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An Agent-Based Model for Greening the City of Ravenna and Reducing Flooding at a Cultural Heritage Site
No full textPublisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.The paper presents an agent-based model exploring the impact of greening a city for groundwater flood risk reduction as a discussion tool to support urban planning decisions. The case study is an urban archaeological site in Ravenna, Italy. The model aimed to provide insights to generate discussion between researchers at the University of Bologna and the local authorities. The city map was divided into cells potentially suitable for modelled greening, combined with Ravenna precipitation and temperature data, and estimated scores for evapotranspiration and permeability. Generally, results indicate that more greening measures introduced correspond to a reduction in the volume of excess rainwater, with particular effectiveness in greened streets. Our results demonstrate the benefits of agent base modelling in the field of disaster risk management for testing measures prior to their implementation.Peer reviewe
Blockchain-based refurbishment certification system for enhancing the circular economy
Get PDFPublisher Copyright: © 2023 The AuthorsAs the global population continues to grow, the enormous stress on our environment and resources is becoming impossible to ignore. A focus on producing and consuming as cheaply as possible has created an economy in which objects are briefly used and then discarded as waste, featuring a linear lifecycle that creates an enormous amount of waste. The alternative to the linear economy “take-make-waste” is called the “circular economy”. Under this paradigm, materials are recycled to build new products or components that are designed and built to promote their reuse and refurbishment. This assures the continuous (re-)exploitation of existing resources, reducing the extraction of new raw materials. However, customers often reject these reused or refurbished products under the suspicion that they do not meet the same usability, safety, or performance levels of new products. In this sense, trustworthy records of historical details of refurbished products could increase consumers’ confidence in products and components of the “circular economy”, prioritizing trustworthiness, reliability, and transparency. This work presents a new certification tool based on blockchain technology to guarantee trusted, accurate, transparent, and traceable lifecycle information of products and their components and to generate trustworthy certificates to probe refurbished product historical details. This tool aims to enhance refurbished product visibility by creating the basis for making the circular economy a reality in any domain.Peer reviewe
Predictive-Cognitive Maintenance for Advanced Integrated railway Management
Get PDFPublisher Copyright: © 2024 11th European Workshop on Structural Health Monitoring, EWSHM 2024. All rights reserved.Railway systems play a vital role in modern transportation, and Predictive-Cognitive Maintenance (PCM) has emerged as a transformative approach in the context of Advanced Integrated Railway Management to ensure the safety, reliability, and efficiency of these systems. PCM leverages data analytics and machine learning to optimize railway system maintenance. This requires effective structural health monitoring (SHM) using low-cost sensor devices. This paper presents a prototype solar-powered wireless sensor node with a 3-axis MEMS accelerometer and energy-harvesting features for monitoring rail-track vibrations. The node contains a microcontroller that runs embedded machine learning models to preprocess the vibration data after train crossing. Abnormal vibrations indicative of defects were detected in real time using the TinyML inference at the edge. Instead of raw data, only the model results were wirelessly transmitted to a digital twin in the cloud. The digital twin aggregates data across the rail network for the system-level assessment of RUL and maintenance planning. This edge computing approach minimizes wireless transmission and cloud storage compared to raw sensor streaming. Embedded ML enables real-time damage detection, whereas cloud digital twins provide system-level prognostic insights. The solar-powered platform enables long-term remote monitoring at low cost without wiring or battery changes. A full-scale physical model was used to validate the edge node prototypes against calculation models and wired accelerometers for impulse loads. The results demonstrated that these nodes can provide a sensor layer for cost-effective PCM in railway systems. In summary, this study proposes an edge computing and embedded ML approach for SHM that integrates cloud-based digital twins to enable the predictive-cognitive maintenance of railway infrastructure. Wireless nodes demonstrate potential for low-cost, convenient, and automated rail health monitoring.Peer reviewe
Quantum Optimization Methods for Satellite Mission Planning
Get PDFPublisher Copyright: © 2013 IEEE.Satellite mission planning for Earth observation satellites is a combinatorial optimization problem that consists of selecting the optimal subset of imaging requests, subject to constraints, to be fulfilled during an orbit pass of a satellite. The ever-growing amount of satellites in orbit underscores the need to operate them efficiently, which requires solving many instances of the problem in short periods of time. However, current classical algorithms often fail to find the global optimum or take too long to execute. Here, we approach the problem from a quantum computing point of view, which offers a promising alternative that could lead to significant improvements in solution quality or execution speed in the future. To this end, we study a planning problem with a variety of intricate constraints and discuss methods to encode them for quantum computers. Additionally, we experimentally assess the performance of quantum annealing and the quantum approximate optimization algorithm on a realistic and diverse dataset. Our results identify key aspects like graph connectivity and constraint structure that influence the performance of the methods. We explore the limits of today's quantum algorithms and hardware, providing bounds on the problems that can be currently solved successfully and showing how the solution degrades as the complexity grows. This work aims to serve as a baseline for further research in the field and establish realistic expectations on current quantum optimization capabilities.Peer reviewe
