Damping of solitons by coastal vegetation

Mangroves are a natural defence of the coastal strip against extreme waves. Furthermore, innovative techniques of naturally based coast defence are used increasingly, according to the canons of eco-hydraulics. Therefore, it is important to correctly evaluate the transmission of waves through cylinder arrays. In the present paper, the attenuation of solitary waves propagating through an array of rigid emergent and submerged cylindrical stems on a horizontal bottom is investigated theoretically, numerically and experimentally. The results of the theoretical model are compared with the numerical simulations obtained with the smoothed particle hydrodynamics meshless Lagrangian numerical code and with experimental laboratory data. In the latter case, solitary waves were tested on a background current, in order to reproduce more realistic sea conditions, since the absence of circulation currents is very rare in the sea. The comparison confirmed the validity of the theoretical model, allowing its use for the purposes indicated above. Furthermore, the present study allowed for an evaluation of the bulk drag coefficient of the rigid stem arrays used, as a function of their density, the stem diameter, and their submergence ratio

Hydrogeological modelling of a coastal karst aquifer using an integrated SWAT-MODFLOW approach

The complexity of modelling in karst environments necessitates substantial adjustments to existing hydrogeological models, with particular emphasis on accurately representing surface and deep processes. This study proposes an advanced methodology for modelling regional coastal karst aquifers using an integrated SWAT-MODFLOW approach. The focus is on the regional coastal karst aquifer of Salento (Italy), which is characterised by significant heterogeneity, anisotropy and data scarcity, such as limited discharge measurements and water levels over time. The integrated SWAT - MODFLOW approach allows an accurate description of both surface and subsurface hydrological processes specific to karst environments and demonstrates the adaptability of the models to karst-specific features such as sinkholes, dolines and fault permeability. The study successfully addresses the challenges posed by the distinctive characteristics of karst systems through the integration of SWAT-MODFLOW. Additionally, incorporating of satellite data enhances the precision and dependability of the model by augmenting the traditional datasets. The entire simulation period, which included both the calibration and validation phases, extended from 2008 to 2018. The calibration phase occurred between 2008 and 2011, followed by the validation phase between 2015 and 2018. The temporal choices were exclusively based on the availability of meteorological and hydrogeological data. During calibration, satellite data, previous study results, and groundwater level measurements were used to optimize the SWAT and MODFLOW models. Validation subsequently confirmed model accuracy by comparing simulated groundwater levels with observed data, demonstrating a satisfactory root mean square error (RMSE) of 0.22 m. Modelling results indicate that evapotranspiration is the predominant hydrological process, and excessive withdrawals could lead to a water deficit. Simulated piezometric maps provide crucial information on recharge areas and hydraulic compartments delineated by faults. The study not only advances the understanding of the hydrogeology of the specific case study but also provides a valuable reference for future modelling of karst aquifers. Additionally, it highlights the crucial need for ongoing enhancement in the management and monitoring of coastal karst aquifers

An Assignment Model for High-Cognitive-Workload Maintenance Activities in Industry 5.0

Industry 5.0 paradigm emphasises human-centricity, sustainability, and resilience in production systems. If, on the one hand, Industry 4.0 (I4.0) promoted production efficiency and quality through the development and implementation of advanced technologies, on the other hand, this paradigm has main limitations due to the limited consideration of industrial sustainability and workers’ welfare. In the I4.0 context, the operator may face cognitive overload due to the inherent complexity of ordinary activities. In this scenario, maintenance operations are of utmost relevance. They are indeed critical in any production context, as they are not value-adding but directly determine factors such as the safety and performance of industrial systems. In the context of I4.0, a paradigm known as Maintenance 4.0 has developed, which involves adopting advanced technologies for maintenance activities. While this paradigm allowed for advantages such as the implementation of predictive maintenance policies, it has also complicated ordinary activities, especially from a cognitive point of view. To this concern, the objective of the present work consists of a task assignment model that supports the company in identifying the proper operator/s to accomplish maintenance tasks with high cognitive workloads. Identifying the proper operator for each task led to reducing the probability of accidents, increasing human well-being, and improving the reliability of the maintained assets. A numerical application of the proposed model proved its effectiveness in identifying the operator to be assigned a specific maintenance activity based on its skills and considering the cognitive workload of previous maintenance tasks assigned to the same operator

