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Background Energy systems, as critical infrastructures (CI), constitute Cyber-Physical-Social Systems (CPSS). Due to their inherent complexity and the importance of service continuity of CIs, digitization in this context encounters significant practical challenges. Digital Twins (DT) have emerged over the recent years as a promising solution for managing CPSSs by facilitating real-time interaction, synchronization, and control of physical assets. The selection of an appropriate architectural framework is crucial in constructing a DT, to ensure integration of enabling technologies and data from diverse sources. Objectives This study proposes a Systematic Literature Review (SLR) to examine technological enablers, design choices, management strategies and Computational Challenges of DTs in Smart Energy Systems (SES) by also analyzing existing architectures and identifying key components. Methods The SLR follows a rigorous workflow exploiting a multi-database search with predefined eligibility criteria, accompanied by advanced searching techniques, such as manual screening of results and a documented search strategy, in order to ensure its comprehensiveness and reliability, More specifically, research questions are first defined and then submitted as queries to scientific digital libraries (i.e., IEEE Xplore, Scopus, and WoS) selected due to their coverage and reliability (Google Scholar was excluded for the presence of grey literature and non-peer-reviewed material). Then, inclusion and exclusion criteria are established to filter the results and shortlist the significant publications. Subsequently, relevant data are extracted, summarized, and categorized in order to identify common themes, existing gaps, and future research directions, with the aim of providing a comprehensive overview of the current state of DTs for SESs. Results From the proposed DT-based solutions described in the selected publications, the adopted architectures are examined and categorized depending on their logical building blocks, microservices, enabling technologies, human–machine interfaces (HMI), artificial intelligence and machine learning (AI/ML) implementations, data flow and data persistence choices, and Internet-of-Things (IoT) components involved. Additionally, the integration of edge-cloud computing and IoT technologies in literature are studied and discussed. Finally, gaps, opportunities, future study lines, and challenges of implementing DTs are thoroughly addressed. The results achieved also pave the way for a forthcoming design pattern catalog for DTs in CPSSs capable of supporting the engineering and research communities, by offering practical insights on implementation and integration aspects. Conclusion The proposed SLR provides a valuable resource for designing and implementing DTs of CPSSs in general and of SESs in particular. Furthermore, it highlights the potential benefits of adopting DTs to manage complex energy systems and it identifies areas for future research.

Petrology and geochemistry reconstruct from plutons and eruptive products the underground chemical and thermodynamic conditions of magma at the time of crystallization. Accretionary layers in crystals record the composition of the surrounding melt, as well as the confining pressure and temperature. Such a backward reconstruction should be paired with a forward computation of the solidifying crystals during their transport in convective motions inside a refilled magmatic reservoir. This work develops a framework for the solution of magma fluid-dynamics and for the related Lagrangian trajectories of suspended crystals. Episodes of magma mixing due to injection of fresh magma into a shallow chamber are simulated at first in a Eulerian reference system. Afterwards, the Lagrangian trajectories of passive tracers are computed, tracking the magma composition, pressure and temperature through which these particles move. On the base of the compositional, pressure and temperature conditions, the crystallizing phases are computed with the MELTS code. The history of accretionary layers is thus obtained by interface-controlled growth and solid-state diffusion. Our results show that crystals residing in different parts of the underground system acquire a distinctive signature and are well mixed together. A small population will register the successive refilling episodes, while a substantial one will record each fresh injection.

The model legume Medicago truncatula establishes a symbiosis with soil bacteria (rhizobia) that carry out symbiotic nitrogen fixation (SNF) in plant root nodules. SNF requires the exchange of nutrients between the plant and rhizobia in the nodule that occurs across a plant-derived symbiosome membrane. One iron transporter, belonging to the Vacuolar iron Transporter-Like (VTL) family, MtVTL8, has been identified as essential for bacteria survival and therefore SNF. In this work we investigated the spatial expression of MtVTL8 in nodules and addressed whether it could be functionally interchangeable with a similar nodule-expressed iron transporter, MtVTL4. Using a structural model for MtVTL8 and the previously hypothesized mechanism for iron transport in a phylogenetically-related Vacuolar Iron Transporter (VIT), EgVIT1 with known crystal structure, we identified critical amino acids and obtained their mutants. Mutants were tested in planta for complementation of an SNF defective line and in an iron sensitive mutant yeast strain. An extended phylogenetic assessment of VTLs and VITs showed that amino acids critical for function are conserved differently in VTLs vs. VITs. Our studies showed that some amino acids are essential for iron transport leading us to suggest a model for MtVTL8 function, one that is different for other iron transporters (VITs) studied so far. This study extends the understanding of iron transport mechanisms in VTLs as well as those used in SNF.

