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Multiple drug-resistant bacteria are a severe and growing public health concern. Because relatively few antibiotics have been approved over recent years and because of the inability of existing antibiotics to combat bacterial infections fully, demand for unconventional biocides is intense. Metallic nanoparticles (NPs) offer a novel potential means of fighting bacteria. Although metallic NPs exert their effects through membrane protein damage, superoxide radicals and the generation of ions that interfere with the cell granules leading to the formation of condensed particles, their antimicrobial potential, and mechanisms of action are still debated. This article discusses the action of metallic NPs as antibacterial agents, their mechanism of action, and their effect on bacterial drug resistance. Based on encouraging data about the antibacterial effects of NP/antibiotic combinations, we propose that this concept be thoroughly researched to identify means of combating drug-resistant bacteria.

Triangular orifices are widely used in industrial and engineering applications, including fluid metering, flow control, and measurement. Predicting discharge through triangle orifices is critical for correct operation and design optimization in various industrial and engineering applications. Traditional approaches like empirical equations have accuracy and application restrictions, whereas computational fluid dynamics (CFD) simulations can be computationally costly. Alternatively, artificial neural networks (ANNs) have emerged as a successful solution for predicting discharge through orifices. They offer a dependable and efficient alternative to conventional techniques for estimating discharge coefficients, especially in intricate relationships between input parameters and discharge. In this study, ANN models were created to predict discharge through the triangle orifice and velocity at the downstream of the main channel, and their effectiveness was assessed by comparing the performance with the earlier models proposed by researchers. This paper also proposes a novel hybrid multi-objective optimization model (NSGA-II) that uses genetic algorithms to discover the best values for design parameters that maximize discharge and downstream velocity simultaneously.

In this study, the effect of both cryogenic and dry machining of AZ31 magnesium alloy on temperature and surface roughness was examined. Cryogenic machining experiments were conducted by applying liquid nitrogen at the cutting zone. The cutting parameters (cutting speed, depth of cut, and feed rate) were varied, and their effect on the results was identified. It was found that the cryogenic machining was able to reduce the maximum temperature at the machined surface to about 60% as compared with dry machining. A finite element model was developed to predict the temperature distribution at the machined surface. The simulated results showed good agreement with the experimental data. After analyzing the temperature distribution, the model also suggested that the cryogenic-assisted machining removes heat at a faster rate as to that of the dry machining. An arithmetic model using the response surface method was also developed to predict the maximum temperature at the surface during cryogenic and dry machining. The analysis pointed out that the maximum temperature was greatly affected by the cutting speed followed by feed rate and depth of cut. Cryogenic machining leads to better surface finish with up to 56% reduction in surface roughness compared with dry machining.

Alzheimer’s disease (AD) is a widespread dynamic neurodegenerative malady. Its etiology is still not clear. One of the foremost pathological features is the extracellular deposits of Amyloid-beta (Aβ) peptides in senile plaques. The interaction of Aβ and the receptor for advanced glycation end products at the blood-brain barrier is also observed in AD, which not only causes the neurovascular anxiety and articulation of proinflammatory cytokines, but also directs reduction of cerebral bloodstream by upgrading the emission of endothelin-1 to induce vasoconstriction. In this process, RAGE is deemed responsible for the influx of Aβ into the brain through BBB. In the current study, we predicted the interaction potential of the natural compounds vincamine, ajmalicine and emetine with the Aβ peptide concerned in the treatment of AD against the standard control, curcumin, to validate the Aβ peptide–compounds results. Protein-protein interaction studies have also been carried out to see their potential to inhibit the binding process of Aβ and RAGE. Moreover, the current study verifies that ligands are more capable inhibitors of a selected target compared to positive control with reference to ΔG values. The inhibition of Aβ and its interaction with RAGE may be valuable in proposing the next round of lead compounds for effective Alzheimer’s disease treatment.

