Screened plant species with potential for green belt development can act as eco-sustainable tools for restoring the polluted ecosystem. Eight plant species from two study locations in Ado-Odo, Ota, Ogun State, Nigeria, were examined to identify their air pollution response and performance by deploying two air pollution indices, namely air pollution tolerance index (APTI) and anticipated performance index (API). APTI results identified all screened plants as sensitive species suitable as bio-indicators of air pollution, with Ficus auriculata (2.42) common to the non-industrial location being the most sensitive. API scores categorized Ficus auriculata (56.25%) as a moderate performer, while Syzygium malaccense (75%) and Mangifera indica (75%) were identified as very good performers, suitable for green belt development. The relationship between each biochemical parameter with APTI was investigated using regression analysis and two-way analysis of variance. The model result showed a significant relationship between each biochemical parameter with APTI, and relative water content had the highest influence on APTI (R² = 0.99436). Both indices (APTI and API) are suitable for screening and recommending native plant species for cultivation in the polluted environment, thus promoting ecological restoration. Hence, Syzygium malaccense, Mangifera indica and Ficus auriculata, respectively, were recommended for green belts design. Further intensive screening to identify tolerant species and best to excellent performer’s trees suitable for restoring the ecosystem is advised.

The numerical solutions of linear integrodifferential equations of Volterra type have been considered. Power series is used as the basis polynomial to approximate the solution of the problem. Furthermore, standard and Chebyshev-Gauss-Lobatto collocation points were, respectively, chosen to collocate the approximate solution. Numerical experiments are performed on some sample problems already solved by homotopy analysis method and finite difference methods. Comparison of the absolute error is obtained from the present method and those from aforementioned methods. It is also observed that the absolute errors obtained are very low establishing convergence and computational efficiency.

Differential Evolution (DE) is an evolutionary computational method inspired by the biological processes of evolution and mutation. DE has been applied in numerous scientific fields. The paper presents a literature review of DE and its application in wireless communication. The detailed history, characteristics, strengths, variants and weaknesses of DE were presented. Seven broad areas were identified as different domains of application of DE in wireless communications. It was observed that coverage area maximisation and energy consumption minimisation are the two major areas where DE is applied. Others areas are quality of service, updating mechanism where candidate positions learn from a large diversified search region, security and related field applications. Problems in wireless communications are often modelled as multiobjective optimisation which can easily be tackled by the use of DE or hybrid of DE with other algorithms. Different research areas can be explored and DE will continue to be utilized in this context.

The Schrödinger equation with the interaction of inversely quadratic effective and Mie-type potential has been solved for any angular momentum quantum number l using the Nikiforov-Uvarov method. The bound state energy eigenvalues and the corresponding un-normalized eigenfunctions are obtained in terms of the Laguerre polynomials. Several cases of the potential are also considered and their eigen values obtained.