An Evolutionary Computation-Based Platform for Optimizing Infrastructure-as-Code Deployment Configurations
No full textPublisher Copyright: © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.PIACERE is an H2020 European project which objective is to implement a solution involving the development, deployment, and operation of Infrastructure-as-Code of applications running on cloud continuum. This technical paper is focused on describing a specific module of the whole PIACERE ecosystem: the IaC Optimizer Platform. The main objective of this component is to provide the user with optimized Infrastructure-as-Code configurations deployed on the most appropriate infrastructural elements that best meet the predefined requirements. For properly dealing with this problem, the IaC Optimizer Platform is based on Evolutionary Computation metaheuristics. More specifically, it resorts to NSGA-II and NSGA-III algorithms, depending on user needs. Additionally, we not only describe the IaC Optimizer Platform component in this paper, but we also show how it helps the user to find the most adequate Infrastructure-as-Code configurations.This research was funded by the European project PIACERE (Horizon 2020 Program, under grant agreement no 101000162).Peer reviewe
Evaluation of 30 urban land surface models in the Urban-PLUMBER project: Phase 1 results
Get PDFPublisher Copyright: © 2023 The Authors. Quarterly Journal of the Royal Meteorological Society published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society.Accurately predicting weather and climate in cities is critical for safeguarding human health and strengthening urban resilience. Multimodel evaluations can lead to model improvements; however, there have been no major intercomparisons of urban-focussed land surface models in over a decade. Here, in Phase 1 of the Urban-PLUMBER project, we evaluate the ability of 30 land surface models to simulate surface energy fluxes critical to atmospheric meteorological and air quality simulations. We establish minimum and upper performance expectations for participating models using simple information-limited models as benchmarks. Compared with the last major model intercomparison at the same site, we find broad improvement in the current cohort's predictions of short-wave radiation, sensible and latent heat fluxes, but little or no improvement in long-wave radiation and momentum fluxes. Models with a simple urban representation (e.g., ‘slab’ schemes) generally perform well, particularly when combined with sophisticated hydrological/vegetation models. Some mid-complexity models (e.g., ‘canyon’ schemes) also perform well, indicating efforts to integrate vegetation and hydrology processes have paid dividends. The most complex models that resolve three-dimensional interactions between buildings in general did not perform as well as other categories. However, these models also tended to have the simplest representations of hydrology and vegetation. Models without any urban representation (i.e., vegetation-only land surface models) performed poorly for latent heat fluxes, and reasonably for other energy fluxes at this suburban site. Our analysis identified widespread human errors in initial submissions that substantially affected model performances. Although significant efforts are applied to correct these errors, we conclude that human factors are likely to influence results in this (or any) model intercomparison, particularly where participating scientists have varying experience and first languages. These initial results are for one suburban site, and future phases of Urban-PLUMBER will evaluate models across 20 sites in different urban and regional climate zones.The project's coordinating team is supported by UNSW Sydney, the Australian Research Council (ARC) Centre of Excellence for Climate System Science (grant CE110001028), University of Reading, the Met Office UK, the Bureau of Meteorology, Australia, the ARC Centre of Excellence for Climate Extremes (grant CE170100023) and ERC urbisphere (grant 855005). Computation support from National Computational Infrastructure (NCI) Australia. G.J. Steeneveld and A. Tsiringakis acknowledge support from the NWO VIDI grant ‘The Windy City’ under number 864.14.007. M. Demuzere acknowledges support from the ENLIGHT project, funded by the German Research Foundation (DFG) under grant No. 437467569. Y. Takane acknowledges support from Japan Society for the Promotion of Science (JSPS) KAKENHI Grand Number 20KK0096. Contributions by K.W. Oleson are supported by the National Center for Atmospheric Research (NCAR), sponsored by the National Science Foundation (NSF) under Cooperative Agreement No. 1852977. Computing and data storage resources for CLMU5, including the Cheyenne supercomputer (doi:10.5065/D6RX99HX), were provided by the Computational and Information Systems Laboratory (CISL) at NCAR. K. Nice acknowledges support from NHMRC/UKRI grant (1194959). J.‐J. Baik acknowledges support from the National Research Foundation of Korea (NRF) under grant 2021R1A2C1007044. E. Bou‐Zeid was supported by the US National Science Foundation under award number AGS 2128345 and the Army Research Office under contract W911NF2010216. Work with TERRA model performed by M. Varentsov was supported by the Russian Science Foundation, grant no. 21‐17‐00249. S.‐H. Lee acknowledges support from the Nuclear Safety and Security Commission (NSSC) of the Republic of Korea (No. 2105036). M. De Kauwe acknowledges support from the Natural Environment Research Council (NE/W010003/1). N. Meili and S. Fatichi acknowledge the support of the National University of Singapore through the project ‘Bridging scales from below: The role of heterogeneities in the global water and carbon budgets’, Award No. 22‐3637‐A0001. T. Sun was supported by UKRI NERC Independent Research Fellowship (NE/P018637/1 and NE/P018637/2). D.‐I. Lee acknowledges support from the Korea Meteorological Administration Research and Development Program under grant KMI(KMI2021‐03512).Peer reviewe
