The rapid emergence of antimicrobial resistance (AMR) has made it clear that there is an urgent need for alternative therapeutic strategies. Among the various emerging approaches, green nanotechnology has come forward as an eco-conscious and sustainable platform for fabricating biologically active nanoparticles (NPs) with enhanced functional potential. In the present work, the gold NPs (AuNP) were synthesized using the root extract of Acmella uliginosa as a simple approach for the synthesis of gold NPs, using the phytoconstituents of the extract as a natural reductant and capping agent during the NPs formation. The formation of AuNPs was confirmed by a set of techniques including UV-visible spectroscopy, dynamic light scattering (DLS), X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, and transmission electron microscopy (TEM). The produced NPs were mainly spherical with an average diameter of 30–40 nm. The AuNPs were tested for their biomedical properties of antibacterial, anti-inflammatory, and cytocompatibility to explore their biomedical relevance. The AuNP biosynthesized showed significant inhibitory activity against both Gram-positive (Staphylococcus aureus) and Gram-negative (Escherichia coli) pathogenic strains. The minimum inhibitory concentration (MIC) profiling also confirmed the significantly stronger antibacterial ability of the AuNP compared to pure root extract. The anti-inflammatory activity was tested using the protein-denaturation inhibition assay, and there was a significant reduction in the inflammatory response. Simultaneously, the increased proliferation of L929 fibroblast cells after exposure to AuNP confirmed the excellent cytocompatibility of the synthesized nanomaterial. All of these observations highlight the versatility of Acmella uliginosa AuNPs and their potential in antimicrobial applications, anti-inflammatory therapies, and biomedical engineering.
This study experimentally evaluates the performance of a domestic refrigerator operating with R152a at different refrigerant charge levels to identify the optimum charge mass. The performance obtained with R152a is compared with that of the conventional refrigerator charged with R134a. The evaluation considers key performance parameters, including pull-down time, energy consumption, volumetric cooling capacity, refrigerating effect, compressor power consumption, coefficient of performance (COP), discharge temperature, and pressure ratio. Based on the experimental results, an optimum R152a charges mass of 98 g was identified, representing approximately a 30
This present work is designed and development of Solar-Assisted Autonomous Guided Electric Vehicle (AGEV) for sustainable Indoor Hospital Logistics and Healthcare Applications. The entire system is developed using ROS2, and integrates with advanced algorithms for perception, localization, path planning, and motion control. A multi-sensor fusion framework consisting of LiDAR, stereo depth camera, and radar enables 360° environmental awareness, SLAM, obstacle detection, and autonomous navigation. The A* algorithm is used for optimal path planning, while a PID controller governs the front-axle steering and 250 W DC drive motors. The hardware architecture includes an NVIDIA Jetson edge-AI platform, MS chassis, wheel encoders, and a 24 V, 26 Ah Li-ion battery with BMS. A 300 W solar photovoltaic charging system is incorporated to supplement battery charging, extend operating range, and reduce grid dependency. The proposed system combines autonomous navigation, electric mobility, and renewable energy integration to provide an efficient and sustainable solution for smart logistics and industrial applications.
The increasing usage of fly ash in concrete has environmental and economic benefits by reducing the amount of cement needed and reusing by-products from industry. However, variations in the composition of the mixture, characteristics of the fly ash, and the curing environment might affect the mechanical properties of the fly ash concrete significantly. Therefore, accurate prediction of the flexural strength is crucial to improve the fly ash concrete mixtures. This study developed an ANFIS design to predict the flexural strength of fly ash concrete based on four inputs: water-cementitious-material ratio (W/cm), percentage of fly ash, fly ash LOI, and curing age. The experimentally measured flexural strength was assumed as the output of the system. The analysis of correlation showed that the curing age had the highest positive correlation with flexural strength, and LOI showed a negative correlation. The ANFIS result showed that for the water-cement ratio of 0.42, 69.8
Conventional welding quality inspection techniques are difficult, time-consuming, and frequently result in missing or erroneous examinations. Fast and precise on-site welding quality inspections are necessary as industrial automation increases. So, to give good technical support, we need detection models that are both more accurate and easier to use. This research aims to present an in-situ weld surface defect classification methodology grounded in the EfficientNetB4 and MobileNetV2 algorithms. The studies show that the proposed method for categorizing weld surface faults is better than others since it is more accurate and uses computer resources more efficiently. The strategy described in this study can get over the problems that occur with other ways in order to reach the output for minimize the manpower, reduce the process time, and achieve the higher accuracy. The method addresses the practical need for fast, accurate classification of surface weld defects in complicated industrial goods.
