Dynamic weight parameter for the Random Early Detection (RED) in TCP networks

This paper presents the Weighted Random Early Detection (WTRED) strategy for congestion handling in TCP networks. WTRED provides an adjustable weight parameter to increase the sensitivity of the average queue size in RED gateways to the changes in the actual queue size. This modification, over the original RED proposal, helps gateways minimize the mismatch between average and actual queue sizes in router buffers. WTRED is compared with RED and FRED strategies using the NS-2 simulator. The results suggest that WTRED outperforms RED and FRED. Network performance has been measured using throughput, link utilization, packet loss and delay

A Review and Comparative Study of Firefly Algorithm and its Modified Versions

Firefly algorithm is one of the well-known swarm-based algorithms which gained popularity within a short time and has different applications. It is easy to understand and implement. The existing studies show that it is prone to premature convergence and suggest the relaxation of having constant parameters. To boost the performance of the algorithm, different modifications are done by several researchers. In this chapter, we will review these modifications done on the standard firefly algorithm based on parameter modification, modified search strategy and change the solution space to make the search easy using different probability distributions. The modifications are done for continuous as well as non-continuous problems. Different studies including hybridization of firefly algorithm with other algorithms, extended firefly algorithm for multiobjective as well as multilevel optimization problems, for dynamic problems, constraint handling and convergence study will also be briefly reviewed. A simulation-based comparison will also be provided to analyse the performance of the standard as well as the modified versions of the algorithm

Modeling cross-national differences in automated vehicle acceptance

The technology that allows fully automated driving already exists and it may gradually enter the market over the forthcoming decades. Technology assimilation and automated vehicle acceptance in different countries is of high interest to many scholars, manufacturers, and policymakers worldwide. We model the mode choice between automated vehicles and conventional cars using a mixed multinomial logit heteroskedastic error component type model. Specifically, we capture preference heterogeneity assuming a continuous distribution across individuals. Different choice scenarios, based on respondents’ reported trip, were presented to respondents from six European countries: Cyprus, Hungary, Iceland, Montenegro, Slovenia, and the UK. We found that large reservations towards automated vehicles exist in all countries with 70% conventional private car choices, and 30% automated vehicles choices. We found that men, under the age of 60, with a high income who currently use private car, are more likely to be early adopters of automated vehicles. We found significant differences in automated vehicles acceptance in different countries. Individuals from Slovenia and Cyprus show higher automated vehicles acceptance while individuals from wealthier countries, UK, and Iceland, show more reservations towards them. Nontrading mode choice behaviors, value of travel time, and differences in model parameters among the different countries are discussed

QSRED, an algorithm to solve the mismatch between the microscopic and macroscopic behavior of RED gateways

Network congestion is a phenomenon caused by the extreme demand of restricted network resources. Various congestion control strategies have been proposed to increase network performance. This study suggests that there is a mismatch between the microscopic and macroscopic behavior in (Random Early Detection) RED’s queue management mechanism. This work investigates this problem and propose QSRED (Queue Sectors RED) to avoid unsatisfactory performance. QSRED is simulated against RED and ERED (Effective RED) by measuring: throughput, link utilization, packets loss and average delay using the NS2 simulator. The results suggest that Queue Sectors RED (QSRED) helps RED overcome the mismatch between microscopic and macroscopic behavior of queue length dynamics

Private subnetting one (PS1), an algorithm for private IPv4 address allocation

Network hosts in private enterprises require local connectivity without any external connections. These networks operated using TCP/IP protocol and require a private address space. This article proposes a novel algorithm for IPv4 address allocation for this type of networks. This algorithm can be used by educators of subnetting, ISPs, university students and researchers to simplify the process of IPv4 address allocation. A tool called Private Subnetting One (PS1) programmed under Java is introduced in this article. This tool can be used by network engineers or university tutors to explain complex subnetting problems to students in introductory networking courses, such as CCNA

Modeling Advertising Practices for Product Involvement and Consumer Impulsivity in Branded Apparel: A Case Study of Indian Consumers

