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The shelf life of tomato is limited due to its perishable nature. The fruit is highly susceptible to fungal attack such as Fusarium oxysporum. The edible coating has been used to enhance the postharvest life of different food products. The present study was designed for the development of edible antifungal coating on tomato fruit using pomegranate peel extract in combination with chitosan. Pomegranate peel extract was obtained from pomegranate peel using water distillation method. The antifungal coating was prepared with different concentrations of pomegranate peel extract (To, T1, T2, T3, T4, T5 for 0%, 0.5%, 1.0%, 1.5%, 2.0%, and 2.5%) respectively. Coated tomatoes were subjected to physicochemical, microbial, and sensory analysis during storage. The firmness and lycopene content were decreased during storage. However, the coated tomatoes have more firmness and lycopene content than uncoated tomatoes. Maximum weight loss was observed for To (6.22 ± 0.07%) after 21 days. Lycopene content of coated tomato were improved during storage as compare with uncoated samples. Maximum total plate count was observed for To (6.11 ± 0.07 CFU/g) and minimum was observed for T5 (2.11 ± 0.08 CFU/g). All the treatments were significantly effective to control total plate count and mold count in comparison with the control. The coating materials did not affect the sensory profile of tomatoes. Pomegranate peel extract was proved effective antimicrobial coating material for tomatoes. The study will pave way for the researchers to use coating material for fruits based on the fruit waste. Practical applications: Fruit waste‐based edible coating can prevent postharvest losses in fruits and vegetables. Pomegranate peel extract and chitosan have shown potential to reduce postharvest losses in tomatoes. PPE‐based coating has antimicrobial activity against Fusarium oxysporum. The edible coating can help to reduce qualitative and safety issues in tomatoes during storage. It may be used as a replacement of synthetic preservatives in tomatoes. [ABSTRACT FROM AUTHOR]

Nanotechnology has recently appeared as an important study subject in modern material sciences. Greener synthesis of nanoparticles has gained the attention of many scientists because of its integral characteristics such as effectiveness, eco-friendly, and low cost. In the present study by following the green synthesis approach, zinc oxide nanoparticles (ZnO NPs) were formed for the very first time by using Senecio chrysanthemoides leaf extract as a reducing agent. The UV-Vis spectrophotometer was used to study the synthesized ZnO NPs, and the specific peak was found to be at 349 nm. The characteristic Fourier transform infrared (FTIR) peak was found to be at 449 cm-1 which displays the peak of ZnO molecules. The surface morphology of the ZnO NPs was determined via scanning electron microscopy (SEM). The energy-dispersive X-ray spectroscopy (EDX) study showed that the synthesized ZnO NPs are present at the weight percentage of 66.38%. The X-ray diffraction (XRD) spectrum confirmed the hexagonal phase wurtzite structure, with the average particle size of 31 nm, and demonstrated the crystalline structure of ZnO NPs. Additionally, to all these experiments, we compared the anti-inflammatory properties of biogenic ZnO NPs to a standard drug. Biosynthesized ZnO NPs have revealed an effective anti-inflammatory activity at a higher concentration (100 mL-1) and showed 73% inhibition in comparison with diclofenac sodium drug. Zinc oxide was shown to be compatible with diclofenac sodium, according to the results. The ZnO NPs produced using the greener synthesis process have the potential to be used in a broad range of fields and also used as a good anti-inflammatory agent. [ABSTRACT FROM AUTHOR]

VANET is the spontaneous evolving creation of a wireless network, and clustering in these networks is a challenging task due to rapidly changing topology and frequent disconnection in networks. The cluster head (CH) stability plays a prominent role in robustness and scalability in the network. The stable CH ensures minimum intra- and intercluster communication, thereby reducing the overhead. These challenges lead the authors to search for a CH selection method based on a weighted amalgamation of four metrics: befit factor, community neighborhood, eccentricity, and trust. The stability of CH depends on the vehicle's speed, distance, velocity, and change in acceleration. These all are included in the befit factor. Also, the accurate location of the vehicle in changing the model is very vital. Thus, the predicted location with the Kalman filter's help is used to evaluate CH stability. The results have shown better performance than the existing state of the art for the befit factor. The change in dynamics and frequent disconnection in communication links due to the vehicle's high speed are inevitable. To comprehend this problem, a graphing approach is used to evaluate the eccentricity and the community neighborhood. The link reliability is calculated using the eigengap heuristic. The last metric is trust; this is one of the concepts that has not been included in the weighted approach to date as per the literature. An adaptive spectrum sensing is designed for evaluating the trust values specifically for the primary users. A deep recurrent learning network, commonly known as long short-term memory (LSTM), is trained for the probability of detection with various signals and noise conditions. The false rate has drastically reduced with the usage of LSTM. The proposed scheme is tested on the real map of Chengdu, southwestern China's Sichuan province, with different vehicular mobilities. The comparative study with the individual and weighted metric has shown significant improvement in the cluster head stability during high vehicular density. Also, there is a considerable increase in network performance in energy, packet delay, packet delay ratio, and throughput. [ABSTRACT FROM AUTHOR]