Abstract
We compare two interval time series (ITS) by testing whether their underlying distributions are significantly different or not. To perform hypothesis testing, we make use of the discrete wavelet transform (DWT) which decomposes a time series into a set of coefficients over a number of frequency bands or scales. We obtain the DWT of the radius and centre of each of the two ITS at different scales, and perform randomisation tests. In order to use a randomisation test, the observations must be uncorrelated; this condition is more or less satisfied since at each scale, the DWT coefficients are approximately uncorrelated with each other. Our proposed test statistic is the ratio of the determinants of the covariance matrix of radius and centre DWTs of the two ITS, at each scale. This test statistic ensures that the variability between the upper and lower bounds of each ITS is encompassed. Simulation studies conducted to evaluate the performance of the test show reasonably good estimates of size and power under most conditions, and applications to real interval time series reveal the practical usefulness of this test.

Abstract
Keeping students engaged while learning programming is becoming more and more imperative. Of the several proposed techniques, gamification is presumably the most widely studied and has already proven as an effective means to engage students. However, there is a complete lack of public and customizable solutions to gamified programming education that can be reused with personalized rules and learning material. FGPE Gamification Service (FGPE GS) is an open-source GraphQL service that transforms a package containing the gamification layer – adhering to a dedicated open-source language, GEdIL – into a game. The game provides students with a gamified experience leveraging on the automatically-assessable activities referenced by the challenges. This paper presents FGPE GS, its architecture, data model, and validation.

Abstract
In the Physical Internet supply chain paradigm, modular boxes are one of the main drivers. The dimension of the modular boxes has already been subject to some studies. However, the usage of a modular approach on the container loading problem has not been accessed. In thiswork, we aim to assess the impact of modular boxes in the context of the Physical Internet on the optimization of loading solutions. A mathematical model for the CLP problem is used, and extensive computational experimentswere performed in a set of problem instances generated considering the Physical Internet concept. From this study, it was possible to conclude for the used instances that modular boxes contribute to a higher volume usage and lower computational times.

Abstract
Sound design has been a fundamental component of audiovisual storytelling in linear media. However, with recent technological developments and the shift towards non-linear and immersive media, things are rapidly changing. More sensory information is available and, at the same time, the user is gaining agency upon the narrative, being offered the possibility of navigating or making other decisions. These new characteristics of immersive environments bring new challenges to storytelling in interactive narratives and require new strategies and techniques for audiovisual narrative progression. Can technology offer an immersive environment where the user has the sensation of agency, of choice, where her actions are not mediated by evident controls but subliminally induced in a way that it is ensured that a narrative is being followed? Can sound be a subliminal element that induces attentional focus on the most relevant elements for the narrative, inducing storytelling and biasing search in an immersive non-linear audiovisual environment? Herein, we present a literature review that has been guided by this prospect. With these questions in view, we present our exploration process in finding possible answers and potential solution paths. We point out that consistency, in terms of coherency across sensory modalities and emotional matching may be a critical aspect. Finally, we consider that this review may open up new paths for experimental studies that could, in the future, provide new strategies in the practice of sound design in the context of non-linear media.

Abstract
With an increasing interest in the digitization effort of ancient manuscripts, ancient character recognition becomes one of the most important areas in the automated document image analysis. In this regard, we propose a Convolutional Neural Network (CNN)-based classifier to recognize the ancient Sundanese characters obtained from a digital collection of Southeast Asian palm leaf manuscripts. In this work, we utilize two different preprocessing techniques for the dataset. The first technique involves the use of geometric transformations, noise background addition, and brightness adjustment to augment the imbalanced samples to be fed into the classifier. The second technique makes use of the Otsu's threshold method to binarize the characters and only uses the usual geometric transformations for the data augmentation. The proposed network with different data augmentation processes is trained on the training set and tested on the testing set. Image binarization from the second technique can outperform the performance of the CNN-based classifier upon the first technique by achieving a testing accuracy of 97.74%. (C) 2021 The Authors. Published by Elsevier B.V.

Abstract
Flexibility is key for the decarbonization of the energy sector, contributing to decrease uncertainty in the operation of distribution networks, due to the connection of renewable energy sources and electric vehicles. However, effective deployment requires interoperable and replicable solutions, technologically agnostic and independent from the role of each actor and market models adopted. This paper presents an overview of ongoing projects that aim to deliver and demonstrate interoperable solutions across the full value chain of the energy sector. The main objective and expected results of the H2020 InterConnect, EUniversal and OneNet projects will be presented. © 2021 The Institution of Engineering and Technology.