After the onset of the global COVID-19 pandemic, the deep connections between environmental factors and the transmission of airborne infectious diseases (including COVID-19) has become an area of relevant scientific and social interest. Indoor environments, where we spend a significant part of our daily lives, play a crucial role in shaping the dynamics of disease spread. The mitigation of infection risk related to poor indoor air quality and its link with the transmission of airborne diseases has emerged as a focal point for research and intervention strategies. This paper presents the results of a specific collaborative project in this field, focused on the utilization of Internet of Things (IoT) devices for comprehensive indoor environmental monitoring and infectious risk forecasting. In the frame of developing effective countermeasures for COVID-19 and future pandemic preparedness, our primary goal was to develop a predictive model for infection risk in indoor environments. Parameters such as humidity, temperature, CO 2 , and particulate matter concentrations (namely PM10 and PM2.5), have been assessed and modelled as indicators of indoor air quality, with these measures having been combined to generate a predictive algorithm specifically able to provide information about the transmission dynamics of COVID-19 and airborne infectious diseases within indoor spaces. This newly-developed Algorithm for the Prediction of Risk of Infections (APRI) relies on rigorous analyses and established different risk thresholds based on temperature, humidity, and CO 2 levels. The model showed significant associations between environmental factors, such as temperature, CO 2 levels, humidity, and particulate matter concentrations. A pivotal role of PM10 and PM2.5 in shaping air quality in indoor environments has been highlighted, as low PM concentrations corresponded in our predictive model to a minimal risk of airborne infectious diseases, while medium or high PM levels were associated with variations in temperature, humidity, and CO 2 levels, thus corresponding to an elevated risk of infection, particularly in the frame of highly diffusive diseases like COVID-19.

This paper aims to define design patterns specifically for data ingestion techniques within cloud-based architectures, addressing the challenges associated with high-volume data processing. The approach utilizes a flexible, metadata-driven framework that enhances adaptability and ease of use. This framework supports both incremental and full refresh methods, allowing for seamless changes to ingestion types, schema updates, table additions, and the incorporation of new data sources with minimal intervention from data engineers. The proposed design patterns were validated through experiments conducted on the Azure and Google Cloud platforms. The experiments demonstrate that the proposed design patterns significantly reduce data ingestion time, showcasing their effectiveness in managing high-volume data ingestion. This paper contributes to the field of data management by presenting a comprehensive definition of design patterns tailored for data ingestion in cloud-based architectures, effectively addressing key challenges in high-volume data processing.

Magmas discharged during individual volcanic eruptions commonly display compositional variations interpreted as new arrivals at shallow depth of more primitive, hotter, volatile-rich magma batches mixing with resident, colder, partially degassed magma. Heterogeneities in eruption products are often interpreted as evidence of short times of order tens of hours from new magma arrival to eruption, raising concerns for emergency planning. We show here, through numerical simulations, that magma convection and mixing in a shallow magma chamber can result in long-lived, dynamically stable configurations with coexistence of magmas from nearly pure to variably mixed end-member compositions. Short mixing time scales may therefore relate to sin-eruptive processes, as heterogeneities found in the eruptive products are not necessarily the fingerprint of new magma arrival shortly preceding or triggering the eruption.

Nitrogen is essential for all living species and may be taken up from the environment in different forms like nitrate or peptides. In plants, members of a transporter family named NPFs transport nitrate and peptides across biological membranes. NPFs are phylogenetically related to a family of peptide transporters (PTRs) or proton-coupled oligopeptide transporters (POTs) that are evolutionarily conserved in all organisms except in Archaea. POTs are present in low numbers in bacteria, algae and animals. NPFs have expanded in plants and evolved to transport a wide range of substrates including phytohormones and glucosinolates. Functional studies have shown that most NPFs, like POTs, operate as symporters with simultaneous inwardly directed movement of protons. Here we focus on four structural features of NPFs/POTs/PTRs that have been shown by structural and functional studies to be essential to proton-coupled symport transport. The first two features are implicated in proton binding and transport: a conserved motif named ExxER/K, located in the first transmembrane helix (TMH1) and a D/E residue in TMH7 that has been observed in some bacterial and algal transporters. The third and fourth features are two inter-helical salt bridges between residues on TMH1 and TMH7 or TMH4 and TMH10. To understand if the mechanism of transport is conserved in NPFs with the expansion to novel substrates, we collected NPFs sequences from 42 plant genomes. Sequence alignment revealed that the ExxER/K motif is not strictly conserved and its conservation level is different in the NPF subfamilies. The proton binding site on TMH7 is missing in all NPFs with the exception of two NPFs from moss. The two moss NPFs also have a positively charged amino acid on TMH1 that can form the salt bridge with the TMH7 negative residue. None of the other NPFs we examined harbor residues that can form the TMH1-TMH7 salt bridge. In contrast, the amino acids required to form the TMH4-TMH10 salt bridge are highly conserved in NPFs, with some exceptions. These results support the need for further biochemical and structural studies of individual NPFs for a better understanding of the transport mechanism in this family of transporters.

