Kinetics of Aldehyde Oxidation on Platinum Anode In Aqueous Perchloric Acid & Sulphuric Acid

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LIBRARY AND INFORMATION NEEDS OF DIFFERENTLY-ABLED STUDENTS IN KERALA: A STUDY

The study was conducted to investigate the information needs of differently-abled students in the school libraries of Kerala. The study was done among students belonging to the category of visually challenged (VC), hearing and speech impaired (HI), and physically challenged (PC) from special schools and schools under Inclusive Education of the Disabled at Secondary Stage (IEDSS). The study was based on a questionnaire survey, conducted in the three districts of Kerala state ie; Thiruvananthapuram, Ernakulum & Kozhikode. The analyses revealed that the information needs of differently-abled students have become complex and problematic due to the insufficiency of adequate information sources and services and there are quite a number of challenges faced by these students in accessing information from the libraries. The overall result of the study was that, though the library services provided in the school are useful for their studies, the respondents cannot make use of them because of barriers. The study comes out with some practical suggestions to improve the library services for differently-abled student

Characteristics of Anodic Film on Aluminium in Borate Bath

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Explainable AI Framework for COVID-19 Prediction in Different Provinces of India

In 2020, covid-19 virus had reached more than 200 countries. Till December 20th 2021, 221 nations in the world had collectively reported 275M confirmed cases of covid-19 & total death toll of 5.37M. Many countries which include United States, India, Brazil, United Kingdom, Russia etc were badly affected by covid-19 pandemic due to the large population. The total confirmed cases reported in this country are 51.7M, 34.7M, 22.2M, 11.3M, 10.2M respectively till December 20, 2021. This pandemic can be controlled with the help of precautionary steps by government & civilians of the country. The early prediction of covid-19 cases helps to track the transmission dynamics & alert the government to take the necessary precautions. Recurrent Deep learning algorithms is a data driven model which plays a key role to capture the patterns present in time series data. In many literatures, the Recurrent Neural Network (RNN) based model are proposed for the efficient prediction of COVID-19 cases for different provinces. The study in the literature doesnt involve the interpretation of the model behavior & robustness. In this study, The LSTM model is proposed for the efficient prediction of active cases in each provinces of India. The active cases dataset for each province in India is taken from John Hopkins publicly available dataset for the duration from 10th June, 2020 to 4th August, 2021. The proposed LSTM model is trained on one state i.e., Maharashtra and tested for rest of the provinces in India. The concept of Explainable AI is involved in this study for the better interpretation & understanding of the model behavior. The proposed model is used to forecast the active cases in India from 16th December, 2021 to 5th March, 2022. It is notated that there will be a emergence of third wave on January, 2022 in India.Comment: 12 page

The role of leaf surface wetness in larval behaviour of the sorghum shoot fly, Atherigona soccata

The susceptibility of sorghum to the shoot fly Atherigona soccata Rondani, (Diptera: Muscidae) is affected by seedling age and is highest when seedlings are 8–12 days old. This corresponds with high moisture accumulation on the central leaf which is the path of newly hatched larva as it moves downwards from the oviposition site, towards the growing apex. Studies showed that leaf surface wetness (LSW) of the central shoot leaf was higher in 10-day old seedlings than in seedlings of other ages. Similarly, LSW was much higher in the susceptible sorghum genotype CSH 1 than in the resistant genotype IS 2146. Larvae moved faster towards the growing point and produced deadhearts much earlier in CSH 1 than in IS 2146. They also moved faster in 10-day old seedlings than in seedlings of other ages. It was also shown that the leaf surface wetness of the central shoot leaf is a more reliable parameter of resistance than the glossy leaf trait or trichome density

MULTIPLE OIL PAD DETECTION USING DEEP LEARNING

Deep learning (DL) algorithms are widely used in object detection such as roads, vehicles, buildings, etc., in aerial images. However, the object detection task is still considered challenging for detecting complex structures, oil pads are one such example: due to its shape, orientation, and background reflection. A recent study used Faster Region-based Convolutional Neural Network (FR-CNN) to detect a single oil pad from the center of the image of size 256 × 256. However, for real-time applications, it is necessary to detect multiple oil pads from aerial images irrespective of their orientation. In this study, FR-CNN was trained to detect multiple oil pads. We cropped images from high spatial resolution images to train the model containing multiple oil pads. The network was trained for 100 epochs using 164 training images and tested with 50 images under 3 different categories. with images containing: single oil pad, multiple oil pad and no oil pad. The model performance was evaluated using standard metrics: precision, recall, F1-score. The final model trained for multiple oil pad detection achieved a weighted average for 50 images precision of 0.67, recall of 0.80, and f1 score of 0.73. The 0.80 recall score indicates that 80% of the oil pads were able to identify from the given test set. The presence of instances in test images like cleared areas, rock structures, and sand patterns having high visual similarity with the target resulted in a low precision score