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Maturity stage of fresh banana fruit is an important factor that affects the fruit quality during ripening and marketability after ripening. The ability to identify maturity of fresh banana fruit will be a great support for farmers to optimize harvesting phase which helps to avoid harvesting either under-matured or over-matured banana. This study attempted to use image processing technique to detect the maturity stage of fresh banana fruit by its color and size value of their images precisely. A total of 120 images comprising 40 images from each stage such as under-mature, mature and over-mature were used for developing algorithm and accuracy prediction. The mean color intensity from histogram; area, perimeter, major axis length and minor axis length from the size values, were extracted from the calibration images. Analysis of variance between each maturity stage on these features indicated that the mean color intensity and area features were more significant in predicting the maturity of banana fruit. Hence, two classifier algorithms namely, mean color intensity algorithm and area algorithm were developed and their accuracy on maturity detection was assessed. The mean color intensity algorithm showed 99.1 % accuracy in classifying the banana fruit maturity. The area algorithm classified the under-mature fruit at 85 % accuracy. Hence the maturity assessment technique proposed in this paper could be used commercially to develop a field based complete automatic detection system to take decision on the right time of harvest by the banana growers.

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Workplaces are high-risk environments for SARS-CoV-2 outbreaks and subsequent community transmission. Identifying, understanding, and implementing effective workplace SARS-CoV-2 infection prevention and control (IPC) measures is critical to protect workers, their families, and communities. A rapid review and meta-analysis were conducted to synthesize evidence assessing the effectiveness of COVID-19 IPC measures implemented in global workplace settings through April 2021. Medline, Embase, PubMed, and Cochrane Library were searched for studies that quantitatively assessed the effectiveness of workplace COVID-19 IPC measures. Included studies comprised varying empirical designs and occupational settings. Measures of interest included surveillance measures, outbreak investigations, personal protective equipment (PPE), changes in work arrangements, and worker education. Sixty-three studies from international healthcare, nursing home, meatpacking, manufacturing, and office settings were included, accounting for ~280,000 employees. Meta-analyses showed that combined measures (0.2% positivity; 95%CI 0-0.4%) were associated with lower post-intervention employee COVID-19 positivity estimates than single measures like asymptomatic PCR testing (1.7%; 95%CI 0.9-2.9%) and universal masking (24%; 95%CI 3.4-55.5%). Modelling studies showed that combinations of (i) timely and widespread contact tracing and case isolation, (ii) facilitating smaller worker cohorts, and (iii) effective use of PPE can reduce workplace transmission. Comprehensive COVID-19 IPC measures incorporating swift contact tracing and case isolation, PPE, and facility zoning, can effectively prevent workplace outbreaks. Masking alone should not be considered as sufficient protection from SARS-CoV-2 outbreaks in workplace environments at high risk of virus transmission.

-Due to the massive existing vulnerabilities in mobile ad-hoc networks, they may be insecure against attacks by the malicious nodes. In this paper we have analyzed the effects of Black hole attack on mobile ad hoc routing protocols. Mainly two protocols AODV and Improved AODV have been considered. Simulation has been performed on the basis of performance parameters and effect has been analyzed after adding Black-hole nodes in the network. Finally the results have been computed and compared to stumble on which protocol is least affected by these attacks.