Scientific Results

This catalogue is obtained by conducting a systematic literature review of scientific studies and reviews related to monitoring, forecasting, and simulating the inland water cycle. The analysis maps scientific expertise across research groups and classifies findings by the type of inland water studied, application focus, and geographical scope. A gap analysis will identify missing research areas and assess their relevance to policymaking.

ID ▲	Type	Year	Authors	Title	Venue/Journal	DOI	Abstract
publications-4751	article	2023	Shaikh, Tawseef Ayoub and Rasool, Tabasum and Verma, Prabal	Machine intelligence and medical cyber-physical system architectures for smart healthcare: Taxonomy, challenges, opportunities, and possible solutions	Artificial intelligence in medicine	10.1016/j.artmed.2023.102692	Hospitals use medical cyber-physical systems (MCPS) more often to give patients quality continuous care. MCPS isa life-critical, context-aware, networked system of medical equipment. It has been challenging to achieve high assurance in system software, interoperability, context-aware intelligence, autonomy, security and privacy, and device certifiability due to the necessity to create complicated MCPS that are safe and efficient. The MCPS system is shown in the paper as a newly developed application case study of artificial intelligence in healthcare. Applications for various CPS-based healthcare systems are discussed, such as telehealthcare systems for managing chronic diseases (cardiovascular diseases, epilepsy, hearing loss, and respiratory diseases), supporting medication intake management, and tele-homecare systems. The goal of this study is to provide a thorough overview of the essential components of the MCPS from several angles, including design, methodology, and important enabling technologies, including sensor networks, the Internet of Things (IoT), cloud computing, and multi-agent systems. Additionally, some significant applications are investigated, such as smart cities, which are regarded as one of the key applications that will offer new services for industrial systems, transportation networks, energy distribution, monitoring of environmental changes, business and commerce applications, emergency response, and other social and recreational activities.The four levels of an MCPS's general architecture-data collecting, data aggregation, cloud processing, and action-are shown in this study. Different encryption techniques must be employed to ensure data privacy inside each layer due to the variations in hardware and communication capabilities of each layer. We compare established and new encryption techniques based on how well they support safe data exchange, secure computing, and secure storage. Our thorough experimental study of each method reveals that, although enabling innovative new features like secure sharing and safe computing, developing encryption approaches significantly increases computational and storage overhead. To increase the usability of newly developed encryption schemes in an MCPS and to provide a comprehensive list of tools and databases to assist other researchers, we provide a list of opportunities and challenges for incorporating machine intelligence-based MCPS in healthcare applications in our paper's conclusion.Copyright Β© 2023 Elsevier B.V. All rights reserved.
publications-4752	article	2024	Gu, T and Niu, W and Huo, L and Zhou, L and Jia, Y and Li, R and Wu, Y and Zhong, H	Molasses-based in situ bio-sequestration of Cr(VI) in groundwater under flow condition.	Environmental pollution (Barking, Essex : 1987)	10.1016/j.envpol.2024.123337	The in situ biosequestration of Cr(VI) in groundwater with molasses as the carbon source was studied based on column experiments and model simulation in this study. Compared with biological reduction, molasses-based chemical reduction did not cause significant Cr(VI) removal at molasses concentration as high as 1.14 g L-1. The molasses at a concentration as low as 0.57 g L-1 could support biofilm-based Cr(VI) sequestration under flow conditions and showed better sequestration performances than D-glucose and emulsified vegetable oil (8 g L-1). The existence of molasses (1.14 g L-1) decreased the pH of the effluent from 7.5 to 6.3 and the oxidation-reduction potential from 275 mV to 220 mV in the groundwater, which was responsible for reduction and thus the sequestration of Cr(VI). Advection-dispersion-reaction model well described the process of the Cr(VI) transport with biosequestration in the column (R2 β‰¥ 0.96). Owing to the Cr(VI) toxicity to the biofilms, the removal ratio decreased by 24\% with a rise of Cr(VI) concentration from 8.6 to 43 mg L-1. The prolongation of hydraulic retention time could promote the performance of Cr(VI) biosequestration. The chemical form of Cr deposited as the product of bio-reduction was confirmed as Cr(OH)3Β·H2O and other complexes of Cr(III). Our work demonstrated the efficacy of molasses for in situ sequestration of Cr(VI) under the dynamic flow condition and provide some useful information for Cr-contaminated groundwater remediation.Copyright Β© 2024 Elsevier Ltd. All rights reserved.