A Reconfigurable UGV for Modular and Flexible Inspection Tasks in Nuclear Sites
Get PDFPublisher Copyright: © 2024 by the authors.Current operations involving Dismantling and Decommissioning (D&D) in nuclear and other harsh environments rely on manual inspection and assessment of the sites, exposing human operators to potentially dangerous situations. This work presents a reconfigurable Autonomous Mobile Robot (AMR) able to mount a wide range of nuclear sensors for flexible and modular inspection tasks in these operations. This AMR is part of the CLEANDEM solution, which uses Unmanned Ground Vehicles (UGVs), nuclear sensors, and a Digital Twin to facilitate a tool for improving D&D operations in nuclear sites. Both the AMR used as a UGV and the system have been successfully tested in real nuclear sites, showing that these tools can greatly aid in operations management and hazard reduction.Peer reviewe
Photonic counterdiabatic quantum optimization algorithm
Get PDFPublisher Copyright: © The Author(s) 2024.One of the key applications of near-term quantum computers has been the development of quantum optimization algorithms. However, these algorithms have largely been focused on qubit-based technologies. Here, we propose a hybrid quantum-classical approximate optimization algorithm for photonic quantum computing, specifically tailored for addressing continuous-variable optimization problems. Inspired by counterdiabatic protocols, our algorithm reduces the required quantum operations for optimization compared to adiabatic protocols. This reduction enables us to tackle non-convex continuous optimization within the near-term era of quantum computing. Through illustrative benchmarking, we show that our approach can outperform existing state-of-the-art hybrid adiabatic quantum algorithms in terms of convergence and implementability. Our algorithm offers a practical and accessible experimental realization, bypassing the need for high-order operations and overcoming experimental constraints. We conduct a proof-of-principle demonstration on Xanadu’s eight-mode nanophotonic quantum chip, successfully showcasing the feasibility and potential impact of the algorithm.Peer reviewe
Circular bioeconomy: A review of empirical practices across implementation scales
Get PDFPublisher Copyright: © 2024 The AuthorsThe European Commission has endorsed the transition to a circular bioeconomy as a crucial initiative to achieve a sustainable and climate-neutral European Union. However, this transition is inherently complex, as it involves environmental and social risks, physical limitations, and trade-offs between different dimensions of sustainability. In this context, effective monitoring and modelling of the bioeconomy are essential tools to inform decision-making at various implementation scales. This article reviews empirical research on the monitoring and modelling of the bioeconomy and their implications for decision-making across three main implementation scales: macro (national-global level); meso (regional-city level); and micro (single product, company level). The contribution of this research is twofold: First, it explores existing gaps in the field through the lens of policy implementation levels. Research gaps include the uneven distribution of indicators among sustainability pillars, the lack of standardised monitoring frameworks at the meso and micro levels, and attribution issues in bioeconomy modelling. Second, the article outlines a comprehensive research agenda, indicating specific research lines and their relevance to different implementation scales. Key research avenues, significant at all implementation scales, include enhancing the life cycle perspective, visualising trade-offs and synergies between Sustainable Development Goals, monetising externalities, and further delving into social aspects.Peer reviewe
One-Pot Synthesis of Gold Nanoparticles and Aluminum Hydroxide Hydrogels-Based Nanocomposites with Modulated Optical Properties
No full textPublisher Copyright: © 2024 Wiley-VCH GmbH.In this work, the one-pot synthesis of composites constituted by gold nanoparticles (AuNPs) and aluminum hydroxide hydrogels (AlHG) by employing the Epoxide Route is presented. To modulate the optical properties of the final composites, different anions (X= (Formula presented.), (Formula presented.), (Formula presented.) and (Formula presented.)) were used as nucleophile, complexing and growth directing agents of the AuNPs. In addition, the concentration of the reactants, e. g., the X : Cl ratio, was set in such a way to preserve the alkalization rate, the transparency of the hydrogels supporting the AuNPs, and the stability of the final composites. Consequently, the composites exhibit different plasmonic properties, resulting from the AuNPs with different sizes and morphologies, as confirmed through transmission electron microscopy, depending on the nature of the employed anion, exclusively. Furthermore, this versatile one-pot synthesis strategy was employed to design new composites with different I : Cl ratio and synthesize stable colloidal AuNPs within an aluminum hydroxide sol (AuNP@Alsol) without adding any conventional capping agent. This AuNP@Alsol composite can be used as seed to accelerate the extremely slow AuNPs formation kinetics in AuNP@AlHG(SCN), demonstrating the potential of this synthesis method to create composites susceptible to be applied in the photonic and catalysis areas.Peer reviewe