• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.4061-4076
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• 2017

With the development of high-speed railway technologies, train velocities can now reach speeds up to 350 km/h, and higher in the future. In high-speed railway systems (HSRs), loss of communication can result in serious accidents, especially when the train is controlled through wireless communications. For to this reason, operators of Long Term Evolution for Railway (LTE-R) communication systems install eNodeBs (eNBs) with high density to achieve highly reliable communications. However, densely located eNBs can result in unnecessary frequent handovers (HOs) resulting in instability because, during every HO process, there is a period of time in which the communication link is disconnected. To solve this problem, in this paper, an HO scheme based on the maximum cell residence time (CRT) and adaptive time to trigger (aTTT), which are collectively called CaT, is proposed to reduce unnecessary HOs (using CRT estimations) and decrease HO failures by improving the handover command transmission point (HCTP) in LTE-R HSR communications.

• Applied Microscopy
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• v.35 no.2
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• pp.97-104
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• 2005

Plant epidermis consists of relatively unspecialized cells and more specialized cells of various structure and function. Trichomes are specialized cells originated from the epidermis and much attention has been paid to the plants developing trichomes with peculiar structure and function. The present study has been undertaken to examine the trichome type noticed in the leaf epidermis of Althaea rosea using scanning electron microscopy. Four types, namely simple, short-and long-tufted, and glandular hairs, were detected in their epidermis. Their Distribution, frequency and structure varied by the development and epidermal surface. The most frequently distinguished type was the tufted ones growing in young leaves of the abaxial epidermis, while the simple hairs were rare throught the examination. The short-tufted hairs branched up to seven times having each branchlet about $160{\sim}210{\mu}m$ in length at maturity. The long-tufted hairs exhibited up to ten branchlets, where branchlets could reach up to $900{\sim}1,000{\mu}m$ long when fully expanded. Glandular trichome was the peltate type comprising $1{\sim}2$ secretory head cells, 2 stalk cells and a basal cell. The short peltate glandular hairs, usually not exceeding $40{\mu}m$, differentiated more along the areoles in the adaxial epidermis. The function of these trichomes in A. rosea has been still obscure, but it has been speculated that they probably play a role in protection; non-glandular ones possibly providing a defense against insects and secretory glandular type participating in chemical defense. Structural features of these trichomes at cellular level will be discussed in the following study of transmission electron microscopy.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.27-33
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• 2021

The whole world now is dealing with Coronavirus, and it has turned to be one of the most widespread and long-lived pandemics of our times. Reports reveal that the infectious disease has taken toll of the almost 80% of the world's population. Amidst a lot of research going on with regards to the prediction on growth and transmission through Symptomatic carriers of the virus, it can't be ignored that pre-symptomatic and asymptomatic carriers also play a crucial role in spreading the reach of the virus. Classification Algorithm has been widely used to classify different types of COVID-19 carriers ranging from simple feature-based classification to Convolutional Neural Networks (CNNs). This research paper aims to present a novel technique using a Random Forest Machine learning algorithm with hyper-parameter tuning to classify different types COVID-19-carriers such that these carriers can be accurately characterized and hence dealt timely to contain the spread of the virus. The main idea for selecting Random Forest is that it works on the powerful concept of "the wisdom of crowd" which produces ensemble prediction. The results are quite convincing and the model records an accuracy score of 99.72 %. The results have been compared with the same dataset being subjected to K-Nearest Neighbour, logistic regression, support vector machine (SVM), and Decision Tree algorithms where the accuracy score has been recorded as 78.58%, 70.11%, 70.385,99% respectively, thus establishing the concreteness and suitability of our approach.