Women in Exile: A Study of Chitra Banerjee Divakaruni, Bharati Mukherjee, and Anita Rau Badami

During the post-colonial period, literature has crossed the boundaries of nation, language, and culture. An important feature of the period is the emergence of woman writers. Significant space has been created for women to discuss their peculiar problems and issues. The social, cultural, and literary definitions accumulated over the past by the patriarchal tradition were redefined from alternate and, especially, from feminist or womanist points of view. With more and more women taking part in the public life and the steady increase in the number of independent women, there is a significant rise in women’s writings across the globe.The paper entitled “Transnationalism and Women: A Study of Chitra Banerjee Divakaruni, Bharati Mukherjee, and Anita Rau Badami” discusses how three prominent woman writers of the Diaspora deal with problems of the transnational women

Effect of different herbicide spray volumes on weed control efficiency of a battery-operated Unmanned aerial vehicle sprayer in transplanted rice (Oryza sativa L.)

The effect of spray volume on weed control in transplanted rice ecosystems using the Unmanned aerial vehicle (UAV) needs to be better understood for management in the advancements of UAV-based spraying technology. The present study aimed to find out the influence of varied spray volumes of 15 L/ha, 20 L/ha and 25 L/ha using the UAV and 500 L/ha using a Knapsack sprayer (KS) to compare the weed density, weed dry matter and weed control efficiency and yield in transplanted rice (Oryza sativa L.). Pre-emergence (PE) application of Pyrazosulfuron-ethyl at 25 g a.i./ha at three days after transplanting (DAT) and post-emergence (PoE) application of Bis-pyribac sodium at 25 g a.i./ha at 25 DAT were used as herbicide treatments. The results revealed that varied spray volumes significantly influenced the weed density, dry matter, and weed control efficiency of the UAV and KS. Application of herbicides using KS (500 L/ha) and UAV (25 L/ha) had better control on the weeds by reducing weed density and dry matter at 20, 40, and 60 DAT, with no significant difference. Higher grain yield and straw yield were recorded in KS (500 L/ha) and UAV (25 L/ha), with no significant difference. However, applying 25 L/ha had better weed control efficiency and higher yield, possibly due to optimum deposition. Considering the low volume application of UAV (25 L/ha) as compared with KS (500 L/ha), it is better to go for the optimal application of 25 L/ha, which is an energy-efficient and cost-effective, labour-saving approach compared to KS

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Assessment of land use and land cover mapping using object-based classification techniques for the eastern districts of Tamil Nadu

LULC (Land use and land cover) mapping is crucial for understanding environmental monitoring, supporting sustainable development, and managing natural resources. This study evaluated the accuracy of object-based LULC classification using Sentinel-2 data and machine learning classifiers in the Ariyalur, Perambalur, and Mayiladuthurai districts of Tamil Nadu during the kharif season of 2023. OBIA (Object-based image analysis) clusters pixels based on their spectral and spatial characteristics, utilizing segmentation to generate masks that effectively represent the image content. The OBIA methodology involves multiresolution segmentation using eCognition software to delineate homogeneous image objects based on spectral, spatial, and contextual characteristics. Several widely used machine learning algorithms, including Random forest (RF), Support vector machine (SVM), Decision Tree (DT), Naive bayes (NB) and k-nearest Neighbor (k-NN), were evaluated to improve classification accuracy. The classification results varied across the districts, with the RF algorithm consistently demonstrating high performance. The Perambalur and Mayiladuthurai RF achieved an overall accuracy of 88 %, with a kappa coefficient of 0.76 and 83 % and a kappa coefficient of 0.66. In Ariyalur, the DT model was used, with an accuracy of 85 % and a kappa coefficient of 0.70. The NB and k-NN classifiers achieved lower accuracies in all districts. In contrast, the RF algorithm was the most reliable for LULC classification in these areas, highlighting its strength and efficiency in accurately identifying complex land cover patterns