Abstract
Multi-Label Stream Classification (MLSC) is the classification streaming examples into multiple classes simultaneously. Since new classes may emerge during the streaming process (concept evolution) and known classes may change over time (concept drift) it is challenging task. In real situations, concept drift and concept evolution occur in scenarios where the actual labels of arriving examples are never available; hence it is impractical to update decision models in a supervised fashion. This is known as Extreme Verification Latency, a topic that has not been well investigated in MLSC literature. This paper proposes a new method called MultI-label learNing Algorithm for Data Streams with Binary Relevance transformation (MINAS-BR), integrated with a Novelty Detection (ND) procedure for detecting concept evolution and concept drift, updating the model in an unsupervised fashion. Furthermore, since the label space is not static, we propose a new evaluation methodology for MLSC under extreme verification latency. Experiments over synthetic and real-world data sets with different concept drift and concept evolution scenarios confirmed the strategies employed in the MINAS-BR and presented relevant advances for handling streaming multi-label data.

Abstract
Simulators have been increasingly used on development and tests on several areas. They allow to speed up the development without damage and no extra costs. On realistic simulators, where kinematics play an important role, the modelling process should be imported for each component to be accurately simulated. Some robots are not yet modelled, as for example the Monera. This paper presents a model of a small vibration robot (Monera) that is acquired in a developed test-bed. A localisation ground truth is used to acquire the position of the Monera with actuating it. Linear and angular speeds acquired from real experiments allow to validate the proposed methodology.

Abstract
Optimizing research on the developmental origins of health and disease (DOHaD) involves implementing initiatives maximizing the use of the available cohort study data; achieving sufficient statistical power to support subgroup analysis; and using participant data presenting adequate follow-up and exposure heterogeneity. It also involves being able to undertake comparison, cross-validation, or replication across data sets. To answer these requirements, cohort study data need to be findable, accessible, interoperable, and reusable (FAIR), and more particularly, it often needs to be harmonized. Harmonization is required to achieve or improve comparability of the putatively equivalent measures collected by different studies on different individuals. Although the characteristics of the research initiatives generating and using harmonized data vary extensively, all are confronted by similar issues. Having to collate, understand, process, host, and co-analyze data from individual cohort studies is particularly challenging. The scientific success and timely management of projects can be facilitated by an ensemble of factors. The current document provides an overview of the 'life course' of research projects requiring harmonization of existing data and highlights key elements to be considered from the inception to the end of the project.

Abstract
The delivery of concrete is a crucial process in construction projects, and any delay or error can cause significant setbacks and added costs. Thus, effective real-time management of concrete delivery is essential to ensure timely and successful project completion. In this paper, we will discuss a practical and manufacturer-agnostic approach to real-time management of concrete delivery for construction named BET 4.0 that is being conceived with a close partnership with a construction company. This application provides the possibility to optimize the whole concreting process as it establishes the connection between all the relevant components and stakeholders involved in the construction process, namely the concrete plant, the transport, and the construction site, interfacing with all actors involved, and benefiting from real-time data produced by installed sensors in the several components such as machines, plants, or construction elements.

Abstract
Advancement and opportunity in the Internet of Things (IoT) and circular economy are pushing the technologies required to develop eco-friendly memory devices, computing devices, advanced sensors, and actuators. In this manuscript, a thermally cycled lithium niobate pyroelectric crystal is used to store the surface charges in different dielectric samples (Kapton, Parafilm, and Teflon). Charge storing parameters, such as the effect of temperature ramp, the gap between the dielectric-to-pyroelectric, and the effect of charging cycles, were studied to understand the surface charge formation on dielectric samples. Pyroelectrically charged dielectrics showed a surface potential of up to 400 V, with a linear dependence on the thermal gradient of the pyroelectric crystal. The charged surface showed good charge storage uniformity and stability at high temperatures (90 degrees C) and relative humidity (>85%). Using the pyroelectrically charged dielectrics, wearable motion sensors offering output signals in the range of tens of millivolts and a digital flexible invisible memory encoding with polarity switched (positive and negative charges) electrostatic bits are demonstrated.

Abstract
As the digital world grows, data is being collected at high speed on a continuous and real-time scale. Hence, the imposed imbalanced and evolving scenario that introduces learning from streaming data remains a challenge. As the research field is still open to consistent strategies that assess continuous and evolving data properties, this paper proposes an unsupervised, online, and incremental anomaly detection ensemble of influence trees that implement adaptive mechanisms to deal with inactive or saturated leaves. This proposal features the fourth standardized moment, also known as kurtosis, as the splitting criteria and the isolation score, Shannon's information content, and the influence function of an instance as the anomaly score. In addition to improving interpretability, this proposal is also evaluated on publicly available datasets, providing a detailed discussion of the results.