Volume 9 | Issue 8 |

 Abstract

Abstract: Background: Stomal recurrence means a recurrence experienced by patients with laryngeal carcinoma after total laryngectomy with a high mortality rate. It is essential to identify the risk factors to predict stomal recurrence. However, factors contributing to stomal recurrence indicate controversial results. Tumor expansion, thyroid nodules, and free margins become the risk factors that are easily identified. The objective: This study aims to describe the correlation between tumor expansion, thyroid nodules, and free margins with stomal recurrence after total laryngectomy. Methods: This study applied an analytical observational study with retrospective cohort studies by employing secondary data. The samples included patients who had experienced a total laryngectomy from January 1, 2015, to December 31, 2019, and those who met the study criteria. The samples were divided into groups of T3 and T4 tumor expansion, groups of N1 and N2+ thyroid nodules, as well as groups of positive and negative free margins. These samples were associated with the stomal recurrence and categorized into yes and no accordingly. The Chi-Square Test was employed for correlation analysis and followed by multiple logistic regression. The results were considered correlated if the P-value was less than 0.05. Results: This study obtained 92 patients with 87 males and 5 females. The analysis of the correlation between tumor expansion and stomal recurrence suggested a P-value of 0.770, which was greater than 0.05. Meanwhile, the correlation between thyroid nodules and free margins with stomal recurrence indicated a P-value of 0.068 and 0.147, greater than 0.05. Conclusion: There was no significant correlation between tumor expansion, thyroid nodules, and free margins with stomal recurrence after a total laryngectomy.

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 Keywords

total laryngectomy, tumor expansion, thyroid nodules, free margin, stomal recurrence.

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Stress, Coping, Student, Nursing

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Aim: The purpose of the present experimental study was to find out the best walking intervention protocol which would have significant effect on health related physical fitness of the middle aged people. Methodology: Total 32 subjects have been selected purposively and divided randomly in equal numbers in four groups. Three experimental groups namely Experimental Group 1 (EG -1), Experimental Group 2 (EG -2), and Experimental Group 3 (EG -3), have gone through three different walking protocol for 12 weeks designed with different intensity and volume of walking; whereas the Control Group (CG) did not get any training. Three components of health related physical fitness namely cardio-respiratory endurance, muscular strength and endurance and body fat percentage has been tested through 12 Minute walk/run, one minute bent knee sit ups and skinfold measurement respectively. ANCOVA has been applied to find out the differences among the groups at 0.05 level of significance. LSD test has been applied to find out the best walking protocol. Result & Conclusion: The statistical findings of mean score of pre and post test mean scores have revealed the fact that all the three walking protocol has significantly improved the selected health related physical fitness components. The walking protocol III has been proved to be best protocol may be because of its gradual increase of intensity of speed with fixed distance covered in lesser duration after every four weeks of training.

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Module, Intervention, Protocol, Health-related fitness.

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ABSTRACT: Over 2.52Cr covid-19 cases in India. Using data and algorithm route block rapid progress of covid-19.AI & ML to solve real world problem like the corona virus outbreal.AI used powerful complex algorithm with them process data which them immolated human cognation to interpretation and compression of complicated medical and health care data for any artificial intelligence, need to be big data needed. This paper mostly focuses on how to manage spread of infection using Artificial Intelligence. Also focuses on the topic and the challenges a developing country such as India faces and overcome. The condition is troubling and new scientific, environmental and infrastructure needs to play a crucial role in removing this important problem globally (including India). It focused in this report on how India, a developing country is trying to stop corona spreading, and how artificial intelligence (AI) plays an essential role in controlling and monitoring the disease.

