Dr. C.V. Raman University is a private university located in Kota, Chhattisgarh, India. Established on 3 November 2006 by All India Society for Electronics & Computer Technology (AISECT). It is named after C.V. Raman. C..
We demonstrate a fixed-point theorem for digital photographs in this study. In particular, we prove a special digital fixed-point theorem for four self-mappings in an entire digital metric space. In the setting of digital metric space, our solution is a logical progression of the seminal work of Bhagwat and Singh.
Medicinal plants are the most common biosource of medications in traditional medical systems. The current study used aqueous leaf extracts of the Amaranthaceae plant Aerva lanata to synthesize silver nanoparticles (AgNPs) via green synthesis. The crystalline nature, size, shape, and elemental composition of the biosynthesized AgNPs are analyzed using FTIR, EDAX, Zeta Potentials, FESEM, XRD, and UV-Visible spectroscopy. FESEM and EDAX examination confirmed the green-synthesized AgNPs, which werespherical in shape, having a size range of 66.70 +/- 0.15 to 98.40 +/- 0.05 nm.A prominent absorbance peak confirmed the presence of AgNPs at 344 nm in UV-Visible spectra.XRD spectra evaluate the AgNPs were of FCC crystal having 111, 200, 220, and 311 planes. AgNPs' moderate stability is confirmed by a -22.0 +/- 0.01 mV zeta potential value.
The present investigation offers a novel approach by scientifically revalidating traditional beliefs surrounding the medicinal value of Bengal amloki (Phyllanthus emblica L.) through comprehensive physicochemical, spectroscopic and chromatographic analyses. This study aimed to determine the bioactive potential and establish traditional beliefs within the framework of scientific interpretation, and to characterize the physicochemical and spectroscopic properties of the fruits of Bengal amloki. The fruits' chemical analysis was done to assess their bioactive potential as a source of alternative medicine and their ability to treat ailments. The nutritious fruits of Bengal amloki (also known as amla or Indian gooseberry) revealed C, H, O and N contents of 48.773 +/- 0.211%, 4.858 +/- 0.222%, 42.411 +/- 0.447% and 1.844 +/- 0.057%, respectively. Based on Fourier Transform Infrared Spectroscopy (FTIR) results, strong bonds between C-O, O-H, N-H, O=C=O, C-H, and O-H molecules supported the presence of primary alcohol, carboxylic acid, alkene, carbon dioxide, alkane and phenol, respectively. Several significant bioactive phyto-constituents have also been detected by Gas chromatography-mass spectrometry (GC-MS) screening, whose bioactivities are believed to be useful in the management of various disorders. The results showed that biomass with high fixed carbon (FC), high volatile matter (VM) and low Compositional analysis (CA) had the best efficiency, antioxidant properties, and potential for converting energy.
Collisions between animals and vehicles on roadways are an ongoing topic of safety and conversation concern to humans, particularly when the visibility is severely limited, such as at night time. The traditional monitoring and detection systems have low illumination, glare, and environmental noise that can contribute to the late detection of a situation that results to accidents. To address this problem, we gathered a selected set of images of 927 images by Roboflow that had been captured in night forest and highway setting and annotated in three large animal groups: deer, fox, bear. The proposed technique was the combination of Contrast Limited Adaptive Histogram Equalization (CLAHE) and a Robust Retinex Model to perform illumination correction, YOLOv5 to detect objects in real-time. The hybrid front end offers the advantage of having a better visibility whilst the reduction of false positives caused by glare provides a stable detectable performance in low light. An additional feature of the work is its two-stage pre-processing-detection fusion, consisting of a illumination normalization step and light-weight high-speed model suitable for real-time inference. Model comparison with Precision, Recall, F1 Score and mean Average Precision (mAP) showed the great improvement against naive detectors. Our improved YOLOv5 showed Precision $=0.923$, Recall $=0.773$, mAP ${@} 0.5=0.802$, and it achieved 55-60 FPS processing frequency, demonstrating that the detector was efficient and reliable for real-time night highway safety operation.
