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.

Abstract
Cutting and packing problems have been widely studied in the last decades, mainly due to the variety of industrial applications where the problems emerge. This paper presents an overview of the solution approaches that have been proposed for solving two-dimensional rectangular cutting and packing problems. The main emphasis of this work is on two distinct problems that belong to the cutting and packing problem family. The first problem aims to place onto an object the maximum-profit subset of items, that is, output maximization, while the second one aims to place all the items using as few identical objects as possible, that is, input minimization. The objective of this paper is not to be exhaustive but to provide a solid grasp on two-dimensional rectangular cutting and packing problems by describing their most important solution approaches.

Abstract

Abstract
Intelligent wheelchairs aim to improve mobility limitations by providing ingenious mechanisms to control and move the chair. This paper aims to enhance the autonomy level of intelligent wheelchair navigation by applying reinforcement learning algorithms to move the chair to the desired location. Also, as a second objective, add one more chair and move both chairs in pairs to promote group social activities. The experimental setup is based on a simulated environment using gazebo and ROS where a leader chair moves towards a goal, and the follower chair should navigate near the leader chair. The collected metrics (time to complete the task and the trajectories of the chairs) demonstrated that Deep Q-Network (DQN) achieved better results than the Q-Learning algorithm by being the unique algorithm to accomplish the pair navigation behaviour between two chairs.

Abstract
Infrastructure construction companies encounter numerous challenges in managing large and dispersed teams, along with fragmented or non-existent operational data. These challenges often result in delays, inefficiencies, and over-budget projects. Road pavement works are an example of this, as they heavily rely on expensive heavy construction equipment that requires detailed planning and real-time adjustments. Also, pavement quality is closely linked to the quality of the asphalt mixture in terms of viscosity and compactability, which is significantly influenced by temperature. This paper describes the features, challenges and results of a road paving real-time management system that was conceived in a co-creation environment with a construction company. Such a partnership has allowed to specify the requirements of such an application aligned with the identified needs of a real-world development. According with the state-of-the-art, this innovative system is unique in the way it is manufacturer-agnostic and designed to be compatible with most situations. It is also data production-oriented to allow future developments that may provide business analytics or scientific research in the road paving area. This work also presents the development of sensors such as a high precision geolocalized infrared matricial temperature sensor for the application of the bituminous mixture, the data and communication structure, and a web-based interface that manages the construction projects for different stakeholders.