Recent Submissions
Bridging tradition and innovation: exploring vegetable harvest loss reduction strategies in Sri Lanka
(Emerald Publishing, 2026-01-15) Jayasuriya, N; Yapa, C.G; Rathnayake, T.A; Dilhara, A; Rathnayake, I.D; Mathangadeera, R
Purpose – This study aims to address a significant gap in the literature regarding vegetable harvest loss reduction methods, exploring both traditional and modern perspectives in Sri Lanka, which is largely driven by an agricultural economy. This study explores the diverse strategies employed and how they are going to be integrated by Sri Lankan vegetable farmers, highlighting both traditional and modern pre- and post-harvest practices aimed at improving productivity, sustainability and resilience in agricultural systems. Design/methodology/approach – The study was conducted across key agricultural districts in Sri Lanka, with data collected through semi-structured interviews with vegetable farmers using the snowball sampling method. Thematic analysis was employed to identify patterns and themes in the data. Findings – The findings emphasize the importance of traditional methods, including cultural practices such as cultivating at auspicious times, established pest control and irrigation techniques. These are complemented by advanced agricultural innovations, modern harvest protection methods and improved packing and transportation techniques. This integrated approach showcases farmers' adaptability in reducing vegetable losses despite the challenges they face. Originality/value – Post- and pre-harvest loss reduction in Asian countries can be considered an understudied area. Furthermore, the focus on traditional methods is rare in the field. Therefore, this study provides a clear understanding of traditional and modern methods that are suitable for farmers in developing countries.
Socio-economic and health drivers of suicide: A global analysis across income groups
(Routledge, 2026-03-27) Kankanam Pathiranage, H.S; Kothalawala, C; Jayathilaka, R
Suicide is a critical global public health challenge, with its socio-economic and health determinants varying significantly across contexts. This study provides the first comprehensive analysis of income-group-specific drivers of suicide, leveraging data from 129 countries over two decades. Using panel regression models to capture cross-country variations and temporal trends, the study identifies mental health disorders as the most significant global contributor, while unemployment universally elevates suicide risk. Alcohol consumption exhibits income-specific patterns, with wine increasing suicide rates in high-income nations and spirits in upper-middle-income settings. In low-income countries, HIV/AIDS prevalence significantly heightens vulnerability. These findings underscore the urgent need for targeted interventions, including expanding access to mental health care as part of universal health coverage, strengthening economic safety nets to mitigate the psychological impacts of unemployment, and implementing tailored alcohol regulation policies. By addressing these global and income-group-specific vulnerabilities, this study offers actionable insights to guide transformative policies and accelerate progress toward achieving the Sustainable Development Goals (SDGs).
Boosting CO2-to-C2H4 electrocatalysis on Cu2O with waste-derived porous carbon from coconut shells
(Royal Society of Chemistry, 2026) Qin, C; Li, T; Masakorala, G; Zhi, C; Huang, H; Zhou, C; Wang, X; Shen, B; Zhang, Jian-Rong; Zhou, Y
This study presents a sustainable strategy to boost CO2-to-C2H4 conversion by constructing Cu–C interfaces using Cu2O nanospheres supported on porous carbon derived from waste coconut shells. The Cu2O–10mgC catalyst achieves a 4-fold increase in FE(C2H4) compared with Cu2O and maintains >40% selectivity for 45 h. In situ spectra reveal enhanced *COLFB coverage, confirming that oxygen-rich functional groups at the Cu–C interface promote C–C coupling. This work demonstrates both the catalytic and economic feasibility of waste-derived carbon supports for efficient CO2-to-C2H4 conversion.
Evaluating and prioritizing delay factors in naval ship maintenance using the analytic hierarchy process: a Sri Lanka navy shipyard case study
(Taylor and Francis Ltd., 2026) Fernando, W. J; Silva, N; Perera, C
Timely maintenance of ships and craft is critical for ensuring operational readiness, safety, and economic sustainability in the maritime sector. However, scheduled docking delays remain a persistent challenge globally, incurring significant financial losses and reducing fleet availability. This study presents a systematic, quantitative approach to identify and prioritize 22 critical factors causing delays in scheduled docking. Using the Analytic Hierarchy Process (AHP), the study evaluates the relative importance of these factors to support informed decision-making. A case study of the Sri Lanka Navy (SLN) demonstrates the application of the proposed framework, revealing that 97% of docking delays occur before vessels enter the dock, with 31.8% of these delays attributable to deficiencies in the procurement of materials and spare parts. While the findings are based on a single case study of the SLN shipyard, they offer context-specific insights into the unique challenges faced by naval maintenance operations in developing regions.
EuqAud: Detecting Gender Bias in Audio Datasets Using Polynomial Regression-Based Metric
(Institute of Electrical and Electronics Engineers Inc., 2026) Jayawardena, S; Haddela, P.S; Shyamalee, T; Ekanayake, A; Mudalige, T; Dhanawardhana, I
With the growing adoption of audio based AI systems in high-stakes domains such as healthcare, law enforcement, and social media, ensuring fairness particularly regarding gender bias has become critically important. While prior work on fairness has predominantly focused on disparities in model performance, bias inherent in training datasets remains underexplored. To address this gap, we propose EuqAud, a novel, pre-trained and traceable fairness metric that quantifies gender bias in audio datasets using raw acoustic features such as pitch, energy, amplitude, and voice activity. Unlike methods dependent on demographic labels such as race, age or language, EuqAud is designed to be demographic and language agnostic, enhancing its applicability across diverse contexts. The score is computed using an equation derived from polynomial regression with L2 regularization (Ridge regression), yielding robust and generalizable outputs. It spans a range from −10 to 10, where 0 denotes neutral, positive scores indicate male dominant bias, and negative scores reflect female dominant bias. For clarity, bias severity is categorized into three tiers: Neutral (EuqAud < 2), Moderate Bias (2 ≤ EuqAud ≤ 6), and Strong Bias (EuqAud > 6). Evaluation across multiple datasets demonstrates high predictive performance, with R2 values between 0.95 and 0.99. By focusing on dataset level bias rather than model outcomes, EuqAud offers a scalable and rigorous solution for advancing fairness in audio-based AI systems.
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