The retrieval of important information from a dataset requires applying a special data mining technique known as data clustering (DC). DC classifies similar objects into a groups of similar characteristics. Clustering involves grouping the data around k-cluster centres that typically are selected randomly. Recently, the issues behind DC have called for a search for an alternative solution. Recently, a nature-based optimization algorithm named Black Hole Algorithm (BHA) was developed to address the several well-known optimization problems. The BHA is a metaheuristic (population-based) that mimics the event around the natural phenomena of black holes, whereby an individual star represents the potential solutions revolving around the solution space. The original BHA algorithm showed better performance compared to other algorithms when applied to a benchmark dataset, despite its poor exploration capability. Hence, this paper presents a multi-population version of BHA as a generalization of the BHA called MBHA wherein the performance of the algorithm is not dependent on the best-found solution but a set of generated best solutions. The method formulated was subjected to testing using a set of nine widespread and popular benchmark test functions. The ensuing experimental outcomes indicated the highly precise results generated by the method compared to BHA and comparable algorithms in the study, as well as excellent robustness. Furthermore, the proposed MBHA achieved a high rate of convergence on six real datasets (collected from the UCL machine learning lab), making it suitable for DC problems. Lastly, the evaluations conclusively indicated the appropriateness of the proposed algorithm to resolve DC issues.
According to recent reports, planar structure-based organometallic perovskite solar cells (OPSCs) have achieved remarkable power conversion efficiency (PCE), making them very competitive with the more traditional silicon photovoltaics. A complete understanding of OPSCs and their individual parts is still necessary for further enhancement in PCE. In this work, indium sulfide (In2S3)-based planar heterojunction OPSCs were proposed and simulated with the SCAPS (a Solar Cell Capacitance Simulator)-1D programme. Initially, OPSC performance was calibrated with the experimentally fabricated architecture (FTO/In2S3/MAPbI3/Spiro-OMeTAD/Au) to evaluate the optimum parameters of each layer. The numerical calculations showed a significant dependence of PCE on the thickness and defect density of the MAPbI3 absorber material. The results showed that as the perovskite layer thickness increased, the PCE improved gradually but subsequently reached a maximum at thicknesses greater than 500 nm. Moreover, parameters involving the series resistance as well as the shunt resistance were recognized to affect the performance of the OPSC. Most importantly, a champion PCE of over 20% was yielded under the optimistic simulation conditions. Overall, the OPSC performed better between 20 and 30 °C, and its efficiency rapidly decreases above that temperature.
A new series of ternary metal complexes, including Co(II), Ni(II), Cu(II), and Zn(II), were synthesized and characterized by elemental analysis and diverse spectroscopic methods. The complexes were synthesized from respective metal salts with Schiff's-base-containing amino acids, salicylaldehyde derivatives, and heterocyclic bases. The amino acids containing Schiff bases showed promising pharmacological properties upon complexation. Based on satisfactory elemental analyses and various spectroscopic techniques, these complexes revealed a distorted, square pyramidal geometry around metal ions. The molecular structures of the complexes were optimized by DFT calculations. Quantum calculations were performed with the density functional method for which the LACVP++ basis set was used to find the optimized molecular structure of the complexes. The metal complexes were subjected to an electrochemical investigation to determine the redox behavior and oxidation state of the metal ions. Furthermore, all complexes were utilized for catalytic assets of a multi-component Mannich reaction for the preparation of -amino carbonyl derivatives. The synthesized complexes were tested to determine their antibacterial activity against E. coli, K. pneumoniae, and S. aureus bacteria. To evaluate the cytotoxic effects of the Cu(II) complexes, lung cancer (A549), cervical cancer (HeLa), and breast cancer (MCF-7) cells compared to normal cells, cell lines such as human dermal fibroblasts (HDF) were used. Further, the docking study parameters were supported, for which it was observed that the metal complexes could be effective in anticancer applications.
Under conventional and silica-supported Muffle furnace methods, water-soluble substituted trimeric triaryl pyridinium cations with various inorganic counter anions are synthesized. The solvent-free synthesis method is superior to the conventional method in terms of non-toxicity, quicker reaction times, ease of workup, and higher yields. Trimeric substituted pyridinium salts acted as excellent catalytic responses for the preparation of Gem-bisamide derivatives compared with available literature. To evaluate the molecular docking, benzyl/4-nitrobenzyl substituted triaryl pyridinium salt compounds with VEGFR-2 kinase were used with H-bonds, π-π stacking, salt bridges, and hydrophobic contacts. The results showed that the VEGFR-2 kinase protein had the most potent inhibitory activity. Intriguingly, the compound [NBTAPy]PF6- had a strongly binds to VEGFR-2 kinase and controlled its activity in cancer treatment and prevention.
This research explores the feasibility of using a nanocomposite from multi-walled carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) for thermal engineering applications. The hybrid nanocomposites were suspended in water at various volumetric concentrations. Their heat transfer and pressure drop characteristics were analyzed using computational fluid dynamics and artificial neural network models. The study examined flow regimes with Reynolds numbers between 5000 and 17,000, inlet fluid temperatures ranging from 293.15 to 333.15 K, and concentrations from 0.01 to 0.2% by volume. The numerical results were validated against empirical correlations for heat transfer coefficient and pressure drop, showing an acceptable average error. The findings revealed that the heat transfer coefficient and pressure drop increased significantly with higher inlet temperatures and concentrations, achieving approximately 45.22% and 452.90%, respectively. These results suggested that MWCNTs-GNPs nanocomposites hold promise for enhancing the performance of thermal systems, offering a potential pathway for developing and optimizing advanced thermal engineering solutions.