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In the context of urban sustainability and the development of resilient cities, the use of 4D geospatial data and the integration and association of building information with geographical information are of considerable interest. Achieving this integration is particularly significant in the scientific field from a technical standpoint but poses significant challenges due to the incompatibility between the two environments. This research proposes various methodologies for the effective integration of BIM/GIS data by analyzing their pros and cons and highlights the innovative aspects of the integration between these systems. Starting with the use of commercial software that has enabled the integration of a building's 3D model within a GIS environment (this system is particularly useful for its ease of management and the potential for practical applications), this study progresses to an experimental virtual/augmented/mixed reality app developed by the authors that allows for the virtual integration of a building with its territorial context. It concludes with an innovative methodology that, by using the customizable and extensible libraries of the Cesium platform, facilitates the integration of structural data within a 4D geospatial space. This study demonstrates the feasibility of integrating BIM and GIS data despite inherent incompatibilities. The innovative use of Cesium platform libraries further enhances this integration, providing a comprehensive solution for intelligent and sustainable urban planning. By addressing the challenges of incompatibility, the final solution offers critical insights for a deeper understanding of evolving urban landscapes and for monitoring urban expansion and its environmental impacts. [ABSTRACT FROM AUTHOR]

The uncertainty that characterizes the external mechanical loads to which any connection plate in steel structures is subjected determines the non-uniqueness of the isochoric deformation distributions. Since the eddy currents induced on the plates produce magnetic field maps with a high fuzziness content, similar to those of the isochoric deformations, their use can be exploited to evaluate the extent of the external load that determines a specific induced current map. Starting from an approach known in the literature, according to which the map-external load association is operated through fuzzy similarity computations, in this paper, we generalize this method by reformulating it in terms of intuitionistic fuzzy logic by proposing a classification based on divergence computations. Our approach, acting adaptively on the fuzzification of the maps, results in a better classification percentage, besides significantly reducing the presence of doubtful cases due to the uncertainty of each applied load. Furthermore, a FEM software tool was developed, which turned out to be, to a certain extent, a substitute for the experimental procedure, notoriously more expensive. Even if the procedure was applied on plates subjected to bi-axial loads, it could be used for other types of loads since the classification operator processes the eddy current maps exclusively, regardless of their cause. [ABSTRACT FROM AUTHOR]

Interest in Unmanned Aerial Vehicles (UAVs) has been increasingly growing in recent years, especially for purposes other than those for which they were initially used (civil and military purposes). Currently, in fact, they are used for advanced monitoring and control purposes, for 3D reconstructions of the territory and cultural heritage, and for freight transport. The problem in using these systems consists of the limited flight autonomy. In fact, commercially used drones, today, are sold with a set of batteries of limited duration which do not allow flights over large areas and, therefore, detailed surveys. The present work seeks to overcome these limitations by proposing an intelligent automatic charging system (Intelligent Charging Network) created using PC Engines Alix and an experimental drone prototype using a Raspberry Pi 3 and a Navio 2 module. At the same time, an efficient Intelligent Charging Network–drone communication system and a data transmission system are proposed, which allow images acquired by the drone to be transferred directly to the server used for data storage for their subsequent processing as well as the transmission of the flight plan from the QGroundControl application to the drone. The proposed system represents technological innovation in the field of drones with potential future developments linked to the implementation of sustainable drones. [ABSTRACT FROM AUTHOR]

Non-destructive testing (NDT) techniques are crucial in making informed decisions about reconstructing or repairing building structures. The SonReb method, a combination of the rebound hammer (RH) and the ultrasonic pulse velocity (UPV) tests, is widely used for this purpose. To evaluate the compressive strength, C S , of the concrete under investigation, the ultrasonic pulse velocity V p and the rebound index R must be mapped to the compressive strength C S using a suitable conversion model, the identification of which requires supplementing the NDT measurements with destructive-type measurements (DT) on a relatively large number of concrete cores. An approach notably indicated in all cases where the minimization of the number of cores is essential is to employ a pre-existing conversion model, i.e., a model derived from previous studies conducted in the literature, which must be appropriately calibrated. In this paper, we investigate the performance of Gaussian process regression (GPR) in calibrating the pre-existing SonReb conversion models, exploiting their ability to handle nonlinearity and uncertainties. The numerical results obtained using experimental data collected from the literature show that GPR calibration is very effective, outperforming, in most cases, the standard multiplicative and additive techniques used to calibrate the SonReb models. [ABSTRACT FROM AUTHOR]

Most clinical measurements rely on bioelectromagnetic phenomena. These events allow us to record electric or magnetic signals during the activity of living tissues. In this paper, we put our attention on the bioelectric fields that occur in the muscle activity. In fact during the body movements, the muscle contractions produce a bioelectric potential distribution that can be measured by putting the electrodes on the skin. In clinical applications, the monitoring of muscle activity can be performed in a noninvasive way, by placing a fixed number of electrodes on the skin surface; this technique is called surface electromyography (sEMG), and it is able to reveal the electric field generated by each muscle activity. Unfortunately, the sEMG suffers from the external electromagnetic fields. In recent years, many authors investigated the correlation between muscle and brain activity by performing the sEMG during the functional magnetic resonance imaging (fMRI). The fMRI is the most reliable technique to evaluate the brain activity because it allows us to obtain some images of the human body with very high resolutions. Unfortunately, joint measurement is a very difficult task because of the high electromagnetic interference between the resonance cots (very high magnetic fields) and the sEMG electrodes. In this paper, we present a method based on wavelet analysis to reveal sEMG in voluntary contractions when the measurement is made in a fMRI environment. Index Terms--Bioelectric potentials, biomedical electrodes, electromyography, magnetic field effects, magnetic resonance.