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In this study, we introduce the pied kingfisher optimizer (PKO), a novel swarm-based meta-heuristic algorithm that draws inspiration from the distinctive hunting behavior and symbiotic relationships observed in pied kingfishers in the natural world. The PKO algorithm is structured around three distinct phases: perching/hovering for prey (exploration/diversification), diving for prey (exploitation/intensification), and fostering symbiotic relations. These behavioral aspects are translated into mathematical models capable of effectively addressing a wide array of optimization challenges across diverse search spaces. The algorithm’s performance is rigorously evaluated across thirty-nine test functions, which encompass various unimodal, multimodal, composite, and hybrid ones. Additionally, eight real-world engineering optimization problems, including both constrained and unconstrained scenarios, are considered in the assessment. To gauge PKO’s efficacy, it is subjected to a comparative analysis against 3 categories of rival optimizers. The 1st category comprises well-established and widely-cited optimizers such as particle swarm optimization and genetic algorithm. The 2nd category encompasses recently published algorithms, including Harris Hawks optimization, Whale optimization algorithm, sine cosine algorithm, Grey Wolf optimizer, gravitational search algorithm, and moth-flame optimization. The 3rd category includes advanced algorithms, such as covariance matrix adaptation evolution strategy and Ensemble Sinusoidal Differential Covariance Matrix Adaptation with Euclidean Neighborhood (LSHADE-cnEpSin). The comparative analysis employs various performance metrics, including the Friedman mean rank and the Wilcoxon rank-sum test, to reveal PKO’s effectiveness and efficiency. The overall results highlight PKO’s exceptional ability to tackle intricate optimization problems characterized by challenging search spaces. PKO demonstrates superior exploration and exploitation tendencies while effectively avoiding local optima. The source code for the PKO algorithm is publicly accessible at  https://www.mathworks.com/matlabcentral/fileexchange/160043-pied-kingfisher-optimizer-pko .

Long-term use of agrochemicals in the oil palm plantation sector, particularly chemical pesticides for pest control, can have a negative impact on the environment. The use of biological control has long been advocated as an alternative to pesticides, but empirical evidence in the context of oil palm plantations is very limited. This study aims to assess whether the installation of artificial bird perches can attract predatory birds, thereby enhancing the potential for biological pest control, particularly against rodents in oil palm plantations. We installed six artificial bird perches in a mature oil palm plantation in Keratong, Pahang, Peninsular Malaysia. Our data showed the presence of eight farmland bird species that utilised artificial perches, specifically White-throated Kingfisher, Collared Kingfisher, Oriental Magpie-Robin, Javan Myna, Barn Owl, Spotted Wood-Owl, Crested Serpent-Eagle, and Spotted Dove, and one forest associated species, Crested Goshawk. Our study is the first to describe predation of pest rodents by many of these eight farmland birds in oil palm plantations during daytime. Of the 1013 images taken, 48 showed rodent predation involving White-throated Kingfisher, Collared Kingfisher, Crested Serpent-Eagle, Crested Goshawk, Barn Owl and Spotted Wood-Owl. In a 24 h period, bird visits to artificial perches peaked between 10:00 am and 5:30 pm. Our findings suggest that artificial bird perches can facilitate the occurrence of diurnal and nocturnal predatory birds in oil palm plantations to control rodent infestation. The installation of artificial bird perches within oil palm plantations has the potential to enhance the ecosystem service of rodent biological control and increase avian biodiversity in palm oil producing countries.

Maritime activities have become increasingly frequent with the deepening of economic globalization, highlighting the burgeoning significance of maritime rescue. However, in practical applications, UAVs for maritime rescue face numerous challenges, such as limited endurance and inadequate autonomous planning capabilities. To optimize flight routes and circumvent adverse sea conditions, an improved Pied Kingfisher Optimizer (IPKO) that incorporates refraction reverse learning, variable spiral search, and Cauchy mutation strategies was proposed. Comparative experiments conducted on CEC2005 and CEC2022 datasets with seven traditional algorithms demonstrate that the proposed algorithm exhibits superior precision and convergence speed. Subsequently, a path planning objective function was constructed based on trajectory cost and threat cost to simulate a 3D space for UAV maritime rescue missions, and the IPKO algorithm was applied to address the UAV path planning problem. The results showed that the total cost incurred by the IPKO algorithm decreased by 5.77% compared to the PKO algorithm and by 51.19% compared to the SCA algorithm. Finally, through UAV flight tests validating its practical applicability, it is ascertained that IPKO can enhance rescue efficiency in complex maritime rescue environments.

Previous research has reported avian plastic ingestion in marine bird species. Yet, while research attention on plastic pollution is shifting from marine to freshwater ecosystems, very few information on plastic ingestion is available for freshwater birds. Here, we examined the presence of microplastic in regurgitated pellets of the common kingfisher ( Alcedo atthis ) collected along the Ticino River (North Italy). In total, 133 kingfisher’s pellets were examined between March and October 2019 from 54 transects along the river. Plastic elements were detected and identified by visual inspection followed by μ-FTIR and SEM-EDS. Overall, we found 12 (micro)plastics from at least three different polymers in 7.5% of the pellets. This study provides the first report of plastic uptake of this bird species. It highlights the importance of spectroscopic techniques in plastic monitoring studies in order to avoid misidentification of items found. Documenting the presence of plastic ingestion by top carnivores such as fish-eating birds is necessary to understand the pervasiveness and impact of (micro)plastic pollution in food webs of freshwater ecosystems.