Permutation-Invariant Cascaded Attentional Set Operator for Computational Nephropathology

Key PointsPermutation-invariant cascaded attentional set operator (PICASO) is a versatile set operator that uses Transformers to dynamically aggregate histopathologic features from a set of glomerular crops.For detecting active crescent in patients with IgA nephropathy on internal and external validation sets, PICASO achieved an area under the receiver-operating characteristic curve of 0.99 and 0.96, respectively.In the case-level classification of antibody-mediated rejection in kidney transplants, PICASO performed well, with an area under the receiver-operating characteristic curves of 0.97.BackgroundThe advent of digital nephropathology offers the potential to integrate deep learning algorithms into the diagnostic workflow. We introduce permutation-invariant cascaded attentional set operator (PICASO), a novel permutation-invariant set operator to dynamically aggregate histopathologic features from instances. We applied PICASO to two nephropathology scenarios: detecting active crescent lesions in sets of glomerular crops with IgA nephropathy (IgAN) and case-level classification for antibody-mediated rejection (AMR) in kidney transplant.MethodsPICASO is a Transformer-based set operator that aggregates features from sets of instances to make predictions. It uses initial histopathologic vectors as a static memory component and continuously updates them on the basis of input embeddings. For active crescent detection in patients with IgAN, we obtained 6206 periodic acid-Schiff-stained glomerular crops (5792 no active crescent, 414 active crescent) from three different health institutes. For the AMR classification, we have 1655 periodic acid-Schiff-stained glomerular crops (769 AMR and 886 non-AMR images) from 89 biopsies. The performance of PICASO as a set operator was compared with other set operators, such as DeepSet, Set Transformer, DeepSet++, and Set Transformer++, using metrics including area under the receiver-operating characteristic curve (AUROC), area under the precision-recall curves, recall, and accuracy.ResultsPICASO achieved superior performance in detecting active crescent in patients with IgAN, with an AUROC of 0.99 (95% confidence interval [CI], 0.98 to 0.99) on internal validation and 0.96 (95% CI, 0.95 to 0.98) on external validation, significantly outperforming other set operators (P < 0.001). It also attained the highest AUROC of 0.97 (95% CI, 0.90 to 1.0, P = 0.02) for case-level AMR classification. The area under the precision-recall curve, recall, and accuracy scores were also higher when using PICASO, and it significantly outperformed baselines (P < 0.001).ConclusionsPICASO can potentially advance nephropathology by improving performance through dynamic feature aggregation. © 2025 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Society of Nephrology

Modeling, Positioning, and Deep Reinforcement Learning Path Following Control of Scaled Robotic Vehicles: Design and Experimental Validation

Mobile robotic systems serve as versatile platforms for diverse indoor applications, ranging from warehousing and manufacturing to test benches dedicated to evaluating automated driving (AD) functions. In AD systems, the path following (PF) layer is responsible for defining steering commands to follow the reference path. Recently explored approaches involve artificial intelligence-based methods, such as Deep Reinforcement Learning (DRL). Despite their promising performance, these controllers still suffer from time-consuming training phases and may experience performance degradation when deviating from training conditions. To address these challenges, this paper proposes novel DRL controllers addressing the simulation-to-reality gap in unknown scenarios by: (i) training via an expert demonstrator which also speed up the learning phase; and (ii) a weight adaptation strategy for the resulting neural network (NN) to strengthen controller robustness and enhance PF performance. In addition, an experimentally validated vehicle model is used for training the proposed DRL algorithm and as a model for a federated extended Kalman filter (FEKF) system employed for sensor fusion in vehicle localisation. The proposed DRL-based PF controllers are experimentally evaluated through key performance indicators across multiple maneuvers not considered during training, and it is shown that they outperform benchmarking model-based controllers from the literature. Note to Practitioners - This paper presents a comprehensive toolchain for controlling mobile robots, which includes: (i) a simple yet effective two-stage least-square approach for parameter identification of the longitudinal and lateral dynamics of scaled robotic vehicles; (ii) the utilisation of a no-reset FEKF to enhance positioning leveraging all sensors commonly available on scaled robotic vehicles; (iii) the inclusion of an expert demonstrator to expedite the training phase and address the simulation-to-reality gap resulting from discrepancies between simulation and experimental environments; and (iv) an adaptation strategy for dynamically adjusting the weights of the resulting NN to further improve robustness for scenarios not considered during the traning