publications-4753	article	2021	de Araujo, Carlos Antonio Alves and de Araujo Junior, Carlos Antonio Alves and Villanueva, Juan MoisΓ©s MaurΓcio and Villanueva, Juan Moises Mauricio and de Almeida, Rodrigo JosΓ© Silva and Almeida, Rodrigo and de Medeiros, Isaac Emmanuel Azevedo and Medeiros, Isaac	Digital Twins of the Water Cooling System in a Power Plant Based on Fuzzy Logic	Sensors	10.3390/s21206737	In the search for increased productivity and efficiency in the industrial sector, a new industrial revolution, called Industry 4.0, was promoted. In the electric sector, power plants seek to adapt these new concepts to optimize electric power generation processes, as well as to reduce operating costs and unscheduled downtime intervals. In these plants, PID control strategies are commonly used in water cooling systems, which use fans to perform the thermal exchange between water and the ambient air. However, as the nonlinearities of these systems affect the performance of the drivers, sometimes a greater number of fans than necessary are activated to ensure water temperature control which, consequently, increases energy expenditure. In this work, our objective is to develop digital twins for a water cooling system with auxiliary equipment, in terms of the decision making of the operator, to determine the correct number of fans. This model was developed based on the algorithm of automatic extraction of fuzzy rules, derived from the SCADA of a power plant located in the capital of Paraiba, Brazil. The digital twins can update the fuzzy rules in the case of new events, such as steady-state operation, starting and stopping ramps, and instability. The results from experimental tests using data from 11 h of plant operations demonstrate the robustness of the proposed digital twin model. Furthermore, in all scenarios, the average percentage error was less than 5\% and the average absolute temperature error was below 3 Β°C.
publications-4754	article	2023	Zifarelli, Andrea and Cantatore, Aldo F. P. and Sampaolo, Angelo and Mueller, Michael and Rueck, T. and Hoelzl, Christine and Rossmadl, Hubert and Patimisco, Pietro and Spagnolo, Vincenzo	Multivariate analysis and digital twin modelling: alternative approaches to evaluate molecular relaxation in photoacoustic spectroscopy	Photoacoustics	10.1016/j.pacs.2023.100564	A comparative analysis of two different approaches developed to deal with molecular relaxation in photoacoustic spectroscopy is here reported. The first method employs a statistical analysis based on partial least squares regression, while the second method relies on the development of a digital twin of the photoacoustic sensor based on the theoretical modelling of the occurring relaxations. Methane detection within a gas matrix of synthetic air with variable humidity level is selected as case study. An interband cascade laser emitting at 3.345 ΞΌm is used to target methane absorption features. Two methane concentration ranges are explored targeting different absorptions, one in the order of part-per-million and one in the order of percent, while water vapor absolute concentration was varied from 0.3\% up to 2\%. The results achieved employing the detection techniques demonstrated the possibility to efficiently retrieve the target gas concentrations with accuracy >95\% even in the case of strong influence of relaxation effects.
publications-4755	article	2024	Roudbari, NS and Punekar, SR and Patterson, Z and Eicker, U and Poullis, C	From data to action in flood forecasting leveraging graph neural networks and digital twin visualization.	Scientific reports	10.1038/s41598-024-68857-y	Forecasting floods encompasses significant complexity due to the nonlinear nature of hydrological systems, which involve intricate interactions among precipitation, landscapes, river systems, and hydrological networks. Recent efforts in hydrology have aimed at predicting water flow, floods, and quality, yet most methodologies overlook the influence of adjacent areas and lack advanced visualization for water level assessment. Our contribution is two-fold: firstly, we introduce a graph neural network model (LocalFLoodNet) equipped with a graph learning module to capture the interconnections of water systems and the connectivity between stations to predict future water levels. Secondly, we develop a simulation prototype offering visual insights for decision-making in disaster prevention and policy-making. This prototype visualizes predicted water levels and facilitates data analysis using decades of historical information. Focusing on the Greater Montreal Area (GMA), particularly Terrebonne, Quebec, Canada, we apply LocalFLoodNet and prototype to demonstrate a comprehensive method for assessing flood impacts. By utilizing a digital twin of Terrebonne, our simulation tool allows users to interactively modify the landscape and simulate various flood scenarios, thereby providing valuable insights into preventive strategies. This research aims to enhance water level prediction and evaluation of preventive measures, setting a benchmark for similar applications across different geographic areas.Β© 2024. The Author(s).