Color aberrations in birds corresponds with important ecological functions, including thermoregulation and physiological impacts, camouflage and increased predation, and social interactions with conspecifics. Color aberrations in birds have been reported frequently in the scientific literature, but aberrations in many species remain undocumented or understudied. We investigated records of leucism in malachite kingfishers (Corythornis cristatus) from observations of community scientists on iNaturalist and eBird in Uganda. Leucistic kingfishers were only observed within the Queen Elizabeth National Park (QENP), Uganda. When considering all observations of malachite kingfishers that included photographs within the QENP, leucistic individuals accounted for 13.0% and 10.4% of total malachite kingfisher observations within the study area from iNaturalist and eBird, respectively. Leucistic observations were recorded from September 2015 through February 2017, making up 60.0% and 68.2% of observations of malachite kingfishers within the study area from iNaturalist and eBird during that time, respectively. The localized and short documentation period suggests observations represent a single individual, while the high observation rate likely corresponds with collection bias due to the novelty of the individual. Our findings help to better understand the ecological importance and potential consequences for color‐aberrant individuals, although color aberration did not appear to inhibit our subject's ability to find a mate. Our work also highlights how participatory science can promote the documentation of color‐aberrant individuals in wild populations, although it poses challenges when trying to estimate abundance. We investigated records of hypopigmentation in malachite kingfishers (Corythornis cristatus) from observations of community scientists on iNaturalist and eBird in Uganda. Hypopigmented individuals accounted for 13.0% and 10.4% of total malachite kingfisher observations within the study area from iNaturalist and eBird, respectively; but from September 2015 through February 2017, hypopigmented individuals made up 60.0% and 68.2% of observations. The localized and short documentation period suggests observations represent a single individual, while the high observation rate likely corresponds with collection bias due to the novelty of the individual.

Belted Kingfishers (Megaceryle alcyon) are widely distributed across aquatic environments in North America, but their behavior in marine habitats remains understudied, especially during the post-fledging period. We recorded daily activity budgets of adult and fledgling Belted Kingfishers on San Juan Island, Washington, and investigated the effects of tides and age class on feeding behaviors. Feeding attempt rate was highest at high tide, but strike success was highest at low tide. Adults foraged more successfully than juveniles and caught fish almost twice as large, consistent with juvenile inexperience during the post-fledging period. Strike success rates also varied between study sites for both adults and juveniles, potentially reflecting habitat quality. These results improve our understanding of how Belted Kingfishers behave in marine environments, and to our knowledge provide the first study of marine Belted Kingfishers during the post-fledging period. Follow-up studies quantifying prey availability and comparing seasonal behaviors of kingfishers in freshwater and marine habitats may help clarify the mechanisms behind the patterns we observed. Received 19 February 2019. Accepted 22 July 2021.

In this study, the pied kingfisher optimizer (PKO) algorithm is adapted to the uncapacitated facility location problem (UFLP), and its performance is evaluated. The PKO algorithm is binarized with fourteen different transfer functions (TF), and each variant is tested on a total of fifteen different Cap problems. In addition, performance improvement was realized by adding the Levy flight strategy to BinPKO, and this improved method was named BinIPKO. The experimental results show that the TF1 transfer function for BinIPKO performs very well on all problems in terms of both best and mean solution values. The TF2 transfer function performed efficiently on most Cap problems, ranking second only to TF1. Although the other transfer functions provided competitive solutions in some Cap problems, they lagged behind TF1 and TF2 in terms of overall performance. In addition, the performance of BinIPKO was also compared with the well-known PSO and GWO algorithms in the literature, as well as the recently proposed APO and EEFO algorithms, and it was found that BinIPKO performs well overall. In line with this information, it is seen that the IPKO algorithm, especially when used with the TF1 transfer function, provides an effective alternative for UFLP.

Concrete-filled steel tubes (CFST) are widely utilized in civil engineering due to their superior load-bearing capacity, ductility, and seismic resistance. However, existing design codes, such as AISC and Eurocode 4, tend to be excessively conservative as they fail to account for the composite action between the steel tube and the concrete core. To address this limitation, this study proposes a hybrid model that integrates XGBoost with the Pied Kingfisher Optimizer (PKO), a nature-inspired algorithm, to enhance the accuracy of shear strength prediction for CFST columns. Additionally, quantile regression is employed to construct prediction intervals for the ultimate shear force, while the Asymmetric Squared Error Loss (ASEL) function is incorporated to mitigate overestimation errors. The computational results demonstrate that the PKO-XGBoost model delivers superior predictive accuracy, achieving a Mean Absolute Percentage Error (MAPE) of 4.431% and R2 of 0.9925 on the test set. Furthermore, the ASEL-PKO-XGBoost model substantially reduces overestimation errors to 28.26%, with negligible impact on predictive performance. Additionally, based on the Genetic Algorithm (GA) and existing equation models, a strength equation model is developed, achieving markedly higher accuracy than existing models (R2 = 0.934). Lastly, web-based Graphical User Interfaces (GUIs) were developed to enable real-time prediction.