Design and experimental evaluation of unmanned aircraft systems communications

Le reti 6G mirano a fornire una banda larga globale a bassissima latenza tramite reti non terrestri (NTN), che integrano droni, piattaforme ad alta quota e satelliti. Questo progetto introduce contributi per l'avanzamento delle capacità NTN, a partire dal Drone Control Layer (DCL), una soluzione middleware che consente un funzionamento efficiente di sciami di droni, dotata di interfacce per l'astrazione hardware, la comunicazione e la loro interconnettività. Inoltre, viene presentato IoD-Sim, un simulatore open source per la modellazione di ambienti NTN, tra cui le superfici riflettenti intelligenti (IRS) per una copertura ottimizzata. IoD-Sim consente una simulazione realistica dei protocolli di comunicazione e della mobilità dei droni in diversi scenari. Nell'Internet of Things (IoT), i droni estendono la durata della batteria del dispositivo IoT tramite Wireless Power Transfer (WPT), trasmettendo in modo efficiente i dati ai CubeSat, le cui simulazioni mostrano significativi guadagni nel trasferimento dei dati. Inoltre, la sicurezza e la protezione sono affrontate rispettivamente tramite Explainable AI (XAI), per la consapevolezza spaziale dei droni, e Counter-Unmanned Aircraft System (C-UAS), per il rilevamento non autorizzato dei droni mediante tecniche di fusione multisensore. Infine, un meccanismo di sicurezza a catena di servizi introduce autenticazione e autorizzazione personalizzati per salvaguardare il flusso di dati nei sistemi Terrestrial/NTN, integrati in un'architettura completa basata sul cloud orientata ai servizi. Questi contributi supportano lo sviluppo e l'implementazione di servizi di comunicazione integrati spazio-aria-terra resilienti, a vantaggio sia della ricerca che dell'industria.6G networks aim to deliver ultra-low-latency global broadband through Non-Terrestrial Networks (NTN), which integrate drones, high-altitude platforms, and satellites. This project introduces contributions for advancing NTN capabilities, starting with the Drone Control Layer (DCL), a middleware solution enabling efficient operation of mixed drone swarms, equipped with interfaces for hardware abstraction, communication, and drone interconnectivity. Furthermore, IoD-Sim is presented, an open-source simulator for modelling NTN environments, including Intelligent Reflecting Surfaces (IRS) for optimised coverage. IoD-Sim allows realistic simulation of communication protocols and drone mobility in diverse scenarios. In the Internet of Things (IoT), drones extend IoT device battery life through Wireless Power Transfer (WPT), efficiently transmitting data to CubeSats, which simulations show significant gains in data transfer. Additionally, safety and security are addressed via Explainable AI (XAI), for drone spatial awareness, and Counter-Unmanned Aircraft System (C-UAS), for unauthorised drone detection using multi-sensor fusion techniques. Finally, a secure service chain model introduces custom authentication and authorisation to safeguard data flow in Terrestrial/NTN systems, integrated in a comprehensive cloud-based service oriented architecture. These contributions support the development and deployment of resilient space-air-ground integrated communication services, benefiting both research and industry