The exponential growth of advertising in enhancing consumer impulsivity has drawn the interest of many researchers to explore the various dimensions of advertising and its effective modeling. The branded apparel product market has grown significantly, a large number of competitors have entered into the market with variant quality, and there are a number of attractive advertising practices. The main aim of this study was to analyze the advertising practices and their relationship with consumer product involvement and the impulsive buying behavior of consumers in branded apparel in India. In a survey of 445 Indian customers, the study indicated that advertising practices significantly affect consumer involvement in branded apparel products and enhance their impulsivity toward products. The study also indicated that the level of consumer involvement in the branded apparel product significantly mediates the relationship between advertising practices and consumer impulsive buying behavior. Effective advertising practices will help companies to enhance consumer involvement that will enable branded apparel companies in enhancing consumer impulsiveness toward products. Some of the managerial implications, limitations, and scope of future research are also presented in the study

Bivariate Generalized Shifted Gegenbauer Orthogonal System

For K0,K1≥0, λ>−1/2, we examine Cr∗λ,K0,K1x, generalized shifted Gegenbauer orthogonal polynomials, with reference to the weight Wλ,K0,K1x=2λΓ2λ/Γλ+1/22x−x2λ−1/2Ix∈0,1dx+K0δ0+K1δ1, where the indicator function is denoted by Ix∈0,1 and δx indicates the Dirac δ−measure. Then, we construct a bivariate generalized shifted Gegenbauer orthogonal system ℭn,r,d∗λ,K0,K1u,v,w over a triangular domain T, with reference to a bivariate measure Wλ,γ,K0,K1u,v,w=Γ2λ+1/Γλ+1/22uλ−1/21−vλ−1/21−wγ−1Iu∈0,1−wIw∈0,1dudw+K0δ0u+K1δw−1u, which is given explicitly in the Bézier form as ℭn,r,d∗λ,K0,K1u,v,w=∑i+j+k=nai,j,kn,r,dBi,j,knu,v,w. In addition, for d=0,…,k, r=0,1,…,n, and n∈0∪ℕ, we write the coefficients ai,j,kn,r,d in closed form and produce an equation that generates the coefficients recursively

Using Artificial Neural Network with Prey Predator Algorithm for Prediction of the COVID-19: The Case of Brazil and Mexico

The spread of the COVID-19 epidemic worldwide has led to investigations in various aspects, including the estimation of expected cases. As it helps in identifying the need to deal with cases caused by the pandemic. In this study, we have used artificial neural networks (ANNs) to predict the number of cases of COVID-19 in Brazil and Mexico in the upcoming days. Prey predator algorithm (PPA), as a type of metaheuristic algorithm, is used to train the models. The proposed ANN models’ performance has been analyzed by the root mean squared error (RMSE) function and correlation coefficient (R). It is demonstrated that the ANN models have the highest performance in predicting the number of infections (active cases), recoveries, and deaths in Brazil and Mexico. The simulation results of the ANN models show very well predicted values. Percentages of the ANN’s prediction errors with metaheuristic algorithms are significantly lower than traditional monolithic neural networks. The study shows the expected numbers of infections, recoveries, and deaths that Brazil and Mexico will reach daily at the beginning of 2021

Influence of Electronic Word of Mouth (eWOM) and Relationship Marketing on Brand Resonance: A Mediation Analysis

This study investigates how electronic work of mouth (eWOM) mediates the relationship between marketing relations and brand resonance. Based on the information obtained from 473 customers using an online questionnaire, this study analyses the relationship between eWOM, relationship marketing practices and the brand resonance of lifestyle products in an Indian context. The results from the multiple regression analysis indicate that the proposed hypotheses are valid, that relationship marketing significantly affects brand resonance, and that eWOM significantly mediates the relationship between the relationship marketing and brand resonance of branded apparel. The findings suggest that personalized attention and tangible rewards are effective relationship marketing strategies, and that these relationship-marketing practices—in association with eWOM—build up the strong brand resonance of branded apparel. The present study recommends that marketers should place emphasis on effective online and offline relationship marketing strategies, and should design appropriate eWOM strategies to enhance brand loyalty, brand attachment, brand community and brand engagement. Some of the managerial implications and the future scope of study based on the empirical findings are also highlighted in the present research work