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Areca leaf sheath, a by-product which was earlier wasted by farmers in the field, has now found utility in agrobased industries. Several areca leaf plates and bowl manufacturing units have mushroomed in the hinterland, which escalated the demand for leaf sheaths. Due to the rapid growth of arecanut production, the state has a vast supply of raw materials. However, the existing areca leaf product industries cannot fully utilise this abundance. As a result, entrepreneurs can explore alternative uses for this valuable resource. Start-up India, a flagship scheme of the Government of India that encourages entrepreneurs, has resulted in an enterprise in Shivammogga, Karnataka that converts waste in the form of areca leaf sheath to wealth in the form of palm leather. The innovation lies in the manufacturing of palm leather without causing environmental pollution. The present study examined the economics, economic viability and SWOT factors of this start-up. Arecanut growers reaped net returns of Rs 38,000 from the sale of leaf sheaths. The entrepreneur made net returns of Rs 116.22 from indoor slippers, Rs 76.98 from diary covers and Rs 253.52 from vanity bags. The positive net present worth of Rs 7.2 crores, benefit: cost ratio of 1.49 and internal rate of returns of 134% revealed economic viability. Strengths and opportunities emerged as crucial SWOT elements. The challenge lies in scaling up the enterprise to encash the glaring demand for palm leather. [ABSTRACT FROM AUTHOR]

Depression is a pervasive mental health disorder characterized by persistent sadness and an inability to enjoy activities that were once enjoyable. This study compares the dermatoglyphic patterns of depressed patients with those of healthy, normal individuals in order to determine if dermatoglyphic patterns can be used as biomarkers for early diagnosis and prompt intervention of depression. A total of 100 depressive disorder patients of both sexes between the ages of 18 and 60 were selected for the study. Dermatoglyphic patterns of individual digits were analyzed using the "Ink and Paper" technique. The Whorl, Loop, and Arch patterns, as well as the AFRC, TFRC, and a-b ridge count, were examined using One-Way ANOVA significance and a Chi-Square test using SPSS 20.0. The results showed that the right hand in the depressive diseased group had decreased numbers of Whorls in the 2nd and 5th digits, and decreased numbers of Loops in all five digits. Similarly, decreased numbers of Whorls were found in the 1st, 2nd, 4th, and 5th digits of the left hand, as well as decreased Loops in the 4th digit. However, there were no changes in the Arches of either hand when compared to the control group. The AFRC and TFRC were significantly decreased (p<0.0001), but there was no significant decrease in the a-b ridge count in the depressive diseased group when compared to the control group. Dermatoglyphics, a noninvasive method, can serve as a screening indicator for depressive individuals, the appearance of the decreased count of whorls and loops parameters might be used as an exploratory sign., Competing Interests: The authors declare no conflict of interest., (© 2023 Biomedical Informatics.)

Air pollution is an important issue, especially in megacities across the world. There are emission sources within and also in the regions around these cities, which cause fluctuations in air quality based on prevailing meteorological conditions. Short term air quality forecasting is used not to just possibly mitigate forthcoming high air pollution episodes, but also to plan for reduced exposures of residents. In this study, a model using Artificial Neural Networks (ANN) has been developed to forecast pollutant concentration of PM 10 , PM 2.5 , NO 2 , and O 3 for the current day and subsequent 4 days in a highly polluted region (32 different locations in Delhi). The model has been trained using meteorological parameters and hourly pollution concentration data for the year 2018 and then used for generating air quality forecasts in real-time. It has also been equipped with Real Time Correction (RTC), to improve the quality of the forecasts by dynamically adjusting the forecasts based on the model performance during the past few days. The model without RTC performs decently, but with RTC the errors are further reduced in forecasted values. The utility of the model has been demonstrated in real-time and model validations were performed for the whole year of 2018 and also independently for 2019. The model shows very good performance for all the pollutants on several evaluation metrics. Coefficient of correlations for various pollutants varies from 0.79-0.88 to 0.49-0.68 between the Day0 to Day4 forecasts. Lowest deterioration of performance was observed for ozone over the four days of forecasts. Use of RTC further improves the model performance for all pollutants., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)