Tripura University is a central university, the main public government university of Tripura, India.
This study reports, for the first time, the use of Spermacoce alata (SA) leaf extract as an efficient bioresource for the green synthesis of multifunctional silver nanoparticles (SA-AgNPs). Comprehensive characterization by ultraviolet-visible (UV-Vis), Fourier transform infrared (FTIR), field-emission scanning electron microscopy (FE-SEM), high-resolution transmission electron microscopy (HR-TEM), EDAX, dynamic light scattering (DLS), X-ray diffraction (XRD), and zeta potential analyses confirmed the formation of predominantly spherical, crystalline, and well-dispersed nanoparticles with an average size of similar to 18 nm and good colloidal stability (-22.8 mV). The SA-AgNPs exhibited strong antibacterial activity against Staphylococcus aureus and Pseudomonas aeruginosa, with minimum inhibitory concentrations of 128 and 64 & micro;g mL(-1), respectively. The DCFH-DA assay revealed significant intracellular ROS generation (173% in S. aureus and 79% in P. aeruginosa), indicating an oxidative stress-mediated antibacterial mechanism. Additionally, SA-AgNPs showed concentration-dependent antibiofilm activity against both pathogens, which was further supported by FE-SEM analysis. The nanoparticles demonstrated excellent biocompatibility and enhanced antioxidant activity (70.88%), surpassing butylated hydroxytoluene (55.04%). Moreover, SA-AgNPs displayed efficient catalytic performance in NaBH4-mediated degradation of organic dyes and rapid conversion of toxic p-nitrophenol. Overall, this work establishes SA-AgNPs as promising biogenic nanomaterials for antimicrobial and environmental remediation applications.
Viral infections remain a significant threat to global health, causing widespread morbidity and mortality. The continuous emergence of new viral strains and the limitations of current antiviral therapies highlight the urgent need for effective and safe treatment options. Herbal remedies, long used in traditional medicine, offer promising avenues for antiviral drug discovery. In this study, we explore the antiviral potential of phytoconstituents from Glycyrrhiza glabra (Yasthimadhu), a well-known medicinal herb with established therapeutic properties. Using in silico molecular docking techniques, we investigated the interaction of key bioactive compounds Glycyrrhizin, Shinflavanone, Hispaglabridin A, Glycyrrhetic acid, Glabiridin, and Shinpterocarpin with the SARS-CoV-2 main protease (3CLpro; PDB ID: 6LU7), a critical viral enzyme responsible for as predicted binding of SARS-CoV-2. Our findings reveal that these phytochemicals predicted binding and complex stability consistent with potential SARS-CoV-2 3CLpro inhibition, which warrants experimental validation. This study underscores the promise of Glycyrrhiza glabra phytoconstituents as potential SARS-CoV-2 3CLpro inhibitors, paving the way for further experimental validation and drug development.
This study investigates the potential of organic resistive memory technology to replicate biological synaptic functions in artificial neural networks. A metal-insulator-metal (MIM) device was fabricated using the organic dye Phenol Red sodium salt (PRSS) as the active layer sandwiched between indium tin oxide (ITO) and gold (Au) electrodes. The device exhibited stable write once read many (WORM) characteristics where the conducting filament cannot be ruptured, unlike a bipolar resistive switching (BRS) memory device. However, it successfully emulates essential synaptic behaviors, including learning (potentiation) and forgetting (depression) processes. The observed conductance modulation is attributed to a charge trapping and detrapping mechanism that reversibly alters the effective width of the conducting filament, distinct from the formation and rupture processes seen in BRS switches. Detailed characterizations were performed to analyze the nonlinearity factor (NLF) and the dependence of synaptic weight on pulse amplitude, width, and interval. The device demonstrated paired-pulse facilitation (PPF), a key form of short-term plasticity (STP), with the PPF index following a double-exponential decay relative to the pulse interval. Furthermore, repetitive cycling revealed the device's capability for long-term potentiation (LTP), confirming its potential as a candidate for future memory storage and neuromorphic computing applications.
Ensuring balanced allocation by achieving optimality for known categorical covariates into two treatment groups has been analytically established with regard to D-, A-, D-s- and As-optimality in Hore et al. (2020) and for E- and E-s-optimality has been discussed by the authors earlier. However, the mathematical complexity of expression of the respective optimality function and mathematical computation increases with more number of treatments. In this work, the relationship between D-optimality and balancing criteria for known categorical covariates across three treatment groups is established through analytical derivation and simulation studies. It has been shown that D-optimality ensures a balanced allocation design, at least as a local optimal solution. Furthermore, simulation studies demonstrate that the balanced allocation design performs uniformly better than random allocation designs.
This paper introduces Rough Set Inspired Neutrosophic Sets (RSINS), a model that combines rough sets and neutrosophic sets. Rough sets deal with incomplete data using lower and upper approximations. Neutrosophic sets describe information using three values: truth, indeterminacy, and falsity. In RSINS, lower approximations use minimum truth and maximum indeterminacy, while upper approximations use maximum truth and minimum indeterminacy. Some basic properties of the model are studied to show that it is consistent and reliable. A rough neutrosophic topological structure is also introduced. An algorithm is proposed to compute RSINS approximations. The model is applied to a decision-making problem to show its usefulness. Existing methods have some limitations. Fuzzy sets do not handle indeterminacy clearly, and rough sets do not handle partial truth. RSINS combines both ideas and overcomes these issues. The results show that the proposed method gives more consistent decisions when the data is uncertain or incomplete.
