Abstract
Deep learning approaches are increasingly used to tackle forecasting tasks but require substantial training data. When samples are limited, synthetic data generation techniques can effectively augment datasets to improve model performance. Data augmentation is typically applied offline before training a model. However, when training with mini-batches, some batches may contain a disproportionate number of synthetic samples that do not align well with the original data characteristics. This work introduces an online data augmentation framework that generates synthetic samples during the training of neural networks. By creating synthetic samples for each batch alongside their original counterparts, we maintain a balanced representation between real and synthetic data throughout the training process. This approach fits naturally with the iterative nature of neural network training and eliminates the need to store large augmented datasets. We validated the proposed framework using 3797 time series from 6 benchmark datasets, three neural architectures, and seven synthetic data generation techniques. The experiments suggest that online data augmentation leads to better forecasting performance compared to offline data augmentation or no augmentation approaches. The framework and experiments are publicly available. © 2025 Elsevier B.V., All rights reserved.

Abstract

Abstract
A study of an Eocene fish fossil using portable XRF revealed distinct geochemical differences between the fossil and surrounding sediment. Elements like uranium, yttrium, arsenic, and phosphorus were found only in the fossil, while calcium and iron appeared in both regions. These patterns point to selective elemental incorporation during early fossilization and diagenesis processes. The results highlight XRF's usefulness in verifying fossil authenticity, provenance and understanding the chemical processes during fossilization. © 2025 Elsevier B.V., All rights reserved.

Abstract
Even for the strongest human being, maintaining an elevated arm position for an extended duration represents a significant challenge, as fatigue inevitably accumulates over time. The physical strain is further intensified when the individual is engaged in repetitive tasks, particularly those involving the use of tools or heavy equipment. Such activities increase the probability of developing muscle fatigue or injuries due to overuse or improper posture. Over time, this can result in the development of chronic conditions, which may impair the individual's ability to perform tasks effectively and potentially lead to long-term physical impairment. Exoskeletons play a transformative role by reducing the perceived load on the muscles and providing mechanical support, mitigating the risk of injuries and alleviating the physical burden associated with strenuous activities. In addition to injury prevention, these devices also promise to facilitate the rehabilitation of individuals who have sustained musculoskeletal injuries. This document examines the various types of exoskeletons, investigating their design, functionality, and applications. The objective of this study is to present a comprehensive understanding of the current state of these devices, highlighting advancements in the field and evaluating their real-world impact. Furthermore, it analyzes the crucial insights obtained by other researchers, and by summarizing these findings, this work aims to contribute to the ongoing efforts to enhance exoskeleton performance and expand their accessibility across different sectors, including agriculture, healthcare, industrial work, and beyond.

Abstract
PurposeThis study aims to comparatively evaluate two semantic models, ArchOnto (CIDOC CRM based) and Records in Contexts Ontology (RiC-O), for archival representation within the Linked Open Data framework. The research seeks to critically analyse their ability to represent archival documents, events, activities, and provenance through the application on a case study of historical baptism records.Design/methodology/approachThe study adopted a comparative approach, utilising the two models to represent a dataset of baptism records from a Portuguese parish spanning several centuries. This involved information extraction and conversion processes, transforming XML EAD finding aids into RDF to facilitate more explicit semantic representation and analysis.FindingsThe analysis revealed distinctive strengths and limitations of each semantic model, providing nuanced insights into their respective capacities for archival description. The findings guide cultural heritage institutions in selecting and implementing the most suitable semantic model for their needs and pave the way for semantic alignment between the two models.Research limitations/implicationsAlthough the case study explored the representation of a wide range of features, potential limitations include the specific contextual constraints of parish records and the need for broader comparative studies across diverse archival contexts.Originality/valueThis paper offers original insights into semantic modelling for archival representations by providing a detailed comparative analysis of two ontological approaches. It offers valuable perspectives for archivists, digital humanities researchers, and cultural heritage professionals seeking to enhance the semantic richness of archival descriptions.

Abstract
Formal verification has recently been increasingly used to prove the correctness and security of many applications. It is attractive because it can prove the absence of errors with the same certainty as mathematicians proving theorems. However, while most security experts recognize the value of formal verification, the views of non-technical users on this topic are unknown. We designed and implemented two experiments to address this issue to understand how formal verification impacts users. Our approach started with a formative study involving 15 participants, followed by the main quantitative study with 200 individuals. We focus on the application domain of Password Managers (PMs) since it has been documented that the lack of trust in PMs might lead to lower adoption. Moreover, recent efforts have focused on formally verifying (parts of) PMs. We conclude that formal verification is seen as desirable by users and identify three actionable recommendations to improve formal verification communication efforts.