Autonomous aircraft are the key enablers of future urban services, such as postal and transportation systems. Digital twins (DTs) are promising cutting-edge technologies that can transform the future transport ecosystem into an autonomous and resilient system. However, since DT is a data-driven solution based on AI, proper data management is essential in implementing DT as a service (DTaaS). One of the challenges in DT development is the availability of real-life data, particularly for training algorithms and verifying the functionality of DT. The current article focuses on data augmentation through synthetic data generation. This approach can facilitate the development of DT in case the developers do not have enough data to train the machine learning (ML) algorithm. The current twinning approach provides a prospective ideal state of the engine used for proactive monitoring of the engine’s health as an anomaly detection service. In line with the track of unmanned aircraft vehicles (UAVs) for urban air mobility in smart city applications, this paper focuses specifically on the common hybrid turbo-shaft in drones/helicopters. However, there is a significant gap in real-life similar synthetic data generation in the UAV domain literature. Therefore, rolling linear regression and Kalman filter algorithms were implemented on noise-added data, which simulate the data measured from the engine in a real-life operational life cycle. For both thermal and hybrid models, the corresponding DT model has shown high efficiency in noise filtration and a certain amount of predictions with a lower error rate on all engine parameters except the engine torque.

As real-time data sources expand, the need for detecting anomalies in streaming data becomes increasingly critical for cutting edge data-driven applications. Real-time anomaly detection faces various challenges, requiring automated systems that adapt continuously to evolving data patterns due to the impracticality of human intervention. This study focuses on energy systems (ES), critical infrastructures vulnerable to disruptions from natural disasters, cyber attacks, equipment failures, or human errors, leading to power outages, financial losses, and risks to other sectors. Early anomaly detection ensures energy supply continuity, minimizing disruption impacts, an enhancing system resilience against cyber threats. A systematic literature review (SLR) is conducted to answer 5 essential research questions in anomaly detection due to the lack of standardized knowledge and the rapid evolution of emerging technologies replacing conventional methods. A detailed review of selected literature, extracting insights and synthesizing results has been conducted in order to explore anomaly types that can be detected using Machine Learning algorithms in the scope of Energy Systems, the factors influencing this detection success, the deployment algorithms and security measurement to take in to consideration. This paper provides a comprehensive review and listing of advanced machine learning models, methods to enhance detection performance, methodologies, tools, and enabling technologies for real-time implementation. Furthermore, the study outlines future research directions to improve anomaly detection in smart energy systems.

Background X-linked myotubular myopathy (XLMTM) is a rare congenital myopathy resulting from pathogenic variants in the MTM1 gene. Affected male subjects typically present with severe hypotonia and respiratory distress at birth and they often require intensive supportive care. Long-term survivors are often non-ambulant, ventilator and feeding tube–dependent and they generally show additional organ manifestations, indicating that myotubularin does play a vital role in tissues other than muscle. For XLMTM several therapeutic strategies are under investigation. For XLMTM several therapeutic strategies are under investigation including a study of intravenous MTM1 gene transfer using a recombinant AAV8 vector of which has some concerns arises due to hepatotoxicity. Results We report prospective and retrospective clinical data of 12 XLMTM patients collected over a period of up to 10 years. In particular, we carried out a thorough review of the data about incidence and the course of hepatobiliary disease in our case series. Conclusions We demonstrate that hepatobiliary disease represents a common comorbidity of XLMTM that seems irrespective to age and diseases severity. We recommend to carefully explore and monitor the hepatobiliary function in XLMTM patients. We believe that a better understanding of the pathogenic mechanisms that induce hepatobiliary damage is essential to understand the fatal events that may occur in the gene therapy program.