publications-4756	article	2024	Abd, NH Wahab and Hasikin, K and Wee, K Lai and Xia, K and Bei, L and Huang, K and Wu, X	Systematic review of predictive maintenance and digital twin technologies challenges, opportunities, and best practices.	PeerJ. Computer science	10.7717/peerj-cs.1943	Maintaining machines effectively continues to be a challenge for industrial organisations, which frequently employ reactive or premeditated methods. Recent research has begun to shift its attention towards the application of Predictive Maintenance (PdM) and Digital Twins (DT) principles in order to improve maintenance processes. PdM technologies have the capacity to significantly improve profitability, safety, and sustainability in various industries. Significantly, precise equipment estimation, enabled by robust supervised learning techniques, is critical to the efficacy of PdM in conjunction with DT development. This study underscores the application of PdM and DT, exploring its transformative potential across domains demanding real-time monitoring. Specifically, it delves into emerging fields in healthcare, utilities (smart water management), and agriculture (smart farm), aligning with the latest research frontiers in these areas.Employing the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) criteria, this study highlights diverse modeling techniques shaping asset lifetime evaluation within the PdM context from 34 scholarly articles.The study revealed four important findings: various PdM and DT modelling techniques, their diverse approaches, predictive outcomes, and implementation of maintenance management. These findings align with the ongoing exploration of emerging applications in healthcare, utilities (smart water management), and agriculture (smart farm). In addition, it sheds light on the critical functions of PdM and DT, emphasising their extraordinary ability to drive revolutionary change in dynamic industrial challenges. The results highlight these methodologies' flexibility and application across many industries, providing vital insights into their potential to revolutionise asset management and maintenance practice for real-time monitoring.Therefore, this systematic review provides a current and essential resource for academics, practitioners, and policymakers to refine PdM strategies and expand the applicability of DT in diverse industrial sectors.Β© 2024 Abd Wahab et al.
publications-4757	article	2023	Johannessen, Erlend and Johansson, Jonas and Hartvigsen, Gunnar and Horsch, Alexander and Γ…rsand, Eirik and Henriksen, AndrΓ©	Collecting health-related research data using consumer-based wireless smart scales	International journal of medical informatics	10.1016/j.ijmedinf.2023.105043	Serious public-health concerns such as overweight and obesity are in many cases caused by excess intake of food combined with decreases in physical activity. Smart scales with wireless data transfer can, together with smart watches and trackers, observe changes in the populationβ€™s health. They can present us with a picture of our metabolism, body health, and disease risks. Combining body composition data with physical activity measurements from devices such as smart watches could contribute to building a human digital twin. The objectives of this study were to (1) investigate the evolution of smart scales in the last decade, (2) map status and supported sensors of smart scales, (3) get an overview of how smart scales have been used in research, and (4) identify smart scales for current and future research. We searched for devices through web shops and smart scale tests/reviews, extracting data from the manufacturerβ€™s official website, user manuals when available, and data from web shops. We also searched scientific literature databases for smart scale usage in scientific papers. We identified 165 smart scales with a wireless connection from 72 different manufacturers, released between 2009 and end of 2021. Of these devices, 49 (28\%) had been discontinued by end of 2021. We found that the use of major variables such as fat and muscle mass have been as good as constant over the years, and that minor variables such as visceral fat and protein mass have increased since 2015. The main contribution is a representative overview of consumer grade smart scales between 2009 and 2021. The last six years have seen a distinct increase of these devices in the marketplace, measuring body composition with bone mass, muscle mass, fat mass, and water mass, in addition to weight. Still, the number of research projects featuring connected smart scales are few. One reason could be the lack of professionally accurate measurements, though trend analysis might be a more feasible usage scenario.
