The most classical approach of determining rain attenuation for radio-wave frequency has been to theoretically determine the specific attenuation. At frequency over 10 GHz, rain and precipitation can influence the attenuation a lot; the effect of atmospheric attenuation between the source and destination over wireless communication is of major concern and a proper site visit and proper method are required to control the attenuation level so that the performance can be increased. In this paper exponential model has been used to determine the attenuation level for k-region (India) which can be used for region having similar condition. The analyzed predicted attenuation data have been compared with ITU-R measured rain attenuation, and the results will provide useful estimation of rainfall attenuation on microwave links in tropical regions that have similar conditions as (Almora) Uttarakhand region.
There was an outbreak of pneumonia in the month of December 2019 in Wuhan, China that spread with a rapid rate throughout the country and shook the world by spreading across the globe causing many deaths due. This disease is confirmed by means of molecular method as a novel coronavirus and was named as 2019 novel coronavirus (2019-nCoV) in its initial stage; however, on February 11, 2020, World Health Organization (WHO) renamed this disease COVID-19, which means corona virus disease. COVID-19 has impacted nearly the entire world, affecting more than 100 countries including India. The Coronavirus Study Group consisting of the International Committee on Taxonomy of Viruses renamed this virus, which was provisionally named 2019-nCoV, as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). This nomenclature is based on taxonomy, phylogeny, and established practice. As on March 2020, WHO has confirmed 692,575 number of cases of COVID-19 with 33,099 deaths, which are distributed across the globe: Western Pacific region with 103,775 confirmed cases with 3,649 deaths; European region with 392,757 number of confirmed cases with 23,962 deaths; South East Asia region with 4,084 confirmed cases with 158 deaths; Eastern Mediterranean region with 46,392 confirmed cases with 2,813 number of deaths; America region with 142,081 confirmed cases with 2,457 deaths; African region with 3,486 confirmed cases with 60 deaths. This paper focuses on these areas and regions and tries to find establish the relationship between numbers of deaths and number of cases with respect to the temperature. This paper takes the study of specific areas around the world and also the case study of India to study the effect of temperature on the rise of and death due to COVID-19 virus.
In this modern world of biomedical medicine, the classification of breast density has been considered a very important part of the process of breast diagnosis. Furthering the same research, this research aims to determine the patient’s breast density by mammogram image with the help of modern techniques such as computerized devices and machine learning algorithms, which will greatly help the radiologist. To carry out this process, this research paper introduces a Convolutional Neural Network (CNN) model of deep learning that will work as a basis for waveform conversion and fine-tune. This deep learning model will prove effective in automatically classifying a patient’s breast density. With the help of this method, the last two layers which are fully connected are removed and joined with two newly formed layers. This would have helped in addressing a pre-trained AlexNet model that further improved the classification process. In this model, the original or preprocessed images are used at level 1 of the input (which is in sharp contrast to the usual methods based on the CNN model), which also makes the model compatible with the use of redundant wavelet coefficients. Because in the field of radiologists it is very important to underline the difference between scattered density and heterogeneous density, so the main objective of this research is focused on this end. As the proposed method has an accuracy of 82.2%, it shows a better performance. This research paper further compares the effectiveness and performance of the proposed method to traditional fine-tuning CNN models, with satisfactory results. The comparative results of the proposed method suggest that the proposed method is in the field of radiologists representing a helpful tool. This method may be intended to act as a second eye for doctors in the medical field with the intention of classifying the categories of breast density in the patient during breast cancer screening.
Reuse of water has been a popular choice toward balancing water scarcity and managing water availability in defined areas. GW which can be defined as the wastewater that comprises water from baths, showers etc, when managed and treated properly could be valuable resource for sectors like agricultural and horticultural. GW is one of the best option if treated and if not, it will mix with the sewage stream. It is possible to intercept this GW at the household level using minimum change in design, and with the primary and secondary treatment it can be recycled for garden washing, flushing and many purposes. In the present work, GW from student accommodation were collected, characterized and treated through series of natural adsorbent. Various parameters such as TDS, pH, Turbidity, BOD,COD, amount of nitrate and phosphorus were measured and it was found that most of the parameters were considerably in range after treatment. A simple method has been proposed that may be applied at individual household level.
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