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YouTube allows its users to upload and view videos on its platform. YouTube provides notification to the subscribers whenever a channel uploads a new video thereby making the channel subscribers the potential viewers of the video. And thus, they are the first to come to know about any new offering. But later on, the view count also increases due to virality, that is, mass sharing of the content by the users on different social media platforms similar to word-of-mouth in the field of marketing. Therefore, the purpose of this paper is to examine different diffusion patterns as they can help to inflate traffic and generate revenue.

YouTube's view count grows majorly through virality. The pattern of view count growth has generally been considered unimodal in most of the available research in the field of YouTube. In the present work, the growth process due to views through the subscribers and views due to word-of-mouth (virality) is presented. Considering that the impact of virality in view count growth comes later in the video life cycle; the viewing patterns of both the segments have been mathematically modeled; independently.

Different models have been proposed to capture the view count growth pattern and how the impact of virality changes the view count growth curve and thereby results in a multimodal curve structure. The proposed models have been verified on various view count data sets of YouTube videos using SPSS (Statistical Package for the Social Sciences), and their ranks have been determined using a weighted criteria–based approach. The results obtained clearly depict the presence of many modes in the life cycle of view counts.

Till now, the literature is evident of the video life cycle following a bell shape curve. This study claims that the initial thrust is by subscribers and then the contribution in the view count by people watching via word-of-mouth comes into picture and brings in another hump in the growth curve.

Metaheuristic algorithms have been commonly used as an optimisation tool in various fields. However, optimisation of real-world problems has become increasingly challenging with to increase in system complexity. This situation has become a pull factor to introduce an efficient metaheuristic. This study aims to propose a novel sport-inspired algorithm based on a football playing style called tiki-taka.

The tiki-taka football style is characterised by short passing, player positioning and maintaining possession. This style aims to dominate the ball possession and defeat opponents using its tactical superiority. The proposed tiki-taka algorithm (TTA) simulates the short passing and player positioning behaviour for optimisation. The algorithm was tested using 19 benchmark functions and five engineering design problems. The performance of the proposed algorithm was compared with 11 other metaheuristics from sport-based, highly cited and recent algorithms.

The results showed that the TTA is extremely competitive, ranking first and second on 84% of benchmark problems. The proposed algorithm performs best in two engineering design problems and ranks second in the three remaining problems.

The originality of the proposed algorithm is the short passing strategy that exploits a nearby player to move to a better position.

The abuse of alcohol and other drugs (AOD) has become one of the most important social problems in modern society. AOD abuse causes untold personal anguish to the abusers and their families, is associated with high levels of crime, and the health consequences include death and disability. Billions are spent on treatment, prevention, and incarceration. The papers in this special issue focus on two key issues in the study of AOD abuse: etiology and treatment. Etiology is a key factor because it is the foundation for prevention and treatment programs. If the etiology of AOD abuse can be unraveled, abuse can be prevented and treatment can be effectively targeted. But, effective treatment also requires an understanding of how treatment works.

The purpose of this paper is to present a new modification of the multimodal pushover method, named the target acceleration method. The target acceleration is the minimum acceleration of the base that leads to the ultimate limit state of the structure, i.e., the lowest seismic resistance.

A nonlinear numerical model is used to determine the target acceleration, which is achieved using the iterative procedure according to the envelope principle. Validation of the target acceleration method was conducted on the basis of the results obtained by incremental dynamic analysis.

The influence of higher modes is highly significant. The general failure vector corresponding to the target acceleration differs from the first load vector and the form of the load with uniform acceleration according to the height of structure, as contained in the European Standard EN 1998-1. Comparison between the target acceleration, including the equivalent structural damping, and the failure peak ground acceleration obtained from the dynamic response of the structure exhibits notably good agreement. This result implies that the equivalent structural damping as calculated according to the formulation presented in this paper should be greater than that suggested in the literature.

The originally developed procedure named multimodal pushover target acceleration method can reasonably estimate the minimum acceleration of the base that leads to the ultimate limit state of the structure, and consequently provides a reliable tool for the assessment of the lowest seismic resistance.

This paper aims to present three girls’ visual annotations and digital responses that restory a scene in the picturebook I’m New Here. The authors focus on how children use multimodal tools to reflect their critical knowledge of the world by illuminating how this group of girls responded to and incorporated broader social issues.

This study takes place in a third-grade classroom. Using qualitative methods that build on critical multimodal literacy, the authors documented and analyzed children’s visual and digital interpretations. Data were generated from classroom sessions that incorporated interactive readalouds, as well as students’ annotated visual images, sketches, video and digital responses. The collaborative analytic process involved multiple passes to interpret visual, textual and multimodal elements.

The analyses revealed how Aliyah, Tiana and Carissa used multimodal tools to engage in the process of restorying. Through their multimodal composition, they designed images that illuminated their solidarity with the young female character wearing the hijab; their desire to disrupt xenophobic bullying; and their hope for a respectful and inclusive climate in their own classroom.

In this paper, the authors examine how three girls in a third-grade classroom restory using critical multimodal literacy methods. These girls’ multimodal responses reflected how they disrupted dominant storylines of exclusionary practices. Their authentic acts of visual advocacy give us hope for the future.