publications-4758	article	2022	Lambertini, Alessandro and Lambertini, Alessandro and Menghini, Massimiliano and Menghini, Massimiliano and Cimini, J. and Cimini, Jacopo and Odetti, Angelo and Odetti, Angelo and Bruzzone, Gabriele and Bruzzone, Gabriele and Bibuli, Marco and Bibuli, Marco and Mandanici, Emanuele and Mandanici, Emanuele and Vittuari, Luca and Vittuari, Luca and Castaldi, Paolo and Castaldi, Paolo and Caccia, Massimo and Caccia, Massimo and Marchi, Luca De and Marchi, Luca De	Underwater Drone Architecture for Marine Digital Twin: Lessons Learned from SUSHI DROP Project	Sensors	10.3390/s22030744	The ability to observe the world has seen significant developments in the last few decades, alongside the techniques and methodologies to derive accurate digital replicas of observed environments. Underwater ecosystems present greater challenges and remain largely unexplored, but the need for reliable and up-to-date information motivated the birth of the Interreg Italy-Croatia SUSHI DROP Project (SUstainable fiSHeries wIth DROnes data Processing). The aim of the project is to map ecosystems for sustainable fishing and to achieve this goal a prototype of an Unmanned Underwater Vehicle (UUV), named Blucy, has been designed and developed. Blucy was deployed during project missions for surveying the benthic zone in deep waters of the Adriatic Sea with non-invasive techniques compared to the use of trawl nets. This article describes the strategies followed, the instruments applied and the challenges to be overcome to obtain an accurately georeferenced underwater survey with the goal of creating a marine digital twin.
publications-4759	article	2022	Yanes, Abraham Reyes and Yanes, Abraham Reyes and Abbasi, Rabiya and Abbasi, Rabiya and Martinez, Pablo and MartΔ±Μnez, Pablo and Ahmad, Rafiq and Ahmad, Rafiq	Digital Twinning of Hydroponic Grow Beds in Intelligent Aquaponic Systems	Sensors	10.3390/s22197393	The use of automation, Internet-of-Things (IoT), and smart technologies is being rapidly introduced into the development of agriculture. Technologies such as sensing, remote monitoring, and predictive tools have been used with the purpose of enhancing agriculture processes, aquaponics among them, and improving the quality of the products. Digital twinning enables the testing and implementing of improvements in the physical component through the implementation of computational tools in a β€twinβ€™ virtual environment. This paper presents a framework for the development of a digital twin for an aquaponic system. This framework is validated by developing a digital twin for the grow beds of an aquaponics system for real-time monitoring parameters, namely pH, electroconductivity, water temperature, relative humidity, air temperature, and light intensity, and supports the use of artificial intelligent techniques to, for example, predict the growth rate and fresh weight of the growing crops. The digital twin presented is based on IoT technology, databases, a centralized control of the system, and a virtual interface that allows users to have feedback control of the system while visualizing the state of the aquaponic system in real time.
publications-4760	article	2023	Davis, Gordon B. and Rayner, J. C. W. and Donn, Michael J.	Advancing β€_x009c_Autonomousβ€_x009d_ sensing and prediction of the subsurface environment: a review and exploration of the challenges for soil and groundwater contamination	Environmental science and pollution research international	10.1007/s11356-022-25125-8	Can we hope for autonomous (self-contained in situ) sensing of subsurface soil and groundwater pollutants to satisfy relevant regulatory criteria? Global advances in sensors, communications, digital technologies, and computational capacity offer this potential. Here we review past efforts to advance subsurface investigation techniques and technologies, and computational efforts to create a digital twin (representation) of subsurface processes. In the context of the potential to link measurement and sensing to a digital twin computation platform, we outline five criteria that might make it possible. Significant advances in sensors based on passive measurement devices are proposed. As an example of what might be achievable, using the five criteria, we describe the deployment of online real-time sensors and simulations for a case study of a petroleum site where natural source zone depletion (NSZD) is underway as a potential biodegradation management option, and where a high-quality conceptual site model is available. Multiple sensors targeting parameters (major gases and temperature influenced by soil moisture) relevant to the subsurface NSZD biodegradation processes are shown to offer the potential to map subsurface processes spatially and temporally and provide continuous estimates of degradation rates for management decisions, constrained by a computational platform of the key processes. Current limitations and gaps in technologies and knowledge are highlighted specific to the case study. More generally, additional key advances required to achieve autonomous sensing of subsurface soil and groundwater pollutants are outlined.