Enhancing photovoltaic parameter estimation: integration of non-linear hunting and reinforcement learning strategies with golden jackal optimizer.

The advancement of Photovoltaic (PV) systems hinges on the precise optimization of their parameters. Among the numerous optimization techniques, the effectiveness of each often rests on their inherent parameters. This research introduces a new methodology, the Reinforcement Learning-based Golden Jackal Optimizer (RL-GJO). This approach uniquely combines reinforcement learning with the Golden Jackal Optimizer to enhance its efficiency and adaptability in handling various optimization problems. Furthermore, the research incorporates an advanced non-linear hunting strategy to optimize the algorithm's performance. The proposed algorithm is first validated using 29 CEC2017 benchmark test functions and five engineering-constrained design problems. Secondly, rigorous testing on PV parameter estimation benchmark datasets, including the single-diode model, double-diode model, three-diode model, and a representative PV module, was carried out to highlight the superiority of RL-GJO. The results were compelling: the root mean square error values achieved by RL-GJO were markedly lower than those of the original algorithm and other prevalent optimization methods. The synergy between reinforcement learning and GJO in this approach facilitates faster convergence and improved solution quality. This integration not only improves the performance metrics but also ensures a more efficient optimization process, especially in complex PV scenarios. With an average Freidman's rank test values of 1.564 for numerical and engineering design problems and 1.742 for parameter estimation problems, the proposed RL-GJO is performing better than the original GJO and other peers. The proposed RL-GJO stands out as a reliable tool for PV parameter estimation. By seamlessly combining reinforcement learning with the golden jackal optimizer, it sets a new benchmark in PV optimization, indicating a promising avenue for future research and applications.

Evaluation of Heavy Metal Removal of Nanoparticles Based Adsorbent Using Danio rerio as Model.

Nanoparticles are potential candidates for wastewater treatment especially for the removal of heavy metals due to their strong affinity. Many biopolymers are used as adsorbents and encapsulation of nanoparticle onto them can increase their efficiency. In this study, SPIONs, alginate, and SPIONs incorporated on alginate beads have been synthesized and characterized both microscopically and spectroscopically. These were then used for the removal of chromium metal and the percentage of removal was evaluated using a batch adsorption study. The percent removal of chromium using SPIONs, alginate and alginate-SPIONs beads were recorded to be 93%, 91% and 94%, respectively. The adsorption of chromium using SPIONs and alginate-SPIONs beads followed the Tempkin isotherm, whereas adsorption of chromium metal by alginate beads was found to be homogeneous in nature and followed the Langmuir isotherm with an R2 value of 0.9784. An in-vivo study using Danio rerio as a model organism was done to examine the toxicity and the removal efficiency of the samples. It was observed that chromium water treated with alginate-SPIONs beads, which were removed after water treatment showed less damage to the fishes when compared to SPIONs and alginate beads treated with chromium water where the SPIONs and alginate beads were not removed after the treatment period.

Facile synthesis of sericin modified graphene oxide nanocomposites for treating ischemic diseases.

Ischemic heart disease (IHD) is the major reason for death worldwide. Therapeutic angiogenesis serves as an effective approach to treat IHD. Sericin (S), a natural silk protein is widely used in regenerative medicine due to its excellent bioactive properties. Graphene oxide (GO) is extensively used in the field of biomedicine due to its amazing capacity to interact with biomolecules. The main objectives of the present study are to synthesize sericin functionalized graphene oxide (SGO) nanocomposites to treat diseases associated with deficient angiogenesis. Carbodiimide induced cross-linking strategy was employed to functionalize graphene oxide using sericin. The SGO nanocomposites had wrinkled flake like structure with good blood biocompatibility. In vivo chick embryo angiogenesis (CEA) assay was performed to prove the angiogenic potency of SGO nanocomposites. CEA assay results clearly indicated the development of new blood vessels in SGO treated chick embryos when compared with the control.

A comparative study on chitosan nanoparticle synthesis methodologies for application in aquaculture through toxicity studies.