Large-Eddy Simulations of a Laser-Ignited Subscale Rocket Combustor: Modeling Strategies and Experimental Comparison

To predict the reliability of laser ignition in a rocket combustor using large-eddy simulations (LESs), it is essential to first ensure that the pre-ignition jet statistics and the dynamics of the hot kernel generated by the energy deposition are accurately captured. In this manuscript, we compare numerical results with experimental data to evaluate the accuracy of the computational approach. First, the jet LES statistics show good qualitative agreement with the particle imaging velocimetry (PIV) data. Quantitative comparisons at several streamwise locations reveal larger differences near the injector, but with local discrepancies of less than 15 m/s in both the mean and fluctuation statistics. Second, we quantify the mean and uncertainties of the hot kernel modeling parameters through a joint analysis of experimental data and direct numerical simulation (DNS) results. This approach accounts for shot-to-shot variability in the simulations, which demonstrate good agreement with the experimental data regarding the ejecta position

Non-cooperative game theoretical control for green and efficient energy communities

Energy communities are socially driven initiatives that emphasize collective participation in energy production, distribution, and consumption. Differently from smart grids, which focus on the infrastructural and technological side, energy communities are concerned with the market and economical incentive design, aiming at guaranteeing a socially, environmentally, and economically sustainable energy grid. Thus, the study of energy communities is tightly intertwined with the analysis of the behavior that arises when several agents are faced with conflicting needs and resource scarcity. Non-cooperative game theory has proved to be a solid tool for tackling the challenge of optimally controlling energy communities: this dissertation aims to contribute to the topic by addressing aspects such as transactive market design, plug-in electric vehicles (PEVs) integration, and learning-based decision-making. In particular, the first part explores the economical and operational design of energy communities from the transactive perspective. Such a term refers to relational patterns between two or more agents that are based on “transactions”, i.e., on the exchange of one or more commodities or services. Such a setup can be effectively studied under the lens of game theory since any transaction, in order to be successful, requires two or more parties to agree on the quantities to be exchanged. Energy markets are perfectly suitable to be considered as transactive environments, because of the ubiquitous need that agents have to exchange energy. In particular, the analysis focuses on the modelling of energy communities with independent and selfish agents, as well as the subsequent design of decentralized and distributed schemes for the equilibrium seeking of the arising non-cooperative game. Among the diverse actors that characterize the modern energy community, PEVs are the ones that, in the last decade, have caused a consistent push towards a more decentralized and dynamic energy market system. This is due to their inherent unpredictability with respect to their energy demand, which serves the purpose of recharging their batteries. Such a unilateral energy exchange gives rise to the V1G-based energy market. However, being equipped with storage devices, PEVs can be considered as non-static ESSs, which can be used by the grid operators for tasks such as voltage and frequency regulation, but also by prosumers and actors that can experience temporary energy surpluses. Such a bilateral exchange is captured by the V2B and – in the most general sense – V2X protocols. The second part of this dissertation frames PEVs as non-cooperative agents, which participate in the energy market with the aim of recharging their batteries in the most economically efficient way, or providing temporary storage service for a fee. The two aforementioned research directions frame the agents in the energy community as rational entities, whose prerogatives are described by an optimization problem constituted by a certain cost function to minimize and operational constraints to satisfy. Such an approach is reasonable when modelling a perfectly rational entity, e.g., control systems that solely follow its instructions. However, as many grid actors are backed by the decisions and actions made by people, the perfect “rationality assumption” becomes unrealistic: people are often driven by habits and belief systems that do not necessarily yield the “optimal” decisions. The third part collects the result of a work-in-progress which aims at defining a learning-based equilibrium – and its related seeking methods – where the agents’ behavior is not modelled by an optimization problem, where its objective expresses some cost (utility) to minimize (maximize), but through a neural network. The idea is to capture behavioral patterns on the basis of existing data, representing the agents’ response to the environment and the other players’ strategies