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Artificial Intelligence(AI), Data Science, PCR, Drone, Aarogya setu App, Robert,RT-PCR,Mylab ,Google AI

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The COVID-19 pandemic has led to the dramatic destruction of human life worldwide and presents an unprecedented hurdle to public health, livelyhood and the world of work. The social and economical disruption caused by the pandemic is devastating. The outbreak first appeared in to spotlight in December 2019 in Wuhan, China. This was declared as a pandemic by the World Health Organization on 11th March 2020. This article analyses how people react to a pandemic outbreak, how much they are aware of the disease and its symptoms, what preventive measures they are taking, whether people are following government guidelines, etc. The experiments have been conducted on the collected data related to COVID-19 tweets from all over the world. This study was conducted to understand how people from different infected countries cope with the situation

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nlp, sentiment analysis,

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Neural networks have become increasingly popular for the task of language modeling. Whereas feed-forward networks only exploit a fixed context length to predict the next word of a sequence, conceptually, standard recurrent neural networks can take into account all of the predecessor words. On the other hand, it is well known that recurrent networks are difficult to train and therefore are unlikely to show the full potential of recurrent models. These problems are addressed by the Long Short-Term Memory neural network architecture. In this work, we analyze this type of network on an English and a large French language modeling task. Experiments show improvements of about 8% relative in perplexity over standard

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WORD BASED PREDICTION USING LSTM NEURAL NETWORKS FOR LANGUAGE MODELING

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The present paper aimed to study the floristic diversity of Narasapur Reserve Forest dominated forest of Telangana State. Floral diversity patterns of vascular plant species were studied along with the geographical gradients in the Areas of tropical regions of Narasapur Reserve Forest. Narasapur Reserve Forest is located in and around Narasapur town in Medak District. The area of the block is 1752.33 hectares. Vegetation data were sampled from two moist evergreen and deciduous forest fragments. The patterns of plant diversity were evaluated on the basis of species richness as the total number of species at each site and species change between and within sites and in relation to vegetation structure. During the field trips and research it is found the field area is inhabited by various types of plants like Tecton grandis;Terminalia arjuna;Ficus recemosa; Terocarpus marspium; Annona squamosal; Aerva lanata; Crotalaria junceae etc. A total of 66 species belonging to 35 families were recorded. The quantitative features such as density and importance value index of species varied greatly. In the present study the diversity index of shrubs and herbs were found to be higher than the tree species. The forest has very thick canopy and many hillocks. The concentration of dominance was recorded highest in the tree species. The presence of low number of higher girth class of tree species and higher number of the saplings and seedlings indicates that the present forest is young exhibiting frequent regeneration

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Evergreen Forest, Narasapur Reserve Forest, deciduous, forests, floristic diversity, and species diversity

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Automatic face recognition (AFR) technologies have seen dramatic improvements in performance over the past years, and such systems are now widely used for security and commercial applications. An automated system for human face recognition in a real time background which can be useful for a college to mark the attendance of their students. So using Real Time Face Recognition is a real world solution which comes with day to day activities of handling various activities. The task is very difficult as the real time background subtraction in an image is still a challenge. To detect real time human face are used and a simple fast Principal Component Analysis has used to recognize the faces detected with a high accuracy rate. The matched face is used to detect accurate user .Our system maintains the collection of user facial features as datasets and use them for verification. information they.

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FACE DETECTION BASED ATTENDANCE SYSTEM

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Key words: COVID-19, Quality education, social work, students, implication level

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E-Government is the use of information and communication technologies (ICTs) to improve the activities of government agencies. The e-readiness index of India is found to be low as compared to other countries. There are various challenges for the implementation of e-government in India. These challenges are like low literacy, low per capita income and limited financial resource. In this paper a conceptual framework is suggested for the effective implementation of e-government in India. The conceptual framework can be further validated in the real life situation. administration requiring the application of complex legislation and regulations. Most efforts of public administration to bring transactions online have been restricted to simple transactions requiring little or no knowledge of the law, such as change of address notifications. Successful implementation of e-Governance practices offer better delivery of services to citizens, improved interactions with business and industry, citizen empowerment through access to information, better management, greater convenience, revenue growth, cost reductions etc.

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E-Government, India, opportunities and challenges

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Creative Commons Attribution 4.0 and The Open Definition