ground motions, with particular focus on the effects of fling-step and forward-directivity motions. For numerical modeling and analysis, an extensive nonlinear response history analysis (NLRHA) was conducted on SDOF systems incorporating parametric variations in dynamic characteristics of structural systems such as elastic period, displacement ductility, and viscous damping under different ground motion conditions. From numerical modeling, empirical equations are proposed to express the inelastic displacement ratio ((Formula presented) ) and inelastic velocity ratio ((Formula presented) ) using elastic period, viscous damping ratio, displacement ductility, and the type of ground motion. In parallel, neural networks are trained on a dataset of 36,456 samples using additional variables, including the predominant period of the ground motion, moment magnitude, and closest rupture distance. Neural network models achieved (Formula presented) (for (Formula presented) ) and (Formula presented) (for (Formula presented) ) for unseen data, indicating the highest accuracy. Model explanations indicated that the predictions adhere to the domain knowledge. Comparative assessments reveal that while empirical equations capture general trends for design purposes, neural network models accurately predict even minor variations in inelastic responses. These data-driven methods provide a complementary approach in predicting the inelastic response compared to empirical equations.
Global economic uncertainty shocks and macroeconomic dynamics before and after COVID-19: Evidence from Africa and the Americas
(Elsevier Inc., 2026-04-03) Madurawala, R; Navamohan, P; Gamage, D; Hansika, S; Jayathilaka, R
Global economic uncertainty has become a central driver of macroeconomic instability, particularly during large-scale crises like the COVID-19 pandemic. This study examines how global uncertainty shocks affect key macroeconomic variables, particularly suicide rates, economic growth, unemployment, and trade openness across 62 countries in Africa, South America, and North America over the period of 2004–2023 as the countries in these regions exhibit the highest uncertainty post-pandemic. Utilising the COVID-19 pandemic as a natural experiment, the analysis distinguishes between pre- and post-pandemic uncertainty-socioeconomic dynamics to assess the bidirectional and cointegrating relationships across regions. The study employs Multiple Linear Regression to capture short-term macroeconomic responses and panel and country-level cointegration techniques to identify long-run relationships between economic uncertainty and macroeconomic variables. Global uncertainty is proxied using the World Uncertainty Index, which captures broad policy, geopolitical, and crisis-related uncertainty affecting expectations and real economic activity. Unlike, existing studies which reveal insights in a particular region or country, the current findings uncover bi-directional relationships in 21 countries post-pandemic, with notable relationships in Algeria, Botswana, Gabon, Guinea, Madagascar, Republic of Congo, Dominican Republic, Mexico, Bolivia, Paraguay. Moreover, long-run cointegration between uncertainty and macroeconomic indicators strengthens in the post-COVID-19 period, particularly in countries of Africa and North America. By analysing countries in the highest uncertainty regions the study contributes to the international macroeconomics literature by providing new evidence on how global uncertainty shocks reshape macroeconomic dynamics across regions with heterogeneous economic structures, offering important implications for macroeconomic stabilisation in an increasingly uncertain global landscape.
Hybrid ABC–HBA feature optimization with self-training using simulated unlabelled data for robust intrusion detection
(Elsevier Ltd, 2026) Harischandra, S; Rajapaksha, U.U. S; Silva, B.N; Jayawardena, C
The increasing scale and heterogeneity of network traffic pose significant challenges for intrusion detection systems (IDS), particularly in detecting extremely rare attack classes and generalising to previously unseen threats under severe class imbalance. This study proposes a hybrid intrusion detection framework that integrates swarm intelligence–based feature optimisation with self-training using unlabelled data simulation to address these limitations. A novel ABC–HBA feature selection strategy is introduced, combining the efficient exploration capability of the Artificial Bee Colony (ABC) algorithm with the strong global exploitation and fast convergence of the Honey Badger Algorithm (HBA), resulting in a highly discriminative and compact feature subset. A Random Forest(RF) classifier augmented with a pseudo-labelling mechanism is then employed to enhance learning from unlabelled and unseen attack samples, enabling effective detection of novel attack patterns absent from the training set. To further mitigate extreme class imbalance, a hybrid resampling strategy is applied. Experimental evaluation on the KDD Cup 1999 dataset demonstrates that the proposed framework achieves an overall accuracy of 99.95% and a detection rate of 98.16%, while significantly improving the recognition of extremely rare attack classes, including a 92.86% detection rate for U2R attacks, which constitute less than 0.01% of the dataset. The proposed method consistently outperforms baseline RF, ABC-based, and several other state-of-the-art meta-heuristic and deep learning approaches, confirming its effectiveness in enhancing rare attack detection and generalisation to unseen threats in realistic intrusion detection scenarios.
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