Deenbandhu Chhotu Ram University of Science and Technology (DCRUST), formerly Chhotu Ram State College of Engineering, Murthal (CRSCE), is a state university located in Murthal, Sonipat, Haryana, India. It was established in 2006 by an Act of the Government of Haryana, upgrading a 1986 established college.
Central bank digital currency (CBDC) is becoming increasingly attractive as an alternative to traditional currencies, boosting the speed and effectiveness of online transactions. Therefore, the authors conducted a systematic literature review to build a conceptual framework on CBDC adoption and used “partial least squares structural equation modelling (PLS-SEM)” for empirical validation. A self-administered questionnaire was used to gather data from 615 respondents. The outcome demonstrates that performance expectancy, social influence, facilitating conditions, hedonic motivation, personal innovativeness and digital awareness significantly affect individuals’ attitudes and behavioral intentions toward CBDC adoption. Nevertheless, effort expectancy and perceived security are not significantly associated with individuals’ attitude and behavioral intention towards CBDC adoption. In addition, attitude significantly influences behavioral intention to adopt CBDC. The findings will offer a valuable reference for digital currency developers, researchers, financial institutions and policymakers in developing CBDC regulatory frameworks and conducting broad explorations of adoption dynamics. This study used a hybrid analysis technique (SLR and PLS-SEM) to deliver a more inclusive understanding of CBDC adoption. From the perspective of emerging nations, substantial evidence remains lacking for the use of CBDC in financial transactions. By doing so, this article bridges the earlier knowledge gap by offering unique insights into promoting digitalization from developing nations’ perspectives.
In this study, hydrazine ligands were synthesized from thiophene-2-carboxylic acid/benzo[b]thiophene-2-carboxylic acid hydrazides and 1,3-diphenyl-1H-pyrazole-4-carbaldehyde, along with their Co(II), Ni(II), Cu(II), and Zn(II) complexes. The synthesized compounds were thoroughly characterized and originally evaluated for their anticancer and antimicrobial potentials. Building on these findings, the compounds were evaluated against A549 and DU145 cancer cells, the IMR-90 cell line, and clinically relevant microbial strains. Among them, the Cu(II) and Zn(II) complexes (9, 10) emerged as most active, with the Cu(II) complex (9) showing pronounced anticancer efficacy against both cancer cell lines while maintaining lower sensitivity toward IMR-90 cells. Remarkably, the Cu(II) (9) and Zn(II) (6, 10) complexes exhibited outstanding antimicrobial activity, with MIC values (0.0052-0.0104 µmol/mL) comparable to those of established reference drugs, underscoring their strong therapeutic potential. Molecular docking supported the observed bioactivities, with compound (9) showing strong binding to the 4ZXT protein (-9.98 kcal/mol). Similarly, compound (10) revealed favorable binding with target protein, yielding docking scores of -9.43 kcal/mol. These interactions confirm the compounds' strong biological affinity and therapeutic potential. ADMET results indicate favorable pharmacokinetics, supporting their drug-likeness. Their broad bioactivity provides a solid basis for future clinical studies and novel drug development.
This comparative study analyzes crystallite size, strain, BET surface area, pore size, dielectric properties, and AC conductivity of double perovskite compositions: La2NiMnO6 (LNMO), La2CuMnO6 (LCMO), and La2ZnMnO6 (LZMO), synthesized via Sol–Gel Pechini route. X-ray diffraction confirmed monoclinic structures (P21/n). Williamson-Hall analysis showed LNMO with the smallest crystallite size (55.71 nm) and the highest strain (3.24 × 10−3), while LCMO has the largest size (64.22 nm) and the lowest strain (1.43 × 10−3). BET analysis indicated LZMO with the highest surface area (6.673m²/g), and LNMO with the smallest pore size (3.038nm). LCMO exhibited the highest dielectric constant, suggesting a strong potential for energy storage applications, with an inverse correlation between dielectric constant and BET surface area. However, the higher tangent loss for LNMO and LCMO limits their practical applications in low-loss devices. Impedance spectroscopy revealed non-Debye-like relaxation. LCMO also showed the highest AC conductivity, attributed to enhanced charge carrier hopping among Cu/Mn cations. The highest activation energy for LZMO aligns well with the lowest conductivity, highlighting its insulating and semiconducting applications. The study emphasizes the effects of transition metal cations at the M-site, providing a detailed comparison of the compositions.
