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A Casson nanoparticle is used in this study to investigate the effects of toxic reactions, temperatures, and concentrations on convective heat transfer flow across a stretching/shrinking vertical sheet. The goal is to convert the control linear partial differential equations (PDEs) into an ordinary differential equations (ODEs) set and analyses them statistically using the Bvp4c technique in MATLAB software. It is necessary to look how various variables, such as the Casson nanofluid measurement, the chemical change constants and Prandtl numbers, the concentration-thermal-buoyancy ratio, the microorganism-to-thermal-buoyancy rate of return, the Lewis number, and other bioconvection-related parameters (such as the variation in micro-organism concentration and the buoyancy parameter), affect each other. The numerical results of the velocity, temperature, concentration, and bioconvection flow profiles are displayed.

The task of predicting solar irradiance is critical in the development of renewable energy sources. This research is aimed at predicting the photovoltaic plant’s irradiance or power and serving as a standard for grid stability. In practical situations, missing data can drastically diminish prediction precision. Meanwhile, it is tough to pick an appropriate imputation approach before modeling because of not knowing the distribution of datasets. Furthermore, not all datasets benefit equally from using the same imputation technique. This research suggests utilizing a recurrent neural network (RNN) equipped with an adaptive neural imputation module (ANIM) to estimate direct solar irradiance when some data is missing. Without imputed information, the typical projects’ imminent 4-hour irradiance depends on gaps in antique climatic and irradiation records. The projected model is evaluated on the widely available information by simulating missing data in each input series. The performance model is assessed alternative imputation techniques under a range of missing rates and input parameters. The outcomes prove that the suggested methods perform better than competing strategies when measured by various criteria. Moreover, combine the methodology with the attentive mechanism and invent that it excels in low-light conditions.

In this paper, the optimal dynamic scheduling of electric vehicles (EVs) in a parking lot (PL) is proposed to minimize the charging cost. In static scheduling, the PL operator can make the optimal scheduling if the demand, arrival, and departure time of EVs are known well in advance. If not, a static charging scheme is not feasible. Therefore, dynamic charging is preferred. A dynamic scheduling scheme means the EVs may come and go at any time, i.e., EVs’ arrival is dynamic in nature. The EVs may come to the PL with prior appointments or not. Therefore, a PL operator requires a mechanism to charge the EVs that arrive with or without reservation, and the demand for EVs is unknown to the PL operator. In general, the PL uses the first-in-first serve (FIFS) method for charging the EVs. The well-known optimization techniques such as particle swarm optimization and shuffled frog leaping algorithms are used for the EVs’ dynamic scheduling scheme to minimize the grid’s charging cost. Moreover, a microgrid is also considered to reduce the charging cost further. The results obtained show the effectiveness of the proposed solution methods.