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.

Abstract
<p>The Dansgaard-Oeschger (DO) events are one of the most striking examples of abrupt climate change in the Earth's history, representing temperature oscillations of about 8 to 16 degrees Celsius within a few decades. DO events have been studied extensively in paleoclimatic records, particularly in ice core proxies. Examples include the Greenland NGRIP record of oxygen isotopic composition.<br>This work addresses the anticipation of DO events using machine learning algorithms. We consider the NGRIP time series from 20 to 60 kyr b2k with the GICC05 timescale and 20-year temporal resolution. Forecasting horizons range from 0 (nowcasting) to 400 years. We adopt three different machine learning algorithms (random forests, support vector machines, and logistic regression) in training windows of 5 kyr. We perform validation on subsequent test windows of 5 kyr, based on timestamps of previous DO events' classification in Greenland by Rasmussen et al. (2014). We perform experiments with both sliding and growing windows.<br>Results show that predictions on sliding windows are better overall, indicating that modelling is affected by non-stationary characteristics of the time series. The three algorithms' predictive performance is similar, with a slightly better performance of random forest models for shorter forecast horizons. The prediction models' predictive capability decreases as the forecasting horizon grows more extensive but remains reasonable up to 120 years. Model performance deprecation is mostly related to imprecision in accurately determining the start and end time of events and identifying some periods as DO events when such is not valid.</p>

Abstract