PurposeDespite an exponential rise in the frequency of online payments in India, the cause of consumer resistance towards the WhatsApp payment system (WPS) remains unexplored. This research is aimed at exploring the barriers to the adoption of WPS.Design/methodology/approachA research model was proposed using stimulus-organism-response framework and innovation resistance theory. Data were collected from 392 users of the WhatsApp application using the mall intercept technique, which also utilizes digital payment platforms. A co-variance based structural equation modelling was employed to test proposed relationships in this cross-sectional study.FindingsThe study findings indicate that personal innovativeness as a personal stimulus negatively influences the usage and value barrier, while negative word of mouth (NWOM) increases the intensity of tradition and the image barrier. Additionally, value barrier, usage barrier, risk barrier and tradition barrier were found to have a negative influence on the intention to adopt the WhatsApp payment system.Originality/valueThis research is an initial endeavour that sheds light on the consumer cognition resisting the adoption of the WPS.

Muscular dystrophies encompass a heterogeneous group of rare neuromuscular diseases characterized by progressive muscle degeneration and weakness. Among these, Duchenne muscular dystrophy (DMD) stands out as one of the most severe forms. The present study employs an integrative approach combining network pharmacology, quantitative structure-activity relationship (QSAR) modeling, molecular dynamics (MD) simulations, and free energy calculations to identify potential therapeutic targets and natural compounds for DMD. Upon analyzing the GSE38417 dataset, it was found that individuals with DMD exhibited 290 upregulated differentially expressed genes (DEGs) compared to healthy controls. By utilizing gene ontology (GO) and protein-protein interaction (PPI) network analysis, this study provides insights into the functional roles of the identified DEGs, identifying ten hub genes that play a critical role in the pathology of DMD. These key genes include DMD, TTN, PLEC, DTNA, PKP2, SLC24A, FBXO32, SNTA1, SMAD3, and NOS1. Furthermore, through the use of ligand-based pharmacophore modeling and virtual screening, three natural compounds were identified as potential inhibitors. Among these, compounds 3874518 and 12314417 have demonstrated significant promise as an inhibitor of the SMAD3 protein, a crucial factor in the fibrotic and inflammatory mechanisms associated with DMD. The therapeutic potential of the compounds was further supported by molecular dynamics simulation and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) analysis. These findings suggest that the compounds are viable candidates for experimental validation against DMD.

The goal of the present research paper is to study how a spacetime manifold evolves when thermal flux, thermal energy density and thermal stress are involved; such spacetime is called a thermodynamical fluid spacetime (TFS). We deal with some geometrical characteristics of TFS and obtain the value of cosmological constant Λ. The next step is to demonstrate that a relativistic TFS is a generalized Ricci recurrent TFS. Moreover, we use TFS with thermodynamic matter tensors of Codazzi type and Ricci cyclic type. In addition, we discover the solitonic significance of TFS in terms of the Ricci metric (i.e., Ricci soliton RS).

In this research note, we investigated the characteristics of perfect fluid spacetime when coupled with the hyperbolic Ricci soliton. We additionally interacted with the perfect fluid spacetime, with a$ \\varphi(\\mathcal{Q}) $ -vector field and a bi-conformal vector field that admits the hyperbolic Ricci solitons. Furthermore, we analyze the gradient hyperbolic Ricci soliton in perfect fluid spacetime, employing a scalar concircular field, and discuss about the gradient hyperbolic Ricci soliton's rate of change. In the end, we determined the energy conditions for perfect fluid spacetime in terms of gradient hyperbolic Ricci soliton with a scalar concircular field.

We explore the Geometrization of hyperbolic conformal Ricci solitons and examine the properties of bulk viscous fluid string spacetime in conjunction with the hyperbolic conformal Ricci solitons in this research note. A ∅ ( Q ) -vector field and a Ricci bi-conformal vector field that admits the hyperbolic conformal Ricci solitons were also used to interact with the bulk viscous fluid string spacetime. It is proven that the bulk viscous fluid string spacetime admits the hyperbolic conformal Ricci solitons with a proper ∅ ( Q ) -vector field, and the bulk viscous fluid string spacetime holds Ricci collineation, then the spacetime is an Einstein manifold, and hyperbolic conformal Ricci soliton is identified as a conformal Ricci soliton. Additionally, using a scalar concircular field, we study the gradient hyperbolic conformal Ricci solitons in bulk viscous fluid string spacetime and talk about the gradient hyperbolic conformal Ricci solitons’ rate of change. Finally, using a scalar concircular field and a gradient hyperbolic conformal Ricci solitons, we calculated the energy conditions for a bulk viscous fluid string spacetime.