In the quest for sustainable energy, Hydroelectric cells (HECs) have emerged as a groundbreaking alternative to fuel cells and solar cells, offering a cost-effective and eco-friendly route to electricity generation. This study presents a novel approach to engineering high-performance HECs using (1-x) Na0.5Bi0.5TiO3-x Na0.2Mg0.8Fe2O4, (NBT-NMFO) nanocomposites, synthesized via the solid-state reaction method. By strategically tuning oxygen vacancies through compositional variations, a remarkable enhancement in water dissociation efficiency is achieved. Lattice mismatch and ionic radius disparities induced substantial strain and structural defects, creating active sites for water molecule adsorption and dissociation. These modifications were systematically analyzed using X-ray diffraction (XRD), Williamson-Hall (WH) analysis, High-resolution transmission electron microscopy (HRTEM), photoluminescence (PL), and X-ray photoelectron spectroscopy (XPS), confirming a progressive rise in defect density and oxygen vacancies with increasing NMFO content. Field-emission scanning electron microscopy (FESEM) and Brunauer-Emmett-Teller (BET) confirmed the porous morphology of the synthesized nanocomposites. Dielectric and conductivity analyses in the wet state highlighted their potential for hydroelectric cell (HEC) applications. Electrochemical impedance spectroscopy (EIS) and Nyquist plot modeling revealed a significant reduction in charge transfer resistance, particularly in Na-substituted magnesium ferrite. Notably, Na0.2Mg0.8Fe2O4-based HEC (2 x 2 cm2) achieved the highest offload current, soaring from 1.05 mA (NBT) to 14.65 mA (NMFO), attributed to minimal charge transfer resistance (0.022 Omega), pronounced lattice strain (2.82 x 10-3), enhanced nanoporosity, and abundant defect states. These results establish HECs as a trans-formative technology for next-generation clean energy and a promising step toward sustainable energy independence.
The rapid evolution of wireless communication technologies has intensified the demand for efficient and sustainable network architectures, particularly in Mobile Ad Hoc Networks (MANETs). This study proposes an integrated framework designed to enhance MANET network lifetime through a structured workflow that combines energy awareness, trust management, and swarm intelligence-based optimization. The proposed workflow consists of four sequential and interdependent modules: Communication Behavioral Impact Rate (CBIR), Energy Aware Macro Channel Selection (EAMCS), Optimal Swarm Intelligence-Based Route Selection (OSIBRS), and Cross-Mutation Self-Scheduling Routing Protocol (CMSSRP). The workflow begins with the CBIR module, which analyzes communication behavior, traffic frequency, and node interactions to generate behavioral insights. These insights are then supplied to EAMCS, which utilizes them to perform intelligent macro-channel allocation based on energy consumption models, ensuring optimal energy utilization across the network. The optimized channel assignments from EAMCS serve as input for OSIBRS, which employs swarm intelligence algorithms to identify the most reliable and energy-efficient routing paths under dynamic network conditions. Finally, CMSSRP refines the routing process through a distributed self-scheduling mechanism that dynamically regulates transmission timing, mitigates congestion, conserves energy, and reinforces trust-based communication by prioritizing reliable nodes. This coordinated workflow establishes a closed-loop system where behavioral data, channel management, and routing intelligence operate synergistically to achieve enhanced performance. Experimental evaluation demonstrates that the proposed framework significantly improves throughput, reduces delay, and achieves a network lifetime improvement of 95.7% compared to conventional methods. The results confirm that the integrated energy- and trust-aware swarm intelligence framework offers a scalable and adaptive solution for next-generation MANETs supporting 5G and 6G communication environments.
