Mathematical and Computational Applications

Yellow fever (YF) and malaria co-infections are real public health concerns in Africa, especially in countries such as Nigeria, where mosquitoes carrying both pathogens (Aedes for YF, Anopheles for malaria) coexist. A mathematical model that considers the critical factors influencing the transmission dynamics and control interventions of YF and malaria co-infections is formulated and used to analyse the problem. The essential dynamical features of the model, such as the basic reproduction number and disease-free equilibrium, are determined and analysed. The qualitative analysis of the model illustrates the conditions under which the disease can be eradicated or persists. Further analysis, supported by numerical simulations, reveals the intrinsic dynamics of the model and the impact of control interventions such as yellow fever vaccination, use of insecticide-treated mosquito nets, treatment of malaria-infected humans, and use of insecticides. The results of the analysis demonstrate the impact of interventions; specifically, effective implementations of interventions such as yellow fever vaccination, use of insecticide-treated mosquito nets, and use of insecticides appear to have a significant impact in eradicating YF and malaria co-infections in endemic areas. Effective treatment of malaria-infected humans may lead to a decrease in infections but might not necessarily lead to eradicating infections in endemic areas. These findings are expected to aid in improving the management of YF and malaria co-infections in endemic regions for expeditious disease eradication.

This article proposes a bat algorithm that incorporates novel techniques inspired by maritime radars, referred to as the Radar-Bat algorithm. This proposed method allows each virtual bat to identify the position of the best solution at a given distance within the search space. It incorporates an adaptive threshold to maintain a constant false alarm rate (CFAR), enabling the acceptance of solutions based on the best value found, thus improving the exploitation of the search space. Furthermore, a systematic directional sweep balances exploration and exploitation effectively. This algorithm is used to solve complex optimization problems, essentially those with multimodal functions, demonstrating that the proposed algorithm achieves better convergence and robustness compared to the basic bat algorithm, highlighting its potential as a novel contribution to the field of metaheuristics. To evaluate the performance of the proposed algorithm against the basic bat algorithm, the Wilcoxon and Friedman non-parametric tests are applied, with a significance level of 5%. Computational experiments show that the proposed algorithm outperforms the state-of-the-art algorithm. In terms of quality, the proposed algorithm shows clear superiority over the basic bat algorithm across most benchmark functions. Regarding efficiency, although Radar Bat incorporates additional mechanisms, the experimental results do not indicate a consistent disadvantage in execution time, with both algorithms exhibiting comparable performance depending on the problem and dimensionality.

Accurate mapping of land use and land cover (LULC) is crucial for various applications such as urban planning, environmental management, and sustainable development, particularly in rapidly growing urban areas. African cities such as Yaoundé, Cameroon, are particularly affected by this rapid and often uncontrolled urban growth with complex spatio-temporal dynamics. Effective modeling of LULC indicators in such areas requires robust algorithms for high-resolution images segmentation and classification, as well as reliable data with great spatio-temporal distributions. Among the most suitable data sources for these types of studies, Sentinel-2 image time series, thanks to their high spatial (10 m) and temporal (5 days) resolution, are a valuable source of data for this task. However, for an effective LULC modeling purpose in such dynamic areas, many challenges remain, including spectral confusion between certain classes, seasonal variability, and spatial heterogeneity. This study proposes a hybrid deep learning architecture combining U-Net and Convolutional Long Short-Term Memory (ConvLSTM) layers, allowing the spatial structures and temporal dynamics of the Sentinel-2 series to be exploited jointly. Applied to the Yaoundé region (Cameroon) over the period 2018–2025, the hybrid model significantly outperforms the U-Net and ConvLSTM models alone. It achieves a macro-average F1 score of 0.893, an accuracy of 0.912, and an average IoU of 0.811 on the test set. These segmentation performances reached up to 0.948, 0.953, and 0.910 for precision, F1-score, and IoU, respectively, on the built-up areas class. Moreover, despite its better performance, in terms of complexity, the figures confirm that the hybrid does not significantly penalize evaluation speed. These results demonstrate the relevance of jointly integrating space and time for robust LULC classification from multi-temporal satellite images.