Chitosan nanoparticles (CSNPs) have been recently used for various applications in aquaculture, especially as drug carriers. The aim of this study was to synthesise and investigate a superlative method of CSNP synthesis for application in aquaculture through aquaculture-based toxicology screening methods. Two different methods were analysed: the first a direct ionic gelation method (A) and the other involving a low-molecular-weight chitosan microparticle intermediate method (B). Dynamic light scattering characterisation revealed that the CSNP particle sizes were 192.7 ± 11.8 and 22.9 nm from methods A and B, respectively. The LC50 values for brine shrimp toxicity were found to be 1.51 and 0.02 ppt in 24 h for methods A and B, respectively. Acute toxicity studies in Litopenaeus vannamei rendered LC50 values of 3235.94 and 2884.03 ppt in 24 h for methods A and B, respectively. Zebrafish toxicity studies revealed mortality rates of 21.67% and 55% at 20 mg/L concentration for methods A and B, respectively, with an increased expression of intracellular reactive oxygen species in method B. From these findings, it can be concluded that a comparatively reduced toxicity of CSNPs derived from ionic gelation method makes it more appropriate for application in aquaculture.

Gelatin grafted poly(D,L-lactide) as an inhibitor of protein aggregation: An in vitro case study.

Amyloids are a group of proteins that are capable of forming aggregated amyloid fibrils, which is responsible for many neurodegenerative diseases including Alzheimer's disease (AD). In our previous study, synthesis and characterization of star-shaped poly(D,L-lactide)-b-gelatin (ss-pLG) have been reported. In the present work, we have extended our work to study ss-pLG against protein aggregation. To the best of our knowledge, this is the first report on the inhibition of amyloid fibrillation by protein grafted poly(D,L-lactide). Bovine serum albumin (BSA) was chosen as the model protein, which readily forms fibril under high temperature. We found that ss-pLG efficiently suppressed the fibril formation of BSA compared with gelatin (Gel), which was supported by Thioflavin T assay, circular dichroism (CD) spectroscopy and atomic force microscopy (AFM). In addition, ss-pLG significantly curtailed amyloid-induced hemolysis. We also found that incubation of ss-pLG with neuroblastoma cells (MC65) protected the cells from fibril-induced toxicity. The rescuing efficiency of ss-pLG was better than Gel, which could be attributed to the reduced lamella thickness in branched ss-pLG. These results suggest the significance of gelatin grafting, which probably allows gelatin to interact with the key residues of the amyloidogenic core of BSA effectively.

Optimization of bacteriocin production by Lactobacillus sp. MSU3IR against shrimp bacterial pathogens.

BACKGROUND: Aquaculture is one amongst the growing and major food producing sectors. Shrimp culture is one of the subsectors of aquaculture that attracts more attention because of the economic interest. However, the shrimp culture systems have been facing severe consequences and economical losses due to disease outbreaks. Risk of disease outbreak can be combated with the application of probiotics. For economically viable production of such probiotic products, the present study provides information on the optimization and partial purification of bacteriocin produced by a goat milk isolate Lactobacillus sp. MSU3IR against the shrimp bacterial pathogens. RESULTS: Bacteriocin production was estimated as a measure of bactericidal activity (arbitrary Unit/ml) over the test strains. The optimum culture conditions and media components for maximum bacteriocin production by Lactobacillus sp. MSU3IR were: pH: 5.0, temperature: 30°C, carbon source: lactose; nitrogen source: ammonium acetate; NaCl: 3.0% and surfactant: Tween 80. MRS medium was found to extend better bacteriocin production than other tested media. Upon partial purification of bacteriocin, the SDS-PAGE analysis had manifested the presence of two peptide bands with the molecular weight of 39.26 and 6.38 kDa, respectively. CONCLUSION: The present results provide baseline trend for the statistical optimization, scale up process and efficient production of bacteriocin by the candidate bacterial strain Lactobacillus sp. MSU3IR which could be used to replace the usage of conventional chemotherapeutics in shrimp culture systems.