This paper presents a curvilinearly‐established finite‐difference time‐domain methodology for the enhanced 3D analysis of electromagnetic and acoustic propagation in generalised electromagnetic compatibility devices, junctions or bent ducts. Based on an exact multimodal decomposition and a higher‐order differencing topology, the new technique successfully treats complex systems of varying cross‐section and guarantees the consistent evaluation of their scattering parameters or resonance frequencies. To subdue the non‐separable modes at the structures' interfaces, a convergent grid approach is developed, while the tough case of abrupt excitations is also studied. Thus, the proposed algorithm attains significant accuracy and savings, as numerically verified by various practical problems.

Distribution of long-haul goods could be managed via multimodal transportation networks where decision-maker has to consider these factors including the uncertainty of transportation time and cost, the timetable limitation of selected modes and the storage cost incurred in advance or delay arriving of the goods. Considering the above factors comprehensively, this paper establishes a multimodal multi-objective route optimization model which aims to minimize total transportation duration and cost. This study could be used as a reference for decision-maker to transportation plans.

Monte Carlo (MC) simulation is introduced to deal with transportation uncertainty and the NSGA-II algorithm with an external archival elite retention strategy is designed. An efficient transformation method based on data-drive to overcome the high time-consuming problem brought by MC simulation. Other contribution of this study is developed a scheme risk assessment method for the non-absolutely optimal Pareto frontier solution set obtained by the NSGA-II algorithm.

Numerical examples verify the effectiveness of the proposed algorithm as it is able to find a high-quality solution and the risk assessment method proposed in this paper can provide support for the route decision.

The impact of timetable on transportation duration is analyzed and making a detailed description in the mathematical model. The uncertain transportation duration and cost are represented by random number that obeys a certain distribution and designed NSGA-II with MC simulation to solve the proposed problem. The data-driven strategy is adopted to reduce the computational time caused by the combination of evolutionary algorithm and MC simulation. The elite retention strategy with external archiving is created to improve the quality of solutions. A risk assessment approach is proposed for the solution scheme and in the numerical simulation experiment.

Student engagement is a key factor that connects with student achievement and retention. This paper aims to identify individuals' engagement automatically in the classroom with multimodal data for supporting educational research.

The video and electroencephalogram data of 36 undergraduates were collected to represent observable and internal information. Since different modal data have different granularity, this study proposed the Fast–Slow Neural Network (FSNN) to detect engagement through both observable and internal information, with an asynchrony structure to preserve the sequence information of data with different granularity.

Experimental results show that the proposed algorithm can recognize engagement better than the traditional data fusion methods. The results are also analyzed to figure out the reasons for the better performance of the proposed FSNN.

This study combined multimodal data from observable and internal aspects to improve the accuracy of engagement detection in the classroom. The proposed FSNN used the asynchronous process to deal with the problem of remaining sequential information when facing multimodal data with different granularity.

The purpose of this paper is to propose a robust method to estimate the parameters of Jiles‐Atherton model of ferromagnetic hysteresis by fitting the model to symmetrical hysteresis loops. The performance of the method is evaluated by both theoretical and experimental data.

Jiles‐Atherton model with five parameters describes the hysteretic behaviour of ferromagnetic materials. To calculate the model parameters, the most common data displaying the hysteresis features are the hysteresis loops stimulated by symmetrical steady state excitations, e.g. sinusoidal sources. Using the characteristic equations at specific points on these hysteresis loops, the Jiles‐Atherton model parameters can be determined by curve fitting and numerical optimized iteration.

Practicality and robustness were not well considered by the conventional parameter estimation method: the initial curve starting from demagnetization is not always available and a direct iterative algorithm to solve the characteristic equations is sensitive to the initial values of the iteration and the evaluation order of the equations because of two main reasons. The first one is that the basic equation group has non‐unique solutions, which is caused by the nonlinearity of the characteristic equations and the fact that there are more unknown quantities (i.e. five model parameters) than the equations available; the second reason is the multimodal feature of the problem, which means that there are many local minima for the iteration algorithm to be trapped in. So curve fitting around the loop tips is proposed before numerical iteration. The goal is to make the initial values, particularly saturation magnetization M_s, not far away from the desired solution to increase the possibility of converging to a physical result. The optimized iterative method with the enhanced ability to avoid local minima and find global roots is then applied to obtain the model parameters.

The proposed method overcomes the difficulties of the other techniques which assume zero remanence. It is also robust as it is guaranteed to converge to physical solutions. The method can facilitate further development and it formed the preliminary basis of our earlier work, where the variation of the Jiles‐Atherton model parameters with different magnetic field strengths was investigated and applied to the simulation of transformer inrush current.

This paper explores the various alternative routes and methods available to garment exporters in Lao PDR, a land‐locked country in South East Asia, when exporting to the European Union. Lao exporters are dependent on the transport systems in place in neighbouring countries (i.e. Vietnam, Thailand, Malaysia and Singapore) for transit purposes. A multimodal transport cost‐model is used to illustrate and clarify multimodal transport routeing alternatives. A confidence index is also introduced for each route, transport modes and nodal links. Five routeing alternatives are presented in this paper and it is shown that the most frequently utilised route via Bangkok (Thailand) is not necessarily the most competitive in terms of time and cost, while the route via Port Klang (Malaysia) potentially offers a better alternative for Lao garment exporters.