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In this study, we report the biosynthesis of Nickel ferrite using Ocimum sanctum leaf extract (OSLE@NiFe₂O${}_4)$. The single-phase cubic spinel structure of the synthesized nickel ferrite was confirmed by powder X-ray diffraction (PXRD) analysis. Structural characteristics were further examined using FTIR analysis. Optical and morphological properties were investigated through UV–DRS and FESEM studies. The magnetic hysteresis loop (M-H loop) confirmed the superparamagnetic nature of OSLE@NiFe₂O₄ nanoparticles. The photocatalytic degradation of Congo Red (CR) dye by the synthesized OSLE@NiFe₂O₄ nanoparticles was explored, revealing a pseudo-first-order kinetic behavior with a degradation efficiency of 99.9% and a rate constant of 0.06min${}^{-1}$. These findings demonstrate the superior photocatalytic activity of the biosynthesized nickel ferrite nanoparticles, highlighting their significant potential for efficient dye removal.

Perovskite-based photovoltaic devices have recently achieved impressively high efficiencies beyond 15% and gained great interest. We show here the formation of perovskite cluster overlayer structures which consist of individual perovskite grains on top of mesoporous TiO₂ films, coexisting with the randomly distributed nanocrystals within the films. Perovskite solution concentration was found to play an important role in modulating the perovskite crystallization and cluster overlayer formation process. Absorbance increase in visible wavelength range and shift of photoluminescence (PL) responses of perovskite films due to the effect of precursor concentration change were observed and investigated in detail. The crystallographic analysis of the CH₃NH₃PbI₃ films shows a gradual decrease of the perovskite lattice parameters and shrinkage of unit volume as precursor solution concentration increases, which is correlated to the changes of optical properties. Finally, perovskite-based solar cell device performance was enhanced at higher precursor concentration.

In order to effectively replace the background of micro moving targets in the video sequences, it is important to solve the problem of micro moving targets extraction. We propose a new algorithm of micro moving target detection in video images. Firstly, Adaboost was used to train skin samples to obtain the skin color data and determine the threshold range of skin color. Secondly, in order to obtain the rough binary images, we fused the binary images of skin detection with the binary images of micro targets by the Otsu. Thirdly, morphological processing and the holes filling processing were used to overcome the rough binary images, in order to obtain the complete binary templates. Lastly, the algorithm successfully accomplished the background replacement of the video sequences. The experiments demonstrate that the proposed algorithm in this paper is effective, which can segment micro moving targets accurately and complete background replacement.

The maturity of a scientific field can be measured by its capacity to formulate models. On this basis coastal modelling is in a mature stage with a range of models that have been developed and tested, both analytically and more importantly, in the field.

This article gives details of numerical packages that can hindcast the seastate, solve the wave and current fields around structures in the nearshore zone, make predictions about medium to long-term morphological developments, and predict the effects of storm attack on sandy beaches and dune systems. The models have an accepted mathematical basis and have been tested over a widerange